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

arXiv: https://arxiv.org/abs/2504.03750 · HTML: https://mlsanity.com/paper/2504.03750/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics](https://mlsanity.com/paper/1910.05266/) (2019)
- [Gated Feedback Recurrent Neural Networks](https://mlsanity.com/paper/1502.02367/) (2015)
- [Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2207.12201/) (2022)
- [Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs](https://mlsanity.com/paper/1909.01185/) (2019)
- [Deep Learning Methods for Credit Card Fraud Detection](https://mlsanity.com/paper/2012.03754/) (2020)
- [A Dynamic Approach to Stock Price Prediction: Comparing RNN and Mixture of Experts Models Across Different Volatility Profiles](https://mlsanity.com/paper/2410.07234/) (2024)
- [How Do Consumers Really Choose: Exposing Hidden Preferences with the Mixture of Experts Model](https://mlsanity.com/paper/2503.05800/) (2025)

## Built on by (most cited first, 1 of 1)

- [The Graph-Embedded Hazard Model (GEHM): Stochastic Network Survival Dynamics on Economic Graphs](https://mlsanity.com/paper/2512.14705/) (2025)

## Abstract (arXiv)

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.