Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems

¹ Associate Professor, Department of CSE – AIML, KG Reddy College of Engineering & Technology, Moinabad, Hyderabad, Telangana - 501504
² Associate Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Hyderabad
³ Assistant Professor, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore 21
⁴ Professor, Department of Computer Science and Engineering, IARE
⁵ Associate Professor, Rajeev Institute of Technology, Hassan, Karnataka
⁶ Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., Andhra Pradesh - 522302, India
⁷ Computer Science Engineer, Oregon State University, Corvallis, Oregon, USA 97331
⁸ Department of IT, GRIET, Hyderabad, Telangana, India
⁹ Lovely Professional University, Phagwara, Punjab, India.

^* Corresponding author: drmaithili@kgr.ac.in

Abstract

Recent developments in e-commerce and e-payment systems have led to a rise in financial fraud incidents, particularly credit card fraud. Software tools to identify credit card theft are essential. Critical characteristics of credit card fraud are crucial in utilizing Machine Learning (ML) for credit card fraud identification and must be selected carefully. This study suggests a An Efficient Machine Learning Algorithm for Reliable Credit Card Fraud Identification (EMLA-RCCFI) was constructed using ML, which utilizes the Genetic Algorithm (GA) to select features. Once the optimum characteristics are determined, the suggested detecting module utilizes the subsequent ML-based classifications. The proposed EMLA-RCCFI system is assessed using a dataset produced by European cardholders to confirm its efficacy. Based on the results, the suggested EMLA-RCCFI method surpassed existing systems regarding accuracy, precision, and F score.