Explainable Deep Learning Models for Detecting Sophisticated Cyber-Enabled Financial Fraud Across Multi-Layered FinTech Infrastructure

Abstract

The proliferation of FinTech platforms has transformed global financial systems by offering innovative, real-time services.However, this evolution has also expanded the surface area for cyber-enabled financial fraud, especially across multi-layered infrastructures comprising mobile banking apps, decentralized finance (DeFi) platforms, digital wallets, and cloud-based services.Traditional machine learning and rule-based systems have demonstrated limited adaptability in detecting increasingly sophisticated attack vectors that span multiple digital layers.This paper presents a comprehensive exploration of explainable deep learning (XDL) models tailored to detect complex cyber-enabled fraud schemes across interconnected FinTech ecosystems.The study begins with an overview of the structural and technological evolution of FinTech infrastructure, followed by an examination of the most prevalent and emerging fraud typologies including synthetic identity fraud, account takeover, transaction laundering, and insider collusion.Emphasis is placed on the limitations of black-box AI models in high-stakes financial environments where interpretability is critical for regulatory compliance, stakeholder trust, and legal recourse.We introduce an explainable deep learning framework incorporating convolutional neural networks (CNNs) for behavioral biometrics, graph neural networks (GNNs) for multi-entity relationship mapping, and attention-based mechanisms for anomaly prioritization.The model integrates SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to improve transparency without compromising predictive performance.Evaluation is conducted using real-world transaction data from anonymized FinTech institutions, with metrics highlighting accuracy, false positive reduction, and interpretability scores.The paper concludes by discussing policy implications, ethical considerations, and future research directions in explainable AI for secure financial innovation.