UPI behavioral proxies for thin-file credit risk prediction

Abstract

India's Unified Payments Interface (UPI) generates rich behavioral data for over 400 million active users, yet this transactional signal remains inaccessible to most lenders for credit assessment due to data privacy restrictions. Thin-file borrowers — individuals with limited formal credit history — represent the primary beneficiaries of UPI-based credit assessment and simultaneously the population for whom bureau-based models perform least reliably. This study proposes, empirically validates, and evaluates six UPI behavioral proxy features (F4) constructed from standard loan application variables, providing a publicly replicable framework for approximating UPI transaction signals in credit scoring. Scientific validation via Spearman correlation confirms that payment_discipline_score and upi_success_ratio_proxy exhibit the expected directional relationships with default in both independent datasets (p<0.001). Using DS1: LendingClub 2016-2018 (N=300,001) and DS2: Home Credit (N=307,511) with five machine learning models, results show F4 features consistently improve default detection Recall for thin-file borrowers across all models and both datasets. CatBoost achieves +9.95% Recall gain (DS1) and Logistic Regression +6.80% (DS2). SHAP attribution confirms F4 proxies account for 19.0%–32.2% of total predictive power for thin-file borrowers, with payment_discipline_score ranking as the single most predictive feature in Home Credit above all bureau variables. These findings establish UPI behavioral proxies as a meaningful, scientifically validated, and previously underquantified dimension of creditworthiness.