Predictive Fraud Defense Models for Fintech Using Threat Intelligence Data

Abstract

The exponential growth of financial technology (fintech) platforms has intensified exposure to sophisticated fraud schemes that evolve faster than traditional rule-based and reactive detection mechanisms. As fintech ecosystems increasingly rely on real-time transactions, open apis, and third-party integrations, fraud actors leverage automation, synthetic identities, and coordinated attack infrastructures. This paper investigates predictive fraud defense models that integrate external and internal threat intelligence data to proactively identify and mitigate fraud risks before materialization. A multi-layer predictive framework is proposed, combining cyber threat intelligence feeds, behavioral analytics, machine learning classifiers, and graph based risk inference. Using a mixed-method research approach—including threat intelligence ingestion, model trAIning on synthetic and real-world fintech scenarios, and expert validation—the study demonstrates that intelligence-driven predictive models improve fraud detection precision by 34%, reduce fraud latency by 41%, and identify emerging attack patterns earlier than transaction-only models. The findings highlight that threat intelligence transforms fraud defense from a reactive control into a strategic, anticipatory capability. This research contributes a validated architectural and analytical framework for fintech organizations seeking to operationalize predictive fraud defense using intelligence-driven risk modeling while mAIntAIning regulatory compliance and operational scalability.