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FutureCentral > AI in Insurance: The Guide for Insurance Professionals
AI in Insurance book cover
Banking & Financial Services · Working Professionals

AI in Insurance

The Guide for Insurance Professionals

Austin PM · FutureCentral Press

AI in insurance for working insurance professionals.

Working ProfessionalsManuscript chapters available
Explore the bookInquire about this bookView contents
AudienceWorking Professionals
Structure27 chapters listed
StatusManuscript chapters available

About the book

Underwriting, claims, actuarial work, distribution, and operations each place different demands on AI. Across insurance lines, the book examines predictive models, generative tools, and agents alongside the data and governance requirements facing insurance professionals.

What you will learn

  • Evaluate insurance AI applications
  • Connect underwriting and claims with data requirements
  • Assess generative and agentic workflows
  • Design accountable insurance AI controls
Read before you decide

Explore the book

Chapter overview

Inside Chapter 1

Chapter topics, summarized from “The Rise of AI in Insurance”:

  • Compare rule-based systems, predictive models, generative AI, and agents in insurance workflows.
  • Assess differences across underwriting, claims, fraud, and distribution.
  • Explain how product lines, distribution channels, and data availability shape Indian insurance applications.

Preview and inspection

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Frameworks and decision tools

The Indian Insurance AI Stack

Introduced in Chapter 1.

The Insurance ML Decision Stack

Introduced in Chapter 2.

The Indian Insurance Data Fabric

Introduced in Chapter 3.

The AI Investment Thesis Stack

Introduced in Chapter 4.

The Life-Insurance AI Stack

Introduced in Chapter 5.

The Health-Insurance AI Stack

Introduced in Chapter 6.

Table of contents

View all 27 chapters

Contents reflect available source documents. Final publication details remain unconfirmed.

  1. The Rise of AI in Insurance
  2. Machine Learning and Deep Learning in Insurance
  3. Data, Digital Signals, and the AI Infrastructure of Insurance
  4. The Strategic Logic of AI Adoption in Insurance
  5. AI in Life Insurance: Underwriting, Pricing, Persistency
  6. AI in Health Insurance: Underwriting, Wellness, Claims, TPAs
  7. AI in Motor and Property Insurance: Telematics, IoT, FNOL
  8. AI in Commercial and Specialty Lines: Marine, Engineering, Liability, Cyber
  9. AI in Agricultural and Crop Insurance: Parametric, Satellite, PMFBY
  10. AI in Reinsurance: Capacity, Treaty, NatCat, GIFT City
  11. AI in Insurance Operations: Policy Administration, Renewal, Servicing
  12. AI in Distribution: Agency, Broking, Bancassurance, Online Aggregators
  13. Actuarial Modeling, Reserving, and Capital in the AI Era
  14. AI in Claims Operations: FNOL, Triage, Settlement, Subrogation
  15. Insurance Fraud Analytics
  16. Operational Risk, Cybersecurity, and AI-Driven Threats in Insurance
  17. What Generative AI Means for the Insurer
  18. LLMs in the Insurance Workflow
  19. Limits and Risks of Generative AI in Insurance
  20. From Insurance Assistants to Insurance Agents
  21. Agentic Workflows in Practice
  22. Governance, Model Risk, and Responsible AI in Insurance
  23. Regulation, Compliance, and the IRDAI Architecture
  24. Cybersecurity, Privacy, and AI in Insurance
  25. The Role-by-Role Transformation
  26. Skills, Credentials, and the Career Architecture of the AI-First Insurer
  27. The Future of the Indian Insurance Industry: Strategy and Transformation
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