Credit Risk Scoring
“Which borrowers will default within 12 months?”
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
Which borrowers will default within 12 months?
US banks charge off $50B+ in consumer loans annually. Traditional scorecards rely on bureau scores and static application data, missing dynamic behavioral signals like changing payment patterns across multiple credit lines, rising utilization on revolving accounts, and transaction velocity shifts. A mid-size lender with a $20B portfolio estimated that a 10% improvement in default prediction would save $80-120M per year in charge-off losses while approving 5% more creditworthy borrowers currently declined by blunt FICO cutoffs.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo connects loan applications, payment histories, account balances, transaction patterns, and bureau data into a unified relational graph. The model discovers that Borrower B-2041 has a 740 FICO but declining payment consistency across three revolving accounts, rising cash-advance frequency, and a new pattern of minimum-only payments. These cross-table behavioral signals produce a more accurate probability-of-default score than any single-table logistic regression, surfacing risk 3-6 months before a traditional model would flag it.
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
BORROWERS
| borrower_id | fico_score | income | employment_years | dti_ratio |
|---|---|---|---|---|
| B-2041 | 740 | $95,000 | 6.2 | 0.34 |
| B-2087 | 680 | $62,000 | 3.1 | 0.41 |
| B-2103 | 790 | $142,000 | 14.5 | 0.22 |
LOANS
| loan_id | borrower_id | type | principal | rate | origination_date |
|---|---|---|---|---|---|
| L-8001 | B-2041 | Auto | $32,000 | 5.9% | 2024-03-15 |
| L-8002 | B-2087 | Personal | $15,000 | 11.2% | 2024-07-22 |
| L-8003 | B-2103 | Mortgage | $450,000 | 6.5% | 2023-11-01 |
PAYMENTS
| payment_id | loan_id | amount | days_late | timestamp |
|---|---|---|---|---|
| P-001 | L-8001 | $542.18 | 0 | 2025-08-01 |
| P-002 | L-8001 | $542.18 | 12 | 2025-09-01 |
| P-003 | L-8002 | $310.00 | 0 | 2025-09-01 |
CREDIT_LINES
| line_id | borrower_id | type | limit | balance | min_payment_only |
|---|---|---|---|---|---|
| CL-01 | B-2041 | Credit Card | $15,000 | $13,200 | True |
| CL-02 | B-2041 | HELOC | $50,000 | $42,000 | False |
| CL-03 | B-2087 | Credit Card | $8,000 | $3,100 | False |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(LOANS.STATUS = 'default', 0, 12, months) FOR EACH BORROWERS.BORROWER_ID WHERE LOANS.STATUS = 'active'
Prediction output
Every entity gets a score, updated continuously
| BORROWER_ID | FICO | KUMO_PD_SCORE | TRADITIONAL_PD | RISK_BAND |
|---|---|---|---|---|
| B-2041 | 740 | 0.38 | 0.06 | Elevated |
| B-2087 | 680 | 0.22 | 0.18 | Moderate |
| B-2103 | 790 | 0.03 | 0.02 | Low |
Understand why
Every prediction includes feature attributions — no black boxes
Borrower B-2041 (FICO 740)
Predicted: 38% probability of default within 12 months
Top contributing features
Credit card utilization trend
88% and rising
29% attribution
Minimum-only payment pattern
5 of 6 months
24% attribution
Late payment emergence (auto loan)
12 days
20% attribution
Cash advance frequency increase
+300%
16% attribution
Cross-account balance growth
+$18K in 6mo
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: Catch high-FICO borrowers showing early distress signals and reduce charge-off losses by $80-120M annually on a $20B portfolio while safely approving 5% more good borrowers.
Related use cases
Explore more financial services 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.
