Machine Learning Classification Based Techniques for Fraud Discovery in Credit Card Datasets

The frequency of credit card-based online payment frauds has increased rapidly in recent years, forcing banks and e-commerce companies to create automated fraud detection systems that perform mining on massive transaction logs. Machine learning appears to be one of the most promising techniques for detecting illegal transactions since it uses supervised binary classification algorithms appropriately trained using pre-screened sample datasets to differentiate between fraudulent and non-fraudulent cases. This study aims to concentrate on machine learning (ML) methods thereby proposing a credit card fraud discovery scheme to detect fraud. The ML techniques employed are Decision Tree (DT) and K-Nearest Neighbor (KNN) ML classification techniques. The performance outcomes of the two ML classification techniques are evaluated depending on accuracy, precision, specificity, recall, f1-score, and false-positive rate (FPR). The area under the ROC curve (AUC) of the receiver operating characteristics (ROC) curve was similarly drawn built on the confusion matrix for both classifiers. The two classification techniques were evaluated and compared using the performance metrics mentioned earlier and it was demonstrated that the KNN technique outperformed that of the DT with a greater ROC curve value of 91% for KNN and 86% for DT. It was concluded that KNN is considered a better ML classification technique that can be employed to discover credit card fraudulent activities.