Claims Fraud Detection
“Is this claim fraudulent?”
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A real-world example
Is this claim fraudulent?
Insurance fraud costs the US industry $80B+ annually (FBI), with 10% of all P&C claims containing some element of fraud (Coalition Against Insurance Fraud). Special Investigation Units (SIUs) can only investigate 5-10% of flagged claims, and legacy rules-based systems generate 80-90% false positives, burying real fraud in noise. Organized fraud rings are particularly hard to detect because they coordinate across multiple policies, claimants, providers, and repair shops. A single fraud ring can cost an insurer $5-20M before detection.
How KumoRFM solves this
Relational intelligence built for insurance data
Kumo connects claims, policies, claimants, providers, repair facilities, adjusters, and geographic data into a single relational graph. The model detects that Claim CLM-9201 involves a claimant who shares a phone number with two other recent claimants, all three used the same body shop, and the repair estimates follow an identical pattern. These multi-hop connections reveal fraud rings invisible to single-claim analysis. The graph also catches soft fraud: Claim CLM-9205 has inflated damage estimates based on the vehicle's age, repair-shop pricing patterns, and historical claim amounts for similar incidents.
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 | policy_id | type | amount | loss_date | filed_date |
|---|---|---|---|---|---|
| CLM-9201 | POL-4401 | Auto Collision | $12,400 | 2025-09-01 | 2025-09-03 |
| CLM-9205 | POL-4418 | Auto Collision | $8,900 | 2025-09-05 | 2025-09-06 |
| CLM-9210 | POL-4425 | Property Fire | $45,000 | 2025-09-08 | 2025-09-10 |
CLAIMANTS
| claimant_id | name | phone | address | claims_12mo |
|---|---|---|---|---|
| CL-801 | Michael Torres | 555-0142 | 88 Pine St | 3 |
| CL-802 | Lisa Chen | 555-0142 | 220 Oak Ave | 2 |
| CL-803 | James Wilson | 555-0199 | 88 Pine St | 1 |
PROVIDERS
| provider_id | name | type | avg_estimate | claims_volume |
|---|---|---|---|---|
| PRV-101 | QuickFix Auto Body | Repair Shop | $11,800 | 42/mo |
| PRV-102 | City Auto Repair | Repair Shop | $7,200 | 28/mo |
| PRV-103 | Dr. Smith Chiro | Medical Provider | $4,500 | 65/mo |
CLAIM_NETWORK
| claim_id | claimant_id | provider_id | adjuster_id | shared_attributes |
|---|---|---|---|---|
| CLM-9201 | CL-801 | PRV-101 | ADJ-05 | Phone, Provider |
| CLM-9202 | CL-802 | PRV-101 | ADJ-05 | Phone, Provider |
| CLM-9203 | CL-803 | PRV-101 | ADJ-12 | Address, Provider |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(CLAIMS.FRAUD_CONFIRMED = 'True', 0, 0, days) FOR EACH CLAIMS.CLAIM_ID WHERE CLAIMS.AMOUNT > 5000
Prediction output
Every entity gets a score, updated continuously
| CLAIM_ID | AMOUNT | FRAUD_SCORE | RING_DETECTED | SIU_PRIORITY |
|---|---|---|---|---|
| CLM-9201 | $12,400 | 0.91 | Ring-A (3 claims) | Critical |
| CLM-9205 | $8,900 | 0.62 | None | High |
| CLM-9210 | $45,000 | 0.15 | None | Low |
Understand why
Every prediction includes feature attributions — no black boxes
Claim CLM-9201 (Auto Collision, $12,400)
Predicted: 91% fraud probability, Ring-A detected
Top contributing features
Shared phone with other claimants
2 matches
28% attribution
Common repair shop (high-volume)
PRV-101, 42/mo
24% attribution
Shared address pattern
88 Pine St
20% attribution
Claim timing cluster
3 in 10 days
17% attribution
Estimate vs vehicle value ratio
68% of ACV
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: Reduce fraud losses by 30-50% and cut SIU false-positive rates by 40%, saving $40-80M 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.
