Developing a prediction model for customer churn from electronic banking services using data mining. This study applies and compares five traditional machine .
Developing a prediction model for customer churn from electronic banking services using data mining Apr 23, 2021 · The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. Financial Jan 3, 2019 · The following paper is an outline of the current author’s research on the churn prediction in electronic banking. Dec 12, 2021 · This study applied data mining techniques to predict customer churn in the banking sector using three different classification algorithms, namely: decision tree (J48), random forest (RF), and Abstract. The availability of stored customer data in the form of big data, together with the use of advanced and tuned machine learning (ML) algorithms, have paved the way for the realisation Apr 1, 2023 · The proposed combined knowledge mining model enable us to conduct a benchmark study on the prediction of bank customer behaviour. A real bank customer dataset, drawn from 24,000 active and inactive customers, is used for an experimental analysis, which sheds new light on the role of feature engineering in bank customer classification. Apr 1, 2022 · However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. M. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization. Predicting customer churn can help banks take proactive measures to retain valuable customers. Churn customer prediction is an activity carried out to predict whether the customer will leave the company or not. Customer churn can lead to an 85% profit increase with a 5% retention rate improvement. International Journal of Electronic Banking, 2(3), 185-204. Businesses can take proactive steps to retain these customers by predicting churn, such as Jan 1, 2022 · The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the Oct 4, 2024 · Here’s what we’ll cover in the following sections: Data Collection: Understanding what data is useful for churn prediction. The solution is built with Random Forest and XGBoost algorithms, which were chosen for their efficiency in handling structured data and providing high predictive accuracy. Some of the benefits of churn prediction in the banking sector are cost savings, increased customer satisfaction, improved revenue, etc. This involves analysing customer behaviour, usage patterns, and other relevant data to forecast which customers are at risk of leaving. Methods:Being based on existing Apr 12, 2024 · Conventional algorithms have been utilized to predict churn and then develop a number of client retention strategies. Jan 9, 2025 · What is Churn prediction? Churn prediction is the process of identifying customers who are likely to stop using a company’s products or services in the near future. Mirmohammadi, “Developing a prediction model for customer churn from electronic banking services using data mining,” Financial Innovation, vol. The CRISP-DM methodology guided the Conclusions: Bank managers can identify churners in future using the results of decision tree. Keywords: Customer churn, Data mining, Electronic banking services, Decision tree, Classification " Developing a prediction model for customer churn from electronic banking services using data mining," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. This literature focuses on discussing related work that used data mining methods to apply a model of prediction. The contribution of four variable categories: customer information, card information, risk information, and transaction activity information are examined. 2, no. Academics can use the findings to explore advanced machine learning applications and develop new churn prediction frameworks. By analyzing diverse customer data (transactions, demographics, activity, interactions), the model will predict customers at Conclusions: Bank managers can identify churners in future using the results of decision tree. Dec 12, 2021 · Many studies have used various techniques for data mining to make churn predictions. The prediction of customer churn represents one of the most critical challenges in the banking industry, with direct implications for revenue stability, customer lifetime value, and competitive positioning. This study applies and compares five traditional machine Nov 16, 2022 · The credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. This model can help banks identify at-risk customers and take proactive steps to Mar 28, 2022 · Learn how to use Python machine learning models to predict customer churn rates, turning marketing data into meaningful insights. We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics. In addition, this paper applied the churn prediction model and the enablement of customer categorisation on their churn risk. The aim is to identify factors influencing customers' decisions to leave the bank and predict future churn. (Nieae et al. This study applied data mining techniques to predict customer churn in the banking sector using three Jul 1, 2018 · PDF | On Jul 1, 2018, Nadeem Ahmad Naz and others published A REVIEW ON CUSTOMER CHURN PREDICTION DATA MINING MODELING TECHNIQUES | Find, read and cite all the research you need on ResearchGate May 12, 2024 · In this article, I’ll share my project on building a