No abstract

The main challenge of data mining approaches to detect fraud in financial transaction data is the imbalance of data classes in available datasets, with a much smaller fraud class proportion than the non-fraud. This imbalance affects the f1-score to be low due to unbalanced precision and recall. Therefore, the model can predict one class well, but it does not apply to another class. In addition, the lengthy training time duration and high computational resource requirements in implementing data mining also make them a particular concern. Therefore, solely handling imbalanced data is still insufficient to produce the expected performance. Reduction of data dimensions can be a solution to increase the speed of the process. However, this method actually reduces the classifier’s performance when it comes to classification. Furthermore, this study intends to improve the performance of the data mining approach based on the Support Vector Machine (SVM) classifier aiming at detecting financial fraud transactions. The SVM performance was refined by tuning the kernel and hyperparameter integrated with the Random Under Sampling (RUS) and our Minimum error-based Principal Component Analysis (MebPCA). The RUS was used to handle imbalanced data, while MebPCA modified data dimension reduction techniques based on classification errors to speed up computational time without disturbing the performance of SVM. This combination improves the classifier's performance in detecting fraud effectively with a precision improvement of 29.31% and f1-score of 19.8%, and efficiently reduces the duration of training time significantly by 36.39% compared to previous research regarding the SVM method for fraud detection.