A Convolutional Neural Network model for Credit Card Fraud detection

• Main Authors: Gambo, Muhammad Liman, Zainal, Anazida, Kassim, Mohamad Nizam
• Format: Conference or Workshop Item
• Published: 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/98908/; http://dx.doi.org/10.1109/ICoDSA55874.2022.9862930

Nowadays, online transactions through various ecommerce platforms are becoming more prevalent, and Credit Card (CC) is significantly used in various online transactions. However, Credit Card Fraud (CCF) strategies continue to evolve with the business transformation, causing customers as well as the financial institutions to lose billions of dollars annually. Hence, effective detection of fraudulent transactions initiated by fraudsters from the voluminous array of normal transactions is ever necessary. Hence, a Convolutional Neural Network (CNN) model for credit card fraud detection is proposed in this study using Adaptive Synthetic (ADASYN) sampling technique to address the imbalance dataset. The proposed model has achieved 0.9982, 0.9965, and 0.9999, accuracy, precision, and recall, respectively compared to other existing studies.