customer churn prediction model using machine learning. Feb 28, 2023 · Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Oct 5, 2024 · This project focuses on using machine learning to build a churn prediction model using the Telco Customer Churn Dataset. Apr 22, 2021 · So, in this paper the standard CRISP-DM method has been used for data mining's road map. The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. This project aims to develop a predictive model using Jan 1, 2023 · The results show that the banking industry will gain a more dynamic and efficient customer relationship management system by using this model. Keramati, H. , Ghaneei, H. This has been inspired by the fact that there are around 1,5 million churn customers in a year which is increasing every year. Keramati, et al. Keywords: Customer churn, Data mining, Electronic banking services, Decision tree, Classification Keywords: Customer churn, Data mining, Electronic banking services, Decision tree, Classification The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. Feature Engineering: How to create meaningful features from raw data. (2016) Developing a Prediction Model for Customer Churn from Electronic Banking Services Using Data Mining. One way to predict Aug 29, 2023 · The study utilizes data mining and predictive analytics techniques to analyse customer behaviour, identify churn patterns, and develop predictive models. Dec 4, 2024 · Introduction Predicting customer churn is a crucial aspect of any business that relies on customer subscriptions, such as streaming services, software companies, or telecommunication providers. Developing a prediction model for customer churn from electronic banking services using data mining Financial Innovation A decision tree model effectively predicts customer churn in electronic banking services. May 16, 2023 · A bank customer churn prediction model utilizes machine learning techniques to analyze historical customer data, identify patterns, and make predictions about the likelihood of a customer churning Nov 7, 2024 · Customer churn is a common problem faced by many industries, including telecommunication industries. 1, 2016. A new method for customer churn analysis and prediction has been proposed. Aug 11, 2021 · Learn how to build a data pipeline in Python to predict customer churn. Provide personalized suggestions based on customer behavior and preferences to improve customer satisfaction and loyalty. Banks are no exception to this rule. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customers. 1186/s40854-016-0029-6 In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. We demonstrate that customer churn may be affected by two additional important factors, namely customer's age and customer's Dec 26, 2024 · The cost-efficiency of retaining existing customers in the realm of e-commerce, the pursuit of new customer acquisition is no longer considered a prudent strategy. doi: 10. . “Developing a Prediction Model for Customer Churn from Electronic Banking Services Using Data Mining. By developing a comprehensive customer analytics platform that combines customer segmentation, recommendation, and churn prediction techniques to counter customer attrition, this work fills a gap in the literature. 2-13, 2016. This paper involves data collection, data exploration, data visualization, and creation of a churn prediction model that can be integrated into a company's existing customer retention strategy. Discover tools, strategies, and the importance of predictive analytics to retain customers and boost loyalty. Learn how to build a predictive model for customer churn using Python and Scikit-learn in this hands-on tutorial. The study came to the conclusion that decision trees and logistic regression Oct 2, 2024 · Thereby using the method of identifying customer churn and machine learning algorithms to build a model to predict customer churn from the Bank, then apply to build a system so that the Bank can come up with strategies to reduce customer churn. Methods Being based on existing information technologies which allow one to collect data from organizations' databases, data mining introduces a powerful tool for the extraction of knowledge from The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Dec 31, 2023 · Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall Oct 28, 2022 · [6] A. There-fore, banks use customer churn forecasting methods when selecting the necessary measures to reduce the impact of this problem. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Jul 6, 2022 · The diversity of data collected on both social networks and digital interfaces is extremely increased, raising the problem of heterogeneous variables that are not often favourable to classification algorithms. This has resulted in the development of advanced techniques for the prediction and prevention of customer churn. Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. Feb 20, 2024 · This project aims to develop a robust churn prediction model for banks. Investigating factors affecting customer churn in electronic banking and developing solutions for retention. M. Methods: Being based on existing information technologies which allow one to collect data from organizations’ databases, data mining introduces a powerful tool for the extraction of knowledge from May 3, 2025 · Customer churn prediction is a critical business problem that directly impacts revenue retention and customer relationship management. