AI USE CASE
ML-Driven Subrogation Opportunity Detection
Automatically flag claims with recovery potential so insurers can pursue subrogation faster and more systematically.
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. 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.
Example vendors
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