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Professional Development Programme · May 2026 Edition

Course Title: AI Innovation for Insurance Professionals

Duration
2-Day Programme
(16 Hours Total)
Delivery
In-Person |
Virtual-Instructor-Led
Level
Practitioner
No prior tech knowledge
Pre-requisites
Familiarity with
insurance operations

Programme Overview

The insurance industry is undergoing rapid transformation driven by advancements in artificial intelligence (AI). Across underwriting, claims, distribution, and customer engagement, AI is reshaping how insurers assess risk, deliver services, and create value. This programme provides a structured and practice-oriented understanding of AI technologies and their application within the insurance value chain. It focuses on enabling organisations to harness AI for innovation, operational efficiency, and enhanced decision-making, while ensuring alignment with governance, regulatory, and ethical considerations.

Programme Objectives

This programme aims to:

  • • Provide a comprehensive overview of AI technologies and their relevance to the insurance industry.
  • • Examine the application of AI across core insurance functions including underwriting, claims, actuarial, and customer engagement.
  • • Explore emerging AI capabilities such as generative AI, agentic AI, and decision intelligence in insurance operations.
  • • Introduce approaches to developing, deploying, and scaling AI use cases within insurance organisations.
  • • Highlight governance, regulatory, and ethical considerations in the adoption of AI.

Learning Outcomes

Upon completion of the programme, participants will be able to:

  • → Explain key AI concepts, including generative AI, large language models (LLMs), and agentic AI, in an insurance context.
  • → Apply AI techniques to support underwriting, claims management, fraud detection, and customer engagement.
  • → Develop and evaluate AI-driven use cases for insurance operations.
  • → Utilise appropriate tools and approaches for building or implementing AI models, including low-code solutions.
  • → Assess risks and implement governance practices aligned with responsible AI principles and regulatory expectations.
  • → Formulate an AI adoption roadmap relevant to their organisational context.

Programme Outline

A comprehensive, modular deep-dive meticulously tailored for the insurance value chain.

MODULE 1

AI Fundamentals in Insurance

  • Overview of AI, machine learning, and deep learning
  • AI applications across the insurance value chain
  • Value creation and business impact
MODULE 2

Data Strategy and Digital Transformation

  • Data governance, data quality, and data architecture
  • Enabling AI-ready organisations
  • Integration with digital platforms and ecosystems
MODULE 3

Generative AI and Large Language Models

  • Foundations of generative AI
  • Use cases in product development, underwriting support, and documentation
  • Prompt engineering and AI copilots
MODULE 4

Agentic AI in Insurance Operations

  • Concepts of autonomous and semi-autonomous AI systems
  • Workflow orchestration and claims automation
  • Human-in-the-loop controls and oversight
MODULE 5

Custom AI Modelling for Underwriting and Risk Assessment

  • Predictive modelling techniques
  • Risk scoring and underwriting optimisation
  • Low-code and no-code model development
MODULE 6

Predictive Analytics and Actuarial Intelligence

  • Augmented analytics and AI-assisted pricing intelligence for reserving
  • Reinforcement learning from human feedback (RLHF) and causal inference in dynamic pricing
  • Explainable AI (XAI)-powered actuarial intelligence and real-time risk signal processing
MODULE 7

AI for Claims Management and Fraud Detection

  • Claims triage and automation
  • Fraud detection using machine learning and behavioural analytics
  • Use of image and text analytics in claims
MODULE 8

AI-Enhanced Customer Experience and Personalisation

  • Conversational AI and customer interaction
  • Personalisation and recommendation engines
  • AI in marketing and distribution
MODULE 9

AI for Emerging Risk Domains

  • Cyber risk and security analytics
  • ESG risk assessment
  • Embedded insurance models and ecosystem integration
MODULE 10

Ethical and Responsible AI

  • Explainability, fairness, and transparency
  • Bias management and accountability
  • Responsible AI frameworks
MODULE 11

Regulatory, Compliance, and Governance

  • AI governance frameworks in financial services
  • Model risk management and auditability
  • Regulatory considerations and industry guidelines
MODULE 12

Decision AI in Insurance Operations

  • What is Decision AI? Distinguishing prediction, recommendation, and automated decision-making
  • Decision intelligence frameworks: integrating ML models, business rules, and optimisation engines
  • Insurance use cases: automated underwriting decisions, claims settlement authority, and dynamic pricing guardrails
  • Human-in-the-loop architecture and escalation protocols for high-stakes decisions
  • Governance and auditability of AI-driven decisions in regulated environments
MODULE 13

Emerging Trends in AI for Insurance

  • Synthetic data and privacy-preserving AI
  • AI copilots across insurance functions
  • Transition from embedded to autonomous insurance
MODULE 14

Capstone Project - Designing an AI Innovation Roadmap

  • Identification of high-impact use cases
  • Implementation considerations
  • Presentation of an organisational roadmap

Programme Methodology

Facilitated lectures & discussions
Case studies & industry examples
Demonstrations of AI tools
Group exercises & workshops
Capstone project presentation

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