Modelling highly imbalanced credit card fraud detection data using statistical learning

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Credit card fraud is a major concern for businesses worldwide, yielding losses of up to $67 billion per year in major banks and institutions. Machine learning techniques used to detect fraudulent transactions face several challenges when dealing with highly imbalanced data, which is often the case with fraud detection. Whilst different sampling techniques are generally used to reduce the imbalance, minimal studies have focussed on the effect the level imbalance has on the predictive capabilities of various statistical learning techniques. This study investigates the effect of three factors on model performance: 1) sampling technique, 2) supervised learning method, and 3) prevalence rate, also known as imbalance ratio (IR), which refers to the proportion of majority class samples compared to that of the minority class. Three sampling techniques are utilised in the study: Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), and Random Undersampling (RUS). These methods are used to create varying levels of imbalance in the datatset, at the prevalence rates of 0.2%, 1%, 10%, 20%, 30%, 40%, and 50%. Six supervised learning models are then used to identify fraudulent transactions: Logistic Regression (LR), C4.5 Decision Trees (DT), Random Forests (RF), XGBoost, and Neural Network (NN) models. Precision, recall and F2 score are the primary metrics used to assess model performance. The results suggest that the ROS and SMOTE sampling techniques performed best in terms of F2 score. The best supervised learning models are RF and XGBoost. The tree models were generally well suited to the imbalanced dataset, whilst LR performed the worst, even when applying regularisation. Increasing the prevalence rate surprisingly yielded a decrease in performance. The findings from the experiments can serve as a foundation for selecting the best sampling technique and supervised learning models to utilize with various degrees of dataset imbalance.