Predicting Satisfaction of Online Banking System in Bangladesh by Machine Learning

Shetu, Syeda Farjana; Jahan, Israt; Islam, Mohammad Monirul; Hossain, Refath Ara; Moon, Nazmun Nessa; Nur, Fernaz Narin

doi:10.1109/icaicst53116.2021.9497796

Cited by 16 publications

(4 citation statements)

References 10 publications

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“…If a user has faith in the payment system, then they will have no choice but to use the technology. Research by [54] confirms that such an outlook favourably affects the intent to adopt electronic payment.…”

Section: Attitudementioning

confidence: 77%

Consumer Adoption of Mobile Payment in the Kingdom of Saudi Arabia an Empirical Study

Ali,

Khan,

Nabi

et al. 2024

J. of Law and Sust. Develop.

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Objective: The rapid stride in mobile payment adoption has transformed the payment practices in the 21st Century. Admittedly, m-payment practice has become more intense due to the surge in usage of internet, mobile devices (smart phones), accelerated further by the spread of the Covid-19 Pandemic. Against such a backdrop, it is the need of the hour to investigate: Why are people starting to use mobile payments more and how will that affect their purchasing habits? The study's intention is to add to what's already been written on electronic banking and mobile banking specifically. The study provides a report on m-banking's development, assesses the potential for its adoption, and identifies potential avenues for expansion in Saudi Arabia. They aid Saudi Arabian banking decision-makers in locating and developing strategies for supplying m-banking services that will help them keep their competitive edge. Methods: While electronically distributed surveys can reach more people more quickly and for less money, this method also has the potential to introduce bias into the sample by excluding people who don't have access to the internet. In addition, the data acquired from the respondents in many mobile payment and mobile banking studies can enhance selection bias. Researchers used both paper-print and online survey methods to disseminate survey instruments, reducing selection bias and increasing response rates. Other potential sources of common method bias (CMB) include the retrospective nature of the survey, the use of a commonly used scale format for constructs, and the fact that respondents were asked to provide information about both the independent and dependent variables in the same question. As a result, SEM has emerged as the gold standard in the field of theory testing, especially as it pertains to studies of IS/IT adoption. Statistical modelling with structural equation modelling (SEM) was chosen as the advanced inferential analytic technique for this study. Aim: The present study also aims to identify the perceived risk, trust, self-efficacy, past experience, attitude, anxiety, and customer support associated with the mobile payment. Lastly, this study will be a path-breaking one because of its intended contribution to the policy-makers and to the society at large in the Kingdom of Saudi Arabia. Needless to say, outcome of the study was providing managerial inputs to various mobile payment platforms. Results: Consumers' intent to utilize mobile banking was strongly correlated with their level of trust and Application of Mobile Banking. In similar way Self-efficacy directly and positively related to the consumer intention towards mobile banking adoption. In the same ways, past experience directly and positively related to the consumer intention towards mobile banking adoption. In the same way, there is a positive and direct relationship between customer intention and the use of mobile banking. Lack of a causal relationship between attitude and consumer desire to use mobile banking. Perceived risk not directly and positively related to the consumer intention towards mobile banking adoption Anxiety not directly and positively related to the consumer intention towards mobile banking adoption. Conclusion: This study intends to identify the various drivers of mobile payment adoption by examining how consumers' spending habits have changed in response to the popularity of mobile payment systems and the relationship between various payment mechanisms. The study's primary aim is to establish precisely the point when mobile payments will become the norm can replace debit and credit card transactions. Besides transaction activities, online payment channels are enhancing mobile payment adoption and lead to increase in the online transactions, local penetration further escalating increased transaction activities.

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Section: Attitudementioning

confidence: 77%

Consumer Adoption of Mobile Payment in the Kingdom of Saudi Arabia an Empirical Study

Ali,

Khan,

Nabi

et al. 2024

J. of Law and Sust. Develop.

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“…Additionally, several classifier models were applied to the dataset, and Random Forest (RF) performance takes first place with the highest accuracy rate (91.53%) for predicting Bangladesh's internet user satisfaction levels [55]. In the research of Shetu et al [56], the results show that when predicting customer satisfaction with Bangladesh's online banking system, three algorithms-KNN, Logistic Regression, and Random Forest achieved the best accuracy of 96%. Precisions are 98%, recall is 96%, and F1-score is 97% for these three models.…”

Section: Discussionmentioning

confidence: 99%

Prediction of US airline passenger satisfaction using machine learning algorithms

Hong

Khaw

Chew

et al. 2023

DAAM

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Due to the COVID-19 pandemic, the U.S. financial system and economy have also been severely affected. The U.S. airline industry has been hit particularly hard by the COVID-19 pandemic. Additionally, the aviation industry is also full of competition. One of the ways to attract customers and compete with other airline companies is by improving their service quality. Therefore, this study aims to predict the satisfaction of airlines based on the machine learning model and discover which features are more correlated with the target variable. In this study, the dataset consists of 129,880 observations and 1 target, 22 features or attributes (not including the identification). In this study, the result showed that the features that slightly correlate more with customer satisfaction are 'Online boarding', 'Inflight entertainment', 'Seat comfort', 'On-board service', 'Leg room service', 'Cleanliness', 'Flight Distance' and 'Inflight wifi service'. Then, K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB) and AdaBoost were used to build the classification models. Data cleaning, exploratory data analysis, feature selection and One Hot Encoding were also performed before building the models. Finally, the models were evaluated based on their accuracy, precision, recall and F1-score. The results suggest that the champion model for this study is Random Forest, which achieved 89.20% accuracy, 93.04% precision and 88.80% F1-score. The results of this study can be used as a guide in applying machine learning to predict the satisfaction of airline passengers. This can also contribute to attracting passengers by improving the airline service quality.

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“…To implement this unique model, some papers have been studied. All of them are described below as per the research paper's theme: Syeda et al [34] applied seven approaches of data mining i.e. KNN, Decision Tree, SVM, NN, Naive Bayes, Logistic Regression, and Random Forest to predict user satisfaction and dissatisfaction.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the Level of Safety Feeling of Bangladeshi Internet users using Data Mining and Machine Learning

Alam,

Roy,

Majumder

et al. 2023

IJACSA

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An amazing combination of cutting-edge data mining and machine learning methodologies to predict the level of safety feeling among Bangladeshi internet users, which is a significant departure in this subject. By leveraging cutting-edge algorithms and innovative data sources, this work provides previously unheard-of insights into how this demographic perceives online safety, shedding light on an essential yet underappreciated aspect of their digital lives. This exceptional study's original research increases the body of knowledge of online safety and sets the road for policy recommendations and intervention tactics that will enable Bangladesh to become a global leader in internet security.

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Predicting Satisfaction of Online Banking System in Bangladesh by Machine Learning

Cited by 16 publications

References 10 publications

Consumer Adoption of Mobile Payment in the Kingdom of Saudi Arabia an Empirical Study

Consumer Adoption of Mobile Payment in the Kingdom of Saudi Arabia an Empirical Study

Prediction of US airline passenger satisfaction using machine learning algorithms

Predicting the Level of Safety Feeling of Bangladeshi Internet users using Data Mining and Machine Learning

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