Artificial Intelligence and Machine Learning in the Insurance Industry: Methods, Applications, Risks, and a Roadmap for Responsible Adoption

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming the insurance landscape by enabling greater precision, efficiency, and innovation across core business functions. From underwriting and pricing to claims management, fraud detection, and customer engagement, AI/ML tools allow insurers to harness large-scale data to enhance decision-making and deliver tailored products. Yet, adoption introduces a new class of operational, regulatory, and ethical challenges.

This manuscript presents a comprehensive analysis of AI/ML adoption in insurance, integrating technical methods, reproducible implementation patterns, validation and monitoring frameworks, security and adversarial considerations, and a roadmap for responsible deployment. It synthesizes findings from academic literature, industry practices, and emerging regulatory guidance to equip practitioners with evidence-based approaches for implementation. Key recommendations emphasize explainability, fairness, data governance, and organizational readiness