# Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions

arXiv: https://arxiv.org/abs/2504.15491 · HTML: https://mlsanity.com/paper/2504.15491/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference](https://mlsanity.com/paper/2504.05758/) (2025)
- [Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining](https://mlsanity.com/paper/2504.04032/) (2025)
- [A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection](https://mlsanity.com/paper/2503.21160/) (2025)

## Built on by (most cited first, 4 of 4)

- [Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks](https://mlsanity.com/paper/2509.04973/) (2025)
- [Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling](https://mlsanity.com/paper/2507.14706/) (2025)
- [Cross-Modal Deep Metric Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2509.12540/) (2025)
- [A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems](https://mlsanity.com/paper/2603.13237/) (2026)

## Abstract (arXiv)

This study proposes an algorithm for detecting suspicious behaviors in large payment flows based on deep generative models. By combining Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), the algorithm is designed to detect abnormal behaviors in financial transactions. First, the GAN is used to generate simulated data that approximates normal payment flows. The discriminator identifies anomalous patterns in transactions, enabling the detection of potential fraud and money laundering behaviors. Second, a VAE is introduced to model the latent distribution of payment flows, ensuring that the generated data more closely resembles real transaction features, thus improving the model's detection accuracy. The method optimizes the generative capabilities of both GAN and VAE, ensuring that the model can effectively capture suspicious behaviors even in sparse data conditions. Experimental results show that the proposed method significantly outperforms traditional machine learning algorithms and other deep learning models across various evaluation metrics, especially in detecting rare fraudulent behaviors. Furthermore, this study provides a detailed comparison of performance in recognizing different transaction patterns (such as normal, money laundering, and fraud) in large payment flows, validating the advantages of generative models in handling complex financial data.