ClearBand compiles tree ensembles and neural networks into deterministic, auditable decision artifacts for regulated credit decisioning. Deploy natively to COBOL, Java, SQL, or C++ — no Python runtime required.
Machine learning delivers superior risk discrimination, but SHAP explanations shift between runs, floating-point scores drift across hardware, and regulators (CFPB, EU AI Act, Basel 3.1) are tightening requirements. Complexity is not a defense.
ClearBand separates training from deployment. Train with full ML flexibility (XGBoost, neural networks), then compile the learned decision surface into a deterministic additive artifact using integer-only fixed-point arithmetic. The artifact — not the model — runs in production.
The compiled artifact produces score, risk band, calibrated probability, and a complete forensic waterfall of exact integer contributions — all in one inference pass. No SHAP, no sampling, no approximation.
Designed to support compliance with CFPB adverse action requirements, ECOA/Regulation B, EU AI Act conformity assessments, Basel 3.1 model validation, and OSFI E-23 explainability standards.
Traditional scorecards start from logistic regression. ClearBand starts from state-of-the-art ML and compiles the learned non-linear intelligence into an additive form. The additive structure is an integrated deployment decision interface generated after training — not a modeling constraint.
Email: carlos.ortiz@clearband.ai | Accepting pilot partners for Q2 2026.