Subrogation Recovery
“Which claims have recovery potential?”
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A real-world example
Which claims have recovery potential?
US P&C insurers recover $40-60B annually through subrogation, but industry analysis suggests $15-25B more is left on the table (Verisk). The challenge: subrogation opportunities must be identified early, before evidence is lost and statutes of limitations expire. Most insurers rely on adjusters manually flagging subrogation potential, but 40% of recoverable claims are never flagged because the third-party liability is not obvious at FNOL. A rear-end collision where the other driver was clearly at fault is easy to flag; a water-damage claim where the manufacturer's defective pipe caused the loss requires connecting claims data with product recall databases and building-code records.
How KumoRFM solves this
Relational intelligence built for insurance data
Kumo connects claims, police reports, policy details, third-party information, product databases, building records, and historical recovery outcomes into a relational graph. The model identifies that Claim CLM-9220 (pipe burst) involves a pipe manufactured by a company with an active recall notice, the building was constructed during a period of known plumbing defects, and similar claims in the region have achieved 65% recovery rates. These cross-table signals surface subrogation opportunities that adjusters would otherwise miss, flagging them within 24 hours of FNOL.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
CLAIMS
| claim_id | peril | amount_paid | third_party_info | status |
|---|---|---|---|---|
| CLM-9220 | Water | $11,200 | Unknown at FNOL | Open |
| CLM-9225 | Auto Collision | $18,400 | Other driver: at fault | Open |
| CLM-9230 | Property | $8,600 | Unknown at FNOL | Open |
INCIDENT_DETAILS
| claim_id | cause_code | product_involved | manufacturer | building_year |
|---|---|---|---|---|
| CLM-9220 | Pipe Burst | PEX-200 Fitting | AquaFlow Inc | 2008 |
| CLM-9225 | Rear-End | N/A | N/A | N/A |
| CLM-9230 | Roof Leak | Asphalt Shingles | RoofTech | 2012 |
PRODUCT_RECALLS
| manufacturer | product | recall_date | defect_type | claims_filed |
|---|---|---|---|---|
| AquaFlow Inc | PEX-200 Fitting | 2024-06-15 | Premature Failure | 2,400 |
| RoofTech | 30-Year Shingles (2010-2013) | 2023-11-01 | Premature Wear | 1,800 |
RECOVERY_HISTORY
| peril | cause_code | manufacturer | recovery_rate | avg_recovery | avg_days_to_collect |
|---|---|---|---|---|---|
| Water | Pipe Burst | AquaFlow Inc | 65% | $7,800 | 120 |
| Auto Collision | Rear-End | N/A | 82% | $15,200 | 90 |
| Property | Roof Leak | RoofTech | 45% | $3,900 | 180 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(CLAIMS.SUBROGATION_RECOVERY > 0, 0, 180, days) FOR EACH CLAIMS.CLAIM_ID WHERE CLAIMS.STATUS = 'open'
Prediction output
Every entity gets a score, updated continuously
| CLAIM_ID | PERIL | RECOVERY_PROB | EST_RECOVERY | PRIORITY | RECOVERY_PATH |
|---|---|---|---|---|---|
| CLM-9225 | Auto Collision | 0.88 | $15,200 | High | Third-Party Liability |
| CLM-9220 | Water | 0.72 | $7,300 | High | Product Defect Recall |
| CLM-9230 | Property | 0.48 | $3,900 | Medium | Manufacturer Warranty |
Understand why
Every prediction includes feature attributions — no black boxes
Claim CLM-9220 (Water Damage, pipe burst)
Predicted: 72% recovery probability, est. $7,300
Top contributing features
Active product recall match
AquaFlow PEX-200
30% attribution
Building year in defect window
2008 (2006-2012)
25% attribution
Historical recovery rate for cause
65%
20% attribution
Manufacturer solvency status
Solvent, paying claims
14% attribution
Statute of limitations remaining
18 months
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Identify 20-35% more subrogation opportunities at FNOL and accelerate recovery timelines by 30%, recovering an additional $50-100M annually for a top-20 P&C insurer.
Related use cases
Explore more insurance use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
