Claims Severity Prediction
“What will the total cost of this claim be?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
What will the total cost of this claim be?
Insurers set initial reserves based on adjuster experience and lookup tables, leading to 30-40% inaccuracy at First Notice of Loss (FNOL). Under-reserving creates balance-sheet surprises and regulatory issues. Over-reserving ties up $20-50B in unnecessary capital across the industry (AM Best). Adjusters spend 3-5 hours per claim on initial assessment, with complex claims taking 2-3 weeks to evaluate. A top-20 insurer processing 500K claims per year could save $200-400M annually in reserve accuracy improvements and $50-100M in faster claims handling.
How KumoRFM solves this
Relational intelligence built for insurance data
Kumo connects FNOL details, policy coverage, claimant history, provider networks, geographic risk factors, and historical claim outcomes into a relational graph. At the moment a claim is filed, the model predicts that Claim CLM-9210 (Property Fire) will cost $52,400 based on the property's construction type, local contractor rates, the severity of recent fires in the area, and the claimant's coverage limits. The prediction updates as new information arrives (adjuster photos, repair estimates, medical reports), converging to within 10-15% of final cost within 48 hours.
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 | peril | initial_estimate | fnol_date | description |
|---|---|---|---|---|---|
| CLM-9210 | POL-4425 | Fire | $45,000 | 2025-09-10 | Kitchen fire, partial structure damage |
| CLM-9215 | POL-4432 | Auto BI | $25,000 | 2025-09-12 | Rear-end collision, neck injury |
| CLM-9220 | POL-4440 | Water | $12,000 | 2025-09-14 | Pipe burst, basement flooding |
POLICY_DETAILS
| policy_id | coverage_limit | deductible | property_value | endorsements |
|---|---|---|---|---|
| POL-4425 | $500,000 | $2,500 | $510,000 | Replacement Cost |
| POL-4432 | $100,000/$300,000 | $500 | N/A | UM/UIM |
| POL-4440 | $350,000 | $1,000 | $380,000 | Water Backup |
HISTORICAL_CLAIMS
| peril | region | avg_final_cost | median_duration_days | litigation_rate |
|---|---|---|---|---|
| Fire | West | $58,200 | 45 | 8% |
| Auto BI | Northeast | $32,400 | 120 | 22% |
| Water | Midwest | $14,800 | 21 | 3% |
PROVIDER_COSTS
| region | provider_type | avg_rate | availability | quality_score |
|---|---|---|---|---|
| West | General Contractor | $185/hr | Low (backlog) | 4.2/5 |
| Northeast | Chiropractor | $120/visit | High | 3.8/5 |
| Midwest | Plumber | $95/hr | Medium | 4.0/5 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(CLAIMS.FINAL_PAID, 0, 0, days) FOR EACH CLAIMS.CLAIM_ID WHERE CLAIMS.STATUS = 'open'
Prediction output
Every entity gets a score, updated continuously
| CLAIM_ID | PERIL | INITIAL_EST | KUMO_PREDICTED | CONFIDENCE | TRIAGE_TIER |
|---|---|---|---|---|---|
| CLM-9210 | Fire | $45,000 | $52,400 | High | Senior Adjuster |
| CLM-9215 | Auto BI | $25,000 | $38,700 | Medium | Litigation Watch |
| CLM-9220 | Water | $12,000 | $11,200 | High | Fast-Track |
Understand why
Every prediction includes feature attributions — no black boxes
Claim CLM-9215 (Auto BI, rear-end collision)
Predicted: $38,700 predicted total cost (vs $25K initial estimate)
Top contributing features
Injury type and litigation rate
Neck, 22% lit. rate
28% attribution
Regional medical cost trends
+12% YoY NE
24% attribution
Claimant attorney involvement signal
Likely
21% attribution
Similar claim outcome distribution
$32.4K median
16% attribution
Policy coverage limits
$100K/$300K
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: Improve reserve accuracy by 30-40% at FNOL and triage claims 60% faster, saving $200-400M annually in capital efficiency and claims handling costs for a top-20 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.
