A malpractice insurer estimated $4M a year from catching high-risk claims at first notice of loss, but review was manual and expert-bound. Trexin's machine-learning model learned the rules from history; within a year the Client was on track to take down $4.3M.
A medical-malpractice liability insurer estimated that spotting high-risk-of-indemnity cases at first notice of loss (and managing them sooner) could save over $4M a year. But review was entirely manual, done by scarce, highly compensated experts who were already over-leveraged on the most complex cases. As a result, most new claims went unreviewed and unmanaged. The CIO asked Trexin for an automated solution that reviewed new claims daily and flagged high-risk cases for early intervention.
The hard constraints: a three-month timeline, a large set of rules to identify high-risk claims, and subject-matter experts with little availability to define them. We designed a machine-learning classification model that learned the rules from historical claims data, removing the need for expert availability during development, and staying forward-adaptable without ever re-programming rules.
The model’s predictive accuracy far exceeded the Client’s prior capability, earning strong endorsement from the claims team and senior management. It went to full production within six months, delivering near-term payback, and within a year the Client was on track to take $4.3M of indemnity and expense off the bottom line.
A model that learns the experts’ judgment is faster to build, easier to adopt, and doesn’t go stale the moment the rules change.
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