Fast, accurate credit decisions are critical to SMB lending—but traditional scoring methods often fail in the face of fragmented data, thin files, and dynamic business conditions. We build credit decision tools that combine interpretability, modular design, and modern statistical methods. These models support underwriting workflows with transparent outputs, flexible inputs, and fast turnaround from prototype to deployment.
Problems We Solve
- Thin-file or non-traditional SMB applicants
→ Scoring models that use transactional, behavioral, and alternative data—without requiring FICO or bureau pulls. - Slow or rigid underwriting logic
→ Replace rules-based decision trees with adaptive, probabilistic scoring models that respond to data variation. - Compliance and internal audit concerns
→ Full model traceability with built-in explainability (e.g., SHAP values, monotonic constraints, GAMs). - Engineering friction in production
→ Delivered as containerized APIs with integration-ready scoring logic and fast cloud deployment.
What We Deliver
- Credit scoring models using sparse and behavioral data
- Probability-of-default models (Bayesian or ensemble-based)
- Modular scorecards with audit trails and override logic
- Threshold and offer recommendations based on pricing/risk curves
- Integration-ready APIs for use in loan origination systems
Tech Stack
Python / scikit-learn / XGBoost / LightGBM / PyMC / FastAPI / Docker / AWS / Postgres