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. One of the most critical challenges facing banking institutions is cus-tomer churn, as it dramatically affects a bank’s profits and reputation. Backward elimination method outperformed forward selection in feature subset selection. Methods: Being based on existing information technologies which allow one to collect data from organizations' databases, data mining introduces a powerful tool for the extraction of knowledge from This project focuses on developing a bank customer churn prediction model using Python. This framework Customer churn, the phenomenon where customers discontinue their relationship with a business, is a significant concern in the banking industry. Abstract Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). In [3], the authors examined the problem of customer churn in banks and found that due to the intense competition between banks, they resorted to looking for intelligent ways to help them make Jul 17, 2024 · Keramati, A. The research is based on real anonymised data of 4 million clients from one of the biggest Polish banks. Sep 29, 2020 · In this work, six different methods using machine learning have been investigated on the retail banking customer churn prediction problem, considering predictions up to 6 months in advance. Traditional approaches to churn prediction Jun 8, 2024 · Create a predictive model using proactive advanced analytics and historical data to detect customer churn. Distinctive in its scope, this work covers key stages of churn prediction models comprehensively, contrary to published reviews, which focus on some aspects of churn prediction, such as model development, feature engineering and model evaluation using traditional machine learning-based evaluation metrics. Ghaneei and S. They should be provide some strategies for customers whose features are getting more likely to churner’s features. This has been inspired by the fact Aug 22, 2024 · Churn analytics in banking involves collecting customer data, preprocessing it, identifying patterns, building predictive models, and implementing real-time monitoring to predict and prevent customer churn. ” Financial Innovation, vol. Uncover key benefits, algorithms, and tools for retaining valuable bank customers. Mar 1, 2024 · In our research, we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers. 1, pp. Predicting customer churn can help businesses identify at-risk customers and Sep 13, 2024 · Learn how to build effective customer churn prediction models. and Mirmohammadi, S. Explore machine learning's role in bank churn prediction. In this paper, two data mining algorithms are applied to build a churn prediction model using credit card data collected from a real Chinese bank. At the same time, practitioners, particularly in banking and related industries, can leverage the Gradient Boosting model to improve customer retention strategies and reduce revenue losses associated with churn. The method uses data mining model in banking industries. Customer churn occurs when a customer stops using a product or service, leading to a loss of revenue and potential profits. Despite the significant improvement in machine learning (ML) and predictive analysis efficiency for classification in customer relationship management systems (CRM), their performance Jul 4, 2024 · The importance of customer retention in the competitive banking sector made clear how raising the retention rate can significantly boost a bank's revenues. The project involves data 1 day ago · Churn Prediction is a critical aspect of customer relationship management in the banking sector. Access to real data in such scale is a Dec 29, 2021 · The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. Demographic variables and transaction data are crucial for identifying churn characteristics. 2011) used two data mining algorithms on credit card data that was gathered from a Chinese bank to develop a churn prediction model. Aug 22, 2016 · From our literature review, use of data mining techniques for predicting customer churn is new in the electronic banking context. al. Jan 17, 2025 · This tutorial describes a data science workflow with an end-to-end example of building a model to predict churn. Nieae et. Data collection and feature selection for predicting customer churn in the electronic banking services context is one of the novel aspects of the present research. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. Utilize Explainable AI, specifically the SHAP-explainer, for transparent and understandable model predictions. 2 (1), pages 1-13, December. Different approaches are tested and compared using real data. [7] Oct 1, 2019 · This study tested 5 different classification methods and found that Support Vector Machine (SVM) with a comparison of 50:50 Class sampling data is the best method for predicting churn customers at a private bank in Indonesia. While several studies have been conducted in the customer churn prediction Apr 30, 2025 · [3] A. esppycwbwjabsaiexzovkpkdwknayqbbdfdeogcxrbuqaedhyikorwlwmoyntvxjebczlggawhokwjjqjsmxmcjg