Hybrid Big Bang-Big crunch with cuckoo search for feature selection in credit card fraud detection.

The technological advancements of financial applications and the expansion of e-commerce platforms have increased the daily volume of credit card transactions. Consequently, there has been a substantial rise in instances of credit card fraud that leads to monetary losses for both individuals and financial institutions. The fraudsters continuously develop new technologies to breach security and acquire the credit card credentials of users through fraudulent activities such as scamming, phishing, or exploiting data breaches. There are numerous machine learning and deep learning techniques for detecting credit card frauds. However, due to the higher dimensionality and the imbalance between fraud and legitimate transactions, it becomes challenging to determine credit frauds with effective performance. To address the aforementioned issues, the current work has presented a novel hybrid Big Bang-Big crunch with cuckoo search (HB(3)C(2)S) method for feature selection prior to performing the classification process. Here, both the Big Bang-Big crunch (BB-BC) and cuckoo search (CS) are metaheuristic algorithms, with BB-BC being a physics-inspired algorithm derived from the theory of universe evolution and CS being an inspiration from the cuckoo bird's brood parasitism behavior. In the HB(3)C(2)S method, the BB-BC algorithm is utilized for exploiting the solution space locally and CS to explore the solutions globally. Here, the CS algorithm uses the Levy flight attribute to help the BB-BC agents escape from stagnation and premature convergence. After feature selection, classification is performed using Deep Convolutional Neural Networks (DCNN) and Enhanced DCNN (EDCNN) to improve detection accuracy. The efficacy of the proposed framework is accessed through experiments conducted on the ECC (European Credit Cardholders) dataset. The HB(3)C(2)S-based system achieves 94.59% accuracy with DCNN and 95.61% with EDCNN, outperforming individual BB-BC and CS feature selection techniques. The experimental evaluations also confirm the efficacy of the proposed framework to detect credit card frauds, surpassing state-of-the-art approaches.