Roughly one in five adults has too thin a bureau file for a traditional credit score to be computed at all. Gradient-boosted models (like LightGBM) trained on telecom top-ups, utility bills and transaction-account patterns can fill that gap, learning non-linear signal from data most bureaus never see.
Because alternative-data models can encode proxies for protected characteristics, responsible deployments pair them with explainability tooling (like SHAP values) and fair-lending audits — the score alone is never considered enough justification for a credit decision.
A gradient-boosted ensemble turns an applicant's bureau history and alternative-data signals — telecom, utility, transaction, mobile-usage and rent-payment patterns — into a live credit score, showing how machine learning extends scoring to thin-file applicants.
Feature bars stream particles into a small forest of decision trees; the resulting gauge shows how much alternative data can lift a score for applicants whose bureau file alone would leave them unscored.
Pick an applicant preset or drag the bureau and alt-data sliders, toggle alternative data on and off to see the score gauge respond, and switch on explainability to see each feature's contribution.
Regulators increasingly require lenders to explain adverse credit decisions feature-by-feature — which is why explainability tools like SHAP values are now standard alongside alternative-data models.