Advancing Financial Inclusion through Machine Learning: Techniques, Applications, and Impact

Abstract

Financial services are emerged after the integration of machine learning (ML) as a crucial force in advancing financial inclusion, especially in underserved and economically marginalized populations. The objective of this paper is to provide a comprehensive analysis of ML methodologies which includes supervised, unsupervised, and reinforcement learning and also their deployment in credit scoring, fraud detection, customer segmentation, and alternative data utilization.