EDIC³
WELCOME TO
InsurEDIC³
Insurance Enterprise Decision Intelligence Command & Control Centre
Shared Decision Infrastructure for the Insurance Industry
Every insurer, reinsurer, broker, captive and MGA in this market is being asked to solve the same problem at the same time — how to make faster, better-evidenced, defensible decisions under a tightening AI supervisory regime. Most are building that capability alone, at full cost, behind a wall, and learning nothing from anyone else. InsurEDIC³ is the alternative: one common platform, one AI Control Tower orchestrating the agents, one shared library of frameworks, tools and methodologies — with each participant's proprietary data staying entirely their own.
◈ DATA SOVEREIGNTY UNDERTAKING
No participant's proprietary data is captured, retained, pooled, mined or used to train shared models. Policyholder records, pricing models, claims files, reserves, distribution economics and underwriting appetite never leave the participant's own boundary. The platform supplies frameworks, methods, agents and reference models — not a data lake. What is shared across the industry is method and anonymised aggregate outcome; what is never shared is anyone's book.
SELECT YOUR VIEW
Industry
INSURERS · REINSURERS · BROKERS · MGAs · CAPTIVES
The operating enterprise. The full decision operating system — executive command, decision queue, the eight-layer cognitive stack, underwriting, claims, financial, customer and enterprise risk intelligence, scenario simulation, the executive copilot, twelve AI executive agents, the enterprise knowledge graph, AI governance, decision fabric, value flow and the enterprise digital twin. Everything a carrier would otherwise build alone, available as shared infrastructure.
ENTER INDUSTRY C³ →
InsurEDIC³ / EXECUTIVE COMMAND CENTER
⇄ SWITCH VIEW ⛨ DATA SOVEREIGN ⚠ 2 ALERTS ROLE: CEO ▾ ⌖ TOWER: GREEN REGION: APAC ▾ ◉ LIVE ⌕ SEARCH
INSURANCE ENTERPRISE DECISION OPERATING SYSTEM · SIGNAL → INSIGHT → DECISION → ACTION → OUTCOME

Executive Command Center

Real-time enterprise operating picture for the Board and C-Suite. Every metric answers one question: what decision does this require, and by when? The enterprise is not short of data — it is short of connective tissue between signal and action. This is that tissue.

Gross Written Premium
USD 2.84B
▲ 8.2% YoY · Plan: USD 2.70B · 105% attainment
Net Profit
USD 184M
▲ 3.1% YoY · ROE 12.4% · target 14%
Combined Ratio
97.4%
▲ 1.8pp MoM · claims pressure · decision req.
Solvency Margin
184%
Regulatory min 120% · capital buffer USD 640M
Enterprise Risk Index
68/100
ELEVATED · cyber +fraud leading
NPS Score
+42
Claims NPS: +28 · ▼3pp MoM · action req.
Customer Retention
86.2%
▼ 1.4pp YoY · motor churn elevated
AI Decision Confidence
91%
12 agents · 5 active decisions · learning
Claims Loss Ratio
68.4%
▲ 2.1pp · motor +14% severity · ALERT
Expense Ratio
29.0%
▼ 0.8pp · digital automation savings
Cat Exposure PML
USD 480M
Reinsurance cover USD 380M · net USD 100M
Decision Velocity
68%
5 decisions pending · backlog: 3 overdue

Enterprise Decision Intelligence Engine ACTIVE

👁
SENSE
847 signals
🧠
UNDERSTAND
214 analyzed
📈
PREDICT
38 forecasts
🔮
SIMULATE
12 scenarios
💡
RECOMMEND
5 decisions
PRIORITISE
ranking now
APPROVE
awaiting CEO
🚀
EXECUTE
3 in flight
🔄
LEARN
86 in memory
Motor claims severity alert — Loss ratio ▲14% MoM. Pricing decision required within 8 days to recover USD 18M underwriting margin.
CLAIMS ENGINE · 14 MIN AGO · DECISION #2 TRIGGERED
🔴
Vietnam regulatory approval — Insurance Commission confirmed conditional approval. Board paper due 4 days. IRR model updated to 14.2%.
STRATEGY ENGINE · 2 HR AGO · DECISION #1 ESCALATED TO BOARD
🌀
Cat model update — APAC flood season probability revised +18%. PML exposure recalculated. Reinsurance renewal negotiation window open.
RISK ENGINE · 6 HR AGO · REINSURANCE TEAM NOTIFIED
🤖
Cyber SME product AI pricing complete — 94% model confidence. Market sizing: USD 340M TAM. Launch recommendation ready for CEO review.
PRODUCT ENGINE · 1 DAY AGO · DECISION #4 QUEUED

Executive Alert Level

LEVEL
2
ELEVATED
Claims Pressure
HIGH
Regulatory Risk
MEDIUM
Market Risk
MEDIUM
Cyber Exposure
HIGH
Solvency
SECURE
View Risk Intelligence →
Embedded Value
USD 3.2B
▲ USD 180M YoY · ROEV 6.8%
Digital Adoption
62%
▲ 8pp YoY · mobile-first penetration
Reinsurance Utilisation
79%
Treaty capacity: USD 380M · used USD 300M
Decision Backlog
5
3 overdue · 2 board-level · USD 612M at stake

Signal-to-Decision Health SPINE STATUS

Enterprises rarely fail for lack of data. They fail because insight sits in silos, priorities are unclear, and teams are not aligned on the next action. These four numbers say whether the spine is carrying signal through to outcome.

Time to insight
4.2 days  ▼ from 19
Signal reaching a decision
31%
Decisions with a named owner
92%
Cross-functional alignment
74% · target 90%
Decided ahead of the forcing event
61%
Open the Spine →
Prioritisation Board →

AI Control Tower FLEET SUPERVISION

44 models and 12 agents run inside the enterprise. The Control Tower is the one surface that says whether the estate as a whole is safe to keep flying.

FLEET STATUS
GREEN
42 / 44 nominal
OPEN INCIDENTS
3
1 critical · SLA running
AUTONOMY CEILING
L3
L4 Board-gated
GUARDRAIL PASS
99.2%
18,400 screened MTD
Open Control Tower →
⏻ Kill switch: armed
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai · All figures are hypothetical and for demonstration purposes only.
AI CONTROL TOWER · MODEL FLEET · AGENT SUPERVISION · GUARDRAILS · KILL SWITCH

AI Control Tower

Single supervisory surface for every pricing, reserving, fraud, catastrophe and claims model running inside the insurer. The Control Tower answers one question at all times: is the AI estate safe to keep underwriting?

FLEET STATUS
GREEN
42 of 44 models nominal · 2 under watch
MODELS UNDER WATCH
2
Motor pricing v4.2 drift · Fraud SIU recall dip
OPEN INCIDENTS
3
1 critical · 2 medium · SLA clock running
GUARDRAIL PASS RATE
99.2%
18,400 automated decisions screened MTD
HUMAN-IN-THE-LOOP
100%
All value-at-stake > USD 5M routed to a named human
AUTONOMY CEILING
L3
Recommend + execute within bounds · L4 gated
EXPLAINABILITY COVERAGE
94%
Decisions with reconstructable rationale
TIME TO CONTAIN
11 min
Median detect-to-rollback · target < 15 min

Model & Agent Fleet LIVE SUPERVISION

Model / Agent
Owner
Drift
Autonomy
Status
Control Action
Motor Pricing Engine v4.2
CUO
PSI 0.27
L2 ▼
THROTTLED
Retrain queued · 6 Sep
Fraud SIU Classifier
Head of Claims
PSI 0.16
L3
WATCH
Shadow model in parallel
Reserve Adequacy Model
CFO
PSI 0.04
L3
NOMINAL
None required
Cat Exposure Simulator
CRO
PSI 0.06
L2
NOMINAL
None required
Claims Triage Agent
COO
PSI 0.08
L3
NOMINAL
Cap: USD 5K / claim
Retention Propensity Model
CMO
FAIRNESS Δ
L1 ▼
HELD
Bias review · 9 Sep
Regulatory Signal Agent
GC
PSI 0.03
L2
NOMINAL
None required
⏻ Global Kill Switch
⚙ Set Autonomy Ceiling
↩ Rollback Model
⚖ Open Governance →

Control Gates

No AI output reaches an executive without clearing five gates. A failed gate stops the decision, not the audit trail.

G5
Accountability Gate
A named CUO, CRO or line head owns every material decision
PASS
G4
Value-at-Stake Gate
Exposure above threshold escalates to HITL
PASS
G3
Explainability Gate
Rationale reconstructable end to end
94%
G2
Fairness & Bias Gate
Rating and claims outcome parity across policyholder cohorts
1 FAIL
G1
Data Provenance Gate
Policyholder consent, data residency and lineage verified
PASS
How gates work →

Open Incidents SLA CLOCK RUNNING

🔴
INC-4471 · Motor pricing drift breach — Population stability index crossed 0.20 on 26 August. Autonomy throttled automatically. Estimated mispricing exposure USD 3.4M if unresolved through September renewals.
CRITICAL · OWNER: CUO · 71 HRS OPEN · TARGET CLOSE 6 SEP
🟠
INC-4468 · Renewal fairness variance — Retention model shows a widening acceptance gap across age bands. Model held at recommend-only. Legal and Compliance engaged ahead of the Governance Committee.
MEDIUM · OWNER: CMO · 5 DAYS OPEN · REVIEW 9 SEP
🟠
INC-4463 · Fraud recall degradation — New intake channel changed the feature distribution. Shadow model deployed in parallel; all referrals carry a human review flag until recall recovers above 0.85.
MEDIUM · OWNER: HEAD OF CLAIMS · 9 DAYS OPEN
INC-4455 · Cat model version mismatch — CLOSED — Twin and reinsurance case were running different vendor versions. Reconciled, and a version-pinning control added to the release pipeline.
CLOSED · 22 AUG · CONTROL ADDED

Autonomy Ladder CEILING: L3

Autonomy is granted per model, never per platform. Read the ladder from the bottom up.

L5
Self-Directed NOT AUTHORISED
Agent sets its own objectives — outside board-approved risk appetite
L4
Bounded Autonomy BOARD GATED
Agent may adjust its own bounds within a Board-set envelope
L3
Execute in Bounds CURRENT · 9 AGENTS
Acts within fixed limits — binding authority, sum insured, settlement cap
L2
Recommend Only 2 AGENTS
Ranked recommendation with rationale — the underwriter decides
L1
Assist 1 AGENT
Surfaces claims and exposure evidence only — no recommendation

Control Tower Operating Doctrine

Supervise the estate, not the model
A pricing model dashboard tells you the pricing model is healthy. It cannot tell you that pricing, reserving and fraud models are drifting in the same direction on the same book. The Control Tower is deliberately a fleet view.
FLEET-LEVEL · 44 MODELS · 12 AGENTS
Autonomy is earned, and revocable
Every agent holds a level on the ladder. Pricing drift, a fairness variance on renewal offers, or a reserving incident demotes it automatically — no committee required to take underwriting authority away.
AUTOMATIC DEMOTION · MANUAL PROMOTION
A stopped decision is still a decision
Every gate failure, throttle and rollback writes to Decision Memory with its rationale. What the insurer chose not to automate — and why — is exactly what the regulator will ask about.
FEEDS → DECISION MEMORY
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
SIGNAL → INSIGHT → DECISION → ACTION → OUTCOME · THE OPERATING ARC

Signal-to-Decision Spine

The insurer does not have a data problem. It has a fragmentation problem. Underwriting, claims, actuarial and distribution insight sit in separate systems, priorities across lines are unclear, and functions are not aligned on what to do next. The spine is the connective tissue that carries a signal through to a measured underwriting outcome — so decisions get made before the market, the regulator or the loss experience forces them.

STAGE 01
Signal
Raw enterprise, market and customer signal captured in a structured, retrievable form — not scattered across inboxes and decks.
847/HR · 14 SOURCES
STAGE 02
Insight
Signal becomes a claim with evidence, confidence and a named owner. Stored once — reusable by underwriting, actuarial and claims alike.
214 THIS WEEK · 1 REPOSITORY
STAGE 03
Decision
Insight is framed as a decision object — question, options, criteria, value at stake, decision right, deadline.
14 OPEN · USD 612M AT STAKE
STAGE 04
Action
Each approval converts into an owned commitment — a rate filing, referral rule or treaty instruction — with a date attached.
9 IN FLIGHT · 92% OWNED
STAGE 05
Outcome
Results are written back against the original rationale. The expectation gap is what trains the next decision.
86 LOGGED · FEEDS MEMORY
Time to Insight
4.2 days
▼ from 19 days · 78% reduction since spine live
Insight Reuse Rate
3.4×
Each insight informs 3.4 decisions · was 1.1×
Signal Reaching a Decision
31%
▲ from 6% · the rest is correctly discarded
Decision Alignment
74%
Teams agreeing on next action · target 90%

Fragmentation Diagnostic WHERE THE SPINE BREAKS

Most enterprises are not short of data or tools. They are short of connective tissue. These are the four breaks that stop signal from becoming action — and what the operating system does about each.

Break 1 · Insights live in silos
Underwriting research, claims analytics, market intelligence and customer feedback each sit in their own tool. The same question is answered four times, four different ways.
FIX → SINGLE INSIGHT REPOSITORY · REUSE 1.1× → 3.4×
Break 2 · Priorities are unclear
Motor repricing, a Vietnam entry, a reinsurance restructure and a cyber launch compete for the same capital and the same quarter. Without an explicit ranking method, the loudest line head wins and the highest-value decision waits.
FIX → PRIORITISATION BOARD · EXPLICIT SCORING
Break 3 · Teams are not aligned on next action
A repricing is announced; underwriting reads it as a rate change, distribution as a commission change, claims as nothing at all. Six weeks later the misalignment surfaces at renewal as a delivery problem rather than a decision problem.
FIX → OWNED COMMITMENTS · 92% NAMED OWNER
Break 4 · Rationale is lost
Nobody can reconstruct why the Bangladesh entry was rejected or why the cat retention was set where it was. The same debate reopens at every treaty renewal and the same mistakes stay available to be repeated.
FIX → DECISION MEMORY · 86 DECISIONS RETRIEVABLE
▸ FRAMEWORK ATTRIBUTION · The signal-to-decision arc, the fragmentation diagnostic, and the terms Decision Memory and Decision Infrastructure are drawn from the published work of Yaron Cohen (yaroncohen.com · Signal to Decision · The Detectionist). Their extension into insurance, into an enterprise decision operating system, and into the InsurEDIC³ command surface is the work of Zaid Hamzah / DeliverValue.ai.

Signal Sources → Decision Yield

SourceVolume / wkInsightsDecisionsYield
Claims FNOL & severity telemetry4,180624HIGH
Regulatory wires — MAS · BNM · APRA214283HIGH
Competitor pricing & product moves860342MEDIUM
Customer voice — NPS, complaints, churn1,940412MEDIUM
Distribution & broker telemetry1,120261MEDIUM
Macro, cat and reinsurance capacity380232HIGH
Why yield beats volume →

Foresight Posture DECIDE BEFORE THE MARKET FORCES IT

A decision made under market pressure costs more and yields less than the same decision made twelve weeks earlier. The spine is scored on how early it moves.

Decisions made ahead of forcing event
61%
Decisions made under market pressure
28%
Decisions made after the window closed
11%
Median lead time vs forcing event
9.4 weeks
Value premium on early decisions
USD 42M
18 tracked forcing events →
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
OPTION OVERLOAD · EXPLICIT SCORING · SEQUENCED COMMITMENT · MEMORY CAPTURE

Prioritisation Board

Insurance executives are rarely short of options — a new line, a rate action, a treaty restructure, a market entry. They are short of an agreed method for choosing between them. Every open decision is scored on the same four criteria, sequenced into a commitment horizon, and the reasoning is captured at the moment of choice rather than reconstructed at the next Board offsite.

Open Options
14
Competing for the same capital and quarter
Value at Stake
USD 612M
Across all ranked options
Capacity to Execute
4
Realistic parallel initiatives this quarter
Scoring Coverage
100%
Every option scored on the same criteria

Commitment Horizon RANKED · NOT LISTED

Four columns, and only four slots in NOW. Scarcity in the first column is the whole point — a board where everything is urgent has made no decision at all.

NOW · Q3 · 4 SLOTS
Motor Portfolio Repricing
SCORE 9.2USD 18M8 DAYS
Vietnam Market Entry
SCORE 8.8USD 120M4 DAYS
Reinsurance Renewal Structure
SCORE 8.4USD 380M3 WEEKS
Model Drift Remediation
SCORE 8.1USD 3.4MGATES 3
Cyber SME Product Launch
SCORE 7.6USD 340M TAM
Claims Automation Phase 2
SCORE 7.2USD 24M
Bancassurance Tier 2
SCORE 6.9USD 64M
LATER · 2027
Level 4 Agent Autonomy
SCORE 5.8BOARD GATED
Embedded Insurance Platform
SCORE 5.4USD 90M
Takaful Line Extension
SCORE 5.1USD 55M
PARKED · REVISIT TRIGGER
Bangladesh Market Entry
REJECTED MAR 26TRIGGER SET
Policy Admin Replacement
DEFERREDTRIGGER SET
Standalone Health Line
DEFERREDTRIGGER SET
Crypto Custody Cover
PARKEDTRIGGER SET

Scoring Criteria SAME FOUR · EVERY OPTION

The criteria matter less than the fact that they are fixed, public and applied identically to a rate action and a market entry alike. Consistency is what makes a ranking defensible to a Board and to the regulator.

CriterionWeightWhat it tests
Value at stake35%GWP, underwriting margin or capital created or protected
Urgency & forcing event25%Cost of missing the renewal, filing or treaty window
Confidence in evidence20%Credibility of the experience data and actuarial basis
Reversibility20%Cost of unwinding — a rate filing versus a licence
Why reversibility is weighted at 20% →

Capture at the Moment of Choice

Rationale reconstructed six months later is a story. Rationale captured at the moment of choice is evidence. Every ranking action on this board writes four fields straight into Decision Memory.

01
What we chose
The option, its slot, and the options it displaced
02
What we expected
Target loss ratio, GWP or IRR — stated numerically, before the fact
03
What would change our mind
The named signal — severity, capacity, regulator — that reverses it
04
Who owns it
CUO, CRO or line head — with a first action and a date
Open Decision Memory →
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
CONNECTIVE LAYER · MODELS · AGENTS · DATA · KNOWLEDGE · SYSTEMS · TOOLS · WORKFLOWS · GUARDRAILS

AI Harness

The Harness is not the end product. It is the connective mechanism that allows every component of the decision environment to work as one integrated system rather than as isolated AI tools. It sits directly beneath the AI Control Tower and directly above the enterprise estate — and it is what makes model-agnostic operation possible.

Connected Components
168
Across 8 component classes · all governed
Model Providers
6
Foundation + specialist · no lock-in
Enterprise Systems Bound
14
Policy admin · claims · CRM · finance · data
Orchestration Latency
240ms
Median decision assembly across components

The Eight Component Classes WHAT THE HARNESS BINDS

CLASS 01
AI Models
6 providers · foundation and specialist insurance models
CLASS 02
AI Agents
12 active · task routing and structured return
CLASS 03
Enterprise Data
14 sources · lineage and residency resolved
CLASS 04
Knowledge
Guidelines · policies · regulation · expertise
CLASS 05
Systems
14 bound · read-first, gated writes
CLASS 06
Tools & APIs
Typed, logged, attributable to a decision
CLASS 07
Workflows
Intelligence → structured decision objects
CLASS 08
Guardrails
Where Control Tower gates are applied

Position in the Stack READ BOTTOM UP

The Harness is layer 4 of 6. It is deliberately invisible to the executive — its job is to make the layers above it possible.

6
Enterprise Decisions & Actions
What the system exists to improve
5
AI Control Tower BRAIN & NERVE CENTRE
Orchestration, visibility, governance, control
4
AI Harness YOU ARE HERE
Connects models, agents, data, knowledge, tools, workflows
3
Decision Intelligence Engine
Runs the sense-to-learn reasoning pipeline
2
Enterprise Decision Operating System
Institutionalises decision intelligence across the organisation
1
Decision Intelligence Infrastructure
Foundational layer for decision-centric architecture

Model-Agnostic by Design NO LOCK-IN

We do not need to own every component of the stack. The future insurance enterprise will run multiple foundation models, specialist models, proprietary models, agents, data providers and platforms. The Harness is what lets them work together around a decision.

Component
Swappable
Bound via
Governed
Status
Note
Foundation model
YES
Provider API
Gates 1–5
LIVE
3 providers active
Pricing model
YES
Model registry
Gates 1–5
THROTTLED
Drift — see Control Tower
Cat model
YES
Vendor adapter
Gates 1, 3
LIVE
Version-pinned
Policy admin
READ-FIRST
System adapter
Gate 4
LIVE
Writes gated on value
Knowledge base
PROPRIETARY
Native
Gates 1–5
LIVE
Client-owned, schema ours
Why model-agnostic matters →

Harness Operating Principles

The Harness connects, it does not decide
Every routing action assembles evidence for a decision object. The decision itself is made by the engine above and governed by the Control Tower — never inside the connective layer.
SEPARATION OF CONNECTION AND JUDGEMENT
Nothing reaches a system ungated
Agents cannot write directly to policy admin, claims or finance. Every write passes the Control Tower gates at the Harness boundary, which is where enforcement is physically applied.
ENFORCEMENT AT THE BOUNDARY
Replaceable everywhere except the schema
Models, providers, vendors and systems are all swappable. The insurance decision schema and the decision architecture are not — and that is precisely where the proprietary value sits.
168 COMPONENTS · 2 PROPRIETARY LAYERS
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
INSIGHTS SYSTEMS · ENABLEMENT SYSTEMS · ADOPTION SYSTEMS · FEEDBACK LOOPS

Adoption & Enablement

An operating system nobody uses is an expensive diagram. Adoption is not a rollout event at the end of the build — it is a system in its own right. These are the feedback loops, underwriting and claims playbooks, and the community of practice that keep the decision spine alive across every business line after the launch email.

LAYER A · TOOLS & WORKFLOWS
Insights Systems
Structures how underwriting research, claims analytics, actuarial studies and broker feedback are captured — so a pricing question answered for Motor last quarter is retrievable when Property asks it this quarter. One repository, one schema, one retrieval path.
Active practitioners
142
Recurring weekly usage
34%
Time-to-insight reduction
78%
LAYER B · CONTENT & FRAMEWORKS
Enablement Systems
Even a well-built system fails if underwriters, claims handlers and actuaries do not know how to use it. Line-specific playbooks carry a practitioner from a loss-ratio signal to a priced action without adding complexity of their own.
Playbooks published
9
Practitioners onboarded
86
Median time to competence
11 days
LAYER C · COMMUNITIES & LOOPS
Adoption Systems
Feedback loops and a cross-line community of practice spanning underwriting, claims, actuarial and distribution — so the workflows are actually used at the branch and desk level, and change as the people writing risk learn.
Community of practice
38
Improvements shipped from feedback
27
Practitioner satisfaction
4.3 / 5

Continuous Discovery Loop ADOPTION IS A SYSTEM, NOT A ROLLOUT

Capture
1,940 INPUTS / WK
Structure
214 INSIGHTS
Decide
14 OPEN OPTIONS
Act
9 COMMITMENTS
Measure
86 OUTCOMES
Improve
27 SHIPPED

The loop closes back into capture. Decentralising discovery is what moves insight cycle time from months to weeks — line underwriters and claims managers run their own broker, agent and policyholder conversations rather than queuing behind a central actuarial or research function, while the centre maintains the method rather than doing the work.

Adoption by Function

FunctionPractitionersWeekly useMaturity
Underwriting3441%EMBEDDED
Claims4138%EMBEDDED
Actuarial & Finance2236%EMBEDDED
Distribution & Sales2624%SCALING
Risk & Compliance1229%SCALING
Marketing & Customer714%EARLY
Address the Marketing gap →

Enablement Library

Nine assets, each written for a named insurance role and each moving a practitioner one step along the spine. Short beats comprehensive.

01
Framing a rate-change decision
For pricing actuaries · options, criteria, margin at stake, decision right
LIVE
02
Writing a loss-driver insight others reuse
For claims analysts · claim, evidence, confidence, owner in 200 words
LIVE
03
Running the portfolio prioritisation session
For line heads · 90 minutes, four criteria, four slots, no exceptions
LIVE
04
Overriding an AI underwriting referral
For underwriters · what model confidence means, and when to decline it
LIVE
05
Writing a renewal outcome back to memory
For account managers · expected vs actual loss ratio, and what changes
DRAFT

Adoption Outcomes MEASURED, NOT ASSERTED

Faster, more confident decisions
Time to insight on a loss-ratio deterioration down from 19 days to 4.2 — inside the renewal window rather than after it. Confidence is measured by how rarely a rate or reserve decision is reopened, not by how sure people say they are.
TIME TO INSIGHT ▼78% · REOPEN RATE ▼41%
Alignment across functions
Underwriting, actuarial and claims arrive at the executive table with one severity view rather than three. The measure is the share of decisions where every affected function agrees on the next action.
ALIGNMENT 74% · TARGET 90%
Less friction in the workflow
The system earns its place by removing steps from the referral and settlement path, not adding a governance layer on top of it. Twenty-seven improvements this year came directly from underwriter and claims-handler feedback.
27 IMPROVEMENTS FROM THE COMMUNITY
▸ FRAMEWORK ATTRIBUTION · The three-layer model — Insights Systems (tools & workflows), Enablement Systems (content & frameworks) and Adoption Systems (communities & feedback loops) — together with the continuous discovery loop and the principle that adoption is a system rather than a rollout, are drawn from the published work of Yaron Cohen (yaroncohen.com). Their application to an insurance enterprise decision operating system is the work of Zaid Hamzah / DeliverValue.ai.
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
EXECUTIVE DECISION QUEUE · AI-RANKED BY STRATEGIC VALUE · 5 DECISIONS ACTIVE

Executive Decision Queue

The heart of the platform. Every decision card contains AI recommendation, evidence, scenarios and value at stake. The primary unit of executive work is a high-quality decision.

