A New Approach to Customer Classification According to a Hybrid Non-linear Bayesian and Quantum Approach
الموضوعات :Nazanin Kashani Nikoo 1 , Mahnaz Rabiei 2 , Kiamars Fathi 3
1 - Ph.D. student, Department of Information Technology Management, South Tehran Branch, Islamic Azad University, Tehran, Iran,
2 - Department of Economics, Modeling and Optimization Research Center in Engineering Sciences, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
الکلمات المفتاحية: Banking services, Customer relationship management, Fuzzy clustering and data mining, Quantum.,
ملخص المقالة :
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.
Kalaivani, D., & Sumathi, P. (2019). Factor based prediction model for customer behavior analysis. International Journal of System Assurance Engineering and Management, 10(4): 519-524.
KhatamiFirouzabadi, M. A., TaghaviFard, M., Sajjadi, K., & Bamdad Soufi, J. (2018). A Multi-objective Model of Services Assignment to Bank Customers by Data Mining and Optimization via Simulation. Business Intelligence Management Studies, 7(25): 5-40.
Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Research in International Business and Finance, 62(1): 101744-60.
Safabakhsh, M., & Asayesh, F. (2023). Segmentation of Bank Customers based on Customer Lifetime Value and Their Profit Ability (Case Study: Customers of Entrepreneur Bank). Quarterly Studies in Banking Management and Islamic Banking, 8(19): 53-80.
Kinge, A., Oswal, Y., Khangal, T., Kulkarni, N., & Jha, P. (2022). Comparative study on different classification models for customer churn problem. In Machine Intelligence and Smart Systems: Proceedings of MISS 2021. Singapore: Springer Nature Singapore.
Vidal, A., & Kristjanpoller, W. (2020). Gold volatility prediction using a CNN-LSTM approach. Expert Systems with Applications, 157(1): 113481.
Theodoridis, G., & Tsadiras, A. (2022). Applying machine learning techniques to predict and explain subscriber churn of an online drug information platform. Neural Computing and Applications, 34(22): 19501-19514.
Zoynul Abedin, M., Hajek, P., Sharif, T., Satu, M. S., & Khan, M. I. (2023). Modelling bank customer behaviour using feature engineering and classification techniques. Research in International Business and Finance, 65(1): 101913-20.
Liu, Y., Yang, M., Wang, Y., Li, Y., Xiong, T., & Li, A. (2022). Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China. International Review of Financial Analysis, 79(1): 101971.
Yuan, K., Chi, G., Zhou, Y., & Yin, H. (2022). A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description. Research in International Business and Finance, 59(1): 101536-49.
Ramezani, F., Shokravi, S., & Bagheri, A. (2024). Investigating the Impact of Natural Resource Rent and Political Stability on the Environmental Degradation Index in Selected Developing Countries Using a Combined Data Approach. Agricultural Marketing and Commercialization, 8(1): 93-108.
Noei, M. H. A., & Khoshsima, R. (2023). Identifying possible and superior marketing strategies of Arad Rastin Adib Dana Company and testing the effectiveness of superior strategies on its profitability based on the Quantitative Strategic Planning Matrix (QSPM) approach. Agricultural Marketing and Commercialization, 7(2): 43-60.
De Caigny, A., Coussement, K., De Bock, K. W., & Lessmann, S. (2020). Incorporating textual information in customer churn prediction models based on a convolutional neural network. International Journal of Forecasting, 36(4): 1563-1578.
Jain, H., Yadav, G., & Manoov, R. (2020). Churn prediction and retention in banking, telecom and IT sectors using machine learning techniques. In Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019. Singapore: Springer Singapore.
Chen, C., Geng, L., & Zhou, S. (2021). Design and implementation of bank CRM system based on decision tree algorithm. Neural Computing and Applications, 33(1): 8237-8247.
Alam, N., Gao, J., & Jones, S. (2021). Corporate failure prediction: An evaluation of deep learning vs discrete hazard models. Journal of International Financial Markets, Institutions and Money, 75(1): 101455-69.
Clerkin, N., & Hanson, A. (2021). Debit card incentives and consumer behavior: evidence using natural experiment methods. Journal of Financial Services Research, 60(1): 135-155.
Berggrun, L., Salamanca, J., Díaz, J., & Ospina, J. D. (2020). Profitability and money propagation in communities of bank clients: A visual analytics approach. Finance Research Letters, 37(1): 101387-98.
Long, W., Lu, Z., & Cui, L. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164(1): 163-173.
Abedin, M. Z., Chi, G., Uddin, M. M., Satu, M. S., Khan, M. I., & Hajek, P. (2020). Tax default prediction using feature transformation-based machine learning. IEEE Access, 9(1): 19864-19881.
Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information Sciences, 557(1): 302-316.
Keramati, A., Ghaneei, H., & Mirmohammadi, S. M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financial Innovation, 2(1): 1-13.
Wojnarski, M. (2002). Modeling the Bank Client’s Behavior with LTF-C Neural Network. Institute of Informatics, Warsaw University.
Abbasimehr, H., & Shabani, M. (2021). A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers. Kybernetes, 50(2): 221-242.
Baumann, C., Burton, S., & Elliott, G. (2007). Predicting consumer behavior in retail banking. Journal of Business and Management, 13(1): 79-96.
Noshabadi, S. M. K., Najafabadi, M. O., & Mirdamadi, M. (2023). Factors affecting the use of climate-smart agricultural technologies among wheat farmers in Alborz province with a planned behavior approach. Agricultural Marketing and Commercialization, 7(2): 156-170.
Ghabouli, N., Bafandeh Zendeh, A., & Aali, S. (2023). Detection of Knowledge Governing on Demographic Characteristics of Customers in Selecting Banks by through using Associative Rules in Data Mining. Journal of Modern Management Engineering, 9(3): 96-121.
Kashani Kikoo, N., Rabiei, M., & Fathi Hafshejani K. (2024). Designing a model for banking service allocation. Iranian journal of management sciences. Doi:100/jiams2024.8845.7732
Charte, D., Charte, F., & Herrera, F. (2021). Reducing data complexity using autoencoders with class-informed loss functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12): 9549-9560.
Zadeh, L. A., & Aliev, R. A. (2018). Fuzzy logic theory and applications: part I and part II. World Scientific Publishing.
Lee, R. S. (2020). Quantum finance. Springer Singapore.
