An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud Detection

Abstract

cybercrime, as fraudsters always find ways to breach security measures and access Keywords: customers' confidential information, which they then use to make fraudulent credit card Credit Card Fraud transactions. As a result, financial institutions incur huge losses amounting to billions of Machine learning United States dollars. To avert such losses, it is important to design efficient credit card Concept drift fraud detection algorithms capable of generating accurate alerts. Recently, machine Ensemble selection learning algorithms such as ensemble classifiers have emerged as the most effective and Class imbalanceefficient algorithms in an effort to assist fraud investigators. There are many factors that hinder the financial sector from designing machine learning algorithms that can efficiently and effectively detect credit card fraud. Such factors include the non-stationarity of data related to concept drift. In addition, class distributions are extremely imbalanced, while there is scant information on transactions that would have been flagged by fraud investigators. This can be attributed to the fact that, owing to confidentiality regulations, it is difficult to access public data. In this article, the author designs and assesses a credit card fraud detection system that can adapt to the changes in data distribution and generate accurate alerts.