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

Year 2025, Volume: 33 Issue: 65, 169 - 195, 17.07.2025

Enes Koçoğlu , Filiz Ersöz , Esra Tekez

https://doi.org/10.17233/sosyoekonomi.2025.03.09

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.

Keywords

Classification , Machine Learning , Credit Rating , Feature Selection

Ticari Kredi Limiti için Makine Öğrenimine Dayalı Bir Karar Destek Sistemi

Abstract

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.

Keywords

Sınıflandırma , Makine Öğrenmesi , Kredi Notu , Özellik Seçimi

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