An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps

Abstract

Nowadays, mobile banking apps are becoming an integral part of people lives due to its suppleness and convenience. Despite these benefits, yet its growth in evolving states is beyond expectations. However, using mobiles devices to conduct financial transactions involved a lot of risk. This paper aims to investigate customers’ reasons for non-usage of the new conduits in developing countries with distinct interest in Nigeria. The study adopts two methods of analysis, artificial intelligence-based methods (AI), and structural equations modeling (SEM). A feed-forward neural network (FFNN) sensitivity examination technique was used to choose the most dominant parameters of mobile banking data collected from 823 respondents. Four algebraic directories were used to corroborate the study AI-based model. The study AI results found risk, trust, facilitating conditions, and inadequate digital laws to be the most dominant parameters that affect mobile banking growth in Nigeria, and discovered social influence and service quality to have no influence on Nigerians’ resolve to use moveable banking apps. Moreover, the results proved the superiority of AI-based models above the classical models. Government and pecuniary institutes can use the study outcomes to ensure secured services offering, and improve growth. Finally, the study suggests some areas for future studies.

Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks

“…The EANN was trained as a predictor in the SVR-EANN hybrid model to achieve superior results for BFP prediction without considering the least significant factor determined by the SVR model. The age attribute was removed for SVR-EANN during the training, and the obtained results of the proposed SVR-EANN hybrid model were compared to the results of other benchmark models: feed-forward NN [36], SVR [29], DT [37], RF [38], LR [39], XGBoost [40], and GradBoost [41].…”

“…Support vector machines were initially proposed for classification tasks [28] and implemented successfully in recent studies [29]; however, the model was modified to accept real-valued data and to be implemented for regression problems. The differentiation of support vector regression (SVR) from other machine learning models is the projection of data into another dimension using different kernels and considering the data points of projected kernels, not directly the data.…”

Evaluating the Role of Artificial Intelligence in the Automation of the Banking Services Industry: Evidence from Jordan

“…Mobile banking facilitates the services of banks for customers as compare to their physical visits to branches; so it results in increasing banks' revenues. That's why banking sector is paying close attention to the adaptation and implementation of emerging technologies in an attempt to improve the quality of services and to be competitive in the market (Cavus, Mohammed, & Yakubu, 2021).…”

“…Banks have to incur huge amounts in term of operational costs due to human based processes which are mostly based on heavy paper work, also incorporating the chances for human errors (Cavus et al, 2021). A few banks implement software like RPA (Robotic process automation) that imitates rules based digital tasks performed by humans.…”

“…Therefore, it is evident that the implementation of AI based systems contributes successfully in the enhancement and up gradation of banking services, which would ultimately improve the quality of banking services as well as generate financial benefits for the banking industry. Studies including (Cavus et al, 2021;Kaur et al, 2020;Yu & Song, 2020) stated that the adoption and implementation of new technologies, such as AI helps bank in improving the quality of their services, reducing their costs and ultimately, improving revenues. Furthermore, by relying on the aforementioned findings, the study concludes that employees as well as clients of Jordanian banks have similar perspectives regarding the central role of AI in the enhancement of Jordanian banking system.…”