CREDIT CARD FRAUD DETECTION USING LINEAR DISCRIMINANT ANALYSIS (LDA), RANDOM FOREST, AND BINARY LOGISTIC REGRESSION

The growth of electronic payment usage makes the monetary tension of credit-card deception is changing into major defiance for finance and technology companies. Therefore, pressuring them to continuously advance their fraud detection system is crucial. In this research, we describe fraud detection as a classification issue by comparing three methods. The method used is Linear Discriminant Analysis (LDA), Random Forest, and Binary Logistic Regression. The dataset used is a dataset containing transactions made by credit cards. The challenge in this analysis is that the dataset is highly unbalanced, so Synthetic Minority Oversampling Technique (SMOTE) must perform better on the data. The dataset contains only continuous features that are transformed into Principal Component Scores (PCs). The results show that the binary regression algorithm, the Random Forest algorithm, and the Linear Discriminant Analysis with variables that have SMOTE have Area Under Curve (AUC) values greater than using the original variables. The largest AUC value was obtained by binary logistic regression with 90:10 separation data and Random Forest Algorithm with 60:40 separation data.