Machine Learning and Artificial Intelligence in FinTech: Driving Innovation in Digital Payments, Fraud Detection, and Financial Inclusion

Abstract

This study examines how machine learning (ML) and artificial intelligence (AI) technologies are fundamentally reshaping financial technology (FinTech), with particular emphasis on three interconnected domains: digital payments, fraud detection, and financial inclusion. Despite the rapid proliferation of AI-driven financial services, comprehensive empirical evidence linking specific algorithmic approaches to measurable outcomes remains fragmented across disciplinary boundaries. We employ a mixed-methods research design combining systematic literature review (covering 2018–2023), quantitative analysis of adoption patterns across 45 countries and 125 financial institutions, and detailed case study examination of six leading FinTech implementations. Our quantitative analysis incorporates transaction data from over 50 million digital payment events, fraud detection records encompassing 2.3 million documented incidents, and financial inclusion metrics from the World Bank’s Global Findex Database. Results demonstrate substantial performance improvements across all three domains. AI-enhanced digital payment systems achieve 67% reduction in average processing time while maintaining enhanced security protocols. Machine learning-based fraud detection systems exhibit accuracy rates between 94–98% with false positive reductions approaching 70 % compared to rule-based alternatives. Alternative credit scoring models powered by ML algorithms expand financial access by 25–40% among previously underserved populations, with loan approval rates 67% higher than traditional methods while maintaining comparable or improved default rates. Our conceptual framework positions AI/ML as an enabling infrastructure that simultaneously transforms and is transformed by advances in payments, fraud detection, and inclusion, with feedback loops distinguishing our approach from linear input-output models common in earlier work.