The rapid global shift toward cashless transactions has made digital payment systems — mobile wallets, UPI, card networks, and net-banking platforms — a critical part of everyday economic life. This growth, however, has been matched by an equally rapid rise in cyber threats, including phishing, card skimming, account takeover, malware, SIM-swap fraud, and man-in-the-middle attacks on transaction channels. Existing research tends to examine individual threat categories or individual technologies in isolation, leaving a gap in the comprehensive assessment of cybersecurity risk across the digital payment ecosystem. This paper proposes an integrated framework that combines real-time transaction monitoring, layered authentication, encryption standards, and machine-learning-based anomaly detection to identify, classify, and mitigate cyber threats to digital payment systems. The paper reviews the current threat landscape, presents research objectives and hypotheses, outlines a five-layer security framework, and illustrates its application through an indicative pilot scenario. The findings suggest that a coordinated, data-driven security architecture can materially reduce fraud exposure while preserving the convenience that drives digital payment adoption.
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Dhawale et al. (2026) studied this question.
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