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Journals / Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi / 2026 / Cilt: 21 Sayı: 1

Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework

Çok Katmanlı Makine Öğrenimi Çerçevesi ile Gelişmiş Kredi Onay Tahmini

Ahmet Akusta(KONYA TEKNİK ÜNİVERSİTESİ)0000-0002-5160-3210
Pages
258–278
Publication Date
2026-04-01
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AI Summary & Key Takeaways

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

The research illustrates the framework's capacity to augment credit risk assessment, curtail defaults, and facilitate decision-making, providing financial institutions with a robust instrument for operational efficiency and financial stability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study introduces a multi-stage cascaded machine learning framework for loan approval prediction, which addresses the inefficiencies inherent in traditional processes.
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Key Findings & Data:The framework combines Gradient Boosting, Support Vector Machine, and XGBoost to refine predictions at each stage.
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Conclusion & Impact:The research illustrates the framework's capacity to augment credit risk assessment, curtail defaults, and facilitate decision-making, providing financial institutions with a robust instrument for operational efficiency and financial stability.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kredi Onay Tahmini · Basamaklı Makine Öğrenimi · Kredi Risk Değerlendirmesi

Abstract

This study introduces a multi-stage cascaded machine learning framework for loan approval prediction, which addresses the inefficiencies inherent in traditional processes. The framework combines Gradient Boosting, Support Vector Machine, and XGBoost to refine predictions at each stage. This approach allows the framework to leverage the strengths of each technique in order to handle complex relationships and imbalanced data. The framework was tested on a comprehensive financial dataset, demonstrating the critical role of Gradient Boosting and traditional features like home ownership in improving predictive accuracy. The research illