Detection of Credit Card Fraud using Machine Learning Techniques

Abstract

Due to the rapid advancement of digital payment tools, the number of credit card fraud cases is increasing at a faster rate, posing a greater risk to users and financial institutions and highlighting the flaws of traditional rule-based preventive measures. The greatest challenge in this area is that transaction data is extremely unbalanced, with cases of fraud representing only a small portion of the total, and transaction fraudsters continuously develop new methods to overcome specific limits. This paper presents a comparison of popular supervised machine learning models used for detecting credit card fraud. A significant data set of transactions was treated with logistic regression (LR), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN), with imbalance-holding methods, undersampling and oversampling to provide a fair judgment. These findings suggest that the ensemble models, in general, RF, were able to outperform individual classifiers in the sense of higher rates of detecting fraud, with low rates of false positives, resulting in high sensitivity and specificity even in highly biased statistics. Altogether, the discussion shows that ML-driven systems are much more versatile and reliable compared to conventional strategies and provide financial organisations with the means of minimising risks of fraud and protecting payment systems.