Active Decisions
5
3 urgent · 2 high · USD 612M total value
Avg AI Confidence
87.6%
Range 79%–94% across all decisions
Decisions This Quarter
23
All retrievable with full rationale · 23 this year
Value Realised QTD
USD 84M
From 18 executed decisions · tracking
#1
Approve Vietnam Market Entry
Strategic expansion into Southeast Asia's fastest-growing insurance market
EXPIRES 4 DAYS BOARD DECISION
CAPITAL REQ.
USD 120M
PROJECTED IRR
14.2%
AI CONFIDENCE
91%
ALTERNATIVES
3 options
⬢ AI RECOMMENDATION — CEO AGENT + STRATEGY AGENT
APPROVE with conditions: phased entry Q1 2027, bancassurance-first distribution, cap year-1 loss ratio at 75%. Vietnam Insurance Law amendment (effective Jan 2027) creates 18-month first-mover advantage. IRR sensitivity: base 14.2%, bear 8.1%, bull 19.4%.
✓ APPROVE
↷ DEFER
View Alternatives
Open Decision Pack
OWNER: CEO · CFO · Board
VALUE PROTECTED: USD 180M
#2
Motor Pricing: Implement +7% Rate Increase
Restore combined ratio to 96% target following 14% claims severity surge
8 DAYS CUO APPROVAL
UW IMPROVEMENT
USD 18M
CUSTOMER IMPACT
-0.4 NPS
AI CONFIDENCE
94%
CHURN RISK
+2.1%
⬢ AI RECOMMENDATION — PRICING AGENT + ACTUARIAL AGENT
IMPLEMENT +7% across all motor classes effective 1 Aug 2026. Fleet segment: +9%. Private car: +6.5%. Body shop inflation driving severity; competitor pricing +5.8% average confirms market support. Churn model projects net positive USD 14.2M after attrition.
✓ APPROVE
Simulate Impact
Open Decision Pack
#3
Reserve Adequacy: Increase by USD 42M
Q2 2026 actuarial review — long-tail liability reserve deficiency identified
12 DAYS SOLVENCY RISK
RESERVE INCREASE
USD 42M
SOLVENCY IMPACT
-8pp
AI CONFIDENCE
88%
RISK OF INACTION
HIGH
⬢ AI RECOMMENDATION — ACTUARIAL AGENT + RISK AGENT
APPROVE reserve increase of USD 42M. Long-tail EL and PI claims showing adverse development vs booked reserves. Regulator sensitised. Non-approval risk: regulatory enforcement and investor confidence impact. Solvency post-increase: 176% — remains comfortably above 120% minimum.
✓ APPROVE
Open Decision Pack
OWNER: CFO · CRO · Actuary
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
ENTERPRISE DECISION INTELLIGENCE ENGINE · 9-STAGE PIPELINE · CONTINUOUS OPERATION

Enterprise Decision Intelligence Engine

The AI engine continuously senses the enterprise, predicts outcomes, simulates options and recommends high-confidence decisions. Every stage is monitored and audited in real time.

Decision Pipeline 9 STAGES ACTIVE

👁
SENSE
847 signals/hr
🧠
UNDERSTAND
214 patterns
📈
PREDICT
38 forecasts
🔮
SIMULATE
12 scenarios
💡
RECOMMEND
5 queued
PRIORITISE
ranked live
APPROVE
CEO pending
🚀
EXECUTE
3 in-flight
🔄
LEARN
86 in memory
LAYER 1
Signal Sensing
LIVE · 847 feeds · 14 sources
LAYER 2
Pattern Understanding
RUNNING · 214 patterns
LAYER 3
Outcome Prediction
ACTIVE · 38 forecasts
LAYER 4
Scenario Simulation
QUEUED · 12 scenarios
LAYER 5
Decision Recommendation
5 DECISIONS
LAYER 6
Human Approval Gate
AWAITING · CEO action
LAYER 7
Orchestrated Execution
3 ACTIVE
LAYER 8
Continuous Learning
SYNCING · 18 outcomes

Engine Performance

Signal Volume
847/hr
Decision Latency
4.2 min avg
Recommendation Accuracy
91.4%
Human Override Rate
12%
Decisions This Week
8
Value Decisions Delivered
USD 84M
Model Uptime
99.97%

AI Control Tower CORE

InsurEDIC³
DECISION
ENGINE
12 AGENTS LIVE
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
FINANCIAL PERFORMANCE INTELLIGENCE · DECISION-ORIENTED · NOT REPORTING

Financial Performance Intelligence

Every financial metric is framed as a decision requirement. Why did profit change? What should we do? Where should capital be allocated?

GWP YTD
USD 2.84B
▲ 8.2% vs prior year · 105% of plan
Net Profit
USD 184M
▼ USD 12M vs plan · claims pressure
Combined Ratio
97.4%
Target 95% · ▲ 1.8pp MoM · action req.
ROE
12.4%
Target 14% · capital optimisation req.

Profit Bridge — Why Did Profit Change? AI ANALYSIS

DriverImpactDirectionDecision Required
GWP Growth+USD 28MPOSITIVESustain growth momentum
Motor Claims Severity-USD 22MNEGATIVEPricing +7% — Decision #2
Cat Events Q2-USD 14MNEGATIVEReinsurance structure review
Reserve Strengthening-USD 8MCAUTIONReserve increase — Decision #3
Investment Income+USD 18MPOSITIVEMonitor rate environment
Expense Reduction+USD 11MPOSITIVEAccelerate automation
Tax & Other-USD 1MNEUTRALNo action required

Capital Allocation Recommendation AI

Available Capital
USD 640M
Vietnam Entry
USD 120M · IRR 14.2%
Digital Transformation
USD 85M · ROI 18%
Cyber Product Launch
USD 45M · IRR 22%
Reserve Strengthening
USD 42M · mandatory
Share Buyback
USD 100M · EPS ▲ 4.2%
Capital Buffer
USD 248M retained
AI RECOMMENDATION — FINANCE AGENT
Prioritise: Vietnam + Cyber + Digital (highest IRR portfolio). Defer buyback until Q4 — preserve strategic optionality. Solvency post-allocation: 168% vs 120% floor.
Investment Return
4.8%
▲ 0.6pp YoY · bond yields rising · opportunity
Expense Ratio
29.0%
▼ 0.8pp · automation saving USD 11M pa
Embedded Value
USD 3.2B
▲ USD 180M YoY · new biz value positive
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
UNDERWRITING INTELLIGENCE · PORTFOLIO DECISIONS · AI-POWERED RISK SELECTION

Underwriting Intelligence

Where should we write more business? Where should we shrink? Where should underwriting authority change? AI answers these executive questions continuously.

Motor Loss Ratio
72.4%
▲ 4.2pp · pricing action required urgently
Property Loss Ratio
58.1%
▼ 2.1pp · cat season lower than model
Liability Loss Ratio
64.8%
▲ 1.2pp · EL claims inflation
Overall Combined
97.4%
▲ from 95.6% · motor driving deterioration

Portfolio Intelligence — Expand or Contract? AI

LineLoss RatioCRAI VerdictAction
Motor Private72.4%101%CONTRACT+7% price, de-risk HV
Motor Fleet78.2%107%SHRINKExit unprofitable segments
Property SME58.1%88%GROWIncrease capacity 20%
Liability EL64.8%94%HOLDMonitor inflation trend
Cyber SMEN/AN/ALAUNCHDecision #4 pending
Marine Cargo61.2%91%GROWTarget ASEAN corridors
Trade Credit69.4%99%REVIEWTighten underwriting criteria

Catastrophe Exposure MONITOR

PML 1-in-100
USD 480M
Reinsurance Cover
USD 380M
Net Exposure
USD 100M
Top Concentration
Thailand Flood Zone A
Cat Budget YTD
USD 68M / USD 80M
Flood Season Outlook
ABOVE AVERAGE
⬢ RISK AGENT ALERT
Thailand flood season probability +18% vs model. Recommend: (1) Accelerate reinsurance renewal, (2) Tighten property CAT underwriting in Zone A/B, (3) Pre-position claims teams.
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
CLAIMS INTELLIGENCE · DECISION-ORIENTED · LEAKAGE · FRAUD · SEVERITY

Claims Intelligence

Claims is where insurance profitability is won or lost. AI continuously identifies leakage, fraud, severity trends and settlement inefficiency — and recommends executive actions.

Claims Severity Index
114
▲ 14 pts MoM · motor body shop inflation
Claims Leakage Est.
USD 28M
4.8% of claims paid · target <3.5%
Fraud Probability
USD 12M
Est. fraudulent paid · 2.1% of paid claims
Settlement Efficiency
78%
Within SLA · target 85% · AI automation gap

Large Loss Monitor 3 ALERTS

Claim RefReserveTypeStatusAI Flag
LL-2026-0441USD 4.2MPI LiabilityLITIGATIONSettle early
LL-2026-0387USD 3.8MProperty FireESCALATEDFraud indicators
LL-2026-0312USD 2.9MMotor FleetAGREEDReserve adequate
LL-2026-0298USD 2.1MCat FloodASSESSMENTReinsurance notify
LL-2026-0201USD 1.9MEL DiseaseLEGALIncrease reserve

AI Decision Recommendations AGENT

🔴
Reserve increase required — Long-tail EL cluster showing adverse development. Recommend USD 42M reserve strengthening (Decision #3).
ACTUARIAL AGENT · 2 HR AGO · HIGH CONFIDENCE 88%
🤖
Claims automation opportunity — Motor claims under USD 5K: 68% auto-assessable. AI settlement potential: USD 14M savings pa, 40% faster cycle.
CLAIMS AGENT · 4 HR AGO · CONFIDENCE 92%
Fraud ring detected — 14 claims, 3 workshops, 2 assessment firms. Potential fraud: USD 2.4M. Referral to SIU and police recommended.
FRAUD AGENT · 6 HR AGO · CONFIDENCE 87%
📉
Customer dissatisfaction surge — Claims NPS ▼8pp this month. Top reason: repair delay +12 days. Preferred repairer SLA renegotiation needed.
CUSTOMER AGENT · 1 DAY AGO
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
SALES & DISTRIBUTION INTELLIGENCE · CHANNEL PROFITABILITY · PRODUCER DECISIONS

Sales & Distribution Intelligence

Which channels should we expand? Which producers should we terminate? Where does pipeline velocity need intervention? AI recommends distribution decisions continuously.

Total GWP
USD 2.84B
Agency 48% · Digital 22% · Banca 18% · Broker 12%
New Business GWP
USD 420M
▲ 12% YoY · digital channel leading growth
Persistency
86.2%
▼ 1.4pp · motor churn elevated post-renewal
Cross-Sell Ratio
1.8x
Products per customer · target 2.2x · gap USD 48M

Channel Profitability Matrix AI RANKED

ChannelGWPCRCOAAI Verdict
Digital DirectUSD 625M91%8%EXPAND
BancassuranceUSD 511M93%12%GROW
Agency — Tier 1USD 680M94%18%RETAIN
Agency — Tier 3USD 342M104%22%RATIONALISE
Broker CorporateUSD 341M96%15%REVIEW
Embedded (Partners)USD 340M89%6%ACCELERATE
⬢ DISTRIBUTION AGENT RECOMMENDATION
Accelerate digital + embedded (combined 28% GWP, 90% CR average). Rationalise 210 Tier-3 agents (Decision #5). Reinvest agency savings into embedded insurance partnerships — TAM USD 180M in 24 months.

Sales Pipeline

Pipeline Value
USD 840M
Win Rate
38%
Avg Deal Cycle
42 days
Deals Stalled >60 days
28 deals
AI Conversion Lift
+14%
PIPELINE BY STAGE
Prospect
USD 840M
Proposal
USD 460M
Negotiation
USD 270M
Closing
USD 160M
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
CUSTOMER INTELLIGENCE · LIFETIME VALUE · RETENTION · BEHAVIOUR · CHURN

Customer Intelligence

Understand every customer segment deeply. AI identifies churn risk, cross-sell opportunity and journey bottlenecks — translating customer data into executive decisions.

Total Customers
2.84M
▲ 6.2% YoY · digital acquisition leading
NPS Score
+42
Claims NPS +28 · ▼3pp MoM · action needed
Churn Rate
13.8%
▲ 1.4pp YoY · motor segment worst
Avg Customer LTV
USD 1,840
▲ 8% · digital customers 2.2x higher LTV

Churn Risk by Segment ACTION REQ.

SegmentChurn RiskCustomersLTV at RiskAction
Motor — Renewal Due 90dHIGH 24%48,200USD 89MRetention campaign
Single Product HoldersMED 16%124,000USD 228MCross-sell AI nudge
Claims-DissatisfiedHIGH 31%18,400USD 34MCEO apology + resolve
Digital-Only CustomersLOW 8%420,000USD 774MLoyalty programme
HNW Multi-LineLOW 5%12,800USD 235MPriority service

Voice of Customer AI SENTIMENT

Overall Sentiment
Positive 68%
Claims Experience
Negative 42%
Digital Experience
Positive 81%
Price Satisfaction
Neutral 54%
Top Complaint
Repair Delay
⬢ CUSTOMER AGENT RECOMMENDATION
Priority action: Resolve claims repair delay — SLA breach affecting NPS. Estimated NPS recovery +6pp if repair TAT reduced from 32 to 18 days. Decision: Renegotiate repairer contracts.
Initiate Action
View Complaints
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
ENTERPRISE RISK INTELLIGENCE · STRATEGIC · INSURANCE · CYBER · CLIMATE · REGULATORY

Enterprise Risk Intelligence

AI continuously monitors, predicts and connects risks across the enterprise — surfacing decision implications and weak signals before they become crises.

Enterprise Risk Index
68/100
ELEVATED · cyber + climate leading
Top Risk
Cyber SME
Uninsured exposure + new product risk accumulation
Emerging Risks
7
AI liability, climate transition, PFAS exposure new
Risk Appetite Utilisation
74%
Within board-approved appetite · 4pp headroom

Risk Heat Map LIVE · AI UPDATED

↑ IMPACTLIKELIHOOD →
Low Medium High Critical

Risk Register — Decision Implications

RiskRAGTrendDecision Required
Cyber accumulationCRITICALProduct cap + reinsurance
Climate — flood PMLHIGHTreaty renewal priority
Motor claims inflationHIGHPricing +7% (Decision #2)
Reserve adequacyHIGHUSD 42M increase (Decision #3)
Vietnam country riskMEDIUMEntry approved — mitigation built in
AI model riskMEDIUMGovernance framework Q3
Regulatory — ICS 2.0MEDIUMCapital modelling review
Talent — actuarialLOWSuccession plan in place
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
SCENARIO SIMULATION CENTER · WHAT-IF EXECUTIVE INTELLIGENCE · LIVE RECALCULATION

Scenario Simulation Center

Change any variable — claims inflation, interest rates, cat events, growth — and watch all enterprise KPIs recalculate in real time. AI recommends the best strategy for each scenario.

Executive Variable Controls LIVE SIMULATION

8%
4.0%
USD 80M
+8%
+7%
62%
86%
Base Case
Stress Scenario
Upside Scenario
Cat Event
⬢ SCENARIO RESULT · AI RECALCULATED
NET PROFIT
USD 184M
BASE
COMBINED RATIO
97.4%
TARGET 95%
GWP
USD 2.84B
+8.2%
ROE
12.4%
TGT 14%
SOLVENCY
184%
SECURE
⬢ AI RECOMMENDATION FOR THIS SCENARIO
Base scenario: Implement motor pricing +7% (Decision #2) and Vietnam entry (Decision #1) to protect combined ratio and ROE trajectory. Reserve increase mandatory regardless of scenario.

Scenario Library

🔵 Base Case (current)
CR 97.4% · ROE 12.4%
🔴 Claims Crisis (+20%)
CR 104% · ROE 7.1%
🟡 Cat Major (USD 150M)
CR 102% · Solvency 168%
🟢 Upside (All decisions)
CR 94% · ROE 15.8%
⚡ Rate +10% Scenario
CR 94.2% · ROE 14.4%
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
EXECUTIVE COPILOT · CONVERSATIONAL DECISION INTELLIGENCE · AI-POWERED

Executive Copilot

Ask anything. The AI synthesises evidence across all enterprise data, models and decisions — answering in plain English with supporting evidence and confidence scores.

⬢ INSURED COPILOT · DECISION AI
Good morning. I have analysed today's enterprise picture. 5 decisions require your attention, with USD 612M enterprise value at stake.

Key alerts: Motor claims severity is up 14% MoM — pricing action needed within 8 days. Vietnam Board paper due in 4 days. Reserve adequacy gap of USD 42M identified. I am ready to help you think through any of these.

What would you like to explore first?
Why did profit fall?
Claims +12% impact?
Highest value decision?
Board briefing
Vietnam opportunity
Biggest risk?
Regulator briefing
SEND →
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
AI EXECUTIVE AGENTS · 12 SPECIALISED AGENTS · ALL ACTIVE · DECISION CONTRIBUTION

AI Executive Agents

12 specialised AI agents continuously monitoring, analysing and recommending across every dimension of the enterprise. Each agent contributes to active decisions.

Active Agents
12/12
All agents operational · 99.97% uptime
Recommendations Today
38
CEO agent leading · 14 actioned
Avg Agent Confidence
89.2%
Actuarial 94% · Fraud 87% highest/lowest
Decision Contributions
5
All active decisions have ≥2 agent inputs
👑
CEO Agent
Enterprise strategy synthesis, Board papers, cross-functional decision orchestration
TASK: Vietnam Board Paper · 4-day deadline
ACTIVE · CONF 91%
💰
Finance Agent
P&L analysis, capital allocation, ROE optimisation, embedded value
TASK: Capital waterfall Q3
ACTIVE · CONF 93%
📋
Claims Agent
Severity monitoring, leakage detection, settlement optimisation, reserve adequacy
TASK: Reserve increase evidence — Decision #3
ACTIVE · CONF 88%
Risk Agent
Enterprise risk monitoring, emerging risk sensing, risk interconnectedness
TASK: Cyber accumulation limit review
ACTIVE · CONF 90%
💹
Pricing Agent
Actuarial pricing, rate adequacy, competitor benchmarking, portfolio optimisation
TASK: Motor +7% decision evidence
ACTIVE · CONF 94%
📊
Actuarial Agent
Reserve adequacy, catastrophe modelling, capital modelling, embedded value
TASK: Q2 reserve adequacy review
ACTIVE · CONF 92%
👤
Customer Agent
LTV, churn prediction, sentiment analysis, customer journey optimisation
TASK: Motor renewal retention brief
ACTIVE · CONF 87%
🔍
Fraud Agent
Fraud ring detection, claims anomaly, network analysis, SIU triage
TASK: Fraud ring SIU referral
ACTIVE · CONF 87%
Compliance Agent
Regulatory monitoring, ICS 2.0, licensing, reporting obligations
TASK: Vietnam licence conditions tracking
ACTIVE · CONF 95%
🗺
Strategy Agent
Market expansion, M&A screening, competitive intelligence, strategic planning
TASK: Vietnam entry decision pack
ACTIVE · CONF 89%
🌱
ESG Agent
Climate risk, TCFD reporting, ESG ratings, sustainability strategy
TASK: Climate stress test report
ACTIVE · CONF 84%
📝
Board Secretary Agent
Board papers, committee minutes, regulatory submissions, governance records
TASK: Q2 Board pack — Vietnam paper
ACTIVE · CONF 96%
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
ENTERPRISE KNOWLEDGE GRAPH · EVERYTHING CONNECTED · CLICK TO EXPLORE

Enterprise Knowledge Graph

The full enterprise as an interconnected intelligence map. Policies, claims, customers, reinsurance, regulations, financials and AI models — all relationships visualised and explorable.

Node Legend

Core Entity
Financial
Risk
AI/Model
Customer/Partner

Knowledge Connections

Total Nodes
2,840
Active Relationships
18,420
AI Models Mapped
34
Regulatory Links
142
Reinsurance Nodes
28
Decision Links
5 active
Explore Full Graph
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
DECISION MEMORY · RATIONALE · EXPECTED VS ACTUAL · INSTITUTIONAL LEARNING

Decision Memory

Every decision — who approved it, why, on what evidence, what they expected, and what actually happened. Decision memory is what stops the enterprise relitigating settled questions and repeating available mistakes. The gap between expected and actual is what trains the next recommendation.

Decisions in Memory
86
All retrievable with full rationale · 23 this year
Decision Quality Score
87%
▲ 3.2pp vs prior year · AI learning effect
Value Delivered
USD 84M
Realised from executed decisions QTD
AI Accuracy (vs outcome)
91.4%
Of AI recommendations that proved correct

Decision Log — Rationale & Outcome Trail

Jun 2026
Digital Distribution Expansion — APPROVED DELIVERED
CEO + COO · USD 85M investment · Digital GWP ▲22% YoY · ROI tracking at 18.4% vs 16% target · Decision quality: Excellent
May 2026
Claims Automation Phase 1 — APPROVED IN FLIGHT
COO + CFO · Auto-settlement under USD 5K · 45% implementation complete · SLA improvement: 8 days ahead of plan
Apr 2026
Property Portfolio Expansion +20% — APPROVED DELIVERED
CUO · SME property new GWP USD 48M in 90 days · Loss ratio 57.8% vs 62% modelled · Decision quality: Excellent
Mar 2026
Bangladesh Market Entry — REJECTED CLOSED
Board · Regulatory uncertainty + capital requirement exceeded risk appetite · AI recommendation was DEFER — aligned with Board decision · Lesson: Market entry requires regulatory pre-approval
Feb 2026
Reinsurance Treaty Renewal — APPROVED DELIVERED
CRO + CFO · Cat cover USD 380M secured · Premium -4% vs prior year · New cyber aggregate cover added · AI confidence 96% — outcome matched
Jan 2026
Bancassurance Partnership — MFIN Bank — APPROVED DELIVERED
CEO · 5-year exclusive · 280 branches · GWP potential USD 180M · Year 1 on track at USD 42M · AI recommendation: APPROVE — confidence 89%
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
AI GOVERNANCE · MODEL INVENTORY · AUDIT · EXPLAINABILITY · BIAS · COMPLIANCE

AI Governance

Every AI model used in decision-making is inventoried, monitored and audited. Human oversight is enforced. Explainability, bias and compliance are tracked in real time.

AI Models in Production
34
12 agents · 22 analytical models · all audited
Model Health
96%
2 models under review · 32 healthy
Human Override Rate
12%
▼ 3pp YoY · AI quality improving
Bias Alerts
0
All models within fairness thresholds · clean

Model Inventory 34 MODELS

ModelTypeHealthLast AuditDecisions
Motor Pricing AIPricingHEALTHYJun 2026Decision #2
Claims Severity ModelPredictiveHEALTHYMay 2026Reserve #3
Fraud Detection V3DetectionHEALTHYJun 2026Fraud ring
Churn PredictionRetentionHEALTHYApr 2026Retention
Cat AccumulationRiskREVIEWMar 2026Reinsurance
Vietnam Market AIStrategyHEALTHYJun 2026Decision #1
LTV SegmentationCustomerHEALTHYMay 2026Cross-sell
Reserve Chain LadderActuarialREVIEWJun 2026Decision #3

AI Governance Maturity

L4
Transparency
L4
Auditability
L3
Fairness
L5
Human OL
L3
Privacy
Policy Engine Status
ACTIVE — 142 rules
Explainability (XAI)
All models — LIME/SHAP
Regulatory Framework
MAS FEAT · AIRG (proposed) · MindForge · IMDA · PDPA
Board AI Policy
Approved Mar 2026
Next Governance Review
Sep 2026
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
STRATEGY EXECUTION · CORPORATE OBJECTIVES · DECISION DEPENDENCIES · INITIATIVE TRACKING

Strategy Execution

Corporate strategy as a live, AI-monitored execution system. Every strategic objective has decision dependencies, KPIs and risk signals — updated continuously.

