Catching Fraud Before It Clears: A Real-Time Machine Learning Framework for U.S. Banking and Digital Payment Networks.

Abstract

Fraud in United States banking and digital payment networks has increasingly outpaced the capacity of legacy, rules-based, and post-transaction review systems, which tend to flag suspicious activity only after funds have cleared and losses have already been realized. This paper synthesizes recent scholarship on machine learning, big data analytics, cybersecurity, and management information systems (MIS) agility to propose a conceptual, real-time machine learning framework for fraud detection in banking and digital payment environments. Drawing on explainability-constrained model tuning approaches originally developed for credit card fraud risk scoring, streaming big-data analytics for cybersecurity threat detection, and organizational findings on agile MIS implementation, the proposed framework integrates four interdependent layers: real-time data ingestion, adaptive feature engineering, explainable ensemble scoring, and a governance-and-feedback loop that keeps models current as fraud tactics evolve. The paper further draws a methodological analogy to multi-criteria life-cycle evaluation frameworks used in other applied domains to argue for a structured, multi-dimensional approach to evaluating fraud-detection systems that balance detection accuracy, latency, interpretability, and organizational readiness rather than accuracy alone. Because the contribution is conceptual rather than empirical, no experimental results or performance statistics are claimed; instead, the paper offers literature-grounded architecture and a set of testable propositions intended to guide subsequent empirical validation. The discussion highlights implications for financial institutions, regulators, and MIS researchers, and closes with limitations and directions for future empirical work.