Benefiting Machine Learning Methods to Detect Fraud in the Validation of Bank Customers' Cards

Document Type : Original Article

Authors

1 Department of Computer, Faculty of Engineering, Aghigh University, Shahin shahr, Isfahan, Iran.

2 Department of Computer, Naein Branch, Islamic Azad University, Naein, Isfahan, Iran

10.22034/jbr.2021.284617.1039

Abstract

The existence of money laundering and banking fraud is one of the major challenges of the banking system in any country. Crediting customers based on their track record and performance is a method to address the banking challenges. Classification methods can be used to validate customers, but these methods own high error. In this paper, machine learning methods are applied on banking data set to classify them and to reduce the error in customer validation. To this end, first the machine learning methods are trained and then tested using the banking data set. Experiments on the banking data set show that the accuracy of the proposed method for validating customers is less than 81.6%. So, the accuracy index of the random forest, decision tree, support vector machine, and multilayer artificial neural network are 80.50\%, 80.05\%, 80.93\%, and 81.58\%, respectively. The best performance is related to multilayer artificial neural network and, accordingly, the multilayer artificial neural network method can be used in detection of the validation of bank customers' cards.

Graphical Abstract

Benefiting Machine Learning Methods to Detect Fraud in the Validation of Bank Customers' Cards

Keywords


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