AI-Powered Fraud Detection in Digital Payments: A Machine Learning Perspective

Abstract

With the rapid adoption of digital payments, financial institutions face escalating risks of fraudulent activities. Traditional rule-based fraud detection methods struggle to keep pace with the complexity and scale of modern cyber threats. This paper examines the application of machine learning and artificial intelligence techniques in detecting, preventing, and mitigating payment fraud. Using supervised and unsupervised learning models, including decision trees, neural networks, and clustering algorithms, AI systems can identify suspicious patterns in real-time, significantly reducing false positives while improving detection accuracy. Case studies highlight the success of AI-driven fraud detection frameworks in e-commerce, mobile banking, and peer-to-peer transactions. Additionally, the paper addresses critical issues such as data privacy, adversarial attacks, and explainability in AI-based financial systems. Results indicate that AI not only strengthens the security of digital payments but also enhances consumer trust and confidence in financial technologies.