Statistical Credit Scoring Model
The Challenge
A digital lender was using basic rule-based underwriting, resulting in high default rates (8%) and missed opportunities on creditworthy applicants who lacked traditional credit history.
Our Solution
Built a statistical credit scoring model using logistic regression with feature engineering, incorporating alternative data signals. Applied Bayesian updating for dynamic risk assessment and developed model validation framework.
Our Approach
Analyzed historical loan performance data to identify predictive features
Developed logistic regression model with regularization for credit scoring
Incorporated alternative data signals (transaction patterns, device data)
Built model validation framework with KS statistics and Gini coefficient
Implemented Bayesian updating for dynamic risk adjustment
Technology Stack
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