An Optimized Deep Learning Approach for Detecting Fraudulent Transactions

Abstract

The proliferation of new technologies and advancements in existing ones are altering our perspective of the world. So, continuous improvements are needed. A connected world filled with a vast amount of data was created as a result of the integration of these advanced technologies in the financial sector. The advantages of this connection came at the cost of more sophisticated and advanced attacks, such as fraudulent transactions. To address these illegal transactions, researchers and engineers have created and implemented various systems and models to detect fraudulent transactions; many of them produce better results than others. On the other hand, criminals change their strategies and technologies to imitate legitimate transactions. In this article, the objective is to propose an intelligent system for detecting fraudulent transactions using various deep learning architectures, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM). Furthermore, the Bayesian optimization algorithm is used for hyperparameter optimization. For the evaluation, a credit card fraudulent transaction dataset was used. Based on the many experiments conducted, the RNN architecture demonstrated better efficiency and yielded better results in a shorter computational time than the ANN LSTM architectures.