A SMOTe based Oversampling Data-Point Approach to Solving the Credit Card Data Imbalance Problem in Financial Fraud Detection

Abstract

Credit card fraud has negatively affected the market economic order, broken the confidence and interest of stakeholders, financial institutions, and consumers. Losses from card fraud is increasing every year with billions of dollars being lost. Machine Learning methods use large volumes of data as examples for learning to improve the performance of classification models. Financial institutions use Machine Learning to identify fraudulent patterns from the large amounts of historical financial records. However, the detection of credit card fraud remains as a significant challenge for business intelligence technologies as most datasets containing credit card transactions are highly imbalanced. To overcome this challenge, this paper proposed the use of the data-point approach in machine learning. An experimental study was conducted applying Oversampling with SMOTe, a data-point approach technique, on an imbalanced credit card dataset. State-of-the-art classical machine learning algorithms namely, Support Vector Machines, Logistic Regression, Decision Tree and Random Forest classifiers were used to perform the classifications and the accuracy was evaluated using precision, recall, F1-score, and the average precision metrics. The results show that if the data is highly imbalanced, the model struggles to detect fraudulent transactions. After using the SMOTe based Oversampling technique, there was a significant improvement to the ability to predict positive classes.