Machine learning based Customer retention modeling in banking and Finance

Praneeth Reddy Amudala Puchakayala

Authors

Praneeth Reddy Amudala Puchakayala Data scientist, Regions Bank. Author

Keywords:

Banking, Artificial Intelligence, CRM, KPI, Machine Learning, Prediction

Abstract

In the banking sector, Customers who have been loyal to a bank for a long time don't plan to switch to a different provider. When a business interacts with a customer for the first time and throughout their entire relationship with the business, including any actions taken to keep them as a customer, this is called customer retention. Today's business world is very competitive, so keeping customers happy is very important. To meet customer standards, the bank must go above and beyond its basic tasks. Discussing the challenges and difficulties faced on the wealth management of customer retention. To reshape the financial sector, a game-changing shift in how wealth managers and institutions handle assets on Artificial intelligence (AI) in wealth management on financial environment. Mention the factors helps to contribute the customer retention based on KPI to monitor and regulate the activities. Based on customer retention, various churn prediction techniques to predict the Customer Relationship Management (CRM) policies to improve customer relationship management. To process, feature engineering is used to provide the variables needed for analysis or predictive modelling. Finally, ML plays a role to change the game for companies all over the world by making it possible to predict when customers will leave and comparative analysis is tabulated to discuss out the pros and cons.

References

Verbeke, W., Dejaeger, K., Martens, D., & Baesens, B. (2012). Newinsights into churn prediction in the telecommunication sector: Aprofit driven data mining approach. European Journal of Operational Research, 218(1), 211-229. doi: 10.1016/j.ejor.2011.10.025

Role of Big Data in Revolutionizing Health Management SystemsDOI: 10.5281/zenodo.10702606

Zhang, Z., & Wang, Z. (2016). Customer churn predictionintelecommunications using machine learning in big data platform. Proceedings of the 2016 IEEE International Conference on BigData(Big Data), 3423-3428. doi: 10.1109/BigData.2016.7841070

Telco Customer Churn https://www.kaggle.com/datasets/blastchar/telco-customer-churn[Accessed: Feb. 06, 2020].

Radcliffe, N. J., & Surry, P. D. (1999). Differential gain: Anewmeasure of prediction accuracy for rarely occurring orderedevents. International Journal of Forecasting, 15(3), 227-236. doi: 10.1016/S0169-2070(99)00018-5

Chaturvedi, A., & Dey, L. (2020). A comparative analysis of machinelearning algorithms for customer churn prediction in telecomindustry. Wireless Personal Communications, 112(4), 2807-2830. doi: 10.1007/s11277-020-07018-2

Nguyen, V. A., Dinh, D. H., Nguyen, V. D., & Nguyen, D. T. (2020). Customer churn prediction using machine learning techniques: Acaseof telecommunication companies in Vietnam. Proceedings of the2020 IEEE 10th International Conference on Communication Systems & Networks (ComSNetS), 124-130. doi: 10.1109/ComSNetS48635.2020.9025017

Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing, 12(1), 17-30. doi: 10.1002/(SICI)1520-6653(199824)12:13.0.CO;2-Q

Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: A systematic review and agenda for future research. ElectronicMarkets, 26(2), 173-194. doi: 10.1007/s12525-016-0219-0

Kadiyala, S. R., Chen, Z., & Jindal, P. (2016). Predicting customerchurn using machine learning techniques in the insurance industry. Proceedings of the 2016 IEEE International Conference onDataScience and Advanced Analytics (DSAA), 268-277. doi: 10.1109/DSAA.2016.51

Kim, Y., Choi, Y., Jeong, C., & Han, H. (2007). Ahybrid approachtocustomer churn prediction in the airline industry. Expert Systems withApplications, 32(2), 705-714. doi: 10.1016/j.eswa.2006.01.027

Liu, C., Li, C., & Li, S. (2015). Big data: Applications andopportunities in finance research. International Reviewof Financial Analysis, 40, 186-190. doi: 10.1016/j.irfa.2015.02.016

Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approachtopredict the success of bank telemarketing. Decision Support Systems, 62, 22-31. doi: 10.1016/j.dss.2014.03.001

Shmueli, G., & Koppius, O. R. (2011). Predictive analyticsininformation systems research. MIS Quarterly, 35(3), 553-572.

