Model for forecasting electronic fraud threats on selected electronic payment channels using linear regression

Electronic fraud is a problem that has become a source of concern for businesses of all sizes. Electronic fraud is increasing the margin of loss as criminals go beyond brick and mortar enterprises to target firms with an online presence and electronic payment methods. The purpose of this research paper is to propose a model which decision makers can use to anticipate threats, provide preventive measures and calculate percentage gain in income following execution of the preventive measures. This will assist in safeguarding businesses and consumers from electronic fraud while using selected electronic payment channels. The model will help minimize e-payment fraud and increase customer adoption of electronic payments. The data used was obtained from the Central Bank of Nigeria. The models output offers decision makers in banks and financial technology firms with historical data on the amount of e-payment fraud on each of the selected channels. The model also allows decision makers forecast cyber fraud on various e-payment channels. Similarly, the model offers methods for implementing preventive measures as well as a percentage gain in income following execution. These findings will help to minimize e-payment fraud and increase customer adoption of electronic payments. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.