Challenges in Implementing AI in the Insurance Industry

Artificial Intelligence (AI) has rapidly emerged as one of the most influential forces transforming the insurance sector. Its potential spans across underwriting, pricing, fraud detection, claims automation, customer service, sales forecasting, and risk analytics. Insurers worldwide are turning to AI to improve accuracy, speed, efficiency, and customer experience.

- Yukti Agarwal Associate Editor The Insurance Times

Artificial Intelligence (AI) has rapidly emerged as one of the most influential forces transforming the insurance sector. Its potential spans across underwriting, pricing, fraud detection, claims automation, customer service, sales forecasting, and risk analytics. Insurers worldwide are turning to AI to improve accuracy, speed, efficiency, and customer experience.

However, while AI promises a future of intelligent and automated insurance operations, its implementation is far more complex than it appears. The insurance sector, characterised by strict regulation, sensitive customer data, legacy systems, and high liability, faces unique barriers that make AI adoption challenging, risky, and expensive.

Regulatory Uncertainty and Evolving AI Governance

India's regulatory approach to AI is still taking shape. In January 2025, the Ministry of Electronics and Information Technology's subcommittee published its report on "AI Governance and Guidelines Development," attempting to identify key issues and propose recommendations for developing an Al regulatory framework.

IRDAI introduced the Information and Cyber Security Guidelines in 2023 that mandate regulated entities to adopt a risk-based approach and implement necessary measures to secure data management. Additionally, IRDAI introduced the Regulatory Sandbox Regulations in 2025 to foster innovation while ensuring structured growth.

The regulatory framework also creates friction with the Digital Personal Data Protection Act. The DPDP Act marks a significant shift in India's data protection and privacy landscape, with far-reaching implications around consent requirements and data principal rights. Insurers face uncertainty around:

• Acceptable data sources for model training

• Cross-border data usage and storage

• Mandatory explainability norms

• AI audit frameworks and liability in automated decisions