Credit Scoring
ML-based creditworthiness assessment model with an explainability system for decision-making.
Challenge
A financial institution relied on expert-based rules for creditworthiness assessment, which slowed application processing and produced a high rate of false rejections. Traditional scorecards failed to capture complex behavioral patterns. The client required a system capable of more accurate risk evaluation that could automatically adapt to market changes.
Solution
We built an ML scoring model that analyzes financial history, behavioral patterns, and numerous additional factors. The system identifies nonlinear dependencies inaccessible to traditional methods. The model is continuously retrained on new data, improving prediction accuracy. A built-in explainability system generates justification for each decision.
Results
Technologies
Approach
Current scoring model audit
Analysis of existing rules, identification of weaknesses and growth opportunities.
Data collection, preparation, and feature engineering
Building the training dataset, creating features based on financial and behavioral history.
ML model training and validation
Model training, cross-validation, and decision threshold tuning.
A/B testing and phased rollout
Running old and new models in parallel, gradually shifting traffic to ML-based scoring.
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