Optimizing Machine Learning Classifiers for Credit Card Fraud Detection on Highly Imbalanced Datasets Using PCA and SMOTE Techniques

Abstract

Card fraud detection refers to the process of identifying unauthorized or suspicious transactions made using credit or debit cards. It employs machine learning models, rule-based systems, and anomaly detection techniques to detect patterns indicating potential fraud. There is a growing need for systems that can accurately predict and prevent fraudulent transactions. Reducing financial loss by Implementing advanced detection models to safeguard it from fraud or malicious transactions. Therefore, we proposed machine learning models that will predict credit card fraud at an early stage. Also, the study used feature scaling, Principal Component Analysis (PCA), and the Synthetic Minority Over-sampling Technique (SMOTE) to deal with the class imbalance on the dataset. Moreover, SMOTE is applied to balance the classes by synthesizing examples of the minority class, making classifiers more robust. The results show that LR, SVM, KNN, and XGBoost models correctly predict 97\% of fraudulent and non-fraudulent cases. The Decision Tree and the Random Forest models are capable of achieving at least 96\%, respectively. This research combines advanced machine learning methodologies with real-time processing to give insights into predictive analytics in financial fraud detection, which may enhance accuracy and efficiency in financial security systems.