Strategy Score
74%
6 of 8 pillars on track · 2 at risk
Initiatives Active
18
12 on track · 4 delayed · 2 blocked
Blocked Decisions
2
Vietnam + Reserve blocking 3 downstream initiatives
Strategic Risks
3
Cyber · Climate · Digital disruption

Strategic Pillars — AI-Monitored Execution

Strategic PillarStatusKPITargetActualKey DecisionOwner
Geographic ExpansionAT RISKNew markets2 by 20271 completeVietnam — Decision #1CEO
Digital TransformationON TRACKDigital GWP %30%22% ▲Digital invest approvedCDO
Underwriting ExcellenceAT RISKCombined Ratio95%97.4%Motor pricing Decision #2CUO
Customer CentricityAT RISKNPS+50+42Claims reform initiativeCCO
Capital OptimisationON TRACKROE14%12.4% ▲Capital allocation planCFO
Product InnovationON TRACKNew products3 launches2 completeCyber SME — Decision #4CUO
AI & Data StrategyON TRACKAI adoption80%74%AI governance approvedCDO
Distribution TransformationON TRACKChannel efficiencyCR 92%91% avgAgency rationalisation #5COO
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
DECISION FABRIC · INTERCONNECTED DECISION INTELLIGENCE · NO OTHER PLATFORM DOES THIS

Decision Fabric

Executive decisions are never isolated. The Decision Fabric visualises how one decision cascades through the entire enterprise — showing every downstream consequence before you approve anything.

Decisions in Fabric
5
48 dependency links mapped · 3 critical chains
Cascade Risk
HIGH
Motor pricing triggers 7 downstream effects
Conflict Detected
2
Vietnam + Reserve increase compete for capital
Optimal Sequence
AI SET
Reserve → Pricing → Vietnam → Cyber → Agency

Decision Dependency Graph LIVE · AI MAPPED

DECISION #2 Motor Pricing +7% DECISION #1 Vietnam Market Entry DECISION #3 Reserve Increase USD 42M 1ST ORDER EFFECTS CHURN RISK Customer Churn +2.1% UW MARGIN Combined Ratio -1.2pp CAPITAL Solvency 184%→168% GEO REVENUE Vietnam GWP +USD 40M 2ND ORDER EFFECTS DISTRIBUTION Agency Cost +0.4pp NET PROFIT Profit USD 184M→196M DIVIDEND Dividend Hold H1 2027 SOLVENCY Capital Ratio 168% INVESTOR Re-rating +8% upside OUTCOME EV CREATED +USD 284M HIGH CONF IF DELAYED -USD 128M

Motor Pricing Decision — Full Cascade Chain

💹
Motor Premium +7%
DECISION #2
Customer Churn +2.1%
-USD 89M LTV
Agency Commission Pressure
+0.4pp cost
Underwriting Profit +USD 18M
+USD 18M
Net Profit +USD 14.2M
+USD 14.2M
Capital Generation +USD 8M
+USD 8M pa
Dividend Policy: Maintained
STABLE

Decision Economics — ROI Ranking AI

DecisionEV CreatedCapitalTimeROIRank
Cyber SME Launch+USD 190MUSD 45M18mo422%#1
Vietnam Entry+USD 180MUSD 120M36mo150%#2
Motor Pricing +7%+USD 14.2MUSD 03mo#3
Agency Rationalise+USD 22MUSD 8M12mo275%#4
Reserve IncreaseRisk mitigationUSD 42M0moMandatoryMUST
⬢ AI OPTIMAL DECISION SEQUENCE
Approve in order: (1) Reserve — mandatory risk protection. (2) Motor Pricing — immediate ROI, no capital. (3) Vietnam — highest strategic value creation. (4) Cyber — highest ROI, Q4 launch. (5) Agency — operational improvement, 12-month horizon.
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
ENTERPRISE VALUE FLOW · ABOVE KPIs · VALUE CREATED · PROTECTED · AT RISK · DESTROYED

Enterprise Value Flow

Not KPIs. Value. Every executive decision is framed by its enterprise value consequence — created, protected, at risk or destroyed. This is the level at which Boards and investors think.

▲ VALUE CREATED
USD 284M
From 3 active decisions
Confidence: 88% avg
◎ VALUE PROTECTED
USD 612M
Reserve + Risk + Pricing
5 decisions active
⚠ VALUE AT RISK
USD 128M
If decisions delayed >30 days
3 decisions at deadline
✕ VALUE DESTROYED
USD 42M
Claims severity Q2 2026
Cost of delay: USD 1.4M/day

Decision Value Ledger EVERY DECISION PRICED

#1
Approve Vietnam Market Entry
EV +USD 180M 36 MONTHS CONF 91%
+USD 180M
Cost of delay: USD 2.8M/mo
#2
Motor Pricing +7%
EV +USD 14.2M 3 MONTHS CONF 94%
+USD 14.2M
Cost of delay: USD 1.4M/mo
#3
Reserve Adequacy USD 42M
MANDATORY SOLVENCY RISK CONF 88%
Protected
Risk of inaction: CRITICAL
#4
Cyber SME Product Launch
EV +USD 190M IRR 22% CONF 86%
+USD 190M
Window: 6 months
#5
Agency Rationalisation
EV +USD 22M 12 MONTHS CONF 79%
+USD 22M
Payback: 4.4 months

Value Realisation Timeline AI PROJECTED

Q3 2026 (Reserve + Pricing)+USD 14M
Q4 2026 (Cyber Launch)+USD 48M
Q1 2027 (Vietnam entry)+USD 40M
FY 2027 (All decisions)+USD 284M
FY 2028 (Full maturity)+USD 520M

Cost of Delay Calculator

Every day motor pricing delayed
USD 47K lost
Every day Vietnam decision delayed
USD 93K lost
Every day reserve not increased
Regulatory risk ↑
30-day delay — total cost
USD 4.2M
90-day delay — total cost
USD 12.6M + window lost
⬢ BOARD-LEVEL FRAMING
The cost of good, fast decisions is small. The cost of slow, poor decisions is catastrophic. Every day without Decision #2 is USD 47K destroyed. Approve today.
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
ENTERPRISE DIGITAL TWIN · SIMULATE THE FUTURE COMPANY · RUN BEFORE YOU DECIDE

Enterprise Digital Twin

Not variable sliders. A complete virtual replica of the enterprise. Ask "what if" — and watch a simulated future company unfold in real time before you commit capital or strategy.

Twin Status
LIVE
Synced to enterprise data · 847 signals · real-time
Scenarios Run
48
This session · 12 saved to scenario library
Twin Accuracy
94.2%
Validated against 18 historical outcomes
Active Scenario
BASE CASE
Current operating state · no changes applied
⬡ DIGITAL TWIN — SCENARIO SELECTOR
Base Case
Rates Fall 2%
Major Cat Event
Claims Surge +20%
All Decisions Approved
Recession Scenario
Cyber Attack
Vietnam Entry Only
Current operating state. All decisions pending. No scenario changes applied.
◉ CURRENT COMPANY — NOW
GWP
USD 2.84B
TODAY
Net Profit
USD 184M
TODAY
Combined Ratio
97.4%
TODAY
ROE
12.4%
TODAY
Solvency
184%
TODAY
NPS
+42
TODAY
Customers
2.84M
TODAY
EV
USD 3.2B
TODAY
TWIN SIMULATION
⬡ VIRTUAL COMPANY — 18 MONTHS
GWP
USD 3.07B
▲ 8.1%
Net Profit
USD 196M
▲ USD 12M
Combined Ratio
95.8%
▼ 1.6pp
ROE
13.8%
▲ 1.4pp
Solvency
171%
▼ 13pp
NPS
+44
▲ 2
Customers
2.96M
▲ 4.2%
EV
USD 3.48B
▲ USD 280M

Twin Intelligence Narrative AI GENERATED

Base Case: With current trajectory and decisions pending, the virtual company in 18 months shows moderate improvement: GWP ▲8.1% driven by organic growth, Combined Ratio improving to 95.8% if motor pricing is approved, ROE reaching 13.8%. Capital deployed on Vietnam reduces solvency to 171% — still comfortable. Embedded Value ▲USD 280M. Key risk: If motor pricing is delayed beyond August 2026, Q3 combined ratio breaches 99%, eroding USD 18M profit and forcing more aggressive reserve action.
Save Scenario
Export for Board
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
ENTERPRISE COGNITIVE INFRASTRUCTURE · ECI · BEYOND DASHBOARDS · A NEW MARKET CATEGORY

Enterprise Cognitive Infrastructure

InsurEDIC³ is not a dashboard. It is the world's first Enterprise Cognitive Infrastructure — a living digital layer that gives the enterprise the ability to sense, reason, simulate, decide, execute and learn as a unified cognitive system.

ECI Maturity
Level 3
Decision Intelligence → targeting Level 4 ECI by Q2 2027
Cognitive Layers
8
Fully operational from Sensing to Enterprise Memory
Enterprise Memory
18 decisions
Outcomes fed back · AI accuracy ▲3.2pp last quarter
Market Category
VALIDATED
Gartner MQ for Decision Intelligence Platforms · Jan 2026

Enterprise Nervous System Architecture 8 COGNITIVE LAYERS

LAYER 1
SENSING
👁
Enterprise Sensors
847 signals/hr · 14 live data sources · Claims FNOL, pricing APIs, regulatory wires, cat models, market intelligence — the enterprise's sensory nervous system
LIVE
LAYER 2
MEMORY
🧠
Enterprise Knowledge Graph
2,840 nodes · 18,420 relationships · Organisational memory, ontology, causal maps — how the enterprise understands itself and its environment
ACTIVE
LAYER 3
REASONING
🕸
Decision Graph / Fabric
5 decisions · 48 dependency links · 3 cascade chains — the enterprise's reasoning capability, mapping cause and consequence across every strategic choice
RUNNING
LAYER 4
COGNITION
AI Executive Agents
12 specialised cognitive agents — each a domain expert continuously analysing, recommending and contributing evidence to the decision pipeline. Avg confidence 89.2%
12 LIVE
LAYER 5
IMAGINATION
Enterprise Digital Twin
Complete virtual replica · 48 scenarios available · 94.2% accuracy · The enterprise's imagination — run the future before committing to it
TWIN LIVE
LAYER 6
CORTEX
Decision Intelligence Engine
9-stage pipeline · 5 active decisions · USD 612M value orchestrated — the executive cortex, synthesising every signal into ranked, evidence-backed decisions
RUNNING
LAYER 7
ACTION
🚀
Orchestrated Execution
3 decisions executing · 8 business units · 14 systems orchestrated — decisions don't just get made, they get done, tracked and measured against value targets
3 ACTIVE
LAYER 8
LEARNING
🔄
Enterprise Memory & Learning
18 outcomes captured · AI accuracy ▲3.2pp from learning · The enterprise grows smarter with every decision — organisational experience encoded and reused
LEARNING

ECI Maturity Roadmap — From Dashboard to Cognitive Enterprise

V1 Executive
Dashboard
V2 Decision
Intelligence
← NOW
V3 Decision
Operating
System
V4 Enterprise
Decision
Infrastructure
V5 Enterprise
Cognitive
Infrastructure

Why Enterprise Cognitive Infrastructure? POSITIONING

🧠
Knowledge Graph = Enterprise Memory
Not just a data store — the enterprise knows what it knows, how things connect and what has happened before.
COGNITIVE CAPABILITY · MEMORY
🕸
Decision Fabric = Enterprise Reasoning
The ability to reason across interconnected decisions — not execute them in isolation — is what separates cognitive from intelligent.
COGNITIVE CAPABILITY · REASONING
Digital Twin = Enterprise Imagination
The capacity to simulate a future state before committing to it. This is the definition of strategic intelligence.
COGNITIVE CAPABILITY · IMAGINATION
🔄
Learning Layer = Enterprise Experience
Every decision outcome makes the system smarter. The enterprise accumulates experience — not just data.
COGNITIVE CAPABILITY · EXPERIENCE

Market Category Positioning

Category Name
Enterprise Cognitive Infrastructure
Abbreviation
ECI
vs. BI/Analytics
Decisions, not reports
vs. ERP
Intelligence, not transactions
vs. AI Copilots
Enterprise-wide, not point solutions
vs. Decision Intelligence Platforms
Insurance-native, not horizontal
vs. AI Governance Platforms
Intervenes at runtime, not observes after
vs. Insurance Point AI
Supervises every lane, not one
Defensibility
Knowledge graph + decision learning moat
Beachhead market
Insurance — P&C · Life · Reinsurance · Takaful
Adjacent verticals
Banking · Healthcare · Defence · Government
Expansion logic
Same decision architecture · re-skinned per vertical
⬢ CATEGORY CREATION THESIS
Stop competing in "executive dashboards." The decision layer is now an analyst-recognised category — Gartner published its first Magic Quadrant for Decision Intelligence Platforms in January 2026, naming FICO, SAS, IBM, Aera, ACTICO and Quantexa as Leaders. A category with a quadrant has a budget line. None of the six is insurance-native, and none enforces decisions at runtime. That is the open ground.
Insurance is the beachhead, not the ceiling. It is chosen deliberately: the highest decision density, the heaviest regulatory evidence burden, and the clearest value-at-stake per decision of any vertical. A decision operating system that survives an insurance regulator survives anywhere. The eight cognitive layers, the decision object model and the control tower are vertical-agnostic — only the sensing sources and the domain ontology change when the same architecture is re-skinned for banking, healthcare, defence or government.
▸ PROTOTYPE · ILLUSTRATIVE DATA ONLY · InsurEDIC³ · FUTUREINSURANCE.AI LLP · CONCEPT BY ZAID HAMZAH · delivervalue.ai
GOVERNMENT VIEW · ILLUSTRATIVE AGENCY: MONETARY AUTHORITY OF SINGAPORE · SUPERVISION → DEVELOPMENT → RESILIENCE → GROWTH

Regulatory Command Centre

The supervisory and industry-development picture in one operating view. Two questions run through every panel: what is the industry being asked to comply with, and what is the industry being asked to build. Everything here sits at sector aggregate level — InsurEDIC³ holds no firm-level book, and this view cannot produce one.

Licensed Insurance Entities
337
Insurers, reinsurers, captives and brokers on the MAS register, June 2026 SOURCED
Total Industry Premiums
S$78B
Life, general and reinsurance, end-2024; grew >8% p.a. over 2019–2024 MAS
Industry Workforce
~19,000
Insurance sector employment, MAS SIRC address Nov 2025 MAS
Asia Nat Cat Uninsured
>90%
Of ~US$65B economic losses across Asia in 2025 MAS
Live Regulatory Workstreams
11
Tracked instruments in consultation, transition or in force DERIVED

Four Supervisory Pillars — Current Posture

PILLAR 01
AI & Model Risk TRANSITION
The proposed Guidelines on AI Risk Management move the sector from ethical principles to supervisory expectations across the full AI life cycle, covering generative AI and autonomous agents, with board and senior management accountability at the top and a proposed twelve-month transition.
PILLAR 02
Capital & Valuation IN FORCE
RBC 2 under Notice 133 governs supervisory intervention levels, valuation of policy liabilities and total risk requirements. Amendments have layered in equity counter-cyclical adjustment, structured product and infrastructure treatment, and AT1 / Tier 2 recognition criteria.
PILLAR 03
Operational Resilience TIGHTENING
Four workstreams run in parallel — operational risk management, technology and cyber risk, third-party risk, and business continuity management — with consultations issued on operational and third-party risk and planned updates to the technology risk notices.
PILLAR 04
Growth & Risk Transfer EXPANDING
The development agenda: the proposed Protected Cell Company framework, the refreshed ILS Grant Scheme running 2026–2028, captive growth, and the standing objective of deepening Singapore's role as a regional risk management hub.

Priority Supervisory Signals

HIGH
AI Risk Management Guidelines pending finalisation
MAS
Consultation P017-2025 ran 13 November 2025 to 31 January 2026 and applies to all financial institutions, proportionate to size, activity and risk profile. In a written parliamentary reply on 5 August 2026, MAS Chairman and Deputy Prime Minister Gan Kim Yong stated the proposed Guidelines apply to all AI use cases by financial institutions including agentic AI and will be finalised soon, without giving a date. Platform implication: firms that wait for the final text will have less of the twelve-month transition left to use. The AI inventory, the three-lines-of-defence mapping and the life-cycle control set can be built now against the consultation text.
MEDIUM-HIGH
Third-party AI governance cannot be delegated to vendors
MAS
The proposed Guidelines cover third-party AI tools. An insurer running a vendor pricing engine, a vendor fraud model or a vendor claims triage agent still owns the governance of that model. Platform implication: shared infrastructure only helps if it ships with the evidence — every InsurEDIC³ agent carries a model card, a validation record and a decision trail the participant can hand to a supervisor as their own.
MEDIUM
Transition planning guidelines now final for insurers
SOURCED
Final Guidelines on Transition Planning for banks, insurers and asset managers were published in March 2026, building on the Guidelines on Environmental Risk Management and its transition-planning addendum. Insurers are expected to manage transition and physical climate risk as part of a sound planning process, with climate scenario analysis across their portfolios.
DEVELOPMENT
Protected Cell Company consultation closed 7 August 2026
MAS
Consultation P013-2026 opened 7 July 2026 proposing a new Protected Cell Companies Act — a single legal entity with a Core and legally segregated Cells, open to MAS-licensed entities carrying out captive insurance, insurance-linked securities and sovereign risk pools. A draft PCC Act and the subsidiary legislation will be consulted on separately. FutureInsurance.ai LLP filed a response to this consultation and has since responded to MAS follow-up queries on cell-level re-domiciliation.

What the Regulator Is Asking the Industry to Build

Development objectiveInstrumentWhat it asks of industryStatus
Alternative risk transfer capacityProposed PCC frameworkStand up captive, rent-a-captive, ILS and sovereign risk pool structures without incorporating a separate legal entity per programme — the cost of the vehicle was the deterrent, not the appetiteCONSULTED
ILS market depthILS Grant Scheme, Jan 2026 – Dec 2028Issue catastrophe bonds, collateralised reinsurance and sidecars from Singapore. Property cat bonds with any APAC risk attract 70% of upfront issuance costs capped at S$1m; without APAC risk, 50% capped at S$1m; non-property cat bonds 70% capped at S$1m; collateralised reinsurance and sidecars 70% capped at S$500,000; renewals 30% capped at S$500,000 MASOPEN
Closing the Asian protection gapPolicy priorityBuild products and capacity for underinsured Asian risk — natural catastrophe, longevity, mortality, operational and cyberSTANDING
Centre-of-excellence capabilityGlobal-Asia Insurance PartnershipA tripartite partnership between the global insurance industry, regulators and policymakers, and academia, established in Singapore with an initial focus on pandemic and climate riskOPERATING
Regional disaster risk financingSEADRIFSupport the first regional catastrophe risk facility established in Asia by ASEAN member states, incorporated and licensed as a general insurer in Singapore in October 2019OPERATING
AI-ready workforceGenAI Jobs Transformation MapMAS and IBF, with Workforce Singapore, partnered eleven financial institutions — including Income Insurance, Manulife and Prudential — to pilot workforce transformation and prepare the sector workforce for an AI-enabled future IBFRUNNING
▸ Sources: MAS media releases and consultation papers; MAS Insurance-Linked Securities Grant Scheme page; MAS Insurance and Risk Financing Initiatives page; IBF GenAI Jobs Transformation Map. Retrieved September 2026.
GOVERNMENT VIEW · HORIZON SCANNING · CONSULTATION → TRANSITION → IN FORCE → SUPERVISED

Regulatory Signal Scanner

Every regulatory instrument moves through the same life cycle, and every stage of that life cycle imposes a different decision on a licensed entity. The scanner tracks where each instrument sits, what it triggers, and how much runway remains before the obligation bites.

In Consultation / Pending Final
3
Comment window closed, final text awaited
In Transition
4
Published, implementation runway open
In Force & Supervised
14
Active examination exposure
Development Incentives Open
3
Grant or framework windows available

Instrument Life Cycle Board

InstrumentStageKey dateDecision it forces on a licensed insurer
Guidelines on AI Risk Management
Consultation P017-2025
PENDING FINALConsulted 13 Nov 2025 – 31 Jan 2026 MASBuild the AI inventory now or wait. Board-level AI oversight, three lines of defence, life-cycle controls from development through retirement, and materiality-based proportionality. A twelve-month transition is proposed from issuance
Protected Cell Companies framework
Consultation P013-2026
PENDING FINAL7 Jul – 7 Aug 2026 MASWhether to restructure existing SPV-based risk programmes into Cells once the Act is in force. Draft PCC Act and subsidiary legislation to be consulted on later
Operational Risk Management guidelinesPENDING FINALConsultation issued SOURCEDRe-map the operational risk taxonomy and control library against the revised expectations
Third-Party Risk Management guidelinesTRANSITIONConsultation issued SOURCEDRe-tier the vendor estate, including AI vendors, and rebuild the exit and concentration analysis
Guidelines on Transition PlanningTRANSITIONFinal, March 2026 SOURCEDStand up a transition plan covering physical and transition climate risk for both the underwriting book and the investment portfolio
Technology Risk Management noticesTRANSITIONConsultations planned SOURCEDPrepare for IT asset management, continuous system monitoring and enhanced oversight of critical systems
Notice 133 — AT1 / Tier 2 criteriaTRANSITIONFrom 1 Jan 2026 MASCapital instruments recognised as AT1 or Tier 2 under RBC 2 must be sold only to persons who are not retail investors in Singapore
Notice 133 — Valuation and Capital Framework (RBC 2)IN FORCEAmended 19 Dec 2022 MASSupervisory intervention levels, valuation of life and general policy liabilities, and total risk requirement calculation. Applies to all licensed insurers; sections 2–5 exclude captives, marine mutuals and SPRVs
IFRS 17 Insurance ContractsIN FORCEEffective 1 Jan 2023 SOURCEDPortfolio definition and aggregation level, LIC and LRC calculation tooling, risk adjustment methodology, and revenue recognition pattern aligned to the underwriting plan
Guidelines on Environmental Risk Management (Insurers)IN FORCEPlus transition addendum MASBoard-level sustainability goals and risk tolerance; climate scenario analysis across portfolios; environmental risk disclosure
Guidelines on Individual Accountability and ConductIN FORCEFive high-level outcomes MASName senior managers, map material risk personnel, and evidence conduct standards across all employees
Guidelines on OutsourcingIN FORCERisk management of outsourcing MASRegister, assess and monitor outsourcing arrangements including cloud and analytics
Management of Outward Reinsurance ArrangementsIN FORCERequirements and principles MASDocument reinsurance strategy, counterparty selection and credit exposure control
AML / CFT for general and A&H businessIN FORCEGuidelines MASProcesses and controls to prevent money laundering and counter terrorism financing across general, reinsurance and accident and health business
Policy Owners' Protection SchemeIN FORCEDIPOP-N02 MASDirect insurers comply with scheme membership and levy obligations
ILS Grant SchemeINCENTIVE OPENJan 2026 – Dec 2028 MASWhether to bring an issuance to Singapore. Now extended to non-APAC risks and renewals, while prioritising issuances addressing APAC protection needs
FinTech Regulatory SandboxINCENTIVE OPENStanding MASWhether to test a new insurance innovation in a controlled environment before wider adoption
GenAI Jobs Transformation MapINCENTIVE OPENMAS · IBF · WSG IBFWhether to map AI adoption to specific job roles and enter the upskilling and reskilling pipeline
▸ Compiled from MAS publications, notices, guidelines and consultation pages, the MAS Compliance Toolkit for Direct Insurers and Reinsurers, and IBF publications, retrieved September 2026. Stage classifications are this platform's reading of published status, not a MAS classification. Verify current status against MAS before relying on any entry.
GOVERNMENT VIEW · CONSULTATION P017-2025 · FEAT → VERITAS → MINDFORGE → AIRG

AI Risk Management Readiness

Singapore's AI supervision arrived in pieces — the FEAT Principles in 2018, the Veritas Initiative, the AI Model Risk Management information paper in 2024, Project MindForge on generative AI. The proposed Guidelines pull those pieces into one instrument and shift the register from ethical principles to supervisory expectations. This view maps what that means operationally.

Lineage of the Instrument

2018

FEAT Principles

Fairness, Ethics, Accountability and Transparency in the use of AI and data analytics in the financial sector.

FROM 2019

Veritas Initiative

Industry consortium work turning the FEAT principles into assessment methodology and open-source toolkits.

2024

AI Model Risk Management

Information paper on observed practices in AI model risk management across financial institutions.

2024

Project MindForge

Generative AI risk framework, with attention to hallucination, prompt injection and data leakage.

NOV 2025

Proposed AIRM Guidelines

Consolidated supervisory expectations across the full AI life cycle, including generative AI and autonomous agents.

▸ Lineage compiled from MAS publications and secondary commentary on Consultation P017-2025. SOURCED

Five Expectation Domains — What Each Requires

1 · Oversight and Governance BOARD LEVEL

The draft places the board and senior management at the centre of AI risk governance — the point being that AI risk is a leadership responsibility, not something delegated to the technology function.

Board AI risk mandate
Named, minuted, standing
Three lines of defence
Mapped to AI specifically
Escalation thresholds
Defined per materiality tier
InsurEDIC³ component
AI Control Tower · governance ledger

2 · Risk Management Systems, Policies and Procedures

An enterprise-wide AI risk framework with systematic internal control procedures, applied proportionately to the size and nature of activities, use of AI, and risk profile.

AI inventory
Comprehensive, current, owned
Materiality assessment
Documented method per use case
Scope of "AI"
ML, deep learning, RL, GenAI, agents
InsurEDIC³ component
Model registry · materiality scoring kit

3 · Life Cycle Controls

Controls across development, deployment, monitoring and retirement — not a single gate at go-live.

