Underwriting Risk Assessment
“What is the true risk for this applicant?”
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 is the true risk for this applicant?
Insurers lose $15-30B annually from adverse selection (underpricing risky applicants) and over-conservatism (rejecting profitable applicants), according to Deloitte. Traditional underwriting models use 15-30 rating factors from the application and third-party data, but miss relational signals: an applicant's neighborhood claims history, the correlation between their vehicle type and local theft rates, or the interaction between their occupation and commute pattern. A 5-point improvement in loss ratio on a $10B book translates to $500M in annual savings.
How KumoRFM solves this
Relational intelligence built for insurance data
Kumo connects applicant profiles, claims history, policy data, geographic risk factors, vehicle databases, and external data sources into a relational graph. The model discovers that Applicant APP-3301 lives in a zip code where pipe-burst claims spiked 3x last winter, has a roof older than 15 years (from property records), and owns a breed of dog associated with 2.5x liability claim frequency. These cross-table signals produce a risk score that is 30-40% more predictive than traditional generalized linear models (GLMs), catching both overpriced low-risk and underpriced high-risk applicants.
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
APPLICANTS
| applicant_id | name | age | occupation | credit_tier | zip_code |
|---|---|---|---|---|---|
| APP-3301 | Sarah Mitchell | 42 | Teacher | A | 90210 |
| APP-3302 | David Kim | 35 | Software Engineer | A+ | 10001 |
| APP-3303 | Robert Brown | 58 | Contractor | B | 33101 |
PROPERTY_DATA
| applicant_id | property_type | year_built | roof_age | sqft | replacement_cost |
|---|---|---|---|---|---|
| APP-3301 | Single Family | 1998 | 18 years | 2,400 | $420,000 |
| APP-3302 | Condo | 2019 | 6 years | 1,100 | $280,000 |
| APP-3303 | Single Family | 1985 | 12 years | 3,200 | $510,000 |
ZIP_CODE_RISK
| zip_code | weather_risk | theft_index | claims_per_1000 | trend |
|---|---|---|---|---|
| 90210 | Wildfire: High | Low | 42 | Increasing |
| 10001 | Flood: Medium | High | 38 | Stable |
| 33101 | Hurricane: High | Medium | 55 | Increasing |
CLAIMS_HISTORY
| applicant_id | prior_claims_5yr | total_paid | largest_claim | claim_type |
|---|---|---|---|---|
| APP-3301 | 1 | $8,200 | $8,200 | Water Damage |
| APP-3302 | 0 | $0 | $0 | N/A |
| APP-3303 | 3 | $45,000 | $22,000 | Wind Damage |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(CLAIMS.TOTAL_PAID, 0, 12, months) FOR EACH APPLICANTS.APPLICANT_ID
Prediction output
Every entity gets a score, updated continuously
| APPLICANT_ID | NAME | KUMO_RISK_SCORE | TRADITIONAL_SCORE | EXPECTED_LOSS | RECOMMENDATION |
|---|---|---|---|---|---|
| APP-3301 | Sarah Mitchell | 0.72 | 0.35 | $14,200 | Rate Up 40% |
| APP-3302 | David Kim | 0.18 | 0.22 | $2,800 | Rate Down 15% |
| APP-3303 | Robert Brown | 0.85 | 0.68 | $28,500 | Decline or Restrict |
Understand why
Every prediction includes feature attributions — no black boxes
Applicant APP-3301 (Sarah Mitchell)
Predicted: Risk score 0.72, expected loss $14,200
Top contributing features
Roof age exceeding replacement threshold
18 years
27% attribution
Zip code wildfire risk increasing
High, +15% YoY
24% attribution
Prior water damage claim
$8,200 in 5yr
20% attribution
Property age and construction type
1998 wood frame
17% attribution
Neighborhood claims density
42 per 1,000
12% 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 loss ratios by 5-10 points while writing 8-12% more profitable business, translating to $500M+ in annual savings on a $10B book.
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.
