AI USE CASE
Subrogation Recovery Opportunity Prioritization
Automatically identify and prioritize claims with subrogation recovery potential using NLP and predictive analytics.
What it is
This use case applies NLP to claims notes and documents combined with predictive ML models to flag claims where third-party recovery is viable, estimate expected recovery amounts, and rank pursuit efforts by ROI. Insurers typically recover 20–40% more subrogation value by focusing adjuster effort on high-probability cases rather than manual triage. Teams can expect to reduce average time-to-pursue by several weeks and lower recovery processing costs by 15–30%.
Data you need
Historical claims data including adjuster notes, loss descriptions, liability assessments, third-party involvement records, and past subrogation outcomes.
Required systems
- erp
Why it works
- Involve experienced subrogation adjusters in defining the labeling criteria and validating model outputs before rollout.
- Provide clear model explainability — show adjusters why a claim was flagged — to drive adoption.
- Start with a single high-volume claims line (e.g. auto) to validate lift before expanding to other lines.
- Establish a feedback loop where adjuster outcomes continuously retrain and improve the model.
How this goes wrong
- Claims notes are too unstructured or inconsistently written for NLP models to extract reliable signals.
- Model scores are ignored in practice because adjusters distrust automated prioritization without explainability.
- Historical subrogation outcome data is too sparse or biased to train a well-calibrated recovery estimator.
- Integration with legacy claims management systems is delayed or incomplete, blocking real-time scoring.
When NOT to do this
Do not deploy this if your claims management system holds fewer than three years of resolved subrogation cases — the model will lack sufficient ground truth to produce reliable recovery estimates.
Vendors to consider
Sources
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