Development
Data lineage, bias testing, documentation
Deployment
Independent validation, sign-off, rollback
Monitoring
Drift, performance, fairness, incidents
Retirement
Decommissioning and record retention
InsurEDIC³ component
Decision Ledger · agent lifecycle harness

4 · Capabilities and Capacity SCARCEST

The Guidelines address the capabilities and capacity needed for the use of AI. This is the domain a platform cannot solve with software alone — it is why the Academy sits inside InsurEDIC³ rather than beside it.

Model validation capacity
Sector-wide constraint
Second-line AI competence
Sector-wide constraint
Board AI literacy
Uneven
InsurEDIC³ component
AI for Insurance Academy · role pathways

Third-Party and Agentic AI — the Two Hard Edges

Vendor AI does not transfer the obligation

Third-party AI tools fall within scope. An insurer cannot delegate governance to the vendor. In practice this means every externally supplied model — pricing, fraud, triage, document extraction, chat — needs the same inventory entry, materiality classification, validation evidence and monitoring record as an internally built one.

⬢ HOW INSUREDIC³ ANSWERS THIS
Every agent on the platform ships with an evidence pack the participant owns outright: model card, training and evaluation provenance, validation record, monitoring configuration, decision trail and retirement plan. The participant hands the supervisor its own file, not a vendor's assurance letter. Shared infrastructure that cannot be evidenced is a liability, not a saving.

Agentic AI is explicitly in scope

The proposed scope covers generative AI, AI agents and newer technologies. MAS has confirmed publicly that the Guidelines apply to all AI use cases including agentic AI. An agent that can take an action — bind, decline, settle, refer, price — is a different supervisory object from a model that produces a score a human then acts on.

⬢ HOW INSUREDIC³ ANSWERS THIS
The AI Control Tower is the enforcement point, not an observation deck. Every agent runs inside an autonomy ladder with an explicit authority envelope: what it may decide alone, what it must recommend for human approval, what it may never touch, and the value and materiality thresholds at which authority is withdrawn automatically. Autonomy is granted per agent, per use case, per threshold — and it is revocable at runtime.

Sector Readiness — Anonymised Aggregate

Control domainEstablishedIn progressNot startedWhere the gap concentrates
Board-level AI risk mandate58%31%11%Branches and smaller general insurers
Comprehensive AI inventory34%47%19%Shadow AI in distribution and operations teams
Documented materiality method29%44%27%No industry-standard scale to anchor against
Independent model validation41%38%21%Validation capacity, not validation policy
Post-deployment drift monitoring26%41%33%Vendor-hosted models with no telemetry access
Third-party AI governance22%39%39%Contracts predating the AI question entirely
GenAI-specific controls19%42%39%Hallucination, prompt injection, data leakage
Agentic authority envelopes9%27%64%The newest expectation and the thinnest practice
ILLUSTRATIVE These percentages demonstrate the aggregation mechanism and reporting format. They are not survey results. In operation, figures would be computed from voluntary participant self-assessments with a minimum cell size of five firms, no attribution, and no ability to reverse out any individual firm's position. No participant's proprietary data is used to produce them.
GOVERNMENT VIEW · WHAT A LICENSED INSURER MUST COMPLY WITH

Obligations Register

A single consolidated register of the regulatory instruments a MAS-licensed insurer or reinsurer operates under, grouped by supervisory theme, with the decision each one forces and the InsurEDIC³ component that carries the evidence for it.

Capital
4
Resilience
6
AI & Data
4
Conduct
4
Climate
2
Structural
4

Consolidated Register

ThemeInstrumentApplies toObligation in operating termsInsurEDIC³ evidence component
CAPITALMAS Notice 133 — Valuation and Capital Framework for Insurers (RBC 2)All licensed insurers; s.2–5 exclude captives, marine mutuals, SPRVsSupervisory intervention levels, valuation of life and general policy liabilities, and calculation of the total risk requirement across C1, C2 and other components MASFinancial Intelligence · capital adequacy decision object
CAPITALNotice 133 — AT1 / Tier 2 recognition criteriaInsurers issuing capital instrumentsAdditional criteria for recognition as AT1 or Tier 2 capital, conditional on sale only to non-retail investors in Singapore from 1 January 2026 MASFinancial Intelligence · instrument eligibility check
CAPITALIFRS 17 Insurance ContractsReporting insurersPortfolio definition and level of aggregation, LIC and LRC calculation, risk adjustment methodology, revenue recognition aligned to the underwriting plan, and sensitivity analysis SOURCEDFinancial Intelligence · reserving decision chain
CAPITALManagement of Outward Reinsurance ArrangementsDirect insurersRequirements and guiding principles for reinsurance strategy, counterparty selection, credit exposure and documentation MASEnterprise Risk · reinsurance counterparty board
RESILIENCETechnology Risk ManagementAll FIsTechnology risk governance and controls; planned notice updates on IT asset management, continuous system monitoring and enhanced oversight of critical systems SOURCEDAI Control Tower · critical system register
RESILIENCECyber Hygiene requirementsAll FIsBaseline cyber controls, reinforced by MAS reminders to strengthen defences in view of AI advancement SOURCEDAI Control Tower · control attestation ledger
RESILIENCEOperational Risk Management guidelinesAll FIsUpdated operational risk taxonomy, control library and loss event capture — consultation issued SOURCEDEnterprise Risk · operational risk decision object
RESILIENCEThird-Party Risk Management guidelinesAll FIsVendor tiering, concentration analysis, exit planning — expanded to cover AI vendors — consultation issued SOURCEDAI Control Tower · third-party AI register
RESILIENCEGuidelines on OutsourcingAll FIsRisk management of outsourcing arrangements including cloud and analytics services MASAI Control Tower · outsourcing dependency map
RESILIENCEBusiness Continuity ManagementAll insurersNotification and submission requirements relating to business continuity management, internal audit and compliance functions, board oversight and data breaches MASScenario Simulation · continuity stress library
AI & DATAProposed Guidelines on AI Risk ManagementAll FIs, proportionateOversight of AI risk management, risk systems and procedures, life-cycle controls, and capabilities and capacity — covering generative AI and AI agents MASAI Control Tower · full evidence stack
AI & DATAFEAT PrinciplesAll FIsFairness, ethics, accountability and transparency in the use of AI and data analytics SOURCEDAI Governance · fairness assessment kit
AI & DATAPersonal Data Protection ActAll entitiesConsent, purpose limitation, notification, access and correction, and data breach notification obligations SOURCEDData Sovereignty Charter · boundary controls
AI & DATAInternal controls and business process controls guidanceAll FIsSound practices for the internal control environment and business process controls MASDecision Ledger · control evidence trail
CONDUCTGuidelines on Individual Accountability and ConductAll FIsFive high-level outcomes promoting senior manager accountability, oversight of material risk personnel and conduct standards across all employees MASAI Governance · accountability mapping
CONDUCTFair Dealing GuidelinesAll FIsFair dealing outcomes for customers across product design, sales, advice and post-sale service SOURCEDCustomer Intelligence · fair dealing lens
CONDUCTProduct development and pricing (life and ILP sub-funds)Direct life insurersRequirements for the development and pricing of life insurance products and investment-linked policy sub-funds MASUnderwriting Intelligence · product gate
CONDUCTAML / CFT guidelinesGeneral, reinsurance, A&HProcesses and controls to prevent money laundering and counter terrorism financing MASEnterprise Risk · financial crime signals
CLIMATEGuidelines on Environmental Risk Management (Insurers)Life, general and composite insurersBoard-level sustainability goals, risk tolerance and accountability; environmental risk embedded in enterprise risk management; climate scenario analysis; transparent disclosure MASEnterprise Risk · environmental risk lens
CLIMATEGuidelines on Transition PlanningBanks, insurers, asset managersFinal guidelines published March 2026 setting supervisory expectations for managing transition and physical climate risk through a sound transition planning process SOURCEDScenario Simulation · climate pathway sets
STRUCTURALInsurance Act 1966 — licensing, control and takeoverAll licensed insurersPrior MAS approval for effective control (20% or more of shares or voting power), substantial shareholding, change of key executive person, chairman or director, or reduction in paid-up capital SOURCEDStrategy Execution · corporate action gate
STRUCTURALInsurance business transferLicensed insurersMAS approval required for transfer of the whole or part of the insurance business, with confirmation by the High Court of Singapore SOURCEDStrategy Execution · portfolio transfer chain
STRUCTURALInsurance funds maintenanceLicensed insurersRequirements relating to establishment and maintenance of insurance funds MASFinancial Intelligence · fund segregation
STRUCTURALPolicy Owners' Protection SchemeDirect insurersScheme membership, levy and disclosure obligations under the Deposit Insurance and Policy Owners' Protection Schemes framework MASFinancial Intelligence · scheme obligations
▸ Compiled from MAS notices, guidelines, consultation papers and the MAS Compliance Toolkit for Direct Insurers and Reinsurers, retrieved September 2026. This register is a working reference for a demonstration platform, not legal advice and not a complete statement of any entity's obligations. Applicability varies by licence class, business lines and group structure. Verify against the current MAS instruments and take your own legal advice before relying on any entry.
GOVERNMENT VIEW · AGGREGATE READINESS · NO FIRM IS IDENTIFIABLE FROM THIS SCREEN

Sector Readiness Heatmap

Readiness by obligation theme and entity archetype. The value of a shared platform to a supervisor is not that it reveals more about individual firms — it is that it reveals structural weakness at sector level earlier, without anyone having to hand over a book.

Readiness by Theme and Archetype

80–100  Control set established and evidenced
60–79  Largely established, gaps documented
40–59  In progress, incomplete coverage
20–39  Early stage
0–19  Not started
ILLUSTRATIVE Scores demonstrate the aggregation and display mechanism. They are not survey results and should not be read as a statement about any market or any firm.

Where Structural Weakness Concentrates

Agentic authority envelopes

The thinnest column across every archetype. Agentic AI is explicitly in scope of the proposed Guidelines, but the practice of granting, bounding and revoking machine authority at runtime barely exists in the sector. This is the gap a shared control tower closes fastest, because the design is identical for every carrier.

Third-party AI governance

Concentrated in branches, MGAs and smaller general insurers — the entities most dependent on vendor models and least able to negotiate telemetry access or validation artefacts into a contract. Collective bargaining position is a genuine platform benefit here, distinct from the software.

Model validation capacity

Not a policy gap — a people gap. Firms have written the validation standard and cannot staff it. Software does not fix this. It is the reason the Academy is a first-class component of InsurEDIC³ rather than a marketing add-on.

Archetype Profiles

ArchetypeTypical constraintWhat shared infrastructure changes for them
Global composite insurerLOWLeast dependent on shared build — but gains from the common evidence format that lets one control set satisfy several ASEAN supervisors at once
Domestic life insurerMEDIUMGains most on the AI life-cycle control set and on validation capacity through pooled Academy pathways
Mid-sized general insurerMEDIUM-HIGHThe core case: full obligation set, fraction of the build budget. Pays for the control architecture once, through subscription, rather than designing it alone
Reinsurer / branchMEDIUMGains on local evidence generation where the group model sits offshore and group tooling does not produce Singapore-shaped artefacts
Broker / MGAHIGHLeast able to build alone, most exposed to vendor AI. Shared platform is the difference between having a governance function and having a policy document
Captive / PCC cellHIGHThin operating teams by design. A shared decision layer is the only realistic route to the analytical capability a cell owner actually wants
GOVERNMENT VIEW · NOTICE 133 · RBC 2 · IFRS 17 · WHERE CAPITAL AND ACCOUNTING DECISIONS MEET

Capital, Valuation and IFRS 17

Capital is where regulatory obligation and enterprise decision-making are least separable. Every underwriting, reserving, investment and reinsurance decision resolves into a capital number, and the supervisor reads that number as the summary of everything upstream of it. This view holds the instruments and the decision chain that produces them.

RBC 2 Under Notice 133 — Structure

TOP
Supervisory Intervention Levels
The point of the framework. Capital adequacy is measured against defined intervention thresholds, and crossing one changes the supervisory relationship rather than merely the disclosure
NOTICE 133 S.2
L4
Total Risk Requirement
Aggregation of component requirements into the total risk requirement, with the aggregation logic set out schematically in the Notice appendices
AGGREGATION
L3
C1 and C2 Requirements
Component risk requirements aggregated separately before rolling into the total. Amendments have introduced equity counter-cyclical adjustment and revised treatment for structured products and infrastructure investments
COMPONENTS
L2
Valuation of Policy Liabilities
Life and general business policy liabilities valued on the basis prescribed in the Notice — the assumption set here drives both the capital number and the IFRS 17 result, from different angles
VALUATION
L1
Financial Resources and Regulatory Adjustments
Components of financial resources including regulatory adjustment, reinsurance adjustment and financial resource adjustment. AT1 and Tier 2 recognition now carries additional criteria including the non-retail sale condition from 1 January 2026
RESOURCES
▸ Structure summarised from MAS Notice 133 on Valuation and Capital Framework for Insurers and its published amendments. MAS Section 1 applies to all licensed insurers; sections 2 to 5 apply to all licensed insurers except captive insurers, marine mutual insurers and special purpose reinsurance vehicles; section 6 applies to those three categories only.

IFRS 17 — The Decision Chain Behind the Disclosure

STEP 01
Portfolio definition and level of aggregation
A judgement made once that constrains everything downstream. It must be justified by accounting approach and supportable by the data actually held
STEP 02
LIC and LRC calculation tooling
Liability for incurred claims and liability for remaining coverage, computed by tooling that has to be reproducible under examination
STEP 03 · WHERE JUDGEMENT CONCENTRATES
Risk adjustment methodology
Defined, tested against the standard, and defensible. This is the number a supervisor and an auditor will both probe hardest, because it is the number with the most discretion in it
STEP 04
Revenue recognition pattern
Aligned to the guidance and consistent with the business and underwriting plan — an inconsistency here reads as either a plan problem or a recognition problem, and both are costly
STEP 05
Closing process, controls and sensitivity analysis
Structured analysis of the close, validated data flows, and sensitivity testing of assumptions against alternative scenarios
▸ Chain compiled from published IFRS 17 implementation guidance. SOURCED IFRS 17 became effective 1 January 2023.

Where Decision Intelligence Adds Value in the Capital Chain

Assumption traceability

Every capital and reserving assumption carries a provenance record: who set it, on what evidence, against which prior, and what changed since the last close. A supervisor asking why a number moved gets the chain, not a reconstruction.

Scenario coherence

The same shock runs through capital, reserving, reinsurance and the investment portfolio simultaneously rather than in four disconnected models producing four incompatible answers.

Decision-to-number linkage

Pricing, appetite and reinsurance decisions are recorded as decision objects linked to the capital outcome they moved — so the capital position becomes explainable as a sequence of choices rather than an emergent surprise.

GOVERNMENT VIEW · FOUR PILLARS OF OPERATIONAL RESILIENCE

Operational and Third-Party Resilience

MAS has been working to improve operational resilience across four pillars — operational risk management, technology and cyber risk management, third-party risk management, and business continuity management. AI adoption touches all four at once, which is why resilience and AI supervision cannot be run as separate programmes.

PILLAR 01
Operational Risk Management
Consultation issued on updated guidelines. Expect a revised taxonomy, control library and loss event framework — and expect AI-originated operational loss to need a home in that taxonomy. SOURCED
PILLAR 02
Technology and Cyber Risk
Planned consultations for updates to the Technology Risk Management notices covering IT asset management, continuous system monitoring and enhanced oversight of critical systems. MAS has separately reminded firms to strengthen cyber defences in view of AI advancement. SOURCED
PILLAR 03
Third-Party Risk Management
Consultation issued on updated guidelines. The AI supervisory expectations reinforce this pillar directly — third-party AI tools are in scope and governance cannot be delegated to the vendor. SOURCED
PILLAR 04
Business Continuity Management
Notification and submission requirements covering business continuity management, internal audit and compliance functions, board oversight and data breaches. MAS

The Concentration Risk a Shared Platform Creates — Stated Plainly

An honest account of the risk this platform introduces

A common platform used by many insurers is, by construction, a concentration. If the platform fails, more than one participant is affected simultaneously. That is a real supervisory concern and it deserves a direct answer rather than a marketing one. Four design commitments follow from it:

No operational dependency for licensed activityInsurEDIC³ supports decisions; it does not execute regulated activity. It does not bind risk, issue policies, hold funds or settle claims. A platform outage degrades analytical capability — it does not stop a participant writing business or paying a claim
Participant-side instance and local fallbackEach participant's decision records, model inventory and evidence pack are held in and exportable from its own instance. A participant can operate its control framework from its own export with the platform offline
No shared data store to loseBecause no proprietary data is pooled, a platform compromise does not expose any participant's book. There is no aggregated policyholder dataset on this platform to breach — that is a security property of the architecture, not a policy promise
Exit portability by designFrameworks, methods, control sets and a participant's own decision history are exportable in open formats at any time. Lock-in through data gravity is the failure mode this platform is specifically built to avoid
⬢ SUPERVISORY POSITION
A shared decision layer should be assessed as a third-party arrangement under the third-party risk and outsourcing expectations, and participants should tier it accordingly. The platform's answer is not that it is exempt from that assessment — it is that it is architected to pass one, and that the evidence to support a participant's assessment ships with the subscription.

Third-Party AI Estate — What Each Participant Must Be Able to Show

ArtefactWho produces itWhy a supervisor asks for it
Model card and intended usePlatform ships itEstablishes what the model is for, and by exclusion what it is not for — the boundary a misuse finding turns on
Training and evaluation provenancePlatform ships itWhether the model was built on data appropriate to the participant's book and market
Independent validation recordPlatform ships · participant reviewsValidation cannot be self-certified by the builder alone; the participant must show it reviewed and accepted
Materiality classificationParticipant ownsProportionality is assessed against the participant's own use, scale and risk profile — the platform cannot classify this on the participant's behalf
Monitoring configuration and resultsPlatform ships · participant configuresDrift and performance monitoring is a continuing obligation, not a go-live gate
Decision trailParticipant ownsWhich decisions the model actually influenced, with what inputs and what human intervention
Authority envelope and revocation logPlatform ships · participant setsFor agentic use: what the agent may do alone, and every instance where that authority was narrowed or withdrawn
Exit and retirement planJointHow the participant continues to operate if the arrangement ends, and how records are retained after retirement
GOVERNMENT VIEW · ENVIRONMENTAL RISK MANAGEMENT · TRANSITION PLANNING · CLIMATE SCENARIO ANALYSIS

Environmental Risk and Transition Planning

Insurers carry climate risk twice — once in the underwriting book as physical loss, and once in the investment portfolio as transition and repricing risk. The supervisory expectations address both, and the final Guidelines on Transition Planning published in March 2026 add the forward-looking dimension to what was previously a risk-management instrument.

Global Nat Cat Protection Gap
US$424B
2025, up from US$395B a year earlier SWISS RE
Asia Economic Cat Losses 2025
~30%
Of the global total, against ~5% of insured losses SWISS RE SIGMA
Emerging Asia Cat Resilience
5%
Insurance resilience score, June 2026 analysis SWISS RE
Asia 2025 Nat Cat Losses
~US$65B
Economic losses, more than 90% uninsured MAS

Supervisory Expectations — Operational Reading

Guidelines on Environmental Risk Management

Applies to life, general and composite insurers, shaped to the nature, scale and complexity of operations.

Board
Explicit sustainability goals, risk tolerance, accountability
Framework
Environmental risk embedded in enterprise risk management
Analysis
Climate scenario analysis across portfolios
Disclosure
Transparency and accountability in environmental risk disclosure
Capability
Ongoing staff training and periodic review

Guidelines on Transition Planning FINAL MAR 2026

Published for banks, insurers and asset management. Sets more detailed supervisory expectations for managing transition and physical climate risk as part of a sound transition planning process.

Scope
Underwriting book and investment portfolio
Horizon
Forward-looking, not point-in-time risk measurement
Engagement
Portfolio company engagement on climate-related risk
Data
Improved collection and enhanced scenario analysis
Calibration
Appropriately calibrated across diverse business models

Why Climate Analytics Is the Strongest Case for Shared Infrastructure

The economics are unambiguous here

Climate scenario analysis has the highest fixed cost and the lowest competitive value of any analytical capability an insurer is required to hold. Hazard models, downscaled climate pathways, exposure geocoding and vulnerability functions cost the same to build whether the book behind them is S$50 million or S$5 billion — and no insurer wins business because its physical hazard model is proprietary. Firms compete on appetite, pricing, distribution and claims handling, not on whether their flood depth-damage curve is secret.

Shared without hesitation

Hazard model libraries, climate pathway sets, downscaling methods, vulnerability functions, scenario narratives, disclosure templates

Never shared

The participant's exposure file, its geocoded portfolio, its accumulation position, its pricing loadings, its reinsurance structure

Shared as anonymised aggregate only

Sector-level accumulation by peril and geography, contributed voluntarily, minimum cell size five, no attribution

⬢ THE SUPERVISORY DIVIDEND
When ten insurers each build a different flood model, sector-level accumulation cannot be assessed — the numbers are not comparable. When they run their own books through a common, transparent, documented hazard layer, sector accumulation becomes measurable for the first time, and each firm still holds its own exposure privately. That is a genuine public good produced by a private platform, and it is produced without anyone surrendering data.
GOVERNMENT VIEW · IAC · FAIR DEALING · PRODUCT GOVERNANCE · AI AND THE CUSTOMER OUTCOME

Conduct and Fair Dealing

AI in insurance meets conduct regulation at three points: how a risk is priced, how a claim is decided, and how a product is sold. Each is a place where a model can produce a technically defensible result and an unfair outcome at the same time — which is precisely why fairness is a named FEAT principle rather than an implied one.

Accountability Architecture

Individual Accountability and Conduct

Guidance on five high-level outcomes that financial institutions should achieve to promote the accountability of senior managers, strengthen oversight over material risk personnel, and reinforce conduct standards among all employees. MAS

⬢ THE AI COLLISION POINT
Accountability regimes assume a named human is answerable for an outcome. Agentic AI distributes the causal chain across a model, a configuration, a threshold and an orchestration layer. The accountability question does not disappear — it sharpens. Someone has to own the authority envelope that let the agent act, and that ownership must be recorded before the decision, not reconstructed after it.

Fair Dealing Outcomes

Fair dealing expectations run across product design, sales, advice and post-sale service. Where AI is used in any of those stages, the fairness of the outcome is the participant's obligation regardless of who built the model.

⬢ WHERE MODELS PRODUCE UNFAIR OUTCOMES LAWFULLY
Proxy discrimination through correlated variables; differential claims friction that is statistically justified but experienced as unequal treatment; price optimisation on inferred inelasticity rather than risk; and automated declines with no meaningful explanation for the customer. None of these requires a prohibited variable to be present in the model.

Fairness Testing — the Shared Method Case

TestApplies toWhat it checksShared or private
Protected-attribute proxy scanPricing, underwritingWhether permitted rating variables are jointly reconstructing an attribute that could not lawfully be used directlyMETHOD SHARED
Outcome parity across cohortsClaims, underwritingWhether decline, referral and settlement rates diverge across cohorts beyond what risk explainsMETHOD SHARED
Friction asymmetryClaimsWhether some claimants face systematically more evidence requests, longer cycles or more referrals for the same claim typeMETHOD SHARED
Explanation adequacyAll customer-facing AIWhether an adverse decision can be explained to the affected customer in terms they can act onMETHOD SHARED
Elasticity separationPricingWhether price movement tracks risk or tracks inferred willingness to payMETHOD SHARED
Test results on the participant's own bookThe actual numbers produced when the shared method is run against a participant's portfolioNEVER SHARED
⬢ THE DISTINCTION THAT MAKES THE PLATFORM WORK
The test is shared. The result is not. Every participant runs the same fairness battery, so the industry converges on a common standard of what "tested" means — and no participant ever sees another's outcome. A supervisor gains a sector where fairness testing is comparable; a participant gains a method it did not have to invent; and no one's book moves.
GOVERNMENT VIEW · THE DEVELOPMENT MANDATE · NOT EVERYTHING A REGULATOR ASKS FOR IS A RULE

Industry Growth Agenda

MAS operates two mandates simultaneously — supervising the industry and developing it. Most compliance tooling only sees the first. This view holds the second: what the industry is being invited to build, the instruments available to support it, and where a shared decision layer materially lowers the cost of participation.

