AI USE CASE
ML-Driven Subrogation Opportunity Detection
Automatically flag claims with recovery potential so insurers can pursue subrogation faster and more systematically.
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Run the diagnostic →What it is
This use case applies machine learning and NLP to scan incoming and historical claims data, identifying those where a third party may be liable and recovery is feasible. By prioritising the highest-value subrogation candidates automatically, claims teams can redirect recovery efforts away from low-yield files. Insurers typically recover 20-40% more through systematic ML-driven triage compared to manual review, and reduce the average time-to-initiate-recovery by several weeks. Over a portfolio of tens of thousands of claims, this translates to material bottom-line improvement on loss ratios.
Data you need
Historical claims records including cause-of-loss codes, adjuster notes, liability descriptions, and recovery outcomes for at least 2-3 years of closed claims.
Required systems
- erp
- data warehouse
Why it works
- Curate a high-quality labelled dataset of past subrogation outcomes before model training begins.
- Integrate model scores directly into the claims management workflow so adjusters see flags in their existing system.
- Start with a single high-volume line of business (e.g. auto) where subrogation patterns are most consistent.
- Establish a feedback loop where adjusters confirm or reject flags, enabling continuous model retraining.
How this goes wrong
- Insufficient labelled historical data on which claims were actually subrogated, starving the model of signal.
- Adjuster notes stored as unstructured scanned PDFs make NLP extraction unreliable without OCR pre-processing.
- Model flags high volumes of false positives, eroding adjuster trust and leading to the tool being ignored.
- Recovery rates vary significantly by line of business, so a single model trained on mixed data underperforms across all segments.
When NOT to do this
Do not deploy this for small-volume specialty lines (e.g. marine, aviation) where claim frequency is too low to generate meaningful training data and manual expert review remains more cost-effective.
Vendors to consider
Sources
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