Bank Customer Churn Prediction Using Machine Learning

Authors

  • Dr. Md Jaffar Sadiq  Associate Professor, Information Technology Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, India
  • Devashish Jobanputra  Bachelor of Technology, IT Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, India
  • Tadanki Gayithri Sai Kaushik  Bachelor of Technology, IT Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, India
  • J V V Satya Vrath Rao  Bachelor of Technology, IT Department, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, India

Keywords:

Customer Churn, Machine Learning, Supervised Learning, Gradient Boosting, Banking

Abstract

Banking is a very competitive field where customer relations is one of the top priorities for a bank. The bank aims for each customer to be lifelong with them. Home loans are often the bank's longest-term relationship with any customer. According to statistics and some of the top leading banks, customers they require special offers and incentives to retain their engagement with the company. The term 'customer churn' refers to a situation in which a customer or subscriber ceases to transact business with a firm or service provider. To deal with this, many businesses employ machine learning to anticipate the pace at which consumers would churn, and then devise a strategy or offer to keep their current clients. We use Machine Learning models to forecast customer churn rates, which tells us if a customer is going to stay with the bank or not based on a variety of characteristics. This will assist the bank in determining which customers are most likely to depart. Furthermore, banks can make enticing offers in order to keep their consumers. Well known models such as logistic regression, decision trees, random forest, and various boosting approaches must be utilised in this predictive process to attain a proficient level of accuracy, allowing banks to clearly forecast which customers would depart next based on customer data available.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-06-30

Issue

Volume 8, Issue 3, May-June-2022

Section

Research Articles

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How to Cite

[1]
Dr. Md Jaffar Sadiq, Devashish Jobanputra, Tadanki Gayithri Sai Kaushik, J V V Satya Vrath Rao, " Bank Customer Churn Prediction Using Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.334-341, May-June-2022.