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Journals / Sosyoekonomi / 2026 / Cilt: 33 Sayı: 65

A Decision-Making System Based on Machine Learning for Commercial Credit Limit

Ticari Kredi Limiti için Makine Öğrenimine Dayalı Bir Karar Destek Sistemi

Enes Koçoğlu(KARABÜK ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ, ENDÜSTRİ MÜHENDİSLİĞİ (DR))0009-0002-5623-2030
Filiz Ersöz(Department of Information Engineering, University of Pisa)0000-0002-4964-8487
Esra Tekez(SAKARYA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ENDÜSTRİ MÜHENDİSLİĞİ BÖLÜMÜ, ENDÜSTRİ MÜHENDİSLİĞİ PR.)0000-0002-0429-5593
Pages
169–195
Publication Date
2025-07-17
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AI Summary & Key Takeaways

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

The random forest achieved the highest success rate of 69.40% among the algorithms tested.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This research presents a novel machine learning model that adjusts commercial credit limits based on financial audit data, offering an alternative to traditional models that categorise customers as either 'good' or 'bad.' It identifies key variables for banks' credit rating processes, improving existing methods.
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Key Findings & Data:The model ensures objectivity by focusing on financial data from independent audits and excluding past behaviour.
💡
Conclusion & Impact:The random forest achieved the highest success rate of 69.40% among the algorithms tested.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sınıflandırma · Makine Öğrenmesi · Kredi Notu

Abstract

This research presents a novel machine learning model that adjusts commercial credit limits based on financial audit data, offering an alternative to traditional models that categorise customers as either 'good' or 'bad.' It identifies key variables for banks' credit rating processes, improving existing methods. The model ensures objectivity by focusing on financial data from independent audits and excluding past behaviour. The study proposes a classification system for credit limits as “increasing” or “decreasing”, aiming to attract new customers. The random forest achieved the highest success rate of 69.40% among the algorithms tested.

Özet

Bu araştırma, kredi limiti ayarlamalarını doğru bir şekilde sınıflandıran ve bankaların ticari kredi derecelendirme süreçleri için kritik değişkenleri belirleyen bir makine öğrenimi modeli sunmaktadır. Bankalar, kötü kredileri en aza indirirken geliri maksimize etmeyi hedeflemekte, bu da hassas müşteri sınıflandırmasını gerektirmektedir. Bağımsız denetim raporlarındaki finansal değişkenlere odaklanan bu çalışma, nesnelliği sağlamak amacıyla geçmiş müşteri davranışlarını dikkate almamaktadır. Ayrıca, “artan” ve “azalan” limitleri ayıran bir sınıflandırma sistemi önererek kredi derecelendirme literatüründeki eksiklikleri gidermeyi amaçlamaktadır. Çalışma, çeşitli algoritmaları değerlendirerek rastgele orman algoritmasının %69,40 ile en yüksek başarıyı elde ettiğini göstermektedir.

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Cite This Article

Koçoğlu, E., Ersöz, F., Tekez, E. (2025). A Decision-Making System Based on Machine Learning for Commercial Credit Limit. Sosyoekonomi, Cilt: 33 Sayı: 65, 169–195. https://doi.org/10.17233/sosyoekonomi.2025.03.09

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