Fraud Detection and Cyber Risk Prevention in Digital Commerce: A Machine Learning-Centric Framework

Digital transactions have become ubiquitous, with e-commerce, fintech, and digital banking platforms processing billions of payments daily. However, this surge in digital activity has also led to an alarming rise in cyber fraud incidents, ranging from synthetic identity fraud to bot-driven checkout attacks. The 2023 Cybersecurity Report by Verizon indicated that over 74% of breaches involved human and behavioral manipulation, while over 35% were financially motivated, specifically targeting payment systems
(Nair et al., 2024). In this high-stakes ecosystem, fraud detection systems (FDS) are evolving from static rule engines to dynamic, AI-driven platforms capable of real-time evaluation of transactional trustworthiness.