Total Premiums End-2024
S$78B
Life, general and reinsurance combined MAS
Premium Growth 2019–2024
>8%
Average annual growth across the period MAS
Reinsurance Share of Asia
~21%
Singapore reinsurance premiums as share of Asia's market, 2023 MAS
Life New Business 2025
S$6.53B
Total weighted new business premiums, up 11.3% LIA

MAS Development Instruments — What Is Actually Available

InstrumentWindowTerms and intentWhere InsurEDIC³ lowers the barrier
ILS Grant SchemeJan 2026 – Dec 2028Property cat bonds covering any proportion of APAC risk: 70% of upfront issuance costs capped at S$1m. Not covering APAC risk: 50% capped at S$1m. Non-property cat bonds: 70% capped at S$1m. Collateralised reinsurance and sidecars: 70% capped at S$500,000. Renewals: 30% capped at S$500,000. Applies to natural catastrophe, longevity, mortality, operational and cyber risks. Open to onshore and offshore companies, financial institutions and multilateral organisations MASStructuring analytics, trigger design and basis risk modelling as shared method rather than a per-issuance consulting build
Proposed PCC frameworkConsulted Jul–Aug 2026A single corporate vehicle with assets and liabilities statutorily segregated within the entity, available to MAS-licensed entities carrying out captive insurance, insurance-linked securities and sovereign risk pools MASCell-level decision infrastructure that a thin cell operating team could not otherwise justify building
Global-Asia Insurance PartnershipOperatingA tripartite partnership between the global insurance industry, regulators and policymakers, and academia, established in Singapore as a centre of excellence in insurance and risk management with a focus on Asia, initially on pandemic and climate risk MASA natural counterpart for the Academy's research-to-practice pathway and for methodology validation
SEADRIFOperating since 2019The first regional catastrophe risk facility established in Asia by ASEAN member states, incorporated and domiciled in Singapore, licensed as a general insurer in October 2019, supported by the World Bank in partnership with Japan MASParametric trigger design, impact-data pipelines and payout decision logic as shared components
Natural Catastrophe Data Analytics ExchangeOperatingAn MAS–industry–academia public-private partnership led by NTU's Insurance Risk and Financial Research Centre, focused on Singapore and Asia with global collaborations MASDirect architectural precedent — a shared analytical layer with sovereign data boundaries already exists in this market
FinTech Regulatory SandboxStandingHelps financial institutions and FinTech players test new innovations in a controlled environment before wider adoption in Singapore and abroad MASA defined route for testing agentic decision components under supervision before scaling
GenAI Jobs Transformation MapRunningMAS and IBF, supported by Workforce Singapore, partnered eleven financial institutions — Citibank, DBS, Franklin Templeton, HSBC, Income Insurance, Manulife, OCBC, Prudential, SCB, UBS and UOB — to pilot workforce transformation and unpack what AI adoption means for specific job roles IBFThe Academy's role-based pathways map directly onto this structure
▸ Terms as published by MAS and IBF, retrieved September 2026. Grant terms, caps and eligibility change — confirm current terms with MAS before relying on any figure here. No inference should be drawn that any of these programmes is connected to this platform.

Why the Growth Agenda Needs a Decision Layer, Not Just Capital

Vehicles do not create demand

A PCC lowers the cost of building a structure. It does not create the buyers or the investor base to fill one. What converts a cheaper vehicle into a used vehicle is the analytical case for the risk owner — quantified retained risk, credible loss modelling, and a defensible view of what the captive or cell is actually worth against commercial market pricing. That is a decision problem.

The constraint on ILS in Asia is comfort, not incentive

Market commentary is direct on this: the grant scheme is generous, but the harder problem is investor familiarity and the stickiness that brings sponsors back. Capacity remains concentrated, with slightly over half of direct investor capacity in North America and most of the remainder in Europe. Transparent, reproducible risk analytics travel further with an investor base than a subsidy does.

Protection gap closure is an underwriting problem

Underinsured risk stays underinsured because the loss distribution is poorly characterised, the data is thin, and no single carrier can justify the modelling cost against an uncertain market. Shared method with private books is the only structure that makes that modelling economic — each carrier still competes on price and appetite.

GOVERNMENT VIEW · CONSULTATION P013-2026 · CORE AND CELLS · CAPTIVES · ILS · SOVEREIGN RISK POOLS

PCC and Alternative Risk Transfer

FutureInsurance.ai LLP filed a response to MAS Consultation P013-2026 on the proposed Protected Cell Company framework, and has since responded to MAS follow-up queries on the cell-level re-domiciliation recommendation. This view holds the framework as proposed and the decision infrastructure a cell-based market would need.

Structure as Proposed

The corporate architecture

Legal form
Single legal entity
Components
A central Core plus one or more Cells
Segregation
Cell assets and liabilities legally segregated from other Cells and from the Core
Legislative route
A new Protected Cell Companies Act — primary legislation, not an amendment
Consultation
P013-2026, 7 July – 7 August 2026
Next stages
Draft PCC Act and subsidiary legislation to be consulted on later
Commencement
Not announced
▸ As set out in the MAS consultation paper and media release, 7 July 2026. MAS

Eligible uses

Open to MAS-licensed entities carrying out three activity classes:

USE CASE 01 · CAPTIVE INSURANCE

A self-insurance programme for a firm's own or its affiliates' risks, allowing management of risk and premium rate volatility arising from reliance on the commercial market. Companies could run multiple self-insurance programmes through separate cells, or participate in rent-a-captive arrangements typically sponsored by the insurance management arm of a licensed broker.

USE CASE 02 · INSURANCE-LINKED SECURITIES

Financial instruments that securitise insurance contracts, allowing insurers and reinsurers to transfer specific risks to capital markets investors. The PCC route supports collateralised reinsurance arrangements including sidecars, and more efficient ILS issuance.

USE CASE 03 · SOVEREIGN RISK POOLS

Regional and national risk pooling arrangements — the structural class SEADRIF occupies.

Why a Cell Market Needs Shared Decision Infrastructure

The economics of a cell make a private build impossible

The whole point of the PCC is that a cell costs far less to establish than a standalone entity. But a cell inherits the same analytical questions a standalone captive faces — how much risk to retain, at what attachment, against what loss distribution, with what collateral, and whether the economics beat commercial market pricing this renewal. If answering those questions requires a bespoke actuarial and modelling build, the vehicle is cheap and the capability is not, and the barrier has simply moved.

DECISION 01

Retention optimisation

What to keep in the cell against what to cede, at what attachment and limit

DECISION 02

Loss distribution

Characterising the cell's own loss experience where the data history is short and thin

DECISION 03

Commercial comparison

Whether the cell beats the commercial market at this point in the cycle, and by how much

DECISION 04

Collateral and capital

Funding adequacy for the cell against its own segregated liability, and stress behaviour

⬢ THE PLATFORM PROPOSITION FOR A CELL MARKET
Every one of those four decisions uses the same method for every cell and different data for every cell. That is the exact shape a shared platform fits. InsurEDIC³ supplies the retention model, the loss-distribution fitting method, the commercial comparison framework and the collateral stress logic as common components — and each cell's exposure, experience and pricing stays inside that cell's own boundary, segregated in the analytics exactly as it is segregated in law.

Open Questions a Cell Market Will Have to Answer

QuestionWhy it matters operationally
Cell-level re-domiciliationWhether an individual Cell can migrate between PCCs or jurisdictions without unwinding and reconstituting the programme. This is the point on which MAS raised follow-up queries with FutureInsurance.ai LLP after its consultation response
Cross-cell recourse in stressStatutory segregation is clear on paper; the operational question is how a Core-level failure or a Core-level obligation interacts with Cell assets in practice
Supervisory reporting granularityWhether reporting sits at Core level, Cell level or both — and what that means for the reporting burden on a thin cell team
Cell governance minimumsWhat board, actuarial and risk function a Cell must have in its own right versus what it may draw from the Core or the sponsor
Cell exit and run-offHow a Cell is closed, run off or transferred, and how policyholder or counterparty protection is preserved through that process
Interaction with RBC 2How the Notice 133 framework applies to Cells, given that sections 2 to 5 currently exclude captives, marine mutuals and SPRVs
▸ These are questions this platform considers material for a functioning cell market. They are not MAS positions and MAS has not endorsed this framing. The draft PCC Act and subsidiary legislation, which are to be consulted on separately, may address several of them.
GOVERNMENT VIEW · WHERE RISK EXISTS AND COVER DOES NOT

Protection Gap Observatory

The protection gap is the clearest expression of the industry's unfinished work, and the clearest case for capability that no single carrier will build alone. Every figure on this page is sourced and attributed; where a number is a demonstration figure it is marked as one.

Global Nat Cat Gap 2025
US$424B
Up from US$395B in 2024 as exposure outpaced coverage SWISS RE
Philippines Cat Gap
~98%
Against a global average of 58% GLOBALDATA
Emerging Asia Resilience
5%
Catastrophe insurance resilience score, June 2026 SWISS RE
Asia Loss Asymmetry
30 : 5
~30% of global economic cat losses against ~5% of insured losses, FY2025 SWISS RE SIGMA

Recorded Events and the Gap They Exposed

EventPeriodReported impactNote
Tropical Storm Penha, PhilippinesH1 202612 fatalities · ~US$30m economic lossLanded in one of the region's most underinsured markets AON
June earthquake, Philippines and IndonesiaH1 202693 fatalities · ~US$250m economic lossCross-border event affecting two low-penetration markets simultaneously AON
Indonesia floodingJan–Feb 2026At least 87 fatalitiesMultiple events in a single quarter AON
Lao PDR rainfall and floodingSep 2026US$1.14m paid in five business daysSEADRIF payouts to the Government of Lao PDR and the World Food Programme, triggered by government-reported disaster impact data under the people-affected trigger SEADRIF
Asia aggregateFY2025~US$65bn economic lossMore than 90% uninsured, cited by DPM and MAS Chairman Gan Kim Yong MAS
▸ Compiled from Aon reporting via Insurance Business, Swiss Re Institute analysis, SEADRIF announcements and MAS statements, retrieved September 2026.

Why the Gap Persists — the OECD Reading

Income levels

Across much of ASEAN, insurance penetration remains relatively low, and the OECD attributes this in part to income levels. Premium affordability is a binding constraint before it is a product design constraint.

Financial literacy

Limited financial literacy is cited as a contributing factor. This is a capability problem in the population, and it sits alongside the capability problem in the industry that the Academy addresses.

Distribution

Underdeveloped insurance distribution limits reach even where willingness and affordability exist. Embedded and digital channels are the structural response the region is now testing.

▸ Factors as attributed to the OECD in reporting on ASEAN penetration, retrieved September 2026. SOURCED

What a Shared Decision Layer Contributes to Gap Closure

Characterising thin-data perilsParametric and index-based products need a defensible loss distribution where claims history barely exists. The modelling method is shareable; the resulting book is not
Basis risk quantificationParametric cover fails commercially when basis risk is unquantified and a payout misses a real loss. A shared basis-risk framework is a public good with no competitive value to any single carrier
Trigger design librariesSEADRIF's people-affected impact trigger demonstrates that non-parametric-index triggers can pay in days. Trigger design patterns should be common property
Affordability-constrained product designDesigning to a price point rather than pricing a design — a method most carriers have not systematised because the target segment has never been profitable enough to justify the build
Distribution economics modellingWhether an embedded or microinsurance channel can carry its own acquisition cost at the premium level the segment can bear
GOVERNMENT VIEW · WHAT A REGULATOR CAN SEE · AND WHAT IT CANNOT

Supervisory Data Exchange

A shared industry platform raises an obvious question from both directions: does the regulator get a window into every participant, and does the platform operator get one. The answer to both is no, and the architecture has to make that answer verifiable rather than merely asserted.

Never Leaves the Participant
PRIVATE BY ARCHITECTURE · NOT BY POLICY
  • Policyholder and claimant records
  • Policy, claims and exposure files
  • Pricing models, rating factors and loadings
  • Underwriting appetite and declinature rules
  • Reserving assumptions and reserve balances
  • Reinsurance structures, terms and counterparties
  • Distribution economics and commission structures
  • Financial results ahead of publication
  • Model inventories and validation evidence
  • Individual decision records and their outcomes
SOVEREIGNTY
BOUNDARY
Shared Across the Industry
METHOD · NOT DATA
  • Frameworks, taxonomies and decision templates
  • Control sets mapped to regulatory instruments
  • Model architectures and reference implementations
  • Validation and fairness testing methodologies
  • Hazard model libraries and climate pathway sets
  • Regulatory intelligence and obligation tracking
  • Agent designs and authority envelope patterns
  • Training curricula and certification pathways
  • Anonymised aggregate readiness, minimum cell size five
  • Published market statistics and regulatory text

What a Supervisor Gains — Without Gaining Anyone's Book

Earlier sight of structural weakness

Sector-level readiness against a common control set, refreshed continuously rather than assembled through a thematic review cycle. A supervisor sees that agentic authority envelopes are thin across the market months before an inspection programme would surface it — and still cannot name a single firm.

Comparability of evidence

When participants build against a common control taxonomy and a common evidence format, submissions become genuinely comparable. Supervisory time currently spent normalising heterogeneous submissions is returned to actual supervision.

Faster sector-wide remediation

When a control gap is identified sector-wide, the remediation pattern is published once to the shared library and every participant can adopt it. The current alternative is fifty parallel remediation projects reaching fifty different standards.

A measurable capability baseline

Capabilities and capacity is a named domain in the proposed AI Guidelines and the hardest for any supervisor to assess. Certification completion by role, aggregated across the sector, is a real signal — and it is generated without any firm disclosing anything about its business.

What This Platform Will Not Do

No supervisory back doorThere is no regulator-facing view of an individual participant's instance, and none can be built without re-architecting the platform. Aggregate views are computed from voluntary contributions with a minimum cell size, and cells below it are suppressed rather than estimated
No training on participant dataNo participant's data is used to train, fine-tune or evaluate any shared model. A model improved by one participant's book would carry that book's signal to competitors — the platform is architected so this is impossible, not discouraged
No cross-participant inferenceThe platform does not compute anything that requires reading more than one participant's data together. There is no cross-book analytics surface, so there is nothing to leak through one
No operator access to participant instancesPlatform operations do not require read access to participant decision data. Support is delivered against configuration and telemetry, not content
No silent scope expansionAny change to what leaves a participant instance is a contractual change requiring affirmative consent, not a terms-of-service update
No lock-in through data gravityFrameworks, control sets and a participant's own decision history export in open formats at any time. The platform earns renewal on value, not on the cost of leaving
ASEAN VIEW · TEN MARKETS · TEN SUPERVISORS · ONE PROTECTION GAP

ASEAN Executive View

Ten member states, ten insurance supervisors, ten regulatory regimes at very different stages of development, and one shared exposure to climate, demographic and digital risk. The regional case for a common decision layer is not efficiency — it is that several of these markets cannot build a modern decision capability alone at any price, and the ones that can are building ten incompatible versions of the same thing.

ASEAN Member States
10
Brunei, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam
Regional Population
650M+
Highly prone to disaster and climate shocks SEADRIF
SEA Life GWP 2025
US$104.8B
Forecast US$107–110B in 2026, US$118B by 2030 SOURCED
SEA Health GWP 2025
US$12.3B
Forecast US$12.5–13B in 2026, US$13.8B by 2030 SOURCED
SEA Motor Market 2026
US$15.1B
From US$13.19B in 2025, to US$19.91B by 2032 SOURCED

Three Forces Named by the Region's Own Regulators

Climate risk FRONT LINE

Climate impacts are described as already becoming a major source of financial instability across ASEAN, with the region remaining highly vulnerable to floods, storms, droughts and other extreme weather. The ASEAN Taxonomy for Sustainable Finance is cited as the instrument that should guide future investment and underwriting to improve long-term resilience.

DECISION LAYER IMPLICATION

Hazard modelling, scenario pathways and accumulation methods are the highest-fixed-cost, lowest-competitive-value capability in the region. They should be built once regionally, not eleven times nationally.

Digital transformation ACCELERATING

Digital transformation emerged as a critical priority at the 2025 regional meeting, with insurers across the region rapidly adopting advanced analytics and AI for underwriting, claims and customer engagement. Adoption is running ahead of supervisory frameworks in several markets.

DECISION LAYER IMPLICATION

Where AI adoption outpaces AI governance, the gap becomes a regional risk rather than a national one. Shared governance architecture closes it faster than ten separate regimes can.

Ageing populations STRUCTURAL

Ageing trends were highlighted as reinforcing the need to expand insurance penetration, particularly in markets where coverage is still developing. Demographic transition is arriving in several ASEAN markets before insurance penetration has matured.

DECISION LAYER IMPLICATION

Longevity, morbidity and health-cost modelling in markets with short data histories — the archetypal case for shared method with private books.

The Regional Asymmetry

Two ASEANs, one insurance market

Singapore's insurance industry reached roughly S$78 billion in total premiums at end-2024 with a workforce of around 19,000, and its reinsurance premiums accounted for about 21% of Asia's reinsurance market in 2023. Cambodia's penetration stands at 1.13% with density of US$20.65 per person. These two markets sit in the same regional framework, attend the same annual meeting, and face the same climate exposure. They cannot plausibly build the same decision capability by the same route.

⬢ THE LEAPFROG CASE
A market with density of US$20.65 per capita will never fund a bespoke AI governance stack, a hazard model library, a fairness testing battery and a model validation function out of its own premium base. Shared infrastructure is not a cost saving for these markets — it is the only route to having the capability at all. And for the developed markets in the region, a neighbour with modern decision capability is a better counterparty, a better cedant and a better co-participant in a regional risk pool than one without.

What Sits Above the National Level Already

AIRM
REGULATORS · EST. 1998
AIC
INDUSTRY · EST. 1978
AITRI
TRAINING & RESEARCH
Council of Bureaux
CROSS-BORDER MOTOR
AIEC
EDUCATION COMMITTEE
ANDREWS
NAT DISASTER RESEARCH
SEADRIF
REGIONAL RISK FACILITY
ADRFI
DISASTER RISK FINANCING
▸ The regional architecture already exists institutionally. What it does not yet have is a common analytical and decision layer beneath it. That is the gap InsurEDIC³ addresses — as an analytical layer complementing existing institutions, not replacing any of them.
ASEAN VIEW · TEN MARKETS · EVERY FIGURE CARRIES A DATA CLASS

Market Data Explorer

Insurance data across ASEAN is published unevenly, on different bases, at different frequencies, in different currencies. This explorer holds what is verifiable and declares what is not. A cell marked as a data gap is a data gap — it is not estimated, interpolated or filled from a regional average.

Penetration, Density and Premium Base

MarketPenetrationDensityPremium baseSource and note
SingaporeData gap GAPData gap GAPS$78bn total, end-2024 MASLife, general and reinsurance combined; grew >8% p.a. 2019–2024. Life new business S$6.53bn in 2025, up 11.3% LIA. Reinsurance premiums S$27.6bn in 2023, ~21% of Asia's reinsurance market MAS
Philippines1.79% of GDP ICPHP 4,384.56 ICPHP 502.64bn, 2025 ICTotal premiums up 14.1% from PHP 440.53bn in 2024. Life 80.77%, non-life 16.41%, mutual benefit associations 3.37%. Total benefits PHP 121.88bn. Penetration remains below the regulator's 2% target
Indonesia2.61%, 2024 SOURCEDData gap GAPData gap GAPPenetration down from 2023 and the lowest since 2019. Takaful growth is a noted structural driver given the world's largest Muslim population
Cambodia1.13% SOURCEDUS$20.65 per capita SOURCEDData gap GAPStated by Cambodia's Ministry of Economy and Finance at the 28th AIRM, November 2025. National priorities: strengthening disaster-risk insurance, widening digital distribution, improving public understanding
MalaysiaData gap GAPData gap GAPData gap GAPReported as experiencing significant growth driven by rising awareness and demand for comprehensive financial protection. Confirmed advanced consideration of joining SEADRIF by May 2026 WORLD BANK
VietnamData gap GAPData gap GAPData gap GAPReported double-digit life premium growth buoyed by bancassurance partnerships, expected to continue into 2026 SOURCED
ThailandData gap GAPData gap GAPData gap GAPStrong bancassurance growth reported, with banks integrating insurance into digital banking platforms. Signalled intent to explore SEADRIF membership WORLD BANK
Brunei DarussalamData gap GAPData gap GAPData gap GAPSignalled intent to explore SEADRIF membership WORLD BANK
Lao PDRData gap GAPData gap GAPData gap GAPAmong the first countries to adopt a sovereign disaster risk insurance policy with a people-affected impact trigger, May 2025. Purchased its first disaster risk insurance in 2021, a three-year hybrid flood product WORLD BANK
MyanmarData gap GAPData gap GAPData gap GAPMyanmar Insurance Association recognised as the 15th member of the ASEAN Insurance Council in April 2018 SOURCED
▸ Retrieved September 2026 from the Philippine Insurance Commission via published reporting, Indonesian penetration reporting, statements at the 28th AIRM, MAS and LIA Singapore publications, and World Bank SEADRIF documentation. Figures are on different bases, periods and currencies and are not directly comparable across rows. Nothing in this table should be used for pricing, capital or investment purposes.

Regional Aggregates and Trajectory

Life and health, Southeast Asia

Life GWP 2025
US$104.8bn
Life GWP 2026 forecast
US$107–110bn
Life GWP 2030
US$118bn
Health GWP 2025
US$12.3bn
Health GWP 2030
US$13.8bn
Annual growth 2025–30
~2–3%
SOURCED Growth is described as moderate but distinctly uneven, with Vietnam, Indonesia, the Philippines and Malaysia outpacing more mature markets.

Motor, Southeast Asia

Market size 2025
US$13.19bn
Market size 2026
US$15.11bn
Market size 2032
US$19.91bn
CAGR 2026–32
4.71%
Third-party liability share
~57% of policies
SOURCED Third-party liability dominates because most ASEAN countries legally require basic liability coverage for vehicle operation.

Emerging Asia baseline

Penetration 2012
3.0%
Penetration 2022
3.6%
China per capita spend 2022
US$489
Other emerging Asia 2022
US$86
Asia nat cat uninsured share
~90%
PEAK RE China materially lifts both regional penetration and density figures. Excluding it, the emerging Asia picture is substantially thinner than headline regional averages suggest.
ASEAN VIEW · MARKET-BY-MARKET · SOURCED FIGURES AND DECLARED GAPS SIDE BY SIDE

Country Benchmark Board

Ten market cards. Where a verifiable figure exists it is shown with its source; where it does not, the card says so. The purpose is not to rank the markets — it is to make visible how uneven the regional evidence base actually is, because that unevenness is itself the constraint on regional decision-making.

Singapore
SG · MAS · DEVELOPED HUB
S$78B
Total premiums end-2024. ~19,000 workforce. Reinsurance ~21% of Asia's market, 2023. 337 licensed insurance entities and brokers, June 2026.
REINSURANCE HUB ILS DOMICILE PCC PROPOSED
Philippines
PH · INSURANCE COMMISSION
1.79%
Penetration below the 2% regulatory target. PHP 502.64bn premiums in 2025, up 14.1%. Density PHP 4,384.56. Catastrophe protection gap estimated at ~98%.
98% CAT GAP 14.1% GROWTH
Indonesia
ID · OJK
2.61%
Penetration in 2024, down from 2023 and the lowest since 2019. Takaful growth is a structural driver. Multiple flooding events January–February 2026.
PENETRATION FALLING TAKAFUL GROWTH
Cambodia
KH · IRC · NBFSA
1.13%
Density US$20.65 per person. Hosted the 28th AIRM and 51st AIC in Siem Reap, November 2025. Priorities: disaster-risk insurance, digital distribution, public understanding.
LOWEST DENSITY AIRM 2025 HOST
Malaysia
MY · BANK NEGARA MALAYSIA
GAP
No verified current penetration or density figure established in this build. Reported significant growth on rising protection awareness. Confirmed advanced consideration of SEADRIF membership by May 2026. BNM issued a discussion paper on AI in the Malaysian financial sector in 2025.
DATA GAP SEADRIF CANDIDATE
Vietnam
VN · MINISTRY OF FINANCE
GAP
No verified current penetration figure established in this build. Reported double-digit life premium growth driven by bancassurance, expected to continue into 2026. Hosted the 26th AIRM and 49th AIC in Ha Long, 2023.
DATA GAP BANCASSURANCE LED
Thailand
TH · OIC
GAP
No verified current penetration figure established in this build. Strong bancassurance growth with banks integrating insurance into digital banking platforms. Signalled intent to explore SEADRIF membership. Participated in the SEADRIF-RAISE agricultural risk finance workshop, February 2026.
DATA GAP SEADRIF CANDIDATE
Lao PDR
LA · MINISTRY OF FINANCE
GAP
No verified penetration figure established in this build. Among the first countries to adopt a sovereign disaster risk insurance policy with a people-affected impact trigger, May 2025, premium supported by a US$3.6m grant. Received US$1.14m in SEADRIF payouts within five business days in September 2026.
PARAMETRIC PIONEER DATA GAP
Brunei Darussalam
BN · BDCB
GAP
No verified penetration or premium figure established in this build. Signalled intent to explore SEADRIF membership.
DATA GAP
Myanmar
MM · FRD · IBRB
GAP
No verified penetration or premium figure established in this build. The Myanmar Insurance Association was recognised as the 15th member of the ASEAN Insurance Council in April 2018 and hosted the 22nd AIRM and 45th AIC in Nay Pyi Taw in 2019.
DATA GAP
▸ Six of ten markets carry declared gaps. That is an honest statement of what could be verified to primary or clearly attributed sources in this build, not a claim that the data does not exist. In an operating deployment these cells would be filled from national supervisor publications and ASEANstats, each with its own provenance record and retrieval date.
ASEAN VIEW · THE REGION'S DEFINING NUMBER

Protection Gap and Natural Catastrophe

ASEAN countries, home to more than 650 million people, are highly prone to disaster and climate shocks — floods, tropical storms, droughts, earthquakes and tsunamis have left severe physical, economic and human impacts across the region. The insurance response to that exposure is the smallest it is anywhere in the developed or emerging world.

