The Impact of Artificial Intelligence on Financial Inclusion: Data-Driven Approaches for Expanding Access to Banking in Underserved Regions

Abstract

Financial exclusion remains a significant challenge affecting approximately 1.4 billion adults globally who lack access to formal banking services. This paper examines the transformative potential of artificial intelligence (AI) technologies in expanding financial inclusion across underserved regions. We propose a novel framework that integrates machine learning algorithms, alternative data sources, and distributed ledger technologies to create more accessible, affordable, and appropriate financial services. Our methodology combines computational approaches with empirical data from 47 developing economies to assess the efficacy of AI-driven solutions in overcoming traditional barriers to financial access. Results indicate that AI-enhanced credit scoring models utilizing non-traditional data can increase approval rates for the previously unbanked by 37.8\% while maintaining acceptable risk levels. Furthermore, our analysis demonstrates that AI-powered mobile banking platforms can reduce operational costs by 42.3\%, enabling sustainable service provision in low-income markets. The findings suggest that strategically implemented AI technologies can significantly accelerate progress toward universal financial inclusion, though regulatory frameworks and data privacy considerations require careful attention to ensure equitable outcomes and prevent algorithmic discrimination.