Developing AI-powered Credit Scoring Systems for Underserved Populations

The rapid evolution of fintech and AI-driven credit scoring systems presents transformative potential for financial inclusion, particularly in low- and middle-income countries (LMICs) where traditional credit systems often exclude underserved populations due to reliance on collateral, formal identification, and historical financial data. This research explores how AI and machine learning (ML) leverage alternative data sources such as mobile phone usage, social media activity, and transaction histories—to assess creditworthiness, enabling lenders to uncover hidden financial reliability among underbanked groups. Case studies, including MyBucks in South Africa, demonstrate AI’s efficacy in reducing default rates by 18%, highlighting its capacity to enhance lending accuracy. However, challenges persist, including algorithmic bias, lack of transparency in "black-box" models, and regulatory compliance. Ethical considerations around fairness, discrimination, and data privacy necessitate frameworks to balance innovation with equity. The study underscores the need for inclusive, interpretable AI models and collaborative efforts among policymakers, financial institutions, and regulators to ensure ethical deployment. By addressing these challenges, AI-powered credit systems can democratize financial access while mitigating risks of perpetuating systemic inequalities.[1] Keywords AI credit scoring, Financial inclusion, Underserved populations, Machine learning, Alternative data, Algorithmic bias, Ethical AI, Fintech innovation, Credit risk assessment, Regulatory compliance.