Vector Result Rate (VRR): A Novel Method for Fraud detection in mobile payment systems

Abstract

Mobile payment systems are becoming more popular due to the increasing number of smartphones, attracting fraudsters' attention. Therefore, existing researchers have developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available, and their detection performance is negatively affected by severe class imbalance in financial fraud data. This study aims to propose a new model entitled Vector Result Rate (VRR) for fraud detection based on deep learning while considering the economic consequences of fraud detection systems. The proposed framework is experimentally implemented on a large dataset containing more than six million mobile phone transactions. A comparative evaluation of existing machine learning methods designed to model unbalanced data and detect outliers is performed for the comparison. The results show that the VRR achieves the best results by integrating several classification algorithms with supervision and classifiers regarding standard classification criteria.

Keywords: Fraud detection, Machine learning, Deep learning, Unbalanced data.

DOI: 10.54941/ahfe1004641