Philippines Cat Protection Gap
~98%
Against a global average of 58% GLOBALDATA
Emerging Asia Cat Resilience
5%
Only a small proportion of catastrophe exposure is insured SWISS RE
Asia Uninsured Nat Cat Losses
~90%
Share of losses uninsured, sustained over years SOURCED
Global Gap 2025
US$424B
Up from US$395B, exposure outpacing coverage SWISS RE

The Asymmetry, Stated Precisely

Asia carries the losses and not the cover

In full-year 2025, Asia accounted for approximately 30% of global economic catastrophe losses while representing only around 5% of insured losses. That single ratio is the region's insurance problem in one line: the risk is here, the capital that absorbs it is not.

WHY THE GAP IS WIDENING, NOT CLOSING

Swiss Re Institute attributes the region's large protection gap to rapid urbanisation and economic development outpacing insurance penetration. Exposure is being created faster than cover is being written — so growth in premiums can coexist with a widening gap, and does.

THE MODELLING CONSEQUENCE

Exposure that has never been insured has never been modelled to underwriting standard. There is no claims history because there were no claims — only losses. Characterising these perils requires methods that do not depend on a loss history, which is exactly the capability no single ASEAN carrier can justify building alone.

Recorded Regional Events

EventPeriodReported impactInsurance response
Tropical Storm Penha, PhilippinesH1 202612 fatalities · ~US$30mLanded in a market with ~98% catastrophe protection gap AON
Earthquake, Philippines and IndonesiaJune 202693 fatalities · ~US$250mCross-border event, two low-penetration markets simultaneously AON
Flooding, IndonesiaJan–Feb 2026At least 87 fatalitiesMultiple events in a single quarter AON
Heavy rainfall and flooding, Lao PDRSept 2026US$1.14m paidSEADRIF paid the Government of Lao PDR and the World Food Programme within five business days, triggered by government-reported disaster impact data under the people-affected trigger SEADRIF
⬢ THE LAO PDR CASE IS THE PROOF POINT
A five-business-day payout, triggered by government-reported impact data rather than a modelled index, with complementary payments to both a government and a humanitarian agency from the same trigger event. This is what a decision infrastructure looks like when it works: a pre-agreed trigger, a clean data pipeline, an automated verification path and a payout that lands while the response is still happening. Replicating it across ten markets and many perils is a decision-architecture problem, not a capital problem.

Structural Causes and the Instruments That Address Them

CauseInstrument in playWhat is still missing
Income levels and affordabilityMicroinsurance, embedded distribution, premium subsidy through grants such as the Global Shield Financing FacilityA systematic method for designing to a price point rather than pricing a design
Limited financial literacyNational awareness programmes; public understanding named as a Cambodian priorityProduct structures simple enough that literacy is not a precondition for value
Underdeveloped distributionDigital and embedded channels through e-commerce and ride-hailing platforms across Southeast AsiaDistribution economics that work at the premium sizes the segment can bear
Sovereign fiscal exposureSEADRIF sovereign policies; ADRFI; SEADRIF-SAFE for public infrastructureScale — regional uptake occurred more slowly than anticipated during the initial project phase WORLD BANK
Agricultural exposureSEADRIF-RAISE with FAO, consulted across six countries in February 2026Yield and index modelling for smallholder agriculture at affordable basis risk
Thin loss dataANDREWS natural disaster research work sharing; AHA Centre disaster impact reportingA common analytical layer that turns shared impact data into underwriting-grade loss characterisation
ASEAN VIEW · WHERE THE REGION'S RISKS MOVE TOGETHER

Correlated Exposure

Diversification is the assumption underneath every regional portfolio and every regional risk pool. It only holds where the underlying exposures are genuinely independent. Across ASEAN, several are not — and the correlations that matter most are the ones that are structural rather than statistical.

Structural Correlation Channels

Climate and hydrology HIGH

Regional-scale climate drivers do not respect national boundaries. Monsoon behaviour, El Niño–Southern Oscillation phases and Indian Ocean Dipole conditions modulate flood, drought and cyclone risk across multiple member states in the same season.

EXPOSED SIMULTANEOUSLY

Agricultural yield cover, property catastrophe, business interruption, sovereign disaster policies, parametric drought triggers

Seismic and volcanic MEDIUM-HIGH

The June 2026 earthquake affecting both the Philippines and Indonesia is the illustration: a single tectonic event producing simultaneous loss in two member states, both with low penetration and both plausible members of the same pool.

EXPOSED SIMULTANEOUSLY

Property, marine, engineering, sovereign infrastructure cover, contingent business interruption

Supply chain and trade MEDIUM

Deep intra-ASEAN supply chain integration means a physical loss in one market propagates as contingent business interruption in several others. The insured event and the insured loss occur in different jurisdictions.

EXPOSED SIMULTANEOUSLY

Contingent BI, marine cargo, trade credit, political risk, cyber where a shared logistics platform is involved

Technology and vendor concentration MEDIUM · RISING

The least discussed and the fastest growing. Insurers across the region are rapidly adopting analytics and AI for underwriting, claims and customer engagement — and drawing on a small number of the same vendors. A single model failure or vendor outage becomes a regional event.

EXPOSED SIMULTANEOUSLY

Operational risk, cyber, mispricing across multiple carriers using the same model, claims handling capacity

Correlation Matrix — Peril Channel by Market Cluster

HIGH Structurally correlated · treat as a single exposure for accumulation purposes
MEDIUM Correlated under specific conditions
LOW Diversifying under most scenarios
ILLUSTRATIVE This matrix demonstrates the analytical structure a regional pool requires. The classifications are a reasoned reading of published exposure characteristics, not the output of a calibrated model, and they are not suitable for pricing, capital allocation or pool design. A production instance would derive these from hazard footprints, historical event catalogues and modelled correlation, with each cell carrying its own provenance.

What the Region Cannot Currently See

Regional accumulation by perilNo participant, regulator or pool can currently see how much of a given peril the region's insurers collectively hold, because every carrier models it differently and no comparable aggregate exists
Cross-border contingent exposureWhere an insured loss in one market is triggered by a physical event in another, neither supervisor sees the full chain
Reinsurance concentrationWhether the region's cedants are concentrated on the same reinsurance counterparties, which converts a counterparty failure into a regional solvency event
Shared model concentrationHow many carriers across how many markets are pricing the same peril off the same vendor model, and what a common error in that model would cost the region
⬢ HOW A SHARED LAYER PRODUCES THIS WITHOUT POOLING BOOKS
Each participant runs its own exposure through a common, documented hazard and correlation layer inside its own instance, and contributes only a scored aggregate — no exposure records, no policy data, no locations. With a minimum cell size and no attribution, regional accumulation becomes measurable for the first time, and no carrier discloses its position to any other. The regional picture is a by-product of every participant doing its own analysis properly, on a common basis.
ASEAN VIEW · ADOPTION IS RUNNING AHEAD OF GOVERNANCE

Digital and Insurtech Landscape

Digital transformation was named a critical priority at the 2025 regional regulators' meeting, with insurers across ASEAN rapidly adopting advanced analytics and AI for underwriting, claims and customer engagement. In several markets the adoption curve is ahead of the supervisory framework — which makes shared governance architecture a regional risk-reduction measure rather than a convenience.

APAC Insurtech 2026
US$18.7–28.1B
Two published estimates differ materially — both shown rather than one chosen SOURCED
Projected CAGR to 2035
16.4–19.4%
Estimate range across the same two sources SOURCED
Singapore Position
Largest
Insurtech activity in Asia is predominantly centred on Singapore, the region's largest insurtech hub SOURCED
STP Rate Shift
10–15% → 70–90%
Reported straight-through processing on simple claims in 2026 documentation SOURCED

Where the Market Is Actually Moving

ShiftWhat is reportedDecision-layer consequence
Embedded distributionEmbedded insurance distribution through e-commerce and ride-hailing apps is reshaping traditional agent networks across Southeast Asia, forcing carriers to rebuild underwriting infrastructure around API-first policy issuance and real-time data ingestion SOURCEDUnderwriting decisions move from days to milliseconds — the governance model has to move with them or it is decorative
Continuous underwritingA reported 2026 shift from static annual underwriting to continuous underwriting, with risk assessed in real time from telematics, IoT and streaming data SOURCEDA model that is re-scoring continuously needs continuous monitoring, not annual validation
Agentic workflowsCytora launched Autopilot in March 2026, an agentic capability running end-to-end risk workflows autonomously; Zurich Insurance completed a 90-day rollout across five countries with plans to expand to more than 20 markets SOURCEDAgentic authority envelopes stop being theoretical — this is production autonomy at multi-market scale
Underwriting cycle compressionUnderwriting timelines reported collapsing from three days to three minutes on standard SME risks SOURCEDThe human review step that carried the governance burden has been removed; the control must be designed into the machine path
Parametric agricultureSatellite-indexed crop insurance reported as underpenetrated, with automated payout triggers being piloted SOURCEDTrigger design and basis risk become the core underwriting decision rather than a technical annexe
Cross-border licensingASEAN member states reported to be negotiating mutual recognition frameworks for insurance licences, potentially enabling single-platform distribution across multiple jurisdictions SOURCEDOne product, ten supervisors — a common control and evidence format becomes an operating necessity
Fraud on direct channelsSynthetic identity schemes and staged accidents reported proliferating on direct-to-consumer platforms SOURCEDFraud detection is a shared-method problem — every carrier faces the same rings, and none benefits from a private taxonomy
▸ Market reporting retrieved September 2026. Vendor and carrier names appear as published market facts. No inference should be drawn that any named company is connected to this platform or has agreed to participate.

The Integration Constraint Nobody Prices In

The binding constraint is absorption, not model quality

Published analysis puts the problem directly: for most traditional insurers the real constraint is not model quality but what IT can absorb, with BCG cited for the finding that 70% of insurers fail to execute on innovation because of IT limitations. The policy core works, the cost of replacing it is prohibitive, and any proposal that starts with a migration dies in committee.

⬢ THE ARCHITECTURAL CONSEQUENCE FOR INSUREDIC³
InsurEDIC³ is a decision layer above the core, not a replacement for it. It reads from existing systems, writes decisions and evidence back, and requires no migration. That is not a marketing position — it is the only architecture with a realistic adoption path in a region where most participants are running policy administration systems they cannot and will not replace. A platform that requires a core replacement to deliver value has, in practice, no ASEAN market outside the largest carriers.
ASEAN VIEW · TEN SUPERVISORS · ONE SET OF INTERNATIONAL PRINCIPLES

Regulatory Divergence Matrix

The AIRM continues to exchange updates on improving observance of the International Association of Insurance Supervisors' Insurance Core Principles. That common reference point is what makes convergence conceivable. Divergence in how it is implemented is what makes a common compliance layer valuable.

Common Reference Architecture

LayerStatusWhat it establishes
IAIS Insurance Core PrinciplesCOMMON REFERENCEThe AIRM continues to exchange updates on improving observance of the ICPs — this is the shared standard against which ten supervisors measure themselves ASEAN
ASEAN Insurance Regulators' MeetingEST. 1998Platform to strengthen insurance cooperation in the development of regulatory and supervisory frameworks, and research and capacity building through AITRI ASEAN
ASEAN Taxonomy for Sustainable FinanceIN OPERATIONCited at the 28th AIRM as the instrument that must guide future investment and underwriting to improve long-term resilience SOURCED
ASEAN Council of BureauxOPERATINGThe regional mechanism addressing motor and personal injury matters arising from vehicles crossing borders between ASEAN countries SOURCED
Mutual recognition of licencesREPORTED IN NEGOTIATIONASEAN member states reported to be negotiating mutual recognition frameworks for insurance licences, potentially enabling single-platform distribution across jurisdictions SOURCED
Regional AI supervisionNO COMMON INSTRUMENTNo ASEAN-level AI supervisory instrument identified in this build. Singapore has consulted on Guidelines on AI Risk Management; Bank Negara Malaysia issued a discussion paper on AI in the Malaysian financial sector in 2025 SOURCED

Supervisory Bodies by Market

MarketSupervisory authorityAI instrument identifiedNote
SingaporeMonetary Authority of SingaporeCONSULTEDGuidelines on AI Risk Management consulted Nov 2025 – Jan 2026, applying to all FIs including agentic AI, pending finalisation MAS
MalaysiaBank Negara MalaysiaDISCUSSION PAPERDiscussion Paper on Artificial Intelligence in the Malaysian Financial Sector, 2025 SOURCED
PhilippinesInsurance CommissionNOT VERIFIEDPublishes penetration, density and premium statistics; operates a stated 2% penetration target IC
IndonesiaOtoritas Jasa Keuangan (OJK)NOT VERIFIEDRequires jurisdiction verification
ThailandOffice of Insurance CommissionNOT VERIFIEDRequires jurisdiction verification
VietnamMinistry of FinanceNOT VERIFIEDDeputy Minister of Finance addressed the 26th AIRM in 2023 SOURCED
CambodiaInsurance Regulator of Cambodia · Non-Bank Financial Services AuthorityNOT VERIFIEDHosted the 28th AIRM and 51st AIC, Siem Reap, November 2025 SOURCED
Lao PDRMinistry of FinanceNOT VERIFIEDSovereign disaster risk insurance policyholder with SEADRIF WORLD BANK
Brunei DarussalamBrunei Darussalam Central BankNOT VERIFIEDRequires jurisdiction verification
MyanmarFinancial Regulatory Department · Insurance Business Regulatory BoardNOT VERIFIEDHosted the 22nd AIRM and 45th AIC, Nay Pyi Taw, 2019 SOURCED
▸ Authority names are as identified in this build and require confirmation against each jurisdiction's current arrangements before any operational use. "Not verified" means no AI-specific supervisory instrument was established for that market in this build — it does not mean none exists.

The Multi-Jurisdiction Compliance Case

One control set, ten evidence formats

A regional insurer operating in six ASEAN markets currently maintains six compliance programmes that overlap heavily in substance and not at all in format. The IAIS Insurance Core Principles mean the underlying expectations are largely convergent; the implementation is where the cost sits. A common control taxonomy mapped once to each supervisor's instrument set lets one piece of evidence satisfy several regimes — and where a market has no instrument yet, the common set is a defensible default rather than an absence.

BUILT ONCE

Control taxonomy, evidence schema, test methodology, model documentation standard

MAPPED PER MARKET

Instrument crosswalk, local reporting format, filing calendar, language and disclosure requirements

HELD PER PARTICIPANT

The actual evidence, the actual model inventory, the actual decisions — never shared, never pooled

ASEAN VIEW · THE ARCHITECTURE THAT ALREADY EXISTS

Regional Institutional Map

ASEAN's insurance cooperation architecture is nearly fifty years old and considerably more developed than is generally understood. Any regional decision layer has to complement this architecture rather than propose an alternative to it — which means knowing precisely what each body already does.

Regional Bodies

BodyEstablishedMandateWhere a decision layer complements it
ASEAN Insurance Council (AIC)4 April 1978, JakartaThe main regional platform for insurance leaders, professionals and practitioners to network and share knowledge and expertise across areas of insurance business. An accredited entity of ASEAN comprising 15 members from insurance associations across the ten member countries. Works closely with ASEAN insurance regulators and other stakeholders for the development of the industry SOURCEDTurning shared knowledge into shared operating tools — the AIC convenes the expertise, a decision layer would carry the method
ASEAN Insurance Regulators' Meeting (AIRM)1998Platform to strengthen insurance cooperation in the development of regulatory and supervisory frameworks and in research and capacity building through AITRI. Continues to exchange updates on improving observance of the IAIS Insurance Core Principles ASEANAggregate, anonymised readiness against a common control set — visibility without any firm-level disclosure
ASEAN Insurance Training and Research Institute (AITRI)Under AIRMResearch and capacity building for the region's insurance sector ASEANThe natural regional counterpart to the AI for Insurance Academy — curriculum and certification, delivered at regional scale
ASEAN Council of Bureaux (COB)Working group from 2015Addresses automotive and personal injury matters relating to border crossing among ASEAN countries — the cross-border compulsory motor mechanism SOURCEDCross-border claims decisioning and fraud detection across jurisdictions is a shared-method problem by construction
ASEAN Insurance Education Committee (AIEC)Under AIRM/AICRegional insurance education coordination; a working group whose outcomes are reviewed at the annual AIC meeting SOURCEDCertification portability across the region — a qualification recognised in one market recognised in ten
ASEAN Natural Disasters Research Works Sharing (ANDREWS)Under AIRM/AICRegional sharing of natural disaster research work ASEANResearch-to-underwriting translation: turning shared hazard research into usable loss characterisation for pricing and pools
SEADRIF Insurance CompanyLicensed Oct 2019The first regional catastrophe risk facility established in Asia by ASEAN member states. Incorporated and domiciled in Singapore, licensed as a general insurer. Established and owned by ASEAN+3 countries with financial support from the Governments of Japan and Singapore and technical support from the World Bank MAS · SEADRIFTrigger design, impact data pipelines and payout decision logic as reusable shared components
ADRFI ProgrammeUnder AIRM agendaThe ASEAN Disaster Risk Financing and Insurance Programme, considered at AIRM alongside the ASEAN Taxonomy for Sustainable Finance and the Framework for Circular Economy SOURCEDRisk financing strategy analytics at sovereign level — layered retention, pool and market transfer decisions
AHA CentreASEAN bodyThe ASEAN Coordinating Centre for Humanitarian Assistance on disaster management. Signed a Memorandum of Intent with SEADRIF Insurance Company in Bali on 9 March 2026 on disaster impact reporting and scalable risk financing solutions, considering a regional insurance mechanism for pre-arranged financing of rapid humanitarian response SEADRIFThe disaster impact data spine that parametric and impact-triggered products depend on
Global-Asia Insurance PartnershipSingaporeA tripartite partnership between the global insurance industry, regulators and policymakers, and academia, established as a centre of excellence in insurance and risk management with a focus on Asia, initially on pandemic and climate risk MASMethodology validation and the research-to-practice pathway for shared analytical components
▸ Compiled from the ASEAN main portal, ASEAN Insurance Council materials, MAS publications, SEADRIF announcements and World Bank documentation, retrieved September 2026. No body listed has agreed to participate in this platform or endorsed anything on it.

Annual Cycle

A joint plenary meeting between the AIRM and the industry is held annually, with members from the AIC, the Council of Bureaux, the AIEC and ANDREWS. The plenary serves as the platform to exchange views on the progress and implementation of the various insurance initiatives. ASEAN

2019
22nd AIRM · 45th AIC · Nay Pyi Taw, Myanmar
Hosted by the Financial Regulatory Department and Myanmar Insurance Association. Over 200 delegates from ASEAN countries and over 500 from Myanmar.
2020
23rd AIRM · 46th AIC · Held by videoconference
Planned for Mactan, Cebu, Philippines; convened remotely for the first time in the series' history.
Dec 2023
26th AIRM · 49th AIC · Ha Long, Quang Ninh, Vietnam
Around 200 delegates. Agenda included the ADRFI programme, operation of the ASEAN Taxonomy for Sustainable Finance, the Framework for Circular Economy, and the Young ASEAN Insurance Managers Award.
27 Nov 2025
28th AIRM · 51st AIC · Siem Reap, Cambodia
Theme: fostering collective growth and resilience. Focus on climate risk, digital transformation and ageing populations, and work toward a more integrated regional insurance framework.
2026
29th AIRM · Location to be confirmed
Listed on the AIC events calendar with location and date to be confirmed at the time of this build.
ASEAN VIEW · SEADRIF · THE REGION'S WORKING EXAMPLE OF DECISION INFRASTRUCTURE

Disaster Risk Financing

SEADRIF is the clearest evidence in the region that pre-arranged, decision-driven risk financing works. It is also the clearest evidence of how slowly it scales without shared capability. Both lessons matter for what a regional decision layer should try to be.

Lao PDR Payout Speed
5 days
US$1.14m paid to the Government and WFP after heavy rainfall and flooding, September 2026 SEADRIF
SEADRIF Licensed
Oct 2019
As a general insurer, incorporated and domiciled in Singapore MAS
Lao Premium Support
US$3.6M
Grant financed by the Global Shield Financing Facility and the Risk Finance Umbrella MDTF WORLD BANK
Ownership
ASEAN+3
Established and owned by ASEAN+3 countries, Japan and Singapore financial support, World Bank technical support SEADRIF

What SEADRIF Has Actually Done

Oct 2019
SEADRIF Insurance Company launched and licensed in Singapore
The first regional catastrophe risk facility established in Asia by ASEAN member states, providing ex-ante climate and disaster risk financing solutions to participating countries. MAS
2021
Lao PDR purchases first disaster risk insurance
A three-year hybrid product covering floods. WORLD BANK
May 2025
Lao PDR adopts a people-affected impact trigger
Among the first countries to adopt a sovereign disaster risk insurance policy with a people-affected impact trigger. Issued by SEADRIF Insurance Company with partial risk transferred to international reinsurance markets; a two-year policy providing pre-arranged protection for 2025–2027. The grant also supports contingency planning and strengthened environmental and social safeguards to ensure payouts are deployed swiftly, transparently and responsibly. WORLD BANK
2025
Regional expansion momentum increases
At the ASEAN+3 Deputy Finance Ministers Meeting in Hong Kong SAR, Malaysia confirmed advanced consideration of joining by May 2026, and Thailand, Brunei Darussalam and Timor-Leste signalled intent to explore membership. WORLD BANK
Q1 2026
SEADRIF 2.0 — new policies expected for the Philippines
New SEADRIF policies for the Philippines expected to launch from Q1 2026 under SEADRIF 2.0. WORLD BANK
12 Feb 2026
SEADRIF-RAISE regional workshop, Manila
With FAO, on the Regional Agriculture Insurance and Sustainable Economies initiative. Over 50 representatives from six countries — Cambodia, Lao PDR, Malaysia, the Philippines, Thailand and Viet Nam — including ministries of finance, agriculture and environment, the ASEAN Secretariat, the Mekong Institute, ADB, the World Bank, JICA, Japan's MOFA, the UK FCDO and Singapore's National Climate Change Secretariat. SEADRIF
9 Mar 2026
Memorandum of Intent with the AHA Centre, Bali
Signed at SEADRIF's inaugural Knowledge Exchange Day, co-hosted by Indonesia's Ministry of Finance, with over 70 participants from ASEAN+3 governments, regional bodies, development institutions and the insurance and reinsurance sectors. Cooperation covers disaster impact reporting and the possible development of a regional insurance mechanism for pre-arranged financing of rapid humanitarian response and recovery, aligned with operational response frameworks under the ASEAN Agreement on Disaster Management and Emergency Response. SEADRIF
1 Sept 2026
US$1.14m paid in five business days
Following heavy rainfall and widespread flooding in Lao PDR. The same government-reported disaster impact data triggered complementary payouts to the Government of Lao PDR and to the World Food Programme. SEADRIF

The Honest Lesson from the Implementation Review

Why regional uptake was slower than planned

The World Bank implementation completion report is direct about what did not work. The project assumed a pace of multi-country uptake that proved unrealistic for a newly established regional insurer operating within a multi-tier governance structure, and it did not include a formal onboarding plan, a product-development roadmap, or a sustainability strategy for SEADRIF beyond initial capitalisation. Those gaps limited SEADRIF's ability to attract additional members during implementation, and highlighted the importance of integrating demand diagnostics, regulatory feasibility analysis and readiness work.

⬢ WHAT THIS TELLS A PLATFORM BUILDER
Capital was never the constraint. Onboarding capability, product development capacity, demand diagnostics and regulatory feasibility analysis were — and every one of those is a decision-support function, not a balance-sheet function. A regional facility with a strong balance sheet and no shared analytical capability onboards slowly, because each new member has to reconstruct the entire analytical case from scratch. This is the strongest available argument that the missing regional layer is decision infrastructure, and it comes from the region's own experience rather than from a vendor.

Reusable Components a Regional Decision Layer Would Carry

Trigger design library

Parametric index, modelled loss, and impact-based triggers including the people-affected pattern, each with its documented failure modes, basis risk profile and data dependency.

Impact data pipeline

Government-reported disaster impact data, validated and channelled into a payout decision. The SEADRIF–AHA Centre memorandum addresses exactly this — consistency and quality of disaster impact reporting.

Layered risk financing model

Retention, contingency, pool and market transfer as a layered stack, sized against a sovereign's fiscal capacity and its actual loss distribution.

Demand diagnostic

The function named as missing in the implementation review — establishing whether a country has the exposure, the fiscal case and the institutional readiness before onboarding begins.

Regulatory feasibility analysis

Whether the product can be issued, held and paid under the target jurisdiction's own insurance and public finance law.

Payout governance

How a payout is verified, released, deployed and accounted for — with the safeguards that ensure funds move swiftly, transparently and responsibly.

ASEAN VIEW · PRE-BUILT DECISION SEQUENCES FOR RECURRING REGIONAL SITUATIONS

Regional Playbooks

A playbook is a decision sequence agreed before it is needed. Each one below is a pattern that recurs across ASEAN markets, where every participant currently improvises its own version and none is better for the improvisation.

