FinTech & Surety · Risk Scoring
Risk scoring that goes beyond the credit score.
Credit scores tell you payment history. Surety and specialty finance need behavioral intelligence — how a company manages cash, how its revenue has trended, and its actual bonding exposure relative to working capital. We build custom models trained on your own underwriting decisions.
Beyond credit scores
Behavioral intelligence, not just payment history.
A credit score tells you whether a company has paid its bills. It does not tell you how that company manages cash, how its revenue has trended over the last several periods, or what its real bonding exposure looks like against its working capital. Surety and specialty finance live in exactly those questions.
We build custom risk scoring models trained on your historical underwriting decisions — your approvals, your declines, and your claims — so the model learns your risk appetite rather than a generic one.
How we build it
From your history to a calibrated, explainable score.
A custom model is engineered, trained, calibrated, and monitored against your own outcomes — with a human override workflow throughout.
Feature engineering: Extract 40–80 financial and behavioral features from application data, including the liquidity, leverage, and profitability ratios that drive surety and specialty-finance decisions.
Model training: Train XGBoost or Random Forest models on your historical approvals, declines, and claims so the model reflects real outcomes from your book.
Score calibration: Validate against known outcomes and calibrate the score to your risk appetite, so the numbers map to decisions your underwriters actually make.
Explainability layer: Compute SHAP values per score so underwriters understand the why behind every number, not just the number itself.
Drift monitoring: Detect when model performance degrades as market conditions change, so a stale model never silently drives decisions.
Human override workflow: An underwriter can override any score with documented rationale — preserving human control and the audit trail behind it.
Ratio & capacity analysis
The financial signals behind every score.
Scores are built on interpreted financial ratios and surety-specific capacity math, not raw extracted numbers.
Liquidity ratios
Current ratio, quick ratio, and cash ratio — the near-term solvency signals underwriters weigh first.
Leverage ratios
Debt-to-equity and debt-to-assets, surfacing how much of the balance sheet is financed and where the risk concentrates.
Profitability & trend
Gross margin, net margin, EBITDA, multi-period working-capital trends, and revenue growth-rate analysis behind the score.
Bonding capacity estimation
Surety-specific capacity using the 10x working-capital rule, measuring actual bonding exposure against the company's working capital.
Model output
What every score returns to the underwriter.
Technology
The stack behind the models.
Python, scikit-learn, XGBoost, and LightGBM for modeling; SHAP for explainability; and MLflow for experiment tracking. Models deploy on AWS SageMaker or Azure ML and are served through a FastAPI serving layer.
Every score is reproducible: the input data, the extracted fields, the score factors, and the model version are logged together so each decision can be reconstructed and audited.
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