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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 17

Forecasting credit delinquency with different machine learning methods

Kredi gecikmesinin farklı makine öğrenmesi yöntemleri ile tahmini

Nazmi Soydemir(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0009-0008-4964-556X
Mehmet Onur Olgun(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0002-7568-3235
Pages
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Publication Date
2026-06-03
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AI Summary & Key Takeaways

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

According to the results, the Random Forest algorithm achieved the highest accuracy rate at 92.68% and ROC value at 0.95 while Logistic Regression had the lowest performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to predict credit risk assessment in bank loan repayments using machine learning algorithms.
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Key Findings & Data:In this context, models including Random Forest, Logistic Regression, KNN (K-Nearest Neighbors), Naive Bayes, and Support Vector Machines were implemented on a real dataset of 1840 customers using the WEKA (Waikato Environment for Knowledge Analysis) software.
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Conclusion & Impact:According to the results, the Random Forest algorithm achieved the highest accuracy rate at 92.68% and ROC value at 0.95 while Logistic Regression had the lowest performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kredi risk değerlendirmesi · makine öğrenmesi · WEKA

Abstract

This study aims to predict credit risk assessment in bank loan repayments using machine learning algorithms. The early prediction of payment delays is crucial for minimizing financial risks and optimizing credit portfolios for banks. In this context, models including Random Forest, Logistic Regression, KNN (K-Nearest Neighbors), Naive Bayes, and Support Vector Machines were implemented on a real dataset of 1840 customers using the WEKA (Waikato Environment for Knowledge Analysis) software. These models were evaluated based on accuracy, kappa statistic, ROC (Receiver Operating Characteristic) curve, and confusion matrices. According to the results, the Random Forest algorithm achieved the highest accuracy rate at 92.68% and ROC value at 0.95 while Logistic Regression had the lowest performance.

Özet

Bu çalışma, banka kredi geri ödemelerinde risk değerlendirmesini makine öğrenmesi algoritmaları kullanarak tahmin etmeyi amaçlamaktadır. Kredi gecikmelerinin önceden öngörülmesi, bankaların mali risklerini minimize etme ve kredi portföylerini optimize etme açısından büyük önem taşımaktadır. Bu kapsamda Waikato Environment for Knowledge Analysis (WEKA) yazılımı aracılığıyla Random Forest (RF), Lojistik Regresyon, KNN (En Yakın Komşu), Naive Bayes ve Destek Vektör Makineleri (SVM) algoritmaları kullanılarak 1840 müşteriden oluşan açık kaynaklı banka veri seti üzerinde modelleme yapılmıştır. Modeller, doğruluk oranı, kappa istatistiği, Receiver Operating Characteristic (ROC) eğrisi ve karşılık matrisleri temelinde değerlendirilmiştir. Elde edilen sonuçlara göre RF algoritması %92.68 doğruluk oranı ve 0.95 ROC değeri ile en başarılı model olurken, Lojistik Regresyon en düşük performansı göstermiştir.

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Cite This Article

Soydemir, N., Olgun, M. O. (2026). Forecasting credit delinquency with different machine learning methods. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 17. https://doi.org/10.28948/ngumuh.1712127

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