A novel deep learning-based hybrid Harris hawks with sine cosine approach for credit card fraud detection

Abstract

Credit cards have become an integral part of the modern financial landscape, and their use is essential for individuals and businesses. This has resulted in a significant increase in their usage in recent years, especially with the growing popularity of online payments. Unfortunately, this increase in credit card use has also led to a corresponding rise in credit card fraud, posing a serious threat to financial security and privacy. Therefore, this research introduces a novel deep learning-based hybrid Harris hawks with sine cosine method for credit card fraud detection system (HASC-DLCCFD). The aim of the presented HASC-DLCCFD approach is to identify fraudulent credit card transactions. The suggested HASC-DLCCFD scheme introduces a HASC technique for feature selection, by combining Harris hawks optimization (HHO) with the sine cosine algorithm (SCA). For the purpose of identifying credit card fraud, an architecture of a convolutional neural network combined with long short-term memory (CNN–LSTM) is utilized in this study. Finally, the adaptive moment estimation (Adam) algorithm is utilized as a hyperparameter optimizer of the CNN-LSTM model. The performance of the suggested HASC-DLCCFD approach was experimentally evaluated using a publicly available database. The results demonstrate that the suggested HASC-DLCCFD approach outperforms other current techniques and achieved the highest accuracy of 99.5%.

Keywords

• neural network
• deep learning
• hyperparameter optimization
• features selection
• fraud detection
• credit card