AI-Driven Credit Scoring for Underbanked Population in India
DOI:
https://doi.org/10.31305/rrijm.2025.v10.n6.018Keywords:
AI credit scoring, financial inclusion, underbanked populations, algorithmic bias, alternative data, regulatory frameworkAbstract
The rapid adoption of AI-based credit scoring models presents a revolutionary opportunity to enhance financial inclusion for underbanked populations, including low-income groups, gig workers, and small businesses in emerging markets such as India. This study evaluates the effectiveness of AI-based credit scoring using alternative data sources such as mobile transactions, utility payments, and digital footprints. Additionally, the research analyzes algorithmic biases (such as caste, gender, or geographic discrimination) and proposes fairness-based machine learning solutions. The study assesses the regulatory and ethical challenges associated with deploying AI credit scoring systems, and compares traditional and AI-powered credit scoring methods in terms of accuracy, accessibility, and risk assessment. The findings suggest that while AI models can promote financial inclusion, they require strong regulatory frameworks, transparent algorithms, and ethical foundations. The research provides guidance for the fintech industry, policymakers, and financial institutions to develop balanced and inclusive credit systems.
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This is an open access article under the CC BY-NC-ND license Creative Commons Attribution-Noncommercial 4.0 International (CC BY-NC 4.0).
