AI-Driven Alternative Credit Scoring and Financial Inclusion Enhancing access to Credit for Underserved in India

Abstract

This study, titled "AI-Driven Alternative Credit Scoring and Financial Inclusion Enhancing Access to Credit for Underserved in India," evaluates alternative data weightings, approval rates, financial inclusion growth, and financial feasibility of AI scoring engines in expanding credit access for unbanked and credit-invisible populations in India. Millions of smallholders, informal MSMEs, and gig workers lack traditional bureau credit histories. A five-year project lifecycle (2021-2025) of an AI alternative credit scoring platform is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis reveals that Unified Payments Interface (UPI) and mobile telemetry account for 45% of alternative data scoring weights. Deploying AI alternative scoring raises loan approval rates for credit-invisible borrowers to 84.2% compared to 14.5% under traditional credit bureau rules. Expanding the borrower base to 35.8 million underserved individuals increases average first-time loan sizes to ₹52,500 while improving the Financial Inclusion Index to 94.8 and compressing 90+ DPD default rates to 1.4% by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in AI alternative credit scoring is highly viable, driving sustainable financial inclusion while maintaining strong credit portfolio quality. Keywords: AI Credit Scoring, Financial Inclusion, Alternative Data, UPI Telemetry, Underserved Borrowers, Credit Access, Capital Budgeting, Financial Feasibility.