Data-Driven Risk Detection in Digital Financial Systems: Implications for Financial Crime Prevention and Infrastructure Resilience

Abstract

The increasing digitalization of financial systems has transformed how financial institutions process transactions, deliver services, and manage operational risk. However, the rapid growth of electronic payment systems and online financial platforms has also increased exposure to fraud, identity theft, and cyber-enabled financial crime. This study explores the relationship between the expansion of the digital financial system and financial crime exposure in the United States, using publicly available data from the Federal Trade Commission (FTC) Consumer Sentinel Network and the Federal Reserve Payments Study. Adopting an exploratory and policy-oriented approach, the study uses descriptive trend analysis, comparative visualization, and conceptual financial risk interpretation to examine evolving fraud patterns and digital transaction vulnerabilities. The findings indicate that the growth of digital payment infrastructures has been accompanied by persistently high levels of fraud reports, identity theft incidents, and financial losses, particularly within credit card and electronic transaction environments. In response, the study proposes a conceptual Data-Driven Risk Detection Framework integrating transaction analytics, anomaly detection, compliance monitoring, and adaptive learning mechanisms to strengthen financial crime prevention and infrastructure resilience. The study contributes to the literature by offering a practical, infrastructure-focused perspective on data-driven financial risk governance in increasingly digital financial ecosystems.