Veri madenciliği yöntemleriyle kredi skor tahmini: Performans karşılaştırması ve analizi

Abstract

Kredi skoru tahmini, finansal kuruluşların kredi riskini etkin şekilde yönetmeleri ve sürdürülebilir kârlılık sağlamaları açısından kritik bir öneme sahiptir. Sağlıklı kredi kararlarının alınabilmesi için geçmiş verilere dayalı tahmin modellerinin geliştirilmesi gerekmektedir. Bu çalışmada, kredi notu veri seti üzerinde çeşitli makine öğrenmesi algoritmaları ile birlikte birliktelik kuralı çıkarımına dayalı Apriori algoritması kullanılarak tahmin modelleri oluşturulmuştur. Modelleme sürecinde veri madenciliği ve yapay zekâ tekniklerinden yararlanılmış; farklı sınıflandırma algoritmalarının başarı performansları 10 katlı çapraz doğrulama yöntemi ile doğruluk, hassasiyet (precision), hatırlama (recall) ve F1-skoru gibi metrikler üzerinden değerlendirilmiştir. İstatistiksel analizler (Wilcoxon ve paired t-testi) DNN modelinin Logistic Regression, Naive Bayes ve Random Forest modellerine göre anlamlı şekilde üstün olduğunu ortaya koyarken, MLP, SVM ve XGBoost modelleri ile benzer performanslar sergilediğini göstermiştir. Bu bulgu, DNN’nin özellikle karmaşık veri setlerinde güçlü bir tahmin modeli olduğunu desteklemektedir. Elde edilen sonuçlar, veri tabanlı tahmin yaklaşımlarının kredi risk analizinde etkinliğini göstermekte ve senaryoya uygun algoritma seçiminin önemine vurgu yapmaktadır. Ayrıca, kullanılan algoritmaların avantajları ve sınırlılıkları değerlendirilmiş; uygulama bağlamına göre en uygun yöntemin seçilmesinin kritik olduğu sonucuna varılmıştır. Bu çıktılar, kredi risk tahminine yönelik modelleme çalışmalarına katkı sağlamakta ve finansal kuruluşların karar destek sistemleri için yapay zekâ temelli çözümler geliştirmelerine rehberlik etmektedir.

Keywords

• Veri Madenciliği
• Sınıflandırma Algoritmaları
• Kredi Skor Tahmin
• Birliktelik Analizi
• Hiperparametre Optimizasyonu

Predicting credit scores with data mining methods: performance comparison and analysis

Abstract

Credit scoring prediction is critically important for financial institutions to effectively manage credit risk and ensure sustainable profitability. Accurate credit decisions require the development of predictive models based on historical data. In this study, predictive models were developed using various machine learning algorithms along with the Apriori algorithm based on association rule mining applied to a credit scoring dataset. The modeling process leveraged data mining and artificial intelligence techniques; the performance of different classification algorithms was evaluated using 10-fold cross-validation through metrics such as accuracy, precision, recall, and F1-score. Statistical analyses (Wilcoxon signed-rank test and paired t-test) revealed that the Deep Neural Network (DNN) model outperformed Logistic Regression, Naive Bayes, and Random Forest models significantly, while exhibiting similar performance to MLP, SVM, and XGBoost models. This finding supports the strength of DNN models, particularly on complex datasets. The results demonstrate the effectiveness of data-driven predictive approaches in credit risk analysis and emphasize the importance of selecting algorithms appropriate to the scenario. Furthermore, the advantages and limitations of the applied algorithms were evaluated, concluding that choosing the most suitable method according to the application context is critical. These findings contribute to credit risk prediction modeling efforts and provide guidance for financial institutions in developing AI-based decision support systems.

Keywords

• Data Mining
• Classification Algorithms
• Credit Score Estimation
• Association Analysis
• Hyperparameter Tuning

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