Credit card fraud detection using hybrid Chord-SpinalNet

Due to the advancement of e-commerce systems and information technologies, credit cards act as the popular payment method for both normal and online shopping. Still, fraudulent transactions are a critical issue for online payments and credit card fraud has become more prevalent in recent years. This paper develops a hybrid network-based credit card fraud detection (CCFD) technique, named Chord-SpinalNet by merging the Chord layer with SpinalNet. The credit card fraud detection dataset is used for the evaluation. This data covers more features, and the feature fusion is done by the Jensen difference for feature sorting and the deep neuro fuzzy network (DNFN) for generating fusing coefficients. The Chord-SpinalNet is utilised in the CCFD phase to identify unauthorised transactions. The accuracy, sensitivity and specificity metrics are utilised to determine the performance of the Chord-SpinalNet and it obtained the highest accuracy, sensitivity and specificity of 0.923, 0.915 and 0.926.