Cybersecurity Threats in Digital Payment Systems (DPS): A Data Science Perspective

Abstract

The growing dependence on digital payment systems has resulted in a rise in cyber-attacks, therefore endangering user confidence and financial stability. Understanding these threats, evaluating their effect, and investigating data-driven solutions for reducing cybersecurity risks drives this research. The purpose of the study is to explore the cybersecutiy threats in digital payment systems (DPS) and the role of data science techniques in threat detection and prevention. The technique used in the current study is Exploratory Factor Analysis (EFA). It was found that the following are the three key factors reflecting cybersecurity threats i.e., Data Breach & Privacy Threats, Network & System Vulnerabilities, and Fraud & Identity Theft Threats. Data science finds threats, analyzes patterns, and supports proactive defensive mechanisms through Machine learning (ML) algorithms such as anomaly detection, neural networks, and decision trees.