Use of Machine Learning in Credit Card Fraud Detection

Credit card fraud is a growing concern, and it poses a significant threat as individual information is being misused and causing a substantial monetary loss. Hence, the prevention of credit card fraud is crucial. Credit card fraud detection is used to differentiate the transactions, either as legitimate or fraudulent. Recently, different machine learning techniques have been implemented to detect credit card fraud. However, the main challenge with fraud detection is that the credit card data is highly skewed, with the fraudulent transactions as less as 1% of the total data. This study investigates the performance of the four supervised machine learning algorithms: logistic regression, support vector machine, decision tree, and random forest, along with different sampling techniques to better understand the fraud detection attributes and performance measures associated with it. This review is also concentrated on exploring different works where the model has a better value for all of the performance evaluation metrics: Recall, precision, F1-score, accuracy, MCC, AUC, and area under the precision-recall curve. This will detect credit card fraudulent transactions better and control credit card fraud.

Keywords: Decision tree, Logistic regression, Random undersampling technique, Random forest, Random oversampling technique, Supervised machine learning algorithms, SVM, SMOTE.