OpenCredit: A Transparent UPI-Based Credit Scoring Framework for Financial Inclusion in India

Financial exclusion remains a critical challenge in India, with approximately 190 million adults remaining credit-invisible despite having bank accounts and active UPI usage. This paper introduces OpenCredit, an open-source, transparent credit scoring framework that leverages Unified Payments Interface (UPI) transaction data to assess creditworthiness without relying on traditional credit bureau scores.

Unlike proprietary black-box algorithms employed by credit bureaus, OpenCredit uses a rules-based deterministic engine with complete algorithmic transparency, enabling community validation, regulatory auditability, and borrower understanding. Through a hybrid architecture combining deterministic rule engines for all scoring decisions with fine-tuned large language models for multi-lingual explanations, OpenCredit provides both regulatory compliance and superior user experience.

Our implementation analyzes five key UPI transaction dimensions: velocity, regularity, merchant-category diversity, seasonal consumption patterns, and financial resilience. Simulation-based validation using synthetic datasets representative of Indian demographics demonstrates that OpenCredit achieves an ROC-AUC of 0.742, comparable to published results for alternative credit scoring systems globally, and successfully scores 68% of first-time credit seekers with only 3 months of UPI history.

OpenCredit's open-source release under Apache License 2.0, combined with its deterministic architecture, positions it not merely as an alternative credit scoring tool but as evidence for regulatory reform — demonstrating that transparent, community-governed credit assessment can achieve comparable predictive accuracy while eliminating systemic discrimination embedded in proprietary systems.