PB-01 · Multi-market catastrophe event HIGH FREQUENCY

A single physical event produces loss in two or more ASEAN markets simultaneously — the June 2026 Philippines and Indonesia earthquake is the reference case.

T+0 TO T+6H
Event characterisation
Hazard footprint from common sources; affected geographies identified; preliminary exposure intersection run inside each participant's own instance
T+6H TO T+48H
Exposure and trigger assessment
Each participant quantifies its own exposure privately; parametric and impact triggers evaluated against event data; sovereign policy triggers checked
T+48H TO T+7D · DECISION POINT
Reserve, claims capacity and reinsurance notification
Initial reserve position, claims surge capacity allocation, and reinsurance notification decisions — the three that determine both the financial and the reputational outcome
T+7D ONWARD
Settlement and learning capture
Settlement progress tracked against the plan; post-event review contributed to the shared precedent library as anonymised pattern, never as claims data

PB-02 · Cross-border motor claim RECURRING

A vehicle insured in one ASEAN market causes loss in another. The ASEAN Council of Bureaux exists precisely for this class of matter.

STEP 01
Jurisdiction and bureau routing
Establish the handling bureau, the applicable compulsory limits and the governing law before any liability position is taken
STEP 02
Cross-border evidence assembly
Police report, medical documentation and repair estimation in the loss jurisdiction, translated and normalised to a common claim object
STEP 03 · WHERE MOST TIME IS LOST
Liability and quantum determination
Two legal regimes, two damages conventions, one claimant. This is where a common decision framework converts weeks into days
STEP 04
Settlement, recovery and fraud screen
Settlement in the appropriate jurisdiction, inter-bureau recovery, and a fraud screen run against shared cross-border patterns rather than a single carrier's history

PB-03 · Sovereign risk financing design STRATEGIC

A member state assesses whether and how to transfer disaster risk — the decision sequence the SEADRIF implementation review found was missing as a formal capability.

01
Fiscal exposure quantification
What disaster losses actually cost the budget, historically and prospectively, and how that cost is currently absorbed
02
Demand diagnostic
Whether the exposure, the fiscal case and the institutional readiness support a transfer decision at all — before product design begins
03
Layering
Retention, contingency financing, pool participation and market transfer sized against the loss distribution and the budget's absorptive capacity
04
Trigger selection and basis risk
Parametric index against modelled loss against impact-based, with basis risk quantified rather than assumed acceptable
05
Regulatory feasibility and payout governance
Whether the product can be issued, held and paid under domestic law, and how funds are released, deployed and accounted for

PB-04 · Regional model failure EMERGING

A model used by multiple carriers across multiple markets is found to be materially wrong. Adoption of common vendor models across the region makes this a question of when, not whether.

STEP 01
Detection and scope
Which model, which versions, which use cases, which decision types, over what period — established from each participant's own inventory, not from a central register
STEP 02
Decision population identification
Which decisions the model influenced, and which of those were material. This is only answerable if the decision trail exists — the single strongest operational argument for a decision ledger
STEP 03 · SUPERVISORY DECISION POINT
Remediation and notification
Whether affected customers are remediated, whether the supervisor is notified, and on what timeline — in each affected jurisdiction, under each jurisdiction's own requirements
STEP 04
Rollback and shared learning
Model rolled back or bounded, authority envelope narrowed, and the failure pattern published to the shared library so no other participant repeats it
ASEAN VIEW · EVERY FIGURE ON THIS PLATFORM, WITH ITS SOURCE

Data Register and Provenance

A platform that asks an industry to trust it with method has to be auditable on evidence. This register lists every substantive external figure used across the three views, its source, its period, and its data class. Where a figure could not be verified to an attributable source it is not on the platform at all — it is listed here as a declared gap.

Sourced Figures
48
Published and attributed to a named source
Derived Figures
6
Computed from sourced inputs, method stated
Illustrative Figures
Marked
Demonstration values, never presented as statistics
Declared Data Gaps
17
Excluded from every calculation rather than estimated

Source Register

SourceClassPeriodWhat is drawn from it
MAS media release — AI Risk Management Guidelines consultationPRIMARY13 Nov 2025Scope, applicability to all FIs, proportionality principle, consultation close date of 31 January 2026, and the deputy managing director's statement on proportionate risk-based guidelines
MAS Consultation Paper P017-2025PRIMARYNov 2025Definition of AI scope covering machine learning, deep learning, reinforcement learning, generative AI and AI agents; the four expectation domains
MAS written parliamentary replyPRIMARY5 Aug 2026Confirmation by the MAS Chairman that the proposed Guidelines apply to all AI use cases including agentic AI and will be finalised soon
MAS media release — PCC framework consultationPRIMARY7 Jul 2026PCC structure, eligible activities, consultation dates, the ~US$65bn Asia 2025 nat cat loss figure and the >90% uninsured share
MAS Consultation Paper P013-2026PRIMARY7 Jul – 7 Aug 2026Core and Cell structure, statutory segregation, captive, ILS and sovereign risk pool use cases, rent-a-captive model definition
MAS Notice 133 and amendmentsPRIMARY2022–2026RBC 2 structure, applicability by insurer class, AT1 and Tier 2 recognition criteria effective 1 January 2026, equity counter-cyclical adjustment and structured product treatment
MAS Guidelines on Risk Management Practices — Insurance Core ActivitiesPRIMARYCurrentEnvironmental risk management guidelines and transition planning addendum, outward reinsurance, outsourcing, AML/CFT, product development and pricing, IAC outcomes
MAS Compliance Toolkit for Direct Insurers and ReinsurersPRIMARYCurrentObligation categories: licensing and control, insurance funds, RBC, business continuity, POPS, reinsurance and technology risk management
MAS keynote, 21st Singapore International Reinsurance ConferencePRIMARY3 Nov 2025S$78bn total premiums end-2024, >8% average annual growth 2019–2024, ~19,000 workforce, refreshed ILS Grant Scheme extension to non-APAC risks and renewals
MAS Insurance-Linked Securities Grant Scheme pagePRIMARYJan 2026 – Dec 2028Grant percentages and caps by instrument type, eligible risk classes, eligible applicants
MAS Insurance and Risk Financing Initiatives pagePRIMARYCurrentGlobal-Asia Insurance Partnership, the NTU IRFRC-led natural catastrophe data analytics partnership, SEADRIF domicile and licensing
MAS regulation and licensing pagesPRIMARYCurrentInsurance regulation overview, FinTech Regulatory Sandbox, tax incentives and grant schemes
IBF — GenAI Jobs Transformation MapPRIMARYCurrentMAS, IBF and WSG partnership with eleven named financial institutions including Income Insurance, Manulife and Prudential; Skills Framework for Financial Services; Young Talent Programme for AI in Finance
LIA Singapore industry resultsPRIMARYFY2025S$6.53bn total weighted new business premiums, up 11.3%; 1H2025 S$2.99bn up 7.7%; ILP share 43% of new sales
Philippine Insurance Commission data via published reportingSECONDARYFY2025PHP 502.64bn total premiums up 14.1%; life 80.77%, non-life 16.41%, MBA 3.37%; density PHP 4,384.56; penetration 1.79% below the 2% target; benefits PHP 121.88bn
28th AIRM · 51st AIC reportingSECONDARY27 Nov 2025Meeting theme and agenda; Cambodia penetration 1.13% and density US$20.65; climate, digital and ageing priorities; ASEAN Taxonomy reference
ASEAN main portal — sectoral bodiesPRIMARYCurrentAIRM establishment 1998, AITRI mandate, joint plenary composition with AIC, COB, AIEC and ANDREWS, IAIS Insurance Core Principles observance
ASEAN Insurance Council materialsPRIMARYCurrentEstablishment 4 April 1978 in Jakarta; 15 members across ten member countries; accredited ASEAN entity status; 2026 events calendar
SEADRIF announcementsPRIMARY2026AHA Centre memorandum of intent 9 March 2026; SEADRIF-RAISE Manila workshop 12 February 2026; Lao PDR US$1.14m payout in five business days, September 2026; ownership and support structure
World Bank — SEADRIF feature and implementation completion reportPRIMARY2026Lao PDR 2021 first policy and May 2025 impact-trigger policy; US$3.6m premium grant; SEADRIF 2.0 Philippines policies from Q1 2026; Malaysia, Thailand, Brunei and Timor-Leste membership signals; the finding on onboarding, roadmap and sustainability gaps
Swiss Re Institute analysis via published reportingSECONDARY2025–2026US$424bn global nat cat protection gap in 2025 from US$395bn; Emerging Asia catastrophe resilience score of 5%; Asia ~30% of economic losses against ~5% of insured losses in FY2025
Aon catastrophe reporting via published reportingSECONDARYH1 2026Tropical Storm Penha; June 2026 Philippines–Indonesia earthquake; Indonesia January–February 2026 flooding
GlobalData analysis via published reportingSECONDARY2026Philippines catastrophe protection gap of approximately 98% against a global average of 58%
Peak Re insightSECONDARY2012–2022Emerging Asia penetration 3.0% to 3.6%; China per capita US$489 against US$86 elsewhere in emerging Asia; ~90% uninsured share of Asian nat cat losses
Southeast Asia life and health market analysisSECONDARY2025–2030Life GWP US$104.8bn 2025 to US$118bn 2030; health GWP US$12.3bn to US$13.8bn; ~2–3% annual growth; uneven country trajectories
Southeast Asia motor market analysisSECONDARY2025–2032US$13.19bn 2025, US$15.11bn 2026, US$19.91bn 2032, 4.71% CAGR; ~57% third-party liability share
APAC insurtech market analysesSECONDARY2026–2035Two divergent estimates, both reported: US$18.7bn 2026 at 16.4% CAGR, and US$28.08bn 2026 at 19.39% CAGR
Insurance AI vendor landscape reportingSECONDARY2026Category structure across intake, workbench, pricing and claims; Cytora Autopilot launch and Zurich 90-day five-country rollout; Federato funding; Akur8 scale; straight-through processing and underwriting cycle figures; the BCG-cited 70% IT execution constraint
Chambers Insurance & Reinsurance 2026 — SingaporeSECONDARY2026Singapore's principles-based AI approach; FEAT and Veritas lineage; M&A and portfolio transfer approval requirements; insurtech hub positioning
CapitalMarkets.SG licence register cross-referenceSECONDARY18 Jun 2026337 insurance companies and brokers holding active MAS licences, cross-referenced against the MAS Financial Institutions Directory

Declared Gaps

GapWhy it is declared rather than estimated
Penetration for six ASEAN marketsMalaysia, Vietnam, Thailand, Brunei, Lao PDR and Myanmar — no current attributable primary figure established in this build. Regional averages would produce a number that looks precise and means nothing
Density for eight ASEAN marketsOnly Cambodia and the Philippines carry an attributable current density figure. Deriving density from a premium estimate and a population estimate compounds two uncertainties into one confident-looking output
Singapore penetration and densityTotal premiums are published; the penetration ratio requires a GDP denominator on a stated basis and period, which was not established here. The premium figure is shown; the ratio is not
AI supervisory instruments in eight marketsOnly Singapore and Malaysia had an AI-specific financial sector instrument identified. "Not verified" means not established in this build, not confirmed absent
Sector readiness percentagesEvery readiness figure on this platform is marked illustrative. No survey has been conducted. The mechanism is real; the numbers demonstrate the mechanism
Correlation classificationsReasoned from published exposure characteristics, not from a calibrated correlation model. Marked illustrative and unsuitable for pricing, capital or pool design
▸ A declared gap is more useful than an estimate presented as a fact. The discipline is simple: if a number cannot be attributed, it does not appear as a statistic anywhere on this platform, and it does not enter any calculation.
SHARED PLATFORM LAYER · VISIBLE IN ALL THREE VIEWS · AI CONTROL TOWER AS THE BRAIN

InsurEDIC³ Platform Architecture

Eight layers. The AI Control Tower sits at the top as the orchestrating brain — it does not merely observe the layers beneath it, it grants and withdraws their authority at runtime. Layer 1 is the foundation at the base: the participant's own systems, which this platform reads from and never replaces.

The Eight Layers

L8
AI Control Tower — the brain and nerve centre
Orchestrates every agent on the platform. Holds the authority envelope for each one: what it may decide alone, what it must escalate, what it may never touch, and the materiality and value thresholds at which authority is automatically withdrawn. Runs the governance ledger, the escalation routing and the runtime intervention path. This is the layer that makes autonomy revocable rather than theoretical
ORCHESTRATION
L7
Decision Surfaces and Role Workbenches
Where a human meets the platform — the executive command centre, the decision queue, the underwriter and claims workbenches, the regulatory readiness board, the ASEAN market view. Each surface is a role, and each role sees only what that role is entitled to see
INTERFACE
L6
Agent Fabric
The deployed agents themselves, organised into squads by domain. Every agent carries a model card, a validation record, a monitoring configuration and an authority envelope. Agents recommend, prepare, monitor and escalate; the Control Tower governs what any of them may execute
EXECUTION
L5
Model and Method Library
The shared intellectual assets — frameworks, taxonomies, control sets, hazard models, fairness testing batteries, validation methodologies, trigger design patterns, decision templates. This is the layer with the highest fixed cost and the lowest competitive value, which is exactly why it should be built once for the industry rather than fifty times
SHARED IP
L4
Decision Object Model and Semantic Layer
A decision is a first-class object: it has an owner, a trigger, an option set, a recommendation, a rationale, a materiality class, a decision-maker, a timestamp and an outcome. The semantic layer maps each participant's own data vocabulary onto that model without requiring the participant to change its systems
ONTOLOGY
L3
Decision Ledger and Evidence Spine
Every decision, every model influence, every human intervention and every authority change, recorded immutably in the participant's own instance. This is what turns a supervisory question into a retrieval rather than a reconstruction — and what makes a model-failure population identifiable after the fact
EVIDENCE
L2
Sovereignty Boundary and Access Control
The architectural enforcement of the data undertaking. Proprietary data does not cross this boundary in either direction: it does not leave the participant, and no shared model is trained on it. Only method flows in; only voluntarily contributed, minimum-cell-size aggregates flow out
BOUNDARY
L1
Source Systems and Signal Ingestion
The participant's own policy administration, claims, finance, actuarial and CRM systems, plus external signal feeds — regulatory wires, market pricing, catastrophe and hazard data, reinsurance capacity, macroeconomic series. Read-only integration by default. The core stays where it is
FOUNDATION

How the Control Tower Governs an Agent

STAGE 01
Registration
An agent cannot run until it is registered with a model card, a stated intended use, a validation record and a named accountable owner inside the participant's organisation
STAGE 02
Materiality classification
The participant classifies the use case against its own scale, activities and risk profile. Proportionality is assessed by the participant, not asserted by the platform
STAGE 03 · THE CONTROL POINT
Authority envelope assignment
What the agent may decide alone, what it must recommend for human approval, what it may never touch, and the value and materiality thresholds at which its authority is automatically withdrawn. Set per agent, per use case, by the accountable owner
STAGE 04
Runtime enforcement
Every action is checked against the envelope before it executes, not audited after. An action outside the envelope does not happen — it becomes an escalation
STAGE 05
Continuous monitoring
Drift, performance, fairness and incident monitoring against configured thresholds, with automatic narrowing of authority when a threshold is breached
STAGE 06
Retirement
Decommissioning with record retention, and identification of the decision population the agent influenced across its whole operating life
▸ This sequence maps to the life-cycle control expectations set out in the proposed MAS Guidelines on AI Risk Management — development, deployment, monitoring and retirement — and to the requirement that governance of third-party AI cannot be delegated to the vendor. The evidence produced at each stage belongs to the participant.
SHARED PLATFORM LAYER · SIX SQUADS · 28 AGENTS · ONE CONTROL TOWER

Agent Orchestration Map

Agents are organised into squads by decision domain. Every agent below runs inside an authority envelope set by the participant, and every one of them can be narrowed, suspended or withdrawn at runtime by the Control Tower. No agent binds risk, issues a policy, moves money or settles a claim — those remain regulated acts performed in the participant's own systems by the participant.

Squads
6
Grouped by decision domain
Registered Agents
28
Each with a model card and owner
Autonomy Levels
4
Observe · Recommend · Act-with-approval · Act-within-bounds
Never Autonomous
7
Agents permanently restricted to recommend-only
Regulated Acts Executed
0
By architecture, not by configuration

Autonomy Ladder

A0
Observe NO AUTHORITY
Monitors, detects and reports. Cannot recommend a course of action, only surface a condition. Entry level for any newly registered agent
A1
Recommend HUMAN DECIDES
Produces an option set with a rationale and a recommendation. A human makes and owns the decision. This is the permanent ceiling for the seven restricted agents
A2
Act with approval HUMAN GATES
Prepares the action fully and executes only on explicit human approval. The human reviews a complete artefact rather than authoring one
A3
Act within bounds BOUNDED AUTONOMY
Acts alone strictly inside a defined envelope — value ceiling, materiality class, decision type, time window. Anything outside the envelope becomes an escalation. Authority narrows automatically on any monitoring breach
▸ There is no level above A3. An agent that could set its own bounds would defeat the purpose of the envelope. Raising an agent from A2 to A3 is a named human decision recorded in the governance ledger with the thresholds that were set, and it is reversible in one action.

Squad 1 · Regulatory and Compliance

Obligation Tracker
Monitors regulatory publications and maps changes to the participant's control set
A1 · RECOMMEND
ACTIVE
Control Mapper
Maps each obligation to the control that evidences it and flags uncovered obligations
A1 · RECOMMEND
ACTIVE
Evidence Assembler
Assembles the evidence pack for a named obligation from the decision ledger
A2 · WITH APPROVAL
ACTIVE
AI Inventory Steward
Maintains the AI inventory, detects unregistered models and flags stale registrations
A3 · BOUNDED
ACTIVE
Materiality Classifier
Proposes a materiality class for a new AI use case against the participant's own scale
A1 · RECOMMEND
ACTIVE

Squad 2 · Underwriting and Pricing

Submission Triage
Structures inbound submissions and routes by appetite fit and complexity
A3 · BOUNDED
ACTIVE
Appetite Guardrail
Checks a risk against stated appetite and portfolio targets before it reaches an underwriter
A3 · BOUNDED
ACTIVE
Rate Adequacy Monitor
Tracks achieved rate against technical rate by segment and flags drift
A1 · RECOMMEND
ACTIVE
Accumulation Sentinel
Monitors accumulation against limits by peril and geography as the book is written
A3 · BOUNDED
ACTIVE
Pricing Fairness Auditor
Runs the shared fairness battery against pricing outcomes on the participant's own book
A1 · RECOMMEND ONLY RESTRICTED
ACTIVE

Squad 3 · Claims

FNOL Structurer
Structures first notification of loss and routes by complexity and severity
A3 · BOUNDED
ACTIVE
Fraud Signal Detector
Applies shared fraud pattern taxonomies to the participant's own claims
A1 · RECOMMEND ONLY RESTRICTED
ACTIVE
Reserve Adequacy Watch
Monitors case reserve adequacy against development patterns and flags deterioration
A1 · RECOMMEND
ACTIVE
Cycle Time Analyst
Identifies where claims cycles stall and what the stall costs
A1 · RECOMMEND
ACTIVE
Friction Equity Monitor
Detects asymmetric claims friction across cohorts beyond what risk explains
A1 · RECOMMEND ONLY RESTRICTED
ACTIVE

Squad 4 · Capital, Finance and Risk

Capital Position Monitor
Tracks the RBC 2 position against intervention levels and flags trajectory
A1 · RECOMMEND ONLY RESTRICTED
ACTIVE
IFRS 17 Close Assistant
Validates data flows and surfaces assumption changes ahead of the reporting close
A2 · WITH APPROVAL
ACTIVE
Reinsurance Optimiser
Models structure alternatives against retained risk, capital relief and cost
A1 · RECOMMEND
ACTIVE
Counterparty Exposure Watch
Monitors reinsurance and investment counterparty concentration and credit signals
A1 · RECOMMEND
ACTIVE
Cell Economics Analyst
Evaluates captive and PCC cell retention against commercial market pricing
A1 · RECOMMEND
ACTIVE

Squad 5 · Climate and Catastrophe

Hazard Footprint Analyst
Applies shared hazard model libraries to the participant's own exposure
A1 · RECOMMEND
ACTIVE
Event Response Coordinator
Runs the multi-market catastrophe playbook sequence from event detection
A2 · WITH APPROVAL
ACTIVE
Transition Pathway Modeller
Runs climate scenario pathways across underwriting book and investment portfolio
A1 · RECOMMEND
ACTIVE
Parametric Trigger Designer
Designs and stress-tests parametric and impact triggers with basis risk quantified
A1 · RECOMMEND
ACTIVE

Squad 6 · Governance and Capability

Drift Sentinel
Monitors model drift and performance across the registered estate and narrows authority on breach
A3 · BOUNDED
ACTIVE
Validation Coordinator
Schedules, tracks and escalates independent model validation across the estate
A2 · WITH APPROVAL
ACTIVE
Decision Ledger Keeper
Maintains the immutable decision record and reconstructs decision populations on demand
A3 · BOUNDED
ACTIVE
Capability Gap Analyst
Maps role-level AI competence against the participant's own AI estate and flags capacity risk
A1 · RECOMMEND
ACTIVE
Boundary Auditor
Continuously verifies that no proprietary data has crossed the sovereignty boundary
A1 · RECOMMEND ONLY RESTRICTED
ACTIVE
Precedent Librarian
Curates anonymised decision patterns contributed to the shared learning exchange
A1 · RECOMMEND ONLY RESTRICTED
ACTIVE
Model Failure Responder
Runs the regional model failure playbook — scope, decision population, remediation, rollback
A1 · RECOMMEND ONLY RESTRICTED
STANDBY
Cross-Border Claims Router
Routes cross-border motor claims through the Council of Bureaux sequence
A1 · RECOMMEND
IDLE

Why seven agents are permanently restricted to recommend-only

Capital position, pricing fairness, claims friction equity, fraud referral, boundary audit, precedent curation and model failure response are all domains where an autonomous action would either affect a customer adversely without human judgement, or where the agent would be marking its own homework. These are not configuration defaults that a participant can raise — they are architectural ceilings. An agent that could autonomously clear its own boundary audit or autonomously decline a claimant is not a governance improvement over a human doing it badly; it is the same failure at machine speed with a thinner accountability trail.

ILLUSTRATIVE Agent names, confidence bars and status indicators demonstrate the orchestration model. Agent composition in a deployment would be configured per participant against its own use cases, and every agent would be registered, classified and bounded by that participant before running.
SHARED PLATFORM LAYER · THE RESOURCE CENTRE · METHOD IS THE PRODUCT

Frameworks and Methods Library

The core proposition of InsurEDIC³ is that the insurance industry keeps paying, separately and repeatedly, for the same intellectual assets. This library is the shared alternative: frameworks, tools, processes and methodologies that any participant can take, apply to its own book, and adapt — without building them, and without disclosing anything.

