Deploying Production AI in <60 Days to Accelerate Claims Review 67%

A leading global firm transforming insurance subrogation operations with AI found that manual review processes capped their throughput to ~30% of available claims. This bottleneck left significant revenue on the table and froze their ability to scale. The path to automation was further blocked by severe data imbalances where the critical signals for coverage appeared in only a small fraction of claims, making traditional AI models unreliable.

The firm engaged Snorkel to co-develop a comprehensive data-centric solution. By combining Snorkel’s team and AI research expertise with internal expert and data scientist collaboration, the firm was able to utilize programmatic data labeling and data-centric error analysis to rapidly correct training data at scale. This foundation enabled the development of distilled specialist models, turning a fragmented manual process into a high-accuracy automated workflow optimized for production.