Credit Card Fraud Detection System using Machine Learning Algorithms and Fuzzy Membership

Fraudulent transactions have skyrocketed in tandem with the rise in Credit Card users. Since legitimate and fraudulent transactions look similar, it is nearly impossible to tell one from the other. This paper proposes a fraud detection system that uses Machine Learning (ML) and a fuzzy membership function to identify fraudulent transactions. The ML techniques used were Logistic regression (LR), Linear Discriminant Analysis (LDA), and the boosting algorithm XGBoost to create models for the proposed system. The dataset from Kaggle was used for training and testing these models. Many performance metrics were used to evaluate the proposed system models' efficiency: confusion matrix, accuracy, precision, f1, recall, and AUC. The results showed the superiority of the XGBoost model over the other models.