V3I6P15

Securing the Cashless Economy: A Systematic Review of Payment Fraud Typologies, AI-Driven Countermeasures, and Socio-Economic Implications

Divya Mangesh Jakhal^1*, Sandhya Kaprawan²

Abstract

As technologies evolve, the mode of payment has shifted from cash to digital. The cashless economy boosted by innovations like the Unified Payments Interface (UPI) in India and mobile banking globally has opened new avenues for payment fraud. Cybercriminals are advancing their tactics— including social engineering, synthetic identities, account takeovers (ATO), and dynamic QR code manipulation — rendering traditional rule-based detection systems incompetent due to rigidity, slow processing speeds, and high false-positive rates. This paper presents a Systematic Literature Review (SLR) to understand payment fraud typologies and analyse how next-generation countermeasures mitigate these frauds. Our synthesis reveals that hybrid architectures integrating Machine Learning (ML), Artificial Intelligence (AI), CNNs, and RNNs demonstrate superior detection accuracy. Persistent challenges include data imbalance, algorithm opacity, and strict data privacy regulations for AML and KYC compliance. We highlight the need for Explainable AI (XAI) tools — LIME and SHAP — and Federated Learning frameworks to enable privacy-preserving collaboration between financial institutions.

Keywords:

Digital Payments, UPI, Financial Fraud, Machine Learning, Cyber Security, Explainable AI (XAI), Federated Learning, Deep Reinforcement Learning, Fraud Risk Management