Enhanced Detection of Credit Card Fraud Using Machine Learning Techniques

Abstract: As the number of credit card transactions continues to grow, they represent an increasing share of the global payment system. This growth has led to an increase in stolen account numbers and subsequent losses to banks. Machine Learning (ML) plays a crucial role in detecting credit card fraud in both online and offline transactions. Credit card fraud detection, which is a data mining problem, becomes challenging for two main reasons: first, the characteristics of normal and fraudulent behavior are continually changing, and second, the credit card fraud dataset is highly asymmetric. This study proposes an ensemble approach for accurately detecting credit card fraud transactions based on various ML algorithms and compares the performance of each algorithm with the proposed model. The results indicate that the proposed Ensemble model without SMOTE (Synthetic Minority Over-sampling Technique) outperforms all other methods, achieving a 99.94% accuracy rate..