Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness

Nadire Cavus, Yakubu Bala Mohammed, Abdulsalam Ya’u Gital, Mohammed Bulama, Adamu Muhammad Tukur, Danlami Mohammed, Muhammad Lamir Isah and Abba Hassan
Additional contact information
Nadire Cavus: Department of Computer Information Systems, Near East University, Nicosia 99138, Cyprus
Yakubu Bala Mohammed: Department of Computer Information Systems, Near East University, Nicosia 99138, Cyprus
Abdulsalam Ya’u Gital: Department of Mathematics and Computer Science, Abubakar Tafawa Balewa University, Bauchi 0248, Nigeria
Mohammed Bulama: Department of Computer Science, Abubakar Tatari Ali Polytechnic, Bauchi 0094, Nigeria
Adamu Muhammad Tukur: Department of Computer Science, Abubakar Tatari Ali Polytechnic, Bauchi 0094, Nigeria
Danlami Mohammed: Department of Computer Science, Abubakar Tatari Ali Polytechnic, Bauchi 0094, Nigeria
Muhammad Lamir Isah: Department of Computer Science, Abubakar Tatari Ali Polytechnic, Bauchi 0094, Nigeria
Abba Hassan: Department of Software Engineering, Nigerian Army University Biu, Biu 1500, Nigeria

Sustainability, 2022, vol. 14, issue 10, 1-21

Abstract: With recent advances in mobile and internet technologies, the digital payment market is an increasingly integral part of people’s lives, offering many useful and interesting services, e.g., m-banking and cryptocurrency. The m-banking system allows users to pay for goods, services, and earn money via cryptotrading using any device such as mobile phones from anywhere. With the recent trends in global digital markets, especially the cryptocurrency market, m-banking is projected to have a brighter future. However, information stored or conveyed via these channels is more vulnerable to different security threats. Thus, the aim of this study is to examine the influence of security and confidentiality on m-banking patronage using artificial intelligence ensemble methods (ANFIS, GPR, EANN, and BRT) for the prediction of safety and secrecy effects. AI models were trained and tested using 745 datasets obtained from the study areas. The results indicated that AI models predicted the influence of security with high precision (NSE > 0.95), with the GPR model outperformed the other models. The results indicated that security and privacy were key influential parameters of m-payment system patronage (m-banking), followed by service and interface qualities. Unlike previous m-banking studies, the study results showed ease of use and culture to have no influence on m-banking patronage. These study results would assist m-payment system stakeholders, while the approach may serve as motivation for researchers to use AI techniques. The study also provides directions for future m-banking studies.

Keywords: m-banking; security; artificial intelligence; ensemble techniques; machine learning; privacy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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