A New Approach to Customer Classification According to a Hybrid Non-linear Bayesian and Quantum Approach

Abstract

The present study explains a customer classification model according to Bayesian-quantum approaches. This study is applied-exploratory research. This study investigated the information of 98,604 customers of one of Iran's banks. Four approaches were used including data mining, fuzzy, quantum, and nonlinear Bayesian averaging. In this study, information on 22 indicators related to customers was entered into nonlinear Bayesian models. According to the error rate, the BMA model had the highest accuracy. According to the results, four features including account balance, total deposit balance, total current facility balance, and volume of financial transactions were used as the primary features for customer classification. The results showed that the C-MEANS approach has higher accuracy than K-MEANS. Then, according to the C-MEANS approach, 16 clusters were identified and the characteristics of each 16 clusters were analyzed. Thus, the selected variables of the Bayesian averaging approach were used in estimating quantum models. According to the results, the harmonic oscillator approach had higher accuracy than the geometric Brownian motion and Heston approaches. The harmonic oscillator approach of the quantum model has high accuracy in all groups and has higher accuracy in the categories where customers are more loyal.