Advancement in Customer Attrition Prediction: Design of Optimal Triple Refined Indeterminate Neutrosophic Sets in Large-Scale Financial Sectors

Bunyodbek Sultonov^1,*, Dilrabo Akhmedova², Hamdam Matyaqubov³, Natalia Falina⁴, K. Shankar⁵

¹Institute of Economics and Management, Herzen State Pedagogical University of Russia, Saint Petersburg, 191186, Russia

²Department of Economics, Mamun University, Khiva, 220900, Uzbekistan

³Department of History, Urgench State University, Urgench, 220100, Uzbekistan

⁴Finance Department, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, 350044, Russia

⁵Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India

Emails: sultanbuned2000@gmail.com;  axmedova_dilrabo@mamunedu.uz;  matyaqubov.h@mail.ru;  falinanv@mymail.academy;  drkshankar@ieee.org

Abstract

Background: Neutrosophy is the subject area of philosophy that researches all associated with neutralities, owing to the contradictory information, lack of information, imprecise and paradoxical information, among them. The scale's design is organized to take the subjective quality of opinion, being responsible for either uncertainty or the indeterminacy of the respondents' opinions. It relies on the triple refined indeterminate neutrosophic sets (NS) for improved accuracy in understanding the agreement or disagreement level on particular items, like the competence of activities cost and financial management inside the legal services. Currently, customer abrasion is more and more serious in commercial banks, mainly, high-valued customers in retail banking. Therefore, it is stimulated to advance a prediction mechanism and recognize this customer may be at attrition risk. Thus, recognizing and lowering customer churn has become important for financial institutions trying to maintain customers. Currently, several researchers concentrate on customer attrition rate studies utilizing sophisticated machine learning (ML) and deep learning (DL) methods. Methodology: This study develops a Customer Attrition Prediction Using Triple Refined Indeterminate Neutrosophic Sets with an Optimization Algorithm (CAP-TRINSOA) technique. The main aim of the CAP-TRINSOA technique is to improve the attrition prediction of a customer in large-scale financial sectors using state-of-the-art techniques. In the initial stage, the data normalization employs mean normalization to transfer input data into an even format. Furthermore, the classification process is performed by implementing the triple refined indeterminate neutrosophic sets (TRINS). Finally, the honey badger algorithm (HBA) alters the parameter tuning value of the TRINS method optimally and results in greater performance of classification. Results: An extensive set of simulations is accomplished to exhibit the promising results of the CAP-TRINSOA method under the bank customer churn prediction dataset. The experimental validation of the CAP-TRINSOA technique portrayed a superior accuracy value of 97.65% over exisitng model in the customer attrition prediction process.

Keywords: Neutrosophy; Neutrosophic Sets; Triple Refined Indeterminate Neutrosophic Sets; Customer Attrition Prediction; Financial Sectors; Honey Badger Algorithm