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Journals / Ekoist: Journal of Econometrics and Statistics / 2026 / Sayı: 44

Machine Learning-Based Analysis: Retired Customer Attrition and Its Societal Implications

Roza Süleymanoğlu(DOĞUŞ ÜNİVERSİTESİ)0000-0003-2649-7572
Hakan Kaya(Kestel Belediyesi)0000-0002-0812-4839
Pages
289–307
Publication Date
2026-07-03
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate that factors such as estimated salary, age, and tenure significantly influence customer attrition.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Retired customers constitute a stable and valuable customer base for banks.
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Key Findings & Data:This study aims to analyse the attrition rates of retired customers to understand the reasons why customers discontinue banking services and assess the potential societal implications of retired customer attrition.
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Conclusion & Impact:The results indicate that factors such as estimated salary, age, and tenure significantly influence customer attrition.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Retired Customer Churn Prediction · Decision Tree • · Machine Learning

Abstract

Retired customers constitute a stable and valuable customer base for banks. Adapting to the evolving needs of this group to reduce customer attrition (churn) is essential for banks’ sustainability. This study aims to analyse the attrition rates of retired customers to understand the reasons why customers discontinue banking services and assess the potential societal implications of retired customer attrition. Decision tree and random forest machine learning models were trained to identify the key factors influencing customer attrition. The main intention of the research methodology followed in the article is to determine the best possible approach that would aid in effectively predicting customer churn. Within the methodological framework, exploratory data analysis (EDA), performance metrics, data cleaning, feature engineering, confusion matrix, and ROC–AUC analyses were applied. The results indicate that factors such as estimated salary, age, and tenure significantly influence customer attrition. In addition to that, the generalisation abilities of both models proved strong. But the random forest model performed well in terms of accuracy. These results allow banks to target vulnerable clients more effectively, make their customer retention processes more efficient, and improve bank customer retention in general. Furthermore, the exclusion of retirees from the world of financial services increases the vulnerability of the concerned society to financial exclusion, economic insecurity, social isolation, lack of social trust, lack of access to online banking services, and digital inequality. As a result, the detailed research analysis of customer loss of retiree clients has strategic significance in the context of customer management as well as in reducing social inequalities.

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Cite This Article

Süleymanoğlu, R., Kaya, H. (2026). Machine Learning-Based Analysis: Retired Customer Attrition and Its Societal Implications. Ekoist: Journal of Econometrics and Statistics, Sayı: 44, 289–307. https://doi.org/10.26650/ekoist.2026.44.1849181

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