Machine Learning for Fraud Detection in Enterprise Financial Systems

Abstract

Financial fraud imposes large and growing costs on enterprises, and the volume, velocity, and variety of modern transactions have outstripped the capacity of rule-based controls to contain it. Machine learning (ML) has become the dominant paradigm for fraud detection because it can learn evolving fraud patterns from large transactional datasets and score transactions in real time. This article synthesizes the current evidence on ML for fraud detection in enterprise financial systems and organizes it into a coherent analytical framework. It reviews the fraud landscape and its economic scale, formalizes the detection problem and its defining challenge, extreme class imbalance, and specifies the governing evaluation mathematics, including precision, recall, the F-score, ROC-AUC and precision–recall AUC, cost-sensitive and focal losses, and synthetic oversampling. Twelve figures and nine equation blocks develop the analysis, spanning a detection pipeline, an imbalance illustration, a model-comparison benchmark, ROC and precision–recall curves, feature importance, a cost-sensitive threshold analysis, and an enterprise real-time scoring architecture. The synthesis indicates that gradient-boosted tree ensembles and, increasingly, deep and graph-based models achieve the strongest precision–recall balance; that resampling and cost-sensitive learning materially improve minority-class recall; and that in operational settings the binding constraint is the trade-off between fraud caught and analyst workload rather than headline accuracy. The article closes with an implementation framework and a discussion of interpretability, concept drift, and governance. A research-style article synthesizing current evidence. Reported loss statistics are drawn from public sources; model-performance figures are illustrative values consistent with the benchmarking literature rather than results from a single primary experiment.