An empirical study of AML approach for credit card fraud detection-financial transactions

Abstract

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets. The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system. This study has also covered the deep analysis and comparison of the performance (i. e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area. The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently. This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.