Fintech-Based Predictive Risk Systems and Their Mitigating Effect on Fraud and Operational Risks

Abstract

This article investigates how fintech-based predictive risk systems contribute to mitigating fraud and operational risks in highly digital and data-driven financial environments. It addresses the question of when and how machine-learning and analytics-driven tools move beyond improving classification metrics to actually reduce realized fraud losses, service disruptions, and compliance failures. The study draws on a systematic review of peer-reviewed articles published between 2020 and 2024 that analyze predictive models embedded in digital payments, e-commerce, platform-based finance, and related fintech services. The reviewed evidence shows that these systems consistently outperform traditional rule-based approaches, particularly when they integrate granular transactional, behavioral, and network data and operate in near real time. Through narrative and thematic synthesis, the article discusses prevailing design patterns, data sources, modelling techniques, and governance practices surrounding these systems. The main findings underline strong technical performance but fragmented governance, limited measurement of loss reductions, and the need for more integrated, proactive risk architectures around fintech-based predictive systems.