AI-Based Fraud Detection in Digital Payment Systems

Abstract

The rapid expansion of digital payment ecosystems, including mobile wallets, UPI transfers, internet banking, card-not-present transactions, and QR-code-based merchant payments, has significantly improved financial accessibility and transaction convenience. However, the same growth has also increased the attack surface for fraudsters, resulting in identity theft, phishing-enabled account takeover, synthetic identity abuse, mule-account activity, bot-driven transaction bursts, and adaptive social-engineering attacks. Traditional rule-based fraud detection systems remain useful for known patterns but struggle against evolving and low-latency fraud behaviors. This research paper presents a structured academic study of artificial intelligence (AI)-based fraud detection in digital payment systems, emphasizing machine learning, deep learning, anomaly detection, graph intelligence, and hybrid human-in-the-loop decisioning. The paper reviews existing literature, identifies major operational and technical challenges, proposes a layered AI detection framework, and synthesizes representative performance insights from prior studies and industry practice. The study concludes that robust fraud prevention in modern payment platforms requires a multi-model architecture that combines supervised risk scoring, unsupervised anomaly discovery, graph-based link analysis, explainable decision support, continuous feedback loops, and governance controls for privacy, fairness, and compliance.

Keywords: AI, Fraud Detection, Digital Payment Systems, Machine Learning, UPI, Financial Technology, Anomaly Detection, Cybersecurity.