1. Program Overview

This program is designed to equip insurance professionals with practical knowledge and insights into how Artificial Intelligence (AI) and Machine Learning (ML) are transforming underwriting and claims processes. Through interactive sessions, real-world use cases, and expert-led discussions, participants will explore how these technologies enhance risk assessment, fraud detection, customer experience, and operational efficiency.

2. Training Objectives

By the end of the program, participants will be able to:

• Understand the core concepts of AI and ML as applied in the insurance industry.
• Analyze the benefits and risks of integrating AI into underwriting and claims management.
• Identify AI tools and technologies used in decision automation, predictive modeling, and fraud detection.
• Evaluate real-world use cases of AI in underwriting and claims, both globally and locally.
• Develop a roadmap for responsible adoption of AI/ML within their organizations.
• Explore ethical, legal, and regulatory considerations related to algorithmic decision-making in insurance.

3. Target Clientele

This program is ideal for:

• Insurance underwriters and claims managers
• Actuarial and risk analysts
• IT managers in insurance firms
• Insurance company executives and decision-makers
• Business process improvement officers
• Regulatory and compliance officers in the insurance sector
• InsurTech professionals and innovators

4. Main Discussion Items / Modules

Module 1: Introduction to AI and Machine Learning

• Key AI/ML concepts
• The difference between automation, AI, and ML
• Historical evolution and current trends in insurance

Module 2: AI in Underwriting

• Predictive analytics for risk assessment
• Behavioral data in underwriting (IoT, wearables, credit scoring, etc.)
• Real-time underwriting engines
• Personalization of insurance products

Module 3: AI in Claims Management

• Automated claims processing
• AI in fraud detection and anomaly recognition
• Use of image recognition (e.g., in auto and property insurance)
• Chatbots and virtual assistants in claims support

Module 4: Implementation Considerations

• Data requirements and model training
• Integrating AI into legacy systems
• Human-in-the-loop approaches

Module 5: Risks, Ethics, and Regulation

• Bias and fairness in underwriting models
• Data privacy and consent
• Regulatory outlook (local and international)
• Ethical AI use in decision-making

Module 6: Case Studies and Group Exercises

• Global and African examples of AI use in insurance
• Kenyan or regional case studies (if available)
• Simulation of AI-led underwriting or claims processes

5. Training Methodology

This program uses a blended, interactive training approach combining:

• Expert presentations and demonstrations
• Real-life case studies
• Group discussions and breakout sessions
• Hands-on exercises and simulations
• Video-based demos of AI tools in insurance
• Q&A and feedback sessions