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Journals / Journal of Artificial Intelligence and Data Science / 2026 / Cilt: 6 Sayı: 1

An Explainable Hybrid Machine Learning Framework for Loan Approval Prediction Based on Random Forest, Light Gradient Boosting Machine, Combined with Grey Wolf Optimizer Algorithms

Kredi Onayı Tahmini için Rastgele Orman Tabanlı Nitelik Seçimi, LightGBM ve Gri Kurt Optimizasyonuna Dayalı Açıklanabilir Hibrit Makine Öğrenmesi Çerçevesi

Oktay Can Sevimgin(AYDIN ADNAN MENDERES ÜNİVERSİTESİ)0009-0004-7974-0657
Denizhan Demirkol(AYDIN ADNAN MENDERES UNIVERSITY, FACULTY OF ENGINEERING, COMPUTER SCIENCES)0000-0003-1215-0404
Pages
31–48
Publication Date
2026-06-29
DOI
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AI Summary & Key Takeaways

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

The results show that advanced ensemble learning techniques, metaheuristic-based hyperparameter optimization, and explainable AI can be combined to form a single coherent pipeline.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Loan approval prediction remains one of the most vital problems in financial decision-making at financial institutions.
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Key Findings & Data:In recent years, these institutions have seen an increase in credit applications.
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Conclusion & Impact:The results show that advanced ensemble learning techniques, metaheuristic-based hyperparameter optimization, and explainable AI can be combined to form a single coherent pipeline.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Açıklanabilir Yapay Zeka · Gri Kurt Optimizasyonu · Hiperparametre Optimizasyonu

Abstract

Loan approval prediction remains one of the most vital problems in financial decision-making at financial institutions. In recent years, these institutions have seen an increase in credit applications. For these reasons, loan approval prediction has become central to research and practice in credit risk management. This paper presents an explainable hybrid ML approach to predict the loan approval or rejection. The whole process for building the ML model is based on the well-known CRISP-DM process model. A publicly available dataset to predict the loan approval or rejection from Kaggle is used for this study. The raw dataset is first preprocessed to handling outliers, to transform the categorical variables into numerical variables, and then to perform the Min-Max normalization. In the experimental result section, nine different classification algorithms, namely Decision Trees, Random Forest, Gradient Boosting Machines, XGBoost, LightGBM, CatBoost, Support Vector Machines, Naive Bayes, and K-Nearest Neighbors, are employed to produce the prediction performance. To improve the prediction performance of the established methods, the optimized model parameters were obtained by Grey Wolf Optimizer (GWO), and the prediction performance was evaluated by the 3-fold cross-validation (CV). According to the obtained results, state-of-the-art ensemble-based algorithms clearly outperformed the more conventional classifier. It has been observed that the GWO-optimized LightGBM model achieved the highest cross-validation accuracy of 92.17%. Due to the majority of the ensemble methods being black-box, SHAP-Explainable Artificial Intelligence (XAI) was utilized to gain insight into the black-box decision-making process of the models. The results show that advanced ensemble learning techniques, metaheuristic-based hyperparameter optimization, and explainable AI can be combined to form a single coherent pipeline.

Özet

Kredi onayı tahmini, finansal kurumlarda finansal karar verme süreçlerinin en önemli problemlerinden biri olmayı sürdürmektedir. Son yıllarda bu kurumlar kredi başvurularında artış gözlemlemiştir. Bu nedenlerle, kredi onayı tahmini kredi riski yönetiminde hem araştırma hem de uygulama açısından merkezi bir konu hâline gelmiştir. Bu çalışma, kredi onayı veya reddini tahmin etmek amacıyla açıklanabilir hibrit bir makine öğrenmesi yaklaşımı sunmaktadır. Makine öğrenmesi modelinin oluşturulmasına ilişkin tüm süreç, yaygın olarak bilinen CRISP-DM süreç modeline dayanmaktadır. Bu çalışmada, kredi onayı veya reddini tahmin etmek amacıyla Kaggle’dan elde edilen halka açık bir veri seti kullanılmıştır. Ham veri seti öncelikle aykırı değerlerin ele alınması, kategorik değişkenlerin sayısal değişkenlere dönüştürülmesi ve ardından Min-Max normalizasyonunun uygulanması için ön işleme tabi tutulmuştur. Deneysel sonuçlar bölümünde, tahmin performansını ortaya koymak amacıyla Karar Ağaçları, Rastgele Orman, Gradient Boosting Machines, XGBoost, LightGBM, CatBoost, Destek Vektör Makineleri, Naive Bayes ve K-En Yakın Komşu olmak üzere dokuz farklı sınıflandırma algoritması kullanılmıştır. Kurulan yöntemlerin tahmin performansını artırmak amacıyla optimize edilmiş model parametreleri Gri Kurt Optimizasyon Algoritması (GWO) ile elde edilmiş ve tahmin performansı 3 katlı çapraz doğrulama yöntemiyle değerlendirilmiştir. Elde edilen sonuçlara göre, güncel topluluk tabanlı algoritmalar daha geleneksel sınıflandırıcıları açıkça geride bırakmıştır. GWO ile optimize edilmiş LightGBM modelinin %92,17 ile en yüksek çapraz doğrulama doğruluğuna ulaştığı gözlemlenmiştir. Topluluk yöntemlerinin büyük çoğunluğunun kara kutu niteliğinde olması nedeniyle, modellerin kara kutu karar verme süreçlerine ilişkin içgörü elde etmek amacıyla SHAP tabanlı Açıklanabilir Yapay Zeka (XAI) kullanılmıştır. Bulgular, gelişmiş topluluk öğrenmesi tekniklerinin, meta-sezgisel tabanlı hiperparametre optimizasyonunun ve açıklanabilir yapay zekanın tek ve tutarlı bir işlem hattı içinde birleştirilebileceğini göstermektedir.

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Cite This Article

Sevimgin, O. C., Demirkol, D. (2026). An Explainable Hybrid Machine Learning Framework for Loan Approval Prediction Based on Random Forest, Light Gradient Boosting Machine, Combined with Grey Wolf Optimizer Algorithms. Journal of Artificial Intelligence and Data Science, Cilt: 6 Sayı: 1, 31–48. https://ajindex.com/articles/1618284

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