📄 Abstract
This paper examines the role of artificial intelligence (AI) and mobile banking in reducing information asymmetry within lending markets and its implications for financial inclusion and credit market efficiency. Traditional lending has long been constrained by adverse selection and moral hazard arising from limited borrower information, resulting in credit rationing and financial exclusion, particularly among low-income households, small businesses, and the informal sector. Drawing on recent empirical and theoretical literature, the paper demonstrates that AI-powered credit scoring and mobile banking platforms have fundamentally transformed credit assessment by leveraging alternative data, including mobile money transactions, digital payment histories, smartphone usage, and other digital footprints. The convergence of mobile banking infrastructure and AI-powered algorithms enable lenders to generate more accurate and dynamic assessments of borrower creditworthiness while lowering screening costs and expanding access to finance. The review further highlights improvements in operational efficiency, credit risk management, and competition within financial markets resulting from AI-enabled lending systems. However, these advances introduce critical systemic challenges relating to data privacy, algorithmic bias, cybersecurity, the digital divide, and evolving regulatory requirements that risk exacerbating inequality. The paper concludes that while AI and mobile banking significantly mitigate information asymmetry and promote inclusive finance, their long-term success depends on robust governance frameworks, strengthened cybersecurity, and collaborative policies that balance technological innovation with consumer protection, transparency, and financial stability
🏷️ Keywords
📚 How to Cite:
Nelson Mandela Ochieng, Dr. Yasin Ghabon , ARTIFICIAL INTELLIGENCE AND MOBILE BANKING: THE SOLUTION TO INFORMATION ASYMMETRY IN LENDING , Volume 14 , Issue 7, July 2026, EPRA International Journal of Economic and Business Review(JEBR) , Pages: 54 - 60 , DOI: https://doi.org/10.36713/epra28728