Adoption of AI Based Credit Scoring Models and Their Impact on Financial Inclusion in India

Abstract

This study, titled "Adoption of AI Based Credit Scoring Models and Their Impact on Financial Inclusion in India," evaluates sector deployment breakdown, loan approval speed, rural credit disbursal expansion, and financial feasibility of alternative AI credit scoring platforms across Indian retail lending ecosystems. Millions of unbanked and underserved micro-borrowers lack formal credit bureau histories, where digital MFIs represent 42% and FinTech NBFCs account for 28% of AI scoring deployments. A five-year project lifecycle (2021-2025) of an AI credit scoring integration program 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 indicates that deploying AI credit scoring engines reduces micro-loan approval times to 2 minutes compared to 2,880 minutes under legacy paper audits. Algorithmic credit evaluation drives rural credit disbursals to 98,500 Crores while raising the RBI Financial Inclusion Index to 96.2, expanding AI adoption to 91.8% and compressing rural portfolio NPA ratios to 1.1% 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 adopting AI-based credit scoring is highly viable, accelerating financial inclusion and expanding sustainable credit access. Keywords: AI Credit Scoring, Financial Inclusion, Micro-Loans, Rural Disbursals, NPA Compression, Alternative Data, Capital Budgeting, Financial Feasibility.