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Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns

Financial fraud detection remains a critical challenge due to the dynamic and adversarial nature of fraudulent behavior. As fraudsters evolve their tactics, detection systems must combine robustness, adaptability, and precision. This study presents a hybrid architecture for credit card fraud detection that integrates a Mixture of Experts (MoE) framework with Recurrent Neural Networks (RNNs), Transformer encoders, and Autoencoders. Each expert module contributes a specialized capability: RNNs capture sequential behavior, Transformers extract high-order feature interactions, and Autoencoders detect anomalies through reconstruction loss. The MoE framework dynamically assigns predictive responsibility among the experts, enabling adaptive and context-sensitive decision-making. Trained on a high-fidelity synthetic dataset that simulates real-world transaction patterns and fraud typologies, the hybrid model achieved 98.7 percent accuracy, 94.3 percent precision, and 91.5 percent recall, outperforming standalone models and classical machine learning baselines. The Autoencoder component significantly enhanced the system's ability to identify emerging fraud strategies and atypical behaviors. Beyond technical performance, the model contributes to broader efforts in financial governance and crime prevention. It supports regulatory compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) protocols and aligns with routine activity theory by operationalizing AI as a capable guardian within financial ecosystems. The proposed hybrid system offers a scalable, modular, and regulation-aware approach to detecting increasingly sophisticated fraud patterns, contributing both to the advancement of intelligent systems and to the strengthening of institutional fraud defense infrastructures.

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