Continual AE-WGAN for Unsupervised Anomaly Detection in Streaming Data

In today's data-driven world, the timely detection of anomalies in streaming data is of paramount importance across diverse domains, including cybersecurity, industrial processes, and notably, finance. In the financial sector, where transactions occur at a rapid pace, the ability to identify anomalies plays a crucial role in fraud detection and prevention. Traditional anomaly detection methods often face challenges in adapting to the dynamic nature of streaming data, where the underlying data distribution may evolve rapidly. To address this intricate challenge, we propose a cutting-edge unsupervised anomaly detection framework based on Autoencoder (AE) and Wasserstein Generative Adversarial Network (WGAN). This innovative approach leverages continual learning to effectively identify anomalies in streaming tabular data, offering a versatile solution that is particularly well-suited for real-world scenarios. Our framework provides an end-to-end prediction tailored for streaming tabular data, ensuring a robust and adaptive solution for detecting anomalies in financial transactions, thereby enhancing the overall security and integrity of financial systems.