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Quest Training

Generative AI for Insurance Professionals Training Course

This advanced training course provides a practical framework for understanding and applying Generative Artificial Intelligence across the insurance industry. It focuses on how insurance professionals can use generative…

INAI · InsurTech & Artificial IntelligenceAll LevelsClassroomEnglish , Arabic
Duration
5 Days
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Sectors:Insurance & Risk ManagementCategories:InsurTech & Artificial Intelligence

Course Overview

This advanced training course provides a practical framework for understanding and applying Generative Artificial Intelligence across the insurance industry. It focuses on how insurance professionals can use generative AI to improve operational efficiency, support decision-making, enhance customer experiences, and transform traditional insurance processes. The course explores practical applications of Generative AI across underwriting, claims management, customer service, insurance operations, compliance, risk management, document management, reporting, and product development. Participants will learn how generative AI can support information analysis, document summarization, content generation, workflow improvement, and knowledge management while maintaining appropriate human oversight. A strong emphasis is placed on prompt engineering, effective instruction design, output validation, information accuracy, data privacy, confidentiality, cybersecurity, bias, transparency, accountability, and responsible AI use. Participants will learn how to distinguish between useful AI-generated outputs and information that requires professional verification before being used in insurance decisions. Through practical exercises, insurance-focused use cases, and implementation workshops, participants will develop the ability to identify high-value Generative AI opportunities, design practical applications, assess business value and risks, and establish a structured roadmap for responsible adoption within insurance organizations.

Learning Objectives

  • By the end of this course, participants will be able to:
  • Understand the fundamentals of Generative AI and its applications across the insurance industry.
  • Analyze opportunities for using Generative AI to improve insurance processes and services.
  • Evaluate high-value use cases across underwriting, claims, customer service, and operations.
  • Develop effective prompts and instructions for obtaining relevant and reliable AI outputs.
  • Apply Generative AI to insurance documents, reports, correspondence, and unstructured information.
  • Use AI to support underwriting activities, information assessment, and risk analysis.
  • Improve claims processes through summarization, classification, analysis, and workflow support.
  • Apply Generative AI to enhance customer communication and digital insurance services.
  • Evaluate the accuracy, reliability, and relevance of AI-generated outputs.
  • Identify privacy, confidentiality, cybersecurity, bias, and operational risks associated with Generative AI.
  • Establish appropriate human review and oversight mechanisms for AI-supported insurance activities.
  • Develop practical governance controls for responsible Generative AI adoption.
  • Assess the operational and commercial value of Generative AI initiatives.
  • Develop a practical implementation roadmap for Generative AI within an insurance organization.

Who Should Attend

This course is designed for insurance professionals working in underwriting, claims management, risk management, customer service, operations, compliance, governance, digital transformation, data analytics, product development, pricing, and insurance technology. It is particularly relevant for executives, department heads, managers, team leaders, specialists, analysts, and professionals responsible for improving insurance processes, digital services, operational performance, and customer experience. The course is also suitable for innovation, technology, data, compliance, internal audit, fraud management, and risk professionals who need to understand the business opportunities, governance requirements, and practical implications of Generative AI within insurance organizations.

Learning Outcomes

  • Upon completion of the course, participants will be able to:
  • Explain the role of Generative AI in transforming insurance operations and services.
  • Identify high-value Generative AI use cases across the insurance lifecycle.
  • Design effective prompts for professional insurance applications.
  • Summarize and analyze insurance documents and unstructured information using AI.
  • Support underwriting teams through AI-assisted information analysis and risk summarization.
  • Improve claims management through AI-supported document review and claims analysis.
  • Develop AI-supported approaches for customer communication and service delivery.
  • Use Generative AI to prepare professional reports, summaries, correspondence, and management information.
  • Validate AI-generated information before using it in professional or business decisions.
  • Recognize hallucinations, bias, inconsistencies, and other AI-generated risks.
  • Apply data privacy, confidentiality, and information security principles when using AI tools.
  • Establish appropriate human oversight for AI-supported insurance decisions.
  • Evaluate the operational, financial, and customer value of AI initiatives.
  • Define performance indicators for measuring Generative AI effectiveness.
  • Develop a structured roadmap for implementing Generative AI within an insurance organization.

Course Outline

Course Outline

Day 1

Foundations of Generative AI in Insurance

  • Understanding Generative AI and its evolution.
  • Generative AI versus traditional Artificial Intelligence.
  • Large language models and their capabilities.
  • Generative AI applications across the insurance lifecycle.
  • Business and operational opportunities for insurance organizations.
  • Identifying high-value insurance use cases.
  • Key challenges and limitations of Generative AI.
  • Understanding AI adoption requirements within insurance operations.
  • Practical Application: Analyze an insurance business process and identify opportunities for Generative AI enhancement.
Day 2

Prompt Engineering and Professional AI Applications

  • Fundamentals of effective prompt engineering.
  • Structuring prompts using context, objectives, constraints, and output requirements.
  • Improving the quality and consistency of AI-generated responses.
  • Using AI to draft insurance documents and professional correspondence.
  • Summarizing insurance policies and extracting key information.
  • Analyzing unstructured insurance information and documents.
  • Preparing reports and executive summaries using Generative AI.
  • Reviewing and validating AI-generated outputs.
  • Reducing inaccurate or unsupported AI-generated information.
  • Practical Application: Develop professional prompts for underwriting, claims, customer service, and insurance operations.
Day 3

Generative AI for Underwriting and Claims Management

  • Applying Generative AI to underwriting processes.
  • Analyzing insurance applications and customer-provided information.
  • Preparing AI-assisted risk summaries for underwriting teams.
  • Supporting risk classification and identifying missing information.
  • Applying Generative AI across the claims lifecycle.
  • Summarizing claims files and supporting documentation.
  • Supporting claims triage and prioritization.
  • Preparing claims-related correspondence and reports.
  • Identifying unusual patterns and potential fraud indicators.
  • Supporting human review and decision-making.
  • Practical Application: Build an AI-supported workflow for reviewing and prioritizing an insurance claim.
Day 4

Generative AI for Customer Experience, Analytics and Operations

  • Enhancing customer experience through Generative AI.
  • Designing AI-powered virtual assistants and customer support services.
  • Developing consistent and professional insurance communications.
  • Using AI for internal knowledge management.
  • Analyzing customer feedback, complaints, and communications.
  • Supporting operational and management reporting.
  • Using Generative AI in insurance product development.
  • Supporting sales, marketing, and distribution activities.
  • Integrating Generative AI into digital insurance workflows.
  • Identifying opportunities for intelligent process automation.
  • Practical Application: Design a Generative AI use case to improve customer experience and reduce processing time within an insurance operation.
Day 5

AI Governance, Responsible Use and Implementation

  • Establishing Generative AI governance within insurance organizations.
  • Protecting customer data and confidential insurance information.
  • Privacy and information security considerations.
  • Managing AI bias, inaccuracies, and unreliable outputs.
  • Transparency, explainability, and accountability.
  • Human oversight and decision authority.
  • Managing AI model and third-party technology risks.
  • Developing policies and controls for responsible AI use.
  • Defining performance indicators and measuring business value.
  • Prioritizing AI initiatives and developing business cases.
  • Building an organizational Generative AI implementation roadmap.
  • Final Practical Workshop: Develop an integrated Generative AI implementation plan for an insurance function, including the use case, business value, data requirements, risks, governance controls, performance indicators, and implementation priorities.

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