Thomas, R. K., & Krishnan, R. (2017). Does Big Data Analyticscontribute to customer relationship management performance?Ameta-analysis. International Journal of Information Management, 37(6), 1444-1454. doi: 10.1016/j.ijinfomgt.2017.08.003

Kumar, V., Yashoda Bhagwat, and Zhang Xi (2015), ‘‘Regaining ‘‘Lost’’ Customers: The Predictive Power of First-Lifetime Behavior, the Reason for Defection, and the Nature of the Win-Back Offer,’’ Journal of Marketing, 79 (4), 34-55.

Kumar, V. and Werner Reinartz (2012), Customer Relationship Management: Concept, Strategy, and Tools. Berlin, Germany: Springer

Keiningham, Timothy L., Forest V. Morgeson, Lerzan Aksoy, and Luke Williams (2014), ‘‘Service Failure Severity, Customer Satisfaction, and Market Share: An Examination of the Airline Industry,’’ Journal of Service Research, 17 (4), 415-431.

Kumar, V., Yashoda Bhagwat, and Zhang Xi (2015), ‘‘Regaining ‘‘Lost’’ Customers: The Predictive Power of First-Lifetime Behavior, the Reason for Defection, and the Nature of the Win-Back Offer,’’ Journal of Marketing, 79 (4), 34-55.

Kumar, V. and Werner Reinartz (2012), Customer Relationship Management: Concept, Strategy, and Tools. Berlin, Germany: Springer

Rohner, P., & Uhl, M. W. (2018). Increasing Investor Happiness with Holistic and Goal-Based Investment Advice. Journal of Wealth Management, 20(4), 22–28. https://doi.org/10.3905/jwm.2018.20.4.022

Saaty, T. L. (1991). Some mathematical concepts of the analytic hierarchy process. Behaviormetrika, 29(1), 1–9. https://doi.org/10.1007/s41237-020-00125-5

Saaty, T., & Vargas, L. (2012). Models, methods, concepts & applications of the analytic hierarchy process. … -Driven Demand and Operations Management Models, 63–70. https://doi.org/10.1007/978-1-4614-3597-6

Schröder, D. (2013). Asset allocation in private wealth management: Theory versus practice. Journal of Asset Management, 14(3), 162–181. https://doi.org/10.1057/jam.2013.14

Seiler, V., & Rudolf, M. (2014). Customer Satisfaction in Private Banking. Credit and Capital Markets – Kredit Und Kapital, 47(3), 485–520. https://doi.org/10.3790/ccm.47.3.485

Seiler, V., Rudolf, M., & Krume, T. (2013). The influence of socio-demographic variables on customer satisfaction and loyalty in the private banking industry. International Journal of Bank Marketing, 31(4), 235–258. https://doi.org/10.1108/IJBM-10-2012-0101

Svoboda, A. (2016). HOW SWISS PRIVATE BANKS CAN SURVIVE IN HIGHLY REGULATED MARKETS. December.

Beeharry Y, Tsokizep Fokone R (2022) Hybrid approach using machine learning algorithms for customers’ churn prediction in the telecommunications industry. Concur Comput: Pract Exp 34(4):e6627. https://doi.org/10.1002/cpe.6627

Amin A, Anwar S, Adnan A, Nawaz M, Alawfi K, Hussain A, Huang K (2017) Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing 237:242–254. https://doi.org/10.1016/j.neucom.2016.12.009

Bhattacharyya J, Dash MK (2022) What do we know about customer churn behaviour in the telecommunication industry? A bibliometric analysis of research trends, 1985–2019. FIIB Bus Rev 11(3):280–302. https://doi.org/10.1177/23197145211062687

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, 19864–19881.

Abedin, M.Z., Guotai, C., Moula, F.E., Azad, A.S., Khan, M.S.U., 2019a. Topological applications of multilayer perceptrons and support vector machines in financial decision support systems. Int. J. Finance Econ. 24 (1), 474–507.

Abedin, M.Z., Guotai, C., Zhang, T. Fahmida-E-Moula, Hassan, M.K., 2019b. An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data. J. Risk Model Valid. 13 (2), 1–46.

Long, W., Lu, Z., Cui, L., 2019. Deep learning-based feature engineering for stock price movement prediction. Knowl.-Based Syst. 164, 163–173.

Moula, F.E., Guotai, C., Abedin, M.Z., 2017. Credit default prediction modeling: an application of support vector machine. Risk Manage. 19 (2), 158–187.

Mujica, L.E., Melendez, J., Colomer, J., 2002. Modeling the bank’s client behavior using case based reasoning and self-organizing map. (Accessed 20 December 2016).

Ngai, E.W., Xiu, L., Chau, D.C., 2009. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 36 (2), 2592–2602.