Deep Autoencoder-Based Anomaly Detection for Financial Fraud Transactions

This study proposes an efficient and scalable deep learning framework for anomaly detection in financial transactions. By utilizing an unsupervised learning strategy, the model can successfully detect fraudulent activities without the need for labelled training data. Experimental results demonstrate the effectiveness of the proposed approach and its suitability for deployment in practical financial fraud detection environments. Future enhancements of this work may include: ·                  Hybrid Detection Models: Combining autoencoder-based anomaly detection with supervised learning techniques to further improve detection accuracy. ·                  Real-Time Fraud Monitoring: Extending the framework to support real-time transaction analysis and streaming fraud detection systems. ·                  Graph-Based Fraud Analysis: Integrating graph learning methods to capture hidden relationships and complex fraud networks among transactions.

1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. 2. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. 3. West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. IEEE Systems Journal, 11(4), 1–12. 4. Kaggle. (n.d.). Credit card fraud detection dataset. Retrieved from https://www.kaggle.com/datasets 5. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371–3408.