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.

Expediency and suppleness were the main reasons for customers’ patronage of m-banking apps. However, data stored or transmitted in these apps are susceptible to different attacks, threats, and risks. Thus, the need for robust safety mechanisms to cope with these security and privacy challenges. The purpose of this research is to examine the different components of m-banking security that merit investigation, and the vulnerability of present authentication methods in order to propose a more robust verification technique. PRISMA preferred items reporting for Systematic Review and Meta-Analyses approach was used in this study. Six databases were utilized; IEEE-Explore, Scopus, EBSCOhost, Taylor & Francis, ScienceDirect, and Web of Science. About 1,149 articles were extracted from these databases out of which 38 articles met the review selection criteria, thus included in the review. Findings of the study highlight the efficacy of PRISMA method with regard to items reporting and identification of research gaps compared to the usual literature review. Also, the results of the study found intrusion via other apps stored on mobile devices, and device lost or theft were the main safety and privacy issues. Furthermore, the study findings discovered that the present authentication schemes used by banks are becoming weak and open to various attacks due to an increase in online fraud. Based on the review findings, an Artificial Intelligence-based user authentication and anomalies detection model was proposed which may provide direction for upcoming studies. Also, banks and other financial institutions can use the review results to improve m-banking security.