Explaining the Factors Affecting Customer Satisfaction at the Fintech Firm F1 Soft by Using PCA and XAI

Mohan Khanal, Sudip Raj Khadka, Harendra Subedi, Indra Prasad Chaulagain, Lok Nath Regmi and Mohan Bhandari ()
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Mohan Khanal: Department of Management, Kantipur College of Management and Information Technology, Tribhuvan University, Mid-Baneshwor, Kathmandu 44600, Nepal
Sudip Raj Khadka: Department of Science and Technology, Samriddhi College, Tribhuvan University, Lokanthali, Bhaktapur 44800, Nepal
Harendra Subedi: Department of Management, Kantipur College of Management and Information Technology, Tribhuvan University, Mid-Baneshwor, Kathmandu 44600, Nepal
Indra Prasad Chaulagain: Department of ICTE, Mahendra Ratna Campus, Tribhuvan University, Tahachal, Kathmandu 44600, Nepal
Lok Nath Regmi: Department of Electronics and Computer Engineering, Institute of Engineering, Pulchowk Campus, Patan 44700, Nepal
Mohan Bhandari: Department of Science and Technology, Samriddhi College, Tribhuvan University, Lokanthali, Bhaktapur 44800, Nepal

FinTech, 2023, vol. 2, issue 1, 1-15

Abstract: The most significant and rapidly expanding fintech services in Nepal are provided by several fintech firms. Customer satisfaction must be compared side by side even if every organization has made an effort to expand the usage of services. Many studies have concentrated on evaluating the impact of various factors on customer satisfaction, but significantly fewer studies have been conducted to explore the factors and focus of machine learning. Based on the planned behavioural theory (TPB), the study is concentrated on exploring and evaluating customer satisfaction on a different stimulus offered by F1 Soft (a fintech firm in nepal), customers’ loyalty and the compatibility they gain through the company’s services. By exploring various factors affecting customer satisfaction by using principal component analysis (PCA) and explainable AI (XAI), the study explored the eight factors (customer service, compatibility, ease of use, assurance, loyalty intention, technology perception, speed and firm’s innovativeness) which affect customer satisfaction individually. Furthermore, by using support vector machine (SVM) and logistic regression (LR), the major contributing factors are explained with local interpretable model-agnostic explanation (LIME) and Shapley additive explanations (SHAP). SVM holds the training accuracy of 89.13% whereas LR achieves 87.88%, and both algorithms show that compatibilty issues consider the major contributing factor for customer satisfaction. Contributing toward different dimensions, determinants, and the results of customer satisfaction in fintech, the study suggests how fintech companies must integrate factors affecting customer satisfaction in their system for further process development.

Keywords: explainable AI; financial technology; firm innovativeness; LIME; machine learning; mobile payment service; PCA; SHAP (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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