In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

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.

Machine learning (ML) uses algorithms with the complexity to iterate over massive datasets to analyse the data for past behaviour with the aim to predict future outcomes. Financial institutions are using ML to detect Credit Card Fraud (CCF) by learning the patterns that distinguish between legitimate and fraudulent actions from historic data of credit card transactions to combat CCF. The market economic order has been negatively affected by CCF, which has contributed to low consumer confidence in financial institutions, and loss of interest from investors. The CCF loses continue increasing every year despite existing efforts to prevent fraud, which amount to billions of dollars lost annually. ML techniques consume large volumes of historical credit card transaction data as examples for learning. In ordinary credit card datasets, there are far fewer fraudulent transactions than legitimate transactions. In dealing with the credit card data imbalance problem, the ideal solution must have low bias, low variance, and high accuracy. The aim of this study was to provide an in-depth experimental investigation of the effect of using the data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The study focused on finding a novel way to handle imbalanced data, to improve the performance of ML algorithms in identifying fraud or anomaly patterns in massive amounts of financial transaction records, where the class distribution was imbalanced. The experiment led to the introduction of two unique multi-level hybrid data-point approach solutions, namely, Feature Selection with Near Miss Undersampling; and Feature Selection with SMOTe based Oversampling. The results were obtained using four widely used ML algorithms, namely, Random Forest, Support Vector Machine, Decision Tree, and Logistic Regression to build the classifiers. These algorithms were implemented for classification of credit card datasets and the performance was assessed using selected performance metrics. The findings show that using the data-point approach improved the predictive accuracy of the ML fraud detection solution.