Deploying production AI in <60 days to accelerate claims review 67%

An unnamed global insurance firm could only manually review about 30% of available subrogation claims, limiting revenue and scalability, and severe data imbalance (key coverage signals appearing in only a small fraction of claims) made off-the-shelf models unreliable. Snorkel worked with the firm on a data-centric approach combining programmatic labeling, large-scale error analysis, distilled specialist models, and integrated expert/data-scientist knowledge to turn a fragmented manual process into an automated production workflow. Within 60 days the firm saw model delivery 30x faster, more than a 20-point accuracy improvement, a 67% increase in throughput (claims processed rose from 3,000 to 5,000), and 3x faster delivery of new models, unlocking revenue from claims previously left unreviewed.

The customer is anonymised in Snorkel AI’s own case study — not withheld by us.

Snorkel AI is a data-development and evaluation company whose programmatic labeling tools build training and benchmark datasets used to stress-test whether a model approach is viable before a full build.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.