Decision Frameworks
9
How a class of decision is structured
Governance Instruments
8
Control sets, registers and evidence schemas
Analytical Methods
9
Testing, modelling and validation methodologies
Operating Playbooks
6
Pre-agreed decision sequences

Decision Frameworks

AssetApplies toWhat it gives a participant
Decision Object ModelAllA standard structure for what a decision is — owner, trigger, options, rationale, materiality, decision-maker, outcome — so decisions become auditable objects rather than events that happened
Signal-to-Decision SpineAllThe path from raw signal through insight to decision to action to outcome, with the handoff points named and owned
Decision Materiality ScaleAllA common scale for classifying decision materiality, which is the input proportionality depends on and which most firms currently invent ad hoc
Autonomy LadderAllThe four-level authority model — observe, recommend, act with approval, act within bounds — with the criteria for moving between levels
Retention Optimisation FrameworkCaptives, PCC cellsHow much risk to retain at what attachment, against what loss distribution, compared to commercial market pricing at this point in the cycle
Layered Risk Financing ModelSovereign, corporateRetention, contingency, pool and market transfer as a layered stack sized against absorptive capacity
Demand DiagnosticRisk pools, new marketsWhether exposure, fiscal case and institutional readiness support a transfer decision — the capability the SEADRIF implementation review identified as missing
Affordability-Constrained Product DesignProtection gapDesigning to a price point the segment can bear rather than pricing a design it cannot
Multi-Jurisdiction Control CrosswalkRegional operatorsOne control set mapped to several supervisors' instruments, so one piece of evidence satisfies several regimes

Governance Instruments

AssetMaps toWhat it gives a participant
AI Inventory SchemaProposed AIRM GuidelinesA structure for the AI inventory that satisfies the expectation, including third-party and embedded models most inventories miss
AI Life-Cycle Control SetProposed AIRM GuidelinesControls across development, deployment, monitoring and retirement, each with the evidence artefact it must produce
Three-Lines-of-Defence AI MappingProposed AIRM GuidelinesWhich line owns which AI control, and where the boundaries sit for a model built by one line and used by another
Board AI Reporting PackProposed AIRM Guidelines · IACWhat the board actually needs to see to discharge oversight, at a length a board will read
Third-Party AI Evidence StandardThird-party risk · AIRMThe artefacts a participant must obtain from any AI vendor, expressed as contractual requirements
Model Card TemplateAIRM · FEATIntended use, limitations, training provenance, performance, fairness results and known failure modes in one document
Decision Ledger SchemaAll supervisory instrumentsWhat must be recorded about a decision for it to be reconstructable years later under examination
Obligation Register StructureAll supervisory instrumentsHow to hold the obligation set so that a regulatory change updates the affected controls rather than triggering a full re-mapping

Analytical Methods

AssetDomainWhat it gives a participant
Fairness Testing BatteryConductProxy scan, outcome parity, friction asymmetry, explanation adequacy and elasticity separation — the method is shared, the results never are
Model Validation MethodologyAI governanceIndependent validation protocol scaled by materiality class, addressing the capacity constraint rather than restating the policy
Drift Detection ConfigurationAI governanceWhat to monitor, at what threshold, with what automatic authority consequence on breach
GenAI Risk Control SetAI governanceHallucination, prompt injection and data leakage controls, following the Project MindForge focus areas
Hazard Model LibraryClimate · catDocumented, transparent hazard models and vulnerability functions — the highest fixed cost and lowest competitive value capability in the industry
Climate Pathway SetsClimateScenario pathways for climate scenario analysis across underwriting book and investment portfolio
Basis Risk QuantificationParametricHow to quantify the gap between trigger and actual loss, which is the failure mode that kills parametric products commercially
Thin-Data Loss CharacterisationProtection gapCharacterising a loss distribution where there is no claims history because there was never any cover
Fraud Pattern TaxonomyClaimsShared typology of fraud patterns including synthetic identity and staged accident schemes, with detection logic — no participant's claims data attached

Operating Playbooks

Multi-market catastrophe
PB-01 · T+0 TO SETTLEMENT
Cross-border motor claim
PB-02 · COUNCIL OF BUREAUX
Sovereign risk financing
PB-03 · FIVE-STEP DESIGN
Regional model failure
PB-04 · SCOPE TO ROLLBACK
AIRG readiness programme
PB-05 · TRANSITION SEQUENCE
PCC cell establishment
PB-06 · ANALYTICAL CASE
▸ Every asset in this library is versioned, dated and attributed. Where a framework derives from published third-party work, that work is named. Where it is original to this platform, it is marked as such. A participant may adapt any asset for its own use; adaptation does not create an obligation to contribute the adaptation back.
SHARED PLATFORM LAYER · LEARNING FROM ONE ANOTHER WITHOUT SEEING ONE ANOTHER'S BOOK

Industry Learning Exchange

Industry players should be able to learn from each other. The obstacle has never been willingness — it is that the obvious mechanism, pooling data, is commercially impossible and legally fraught. The exchange is built on a narrower and more durable premise: share what went wrong and what worked, never what it was worth or to whom.

Four Exchange Mechanisms

1 · Anonymised pattern contribution

A participant contributes the shape of a decision that went well or badly — the trigger, the option set considered, what was missed, what the corrective was. No amounts, no counterparties, no dates precise enough to identify the event, no lines of business specific enough to identify the contributor.

EXAMPLE OF WHAT IS CONTRIBUTED

"A rate adequacy signal was visible eleven months before the segment turned unprofitable, but sat in a report nobody owned. The corrective was to make rate drift an owned decision object with an escalation threshold." — no segment, no numbers, no firm.

2 · Method peer review

Participants review and improve shared methods, the way an actuarial profession improves a standard. A better fairness test contributed by one participant is a better fairness test for everyone, and it reveals nothing about the contributor's book.

WHY THIS WORKS COMMERCIALLY

No carrier competes on the quality of its proxy discrimination scan. Every carrier competes on price, appetite, distribution and service. Improving a shared test costs a contributor nothing it was monetising.

3 · Minimum-cell-size aggregates

Where a genuine benchmark is useful — readiness against a control set, adoption of a control, certification completion by role — participants contribute a scored result and the platform publishes the aggregate. Minimum cell size five, no attribution, suppression rather than estimation below threshold.

THE HARD LIMIT

No financial benchmarks. No loss ratios, expense ratios, rate movements, retention or premium figures. Those are the numbers that would let a participant infer a competitor's position, and no cell size makes them safe.

4 · Facilitated practitioner forums

Structured sessions where practitioners work a real problem together under a common framework, with a facilitator and a rule set. The oldest and still most effective knowledge transfer mechanism in the industry — the ASEAN Insurance Council has run on this principle since 1978.

WHAT MAKES IT DIFFERENT HERE

The output is not a conference summary. It is a versioned addition to the shared method library that every participant can apply the following week.

The Line That Cannot Move

Never exchanged, under any mechanismWhy
Loss ratios, expense ratios, combined ratiosThe core competitive metrics. No aggregation makes them safe in a market with a small number of large participants
Rate movements or pricing levelsExchange of pricing information between competitors raises competition law issues independent of any commercial concern, and this platform will not host it
Appetite, capacity or retention positionsDirectly commercially sensitive and directly inferable into a competitor's strategy
Claims records or claimant informationPersonal data with its own legal regime, and of no legitimate value to a competitor
Distribution or commission economicsThe most closely held commercial information in the industry
Anything identifying a specific transaction, client or counterpartyAnonymisation that leaves a transaction identifiable is not anonymisation
⬢ COMPETITION LAW POSTURE
A shared platform among competitors attracts competition law scrutiny, and it should. The exchange is designed to be defensible on that basis: no exchange of pricing, capacity or commercially sensitive forward-looking information; a documented rule set applied consistently; a facilitator with authority to stop a discussion; and a published boundary that participants and their counsel can assess in advance. Participants should take their own competition law advice — this platform provides the boundary and the record, not the advice.
SHARED PLATFORM LAYER · THE UNDERTAKING THIS PLATFORM STANDS OR FALLS ON

Data Sovereignty Charter

No insurer will put its decision-making on a platform shared with its competitors unless the data question is answered completely, verifiably and in advance. This charter is that answer. It is written to be tested, not to be reassuring.

The Boundary

Participant Side
NEVER CROSSES OUTWARD
  • Policyholder and claimant personal data
  • Policy, claims, exposure and accumulation files
  • Pricing models, rating factors, technical rates
  • Underwriting appetite and declinature rules
  • Reserving assumptions and reserve balances
  • Capital position ahead of regulatory filing
  • Reinsurance structures, terms, counterparties
  • Distribution and commission economics
  • Financial results ahead of publication
  • The participant's own model inventory and validation evidence
  • Individual decision records and their outcomes
  • Strategy, M&A activity and corporate plans
ENFORCED
IN
ARCHITECTURE
Platform Side
FLOWS INWARD TO THE PARTICIPANT
  • Frameworks, taxonomies, decision templates
  • Control sets mapped to regulatory instruments
  • Model architectures and reference implementations
  • Validation and fairness testing methodologies
  • Hazard model libraries and climate pathways
  • Regulatory intelligence and obligation tracking
  • Agent designs and authority envelope patterns
  • Playbooks and decision sequences
  • Training curricula and certification
  • Anonymised aggregates, minimum cell size five
  • Anonymised failure and success patterns
  • Published market and regulatory data

Nine Commitments — Each One Testable

No.CommitmentHow a participant can verify it
01No training on participant dataModel provenance records state the training corpus for every shared model. A participant may commission an independent audit of that provenance at the platform's cost, once per year
02No cross-participant computationThe platform performs no computation that reads more than one participant's data together. There is no cross-book analytical surface in the architecture, so there is none to audit around
03No operator access to decision contentPlatform support operates against configuration and telemetry, not content. Any access to participant content requires a per-instance, time-boxed grant by the participant, logged and visible to the participant
04Egress transparencyThe participant can see, at any time, every category of information that has left its instance and when. The Boundary Auditor agent runs this check continuously and reports to the participant, not to the platform
05Aggregate contribution is opt-in and reversibleContribution to any aggregate is per-metric opt-in, and withdrawal recomputes the aggregate without the participant's contribution
06Minimum cell size with suppressionNo aggregate is published below five contributing firms. Below the threshold the cell is suppressed and marked as such, never estimated or filled from an adjacent cell
07No financial or pricing benchmarksLoss ratios, expense ratios, rate movements, retention and premium figures are outside the aggregate set entirely. This is an architectural exclusion, not a configuration default
08Full export at any timeFrameworks, control sets, model inventory, evidence packs and the participant's complete decision history export in open, documented formats on demand, with no exit fee and no notice period
09No silent scope changeAny change to what leaves a participant instance requires affirmative written consent. It is a contractual amendment, not a terms-of-service update, and silence is not consent

The Question Every Prospective Participant Asks

"If you learn nothing from our data, what improves over time?"

Method improves. Every participant applying the same framework to a different book surfaces different weaknesses in the framework — and the weakness is reportable without the book being visible. A carrier that finds the fairness battery misses a particular proxy pattern reports the gap in the method, not the finding in its portfolio. Over three years the shared method set becomes considerably better than anything one firm would have built, and no firm has disclosed anything. That is a slower improvement curve than pooling data would give. It is the only curve that is commercially and legally available, and this platform is honest that it chose the slower one deliberately.

"What stops you changing your mind once we depend on you?"

Three things, in descending order of reliability. First, architecture: there is no aggregated data store, so there is nothing to change your mind about — reversing this would require rebuilding the platform, not amending a policy. Second, the export commitment: a participant that can leave with everything at any time, at no cost, is not dependent in the way that makes exploitation profitable. Third, contract: the undertaking is contractual, and change requires affirmative consent. The order matters — a promise backed only by contract is worth exactly as much as the counterparty's incentive to keep it.

SHARED PLATFORM LAYER · WHY BUILDING THIS ALONE IS THE EXPENSIVE OPTION

Shared Platform Economics

The operating model rests on a single observation: the decision infrastructure an insurer needs is almost entirely non-differentiating, and almost entirely fixed-cost. Every participant is currently paying the full fixed cost of an asset that gives it no competitive advantage. That is the inefficiency the platform monetises, and it is the participant who keeps most of the gain.

Build Alone Against Shared Platform

Build Alone
EVERY PARTICIPANT PAYS FULL FIXED COST
Control architecture designDesigned from first principles, once per firm, by people who have not done it before
Framework and method developmentFairness batteries, validation protocols, hazard libraries, trigger design — each invented locally
Regulatory interpretationEvery firm reads the same instrument separately and reaches a slightly different operational answer
Specialist hiringCompeting for the same scarce model validation and AI governance talent as every peer
Vendor negotiationNegotiating AI vendor terms alone, without the leverage to obtain telemetry access or validation artefacts
Time to capabilityMeasured in years, against a transition period measured in months
LearningOnly from own mistakes, and only after they have cost something
Competitive advantage gainedNONE No customer chose an insurer because its model validation protocol was proprietary
Shared Platform
FIXED COST AMORTISED · BOOK STAYS PRIVATE
Control architecture designDesigned once against the published instruments, maintained centrally as they change
Framework and method developmentBuilt once, improved by every participant's application to a different book
Regulatory interpretationInterpreted once, published to all, updated as instruments move from consultation to final
Specialist hiringScarce capability accessed through the platform and built internally through the Academy, in parallel
Vendor negotiationA common evidence standard that vendors must meet to be usable by any participant
Time to capabilityWeeks to a working control set, then adaptation to the participant's own profile
LearningFrom anonymised patterns across the industry, before the mistake is made
Competitive advantage retainedALL OF IT Pricing, appetite, distribution, service and claims handling stay entirely proprietary

What Is Differentiating and What Is Not

Non-differentiating — should be shared

AI governance architectureModel validation protocolFairness testing methodControl taxonomyEvidence schemaHazard modelsClimate pathwaysRegulatory interpretationObligation trackingDecision object modelFraud pattern taxonomyTrigger design patternsBasis risk methodPlaybook sequencesTraining curriculaCertification standards

Nothing on this list has ever won a renewal, a broker relationship or a customer. Every item on it costs real money to build and real money to maintain.

Differentiating — stays private

Risk appetiteTechnical pricingRating factorsDistribution relationshipsClaims serviceProduct designReinsurance structureCapital allocationCustomer relationshipsUnderwriting judgementBrandTalentStrategySegment selection

This is where insurers actually compete, and none of it touches the platform. A participant using InsurEDIC³ competes with a participant using InsurEDIC³ on exactly the dimensions they competed on before — with better instruments on both sides.

Participation Model

TIER 01

Resource Access

The frameworks and methods library, regulatory intelligence, playbooks and the Academy. No platform instance. The entry point for brokers, MGAs, captives and small carriers.

TIER 02

Platform Instance

A participant-controlled instance with the decision ledger, agent fabric and Control Tower, integrated read-only to the participant's own systems. Full sovereignty boundary.

TIER 03

Instance plus Capability Programme

Platform instance with a structured capability-building programme — role pathways, certification and facilitated practitioner forums, sized to the participant's AI estate.

TIER 04

Consortium and Regional

For associations, regional pools and groups of smaller carriers participating collectively — the structure that makes the platform reachable for markets with low premium bases.

▸ Commercial terms are not published on this demonstrator. The tiering describes the participation structure, not a price list. Tier 4 exists specifically because a market with insurance density of US$20.65 per capita cannot reach this capability through individual subscriptions, and a regional platform that only serves the markets that could already afford to build alone has failed at its stated purpose.
SHARED PLATFORM LAYER · CAPABILITY BUILDING · UPSKILL AND RESKILL FOR BETTER DECISIONS

AI for Insurance Academy

The proposed MAS Guidelines on AI Risk Management name capabilities and capacity as an expectation domain in its own right. It is the one domain software cannot satisfy. The Academy exists because a platform that hands an under-skilled organisation more powerful tools has made the risk worse, not better — and because the scarcest thing in this industry is not models, it is people who can judge them.

Modules
18
Across five tracks
Role Pathways
8
Curated sequences by job family
Delivery Modes
4
Self-paced · cohort · in-house · practitioner forum
Assessment
Applied
On the participant's own decision problems, in its own instance
Regional Delivery
10
Designed for delivery across all ASEAN markets

Track A · Foundations

FND-01
What AI Actually Does in an Insurance Business
HALF DAY · ALL ROLES
A working mental model of machine learning, generative AI and agentic systems, framed entirely around insurance workflows — intake, pricing, triage, servicing, fraud, reserving. No mathematics, no hype, no vendor demonstrations.
Outcome: can tell the difference between a task AI is genuinely good at and one where it will produce confident nonsense.
FND-02
Decision Quality Before Decision Speed
HALF DAY · ALL ROLES
Why faster decisions are only valuable if they are better decisions. Decision framing, option generation, the difference between a good decision and a good outcome, and where automation improves quality against where it merely accelerates an existing error.
Outcome: can identify which decisions in their own function should be automated and which should not.
FND-03
Data, Evidence and the Limits of Both
ONE DAY · ANALYTICAL ROLES
Where insurance data comes from, what it systematically omits, how survivorship and selection effects enter a book, and why a model trained on a book reproduces that book's blind spots. Includes the thin-data problem that defines protection gap segments.
Outcome: can interrogate a model's data provenance rather than accepting its accuracy score.
FND-04
Reading a Model Card
HALF DAY · ALL ROLES
How to read intended use, limitations, training provenance, performance characteristics and known failure modes — and what questions to ask a vendor when the model card does not answer them. Uses real model card structures.
Outcome: can assess whether a model is fit for a use case without needing to build one.

Track B · Decision Intelligence

DEC-01
The Decision Object Model
ONE DAY · MANAGERS AND ABOVE
Treating a decision as an object with an owner, a trigger, an option set, a rationale, a materiality class and an outcome. How to convert an existing informal process into an auditable decision chain without adding bureaucracy.
Outcome: can redesign one of their own recurring processes as a decision chain.
DEC-02
Signal to Decision
ONE DAY · ANALYTICAL AND OPERATIONAL
Why organisations drown in signal and starve for decisions. Building the path from raw signal through insight to decision to action to outcome, and finding the handoff points where value is currently lost.
Outcome: can trace one signal in their own business end to end and find where it dies.
DEC-03
Decision Intelligence Under Uncertainty
TWO DAYS · SENIOR ANALYTICAL
Structured decision-making where the loss distribution is poorly characterised — thin data, emerging risk, correlated exposure. Scenario construction, sensitivity to assumption, and how to be honest about what is not known without being paralysed by it.
Outcome: can build a defensible decision case in a segment with no credible loss history.
DEC-04
Designing Authority Envelopes
ONE DAY · RISK, GOVERNANCE, OPERATIONS
The core agentic AI competence. What an agent may decide alone, what it must escalate, what it may never touch, and at what thresholds authority is withdrawn. How to set these deliberately rather than inheriting a vendor default.
Outcome: can write and defend an authority envelope for a live use case in their own function.

Track C · Governance and Regulation

GOV-01
The Supervisory Expectations for AI
ONE DAY · ALL ROLES · REGIONAL VARIANTS
The lineage from FEAT through Veritas and Project MindForge to the proposed Guidelines on AI Risk Management, and what each expectation domain requires operationally. Regional variants cover other ASEAN markets as instruments emerge.
Outcome: can locate their own role's obligations within the supervisory framework.
GOV-02
Building and Maintaining an AI Inventory
ONE DAY · GOVERNANCE, TECHNOLOGY, RISK
The practical work: finding shadow AI, capturing embedded vendor models, classifying materiality against the firm's own profile, and keeping the inventory current when the estate changes weekly.
Outcome: can stand up an inventory that would survive a supervisory examination.
GOV-03
Model Validation as a Practice
TWO DAYS · SECOND LINE, ACTUARIAL, AUDIT
The module addressing the sector's sharpest capacity constraint. Independent validation protocol scaled by materiality, what to test, what evidence to produce, and how to challenge a first-line model owner constructively and effectively.
Outcome: can independently validate a materiality-tier-two model and document it defensibly.
GOV-04
Third-Party AI and Vendor Governance
ONE DAY · PROCUREMENT, RISK, TECHNOLOGY
Why governance cannot be delegated to the vendor. What to require contractually, what artefacts to obtain before deployment, how to monitor a model you did not build and cannot see inside, and how to plan an exit.
Outcome: can write AI requirements into a vendor contract that a supervisor would accept.

Track D · Domain Application

APP-01
AI in Underwriting
TWO DAYS · UNDERWRITING
Submission intake, appetite guardrails, portfolio steering, continuous underwriting from telematics and IoT, and the discipline of underwriting judgement when the cycle compresses from three days to three minutes. Where the underwriter's value moves to when the mechanics are automated.
Outcome: can operate as the accountable decision-maker over an AI-assisted underwriting workflow.
APP-02
AI in Claims
TWO DAYS · CLAIMS
FNOL structuring, triage, fraud signal interpretation, reserve adequacy monitoring, and the conduct dimension — friction asymmetry, explanation adequacy and the customer's experience of an automated decision.
Outcome: can run an AI-assisted claims operation without degrading the customer outcome.
APP-03
AI in Pricing and Actuarial Work
TWO DAYS · ACTUARIAL, PRICING
Transparent machine learning for pricing, why regulators expect an actuary to explain exactly why a rate moved, the proxy discrimination problem, elasticity separation, and reserving under IFRS 17 where model output meets accounting judgement.
Outcome: can defend a machine-learning-derived rate movement to a regulator and a board.
APP-04
AI in Climate and Catastrophe Risk
TWO DAYS · CAT, RISK, ACTUARIAL
Hazard modelling, climate scenario analysis for transition planning, accumulation management, parametric and impact trigger design, and basis risk quantification — the capability set the region's protection gap depends on.
Outcome: can design and stress-test a parametric trigger with basis risk quantified.

Track E · Leadership and Board

LDR-01
Board Oversight of AI Risk
HALF DAY · BOARD AND EXECUTIVE
The draft Guidelines place the board and senior management at the centre of AI risk governance — AI risk is a leadership responsibility, not a technology one. What a board must actually understand, what it should demand to see, what questions expose a weak AI programme in ten minutes, and what a board is accountable for when an agent acts.
Outcome: a board that can discharge AI oversight without becoming technical, and can tell a real programme from a paper one.
LDR-02
Leading an AI-Enabled Insurance Business
TWO DAYS · EXECUTIVE
Where value actually accrues from AI in an insurance business, why most programmes stall at pilot, the IT absorption constraint that stops 70% of insurers executing on innovation, the workforce transition, and how to sequence adoption so governance arrives with capability rather than after it.
Outcome: can sequence an AI programme that survives both the board and the regulator.

Alignment with the National Skills Architecture

MAS and IBF, supported by Workforce Singapore, partnered eleven financial institutions — including Income Insurance, Manulife and Prudential — to pilot workforce transformation initiatives and prepare the sector workforce for an AI-enabled future, unpacking what AI and generative AI adoption means for specific job roles. IBF also maintains the Skills Framework for Financial Services, the Data Analytics and Automation and Generative AI Jobs Transformation Maps, and the Young Talent Programme for AI in Finance. At regional level, the AIRM operates capacity building through AITRI, and the ASEAN Insurance Education Committee coordinates regional insurance education.

The Academy is designed to map onto that architecture rather than compete with it — role-based pathways, defined technical competencies, and applied assessment. No accreditation, recognition, funding eligibility or affiliation with IBF, MAS, WSG, AITRI or the AIEC is claimed or implied. Any such recognition would be applied for on its own merits.

SHARED PLATFORM LAYER · EIGHT ROLE PATHWAYS · APPLIED ASSESSMENT ON REAL DECISIONS

Learning Pathways

A module list is not a capability programme. A pathway is a sequence for a specific role, ending in an applied assessment on that person's own decision problems, in their own instance, on their own data — which never leaves it.

Role Pathways

RoleSequenceDurationApplied assessment
UnderwriterFND-01 → FND-04 → DEC-01 → APP-01 → DEC-046 daysDesign the authority envelope for an AI-assisted underwriting workflow in their own line, and defend the risks they chose to keep human
Claims managerFND-01 → FND-04 → DEC-02 → APP-02 → DEC-046 daysRun the friction equity battery on their own claims cohorts and produce the remediation case for what it surfaces
Actuary / pricingFND-03 → DEC-03 → APP-03 → GOV-038 daysValidate a pricing model to materiality tier two standard and defend a rate movement it produced to a simulated supervisory panel
Risk and second lineFND-01 → FND-03 → GOV-01 → GOV-02 → GOV-03 → DEC-048 daysStand up an AI inventory and materiality classification for their own firm's estate, including shadow and embedded models
ComplianceFND-01 → GOV-01 → GOV-02 → GOV-04 → DEC-015 daysProduce the evidence pack for one named obligation, assembled from the decision ledger, to examination standard
Technology and dataFND-02 → FND-03 → DEC-02 → GOV-02 → GOV-046 daysMap their own AI estate against the life-cycle control set and identify every control with no owner
Catastrophe and climateFND-03 → DEC-03 → APP-04 → GOV-038 daysDesign a parametric trigger for a thin-data peril with basis risk quantified, and stress it against historical events
Board and executiveFND-01 → LDR-01 → LDR-023 daysChair a simulated board AI risk review, including an agent that acted outside its envelope, and decide what to do about it

Progression Model

01
Diagnostic
Where this role's decisions actually sit today, what AI touches them now, and what the person is currently accountable for without knowing it. Run inside the participant's own instance against its own AI inventory.
02
Foundations
Common language across the organisation, so an underwriter, an actuary, a compliance officer and a board member can have the same conversation about the same model without translating.
03
Role depth
The domain track for the specific job family, built on the decisions that role actually owns rather than on generic AI content.
04
Applied assessment
On a real decision problem in the participant's own instance. Not a case study, not a simulation — the person's own work, assessed. Nothing about that work leaves the instance.
05
Practitioner forum
Joining the cross-industry forum for that role, where practitioners work shared problems under the exchange rule set and improve the shared method library.
06
Continuing currency
The supervisory expectations are moving. Currency requires refresh as instruments finalise and as the participant's own AI estate changes — capability is a subscription, not a certificate.

The Capacity Problem This Is Actually Trying to Solve

Every insurer in this market is hiring for the same two roles

Model validation capacity and second-line AI competence are the two constraints that appear in every readiness assessment, and they cannot be solved by hiring because the pool does not exist at the size the sector needs. The industry is bidding against itself for a small number of people, which raises cost for everyone and closes no gap in aggregate.

WHAT DOES NOT WORK

Competing for the same scarce hires. It reallocates capability between firms and adds none to the sector.

WHAT PARTIALLY WORKS

Outsourcing validation to consultancies. Fast, expensive, and leaves the firm without the capability once the engagement ends.

WHAT ACTUALLY WORKS

Converting existing actuarial, audit and risk professionals into AI-competent ones. They already have the judgement and the domain knowledge; what they lack is the specific competence, and that is teachable.

⬢ WHY THIS BELONGS INSIDE THE PLATFORM RATHER THAN BESIDE IT
A person who learns model validation on a generic course then returns to a firm with no inventory, no control set and no decision ledger has nowhere to apply it. A person who learns it against their own firm's actual AI estate, inside the instance that holds it, is productive the same week. Training separated from infrastructure decays; training embedded in infrastructure compounds. That is why capability building is a first-class component of InsurEDIC³ and not a marketing attachment to it.