Anomaly Detection in Financial Data using Deep Learning: A Comparative Analysis

Abstract

Anomaly detection in the financial sector has a critical importance for financial markets, investors, and regulatory authorities. As financial environments change, real-time detection of anomalies becomes more difficult due to the increase in data speed and volume with increasing digitization. Recently, deep learning (DL) algorithms have been used as a promising approach to solving the anomaly detection problem. In this study, DL-based anomaly detection model in the financial sector is presented using various DL algorithms including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1dCNN). In addition, hyperparameter optimization is performed with the grid search method. These methods are applied to two popular financial data, Tesla's stock market and Ethereum cryptocurrency data sets. Accordingly, a comparative analysis is conducted on these data sets with DL algorithms. Experimental results clearly show that GRU algorithm has the highest prediction score in both data sets, while 1dCNN algorithm has the lowest prediction score. In addition, anomaly values are demonstrated graphically with GRU for both data sets.