AI in Insurance: Data Quality, Not Models, Drives Competitive Advantage

Hindustan Times · business

While many insurers are investing in AI for claims, fraud detection, and underwriting, most programs struggle to deliver at scale. The primary obstacle is not the AI model itself, but the fragmented and poor quality of data these models rely on. Insurers possess vast amounts of historical data, but it's often siloed across incompatible legacy systems. Addressing data preparation, cleaning, and governance is crucial for successful AI implementation. Organizations achieving real value from AI are prioritizing data quality and connectedness over model sophistication, ensuring trustworthy outputs for regulated environments.