Churning of Bank Customers Using Supervised Learning

Hemlata Dalmia · Ch V S S Nikil · Sandeep Kumar

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

In the current challenging era, there is prominent competition in bank industry. To improve quality and level of service, bank concentrates on customer retention as well as customer churning. This paper discusses the classification problem of banking industry. It focuses on the customers of a bank concerns towards churning, predicting the departing customers from potential customers. Machine learning is the cutting edge technology that is practical and handy to solve such problems. Using supervised machine learning, a proprietary algorithm (a typical machine learning model) is created to forecast and inform the bank about the customers who are at the highest risk in leaving the bank. A customer churn prediction can be used here as churn and nonchurn customers are to be defined. Using ML, gap is to be resolved between churn and nonchurn customers. Different accuracy levels are achieved by classifiers using different data sheets. A novel approach K-nearest neighbor algorithm (KNN) is presented in which dataset is suitably grouped into training and testing models depending on weighted scales along with XGBooster algorithm for high and improved accuracy.

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References

2019Customer churn prediction in telecom using machine learning in big data platform 2008Customer churn prediction using improved balanced random forests 2016Customer churn prediction in the telecommunication sector using a rough set approach 2012A Customer Churn Prediction Model in Telecom Industry Using Boosting 2018Machine-Learning Techniques for Customer Retention: A Comparative Study 2013Telecommunication subscribers' churn prediction model using machine learning 2014Prediction of Customer Attrition of Commercial Banks based on SVM Model 2016Developing a prediction model for customer churn from electronic banking services using data mining

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2022Predicting Banking Customer Churn based on Artificial Neural Network 2024Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customers Churn Banks 2024An Efficient Customer Churn Prediction Technique Using Combined Machine Learning in Commercial Banks

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