Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction
The banking industry faces significant challenges, from high customer churn rates to threatening long-term revenue generation. Traditionally, churn models assess service quality using customer satisfaction metrics; however, these subjective variables often yield low predictive accuracy. This study e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/4/73 |
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| author | Tahsien Al-Quraishi Osamah Albahri Ahmed Albahri Abdullah Alamoodi Iman Mohammed Sharaf |
| author_facet | Tahsien Al-Quraishi Osamah Albahri Ahmed Albahri Abdullah Alamoodi Iman Mohammed Sharaf |
| author_sort | Tahsien Al-Quraishi |
| collection | DOAJ |
| description | The banking industry faces significant challenges, from high customer churn rates to threatening long-term revenue generation. Traditionally, churn models assess service quality using customer satisfaction metrics; however, these subjective variables often yield low predictive accuracy. This study examines the relationship between customer attrition and account balance using decision trees (DT), random forests (RF), and gradient-boosting machines (GBM). This research utilises a customer churn dataset and applies synthetic oversampling to balance class distribution during the preprocessing of financial variables. Account balance service is the primary factor in predicting customer churn, as it yields more accurate predictions compared to traditional subjective assessment methods. The tested model set achieved its highest predictive performance by applying boosting methods. The evaluation of research data highlights the critical role of financial indicators in shaping effective customer retention strategies. By leveraging machine learning intelligence, banks can make more informed decisions, attract new clients, and mitigate churn risk, ultimately enhancing long-term financial results. |
| format | Article |
| id | doaj-art-5d9cd549a9854acfbf605481af544ce9 |
| institution | OA Journals |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-5d9cd549a9854acfbf605481af544ce92025-08-20T02:17:25ZengMDPI AGAI2673-26882025-04-01647310.3390/ai6040073Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn PredictionTahsien Al-Quraishi0Osamah Albahri1Ahmed Albahri2Abdullah Alamoodi3Iman Mohammed Sharaf4Victorian Institute of Technology (VIT), School of IT, Melbourne, VIC 3000, AustraliaVictorian Institute of Technology (VIT), School of IT, Melbourne, VIC 3000, AustraliaTechnical Engineering College, Imam Ja’afar Al-Sadiq University, Baghdad 10001, IraqGUST Engineering and Applied Innovation Research Center (GEAR), Gulf University for Science and Technology, Mishref 32093, KuwaitDepartment of Basic Sciences, Higher Technology Institute, Tenth of Ramadan City 44629, EgyptThe banking industry faces significant challenges, from high customer churn rates to threatening long-term revenue generation. Traditionally, churn models assess service quality using customer satisfaction metrics; however, these subjective variables often yield low predictive accuracy. This study examines the relationship between customer attrition and account balance using decision trees (DT), random forests (RF), and gradient-boosting machines (GBM). This research utilises a customer churn dataset and applies synthetic oversampling to balance class distribution during the preprocessing of financial variables. Account balance service is the primary factor in predicting customer churn, as it yields more accurate predictions compared to traditional subjective assessment methods. The tested model set achieved its highest predictive performance by applying boosting methods. The evaluation of research data highlights the critical role of financial indicators in shaping effective customer retention strategies. By leveraging machine learning intelligence, banks can make more informed decisions, attract new clients, and mitigate churn risk, ultimately enhancing long-term financial results.https://www.mdpi.com/2673-2688/6/4/73customer churn predictionmachine learning modelspredictive analyticscustomer retention strategiesaccount balance analysisbanking analytics |
| spellingShingle | Tahsien Al-Quraishi Osamah Albahri Ahmed Albahri Abdullah Alamoodi Iman Mohammed Sharaf Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction AI customer churn prediction machine learning models predictive analytics customer retention strategies account balance analysis banking analytics |
| title | Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction |
| title_full | Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction |
| title_fullStr | Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction |
| title_full_unstemmed | Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction |
| title_short | Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction |
| title_sort | bridging predictive insights and retention strategies the role of account balance in banking churn prediction |
| topic | customer churn prediction machine learning models predictive analytics customer retention strategies account balance analysis banking analytics |
| url | https://www.mdpi.com/2673-2688/6/4/73 |
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