An efficient fraud detection framework with credit card imbalanced data in financial services

Abstract

Credit card fraud has adversely impacted market economic order and has broken stakeholders, financial entities, and consumers’ trust and interest. Card fraud losses are increasing annually and billions of dollars are being lost. Therefore, this work provides a framework for fraud card detection to be tackled efficiently. Recently, the imbalanced dataset for fraud card transactions due to the number of ordinary transactions being far greater than the amount of fraud. Before solving the fraud problem, we first have to solve the imbalanced data problem which occurred when one class considerably outnumbers the examples of the other class. So, the classification of fraud come to be very tough as the result may get biased towards the majority group. Thus, this paper aims firstly to use hybrid sampling and oversampling preprocessing techniques to solve the imbalanced data problem, and secondly to resolve the fraud. The performance of the proposed framework is estimated based on different metrics accuracy, precision, and recall in comparing existing algorithms such as KNN, LR, LDA, NB, and CART. The obtained results revealing that when the data is highly imbalanced, the model strives to detect fraudulent transactions. Besides, it can predict positive classes improved significantly, reaching an accuracy of 99.9.