The Application of Machine Learning Models in Fraud Detection and Prevention Across Digital Banking Channels and Payment Platforms

Abstract

This paper presents a comprehensive investigation into the application of machine learning models for fraud detection and prevention across digital banking channels and payment platforms. It examines the unique characteristics of transactional data streams in online banking, mobile banking, peer-to-peer transfers, and emerging payment modalities such as digital wallets and real‐time payment rails. We analyze the efficacy of supervised, semi‐supervised, and unsupervised learning algorithms under various feature representations, including temporal sequence embeddings, graph‐based relational features, and hierarchical behavioral signatures. A detailed exposition of one advanced mathematical formulation frames the detection problem as a stochastic optimization under adversarial perturbations, leveraging concepts from measure‐theoretic probability, reproducing kernel Hilbert spaces, and robust statistical decision theory. The proposed framework integrates incremental learning to adapt to concept drift, and meta‐learning strategies to transfer insights across heterogeneous channels. Experimental evaluation on large‐scale synthetic and anonymized production datasets demonstrates that ensemble architectures combining deep representation learners with probabilistic graphical models can achieve significant improvements in detection latency and false‐positive control, while preserving customer experience through adaptive risk‐scoring thresholds. The findings underscore the trade‐offs between interpretability, computational overhead, and adaptability in real‐time fraud prevention systems. The paper concludes with recommendations for deployment architectures, data governance practices, and future research directions toward fully autonomous fraud resilience.