Enhanced particle swarm optimization-based hyperparameter optimized stacked autoencoder for credit card fraud detection

In recent years, several fraud attempts have been made in various sectors including finance, banking and insurance. In fact, credit card fraud refers to the unauthorized use of a credit card account to obtain money, products or services. It involves the manipulation of card details for fraudulent purchases or withdrawals. These fraudulent incidents result in substantial financial losses of different divisions of businesses. The present manuscript presents an innovative system used to detect credit card fraud employing unsupervised deep learning. However, the effectiveness of deep learning models relies on the configuration of hyperparameter values and the avoiding of overffiting issue, tasks that provide time-consuming and require significant trial and error. The proposed model utilizes an improved particle swarm optimization (PSO) to optimize the training hyperparameters such as global initial connection weights and thresholds, while leveraging stacked autoencoder for classification purposes. Unlike the existing model, that introduced in this work takes into account transactional data and enables the classifier to accurately identify the most crucial transactions within the input sequence, which allows predicting fraudulent transactions more accurately. This approach combines the strengths of three methods: SMOTE-Tomek for handling imbalanced data, local search PSO for hyperparameter optimization, and an enhanced stacked autoencoder used to detect anomaly in credit card fraud transactions. The comparative study reveals that the developed model is the most efficient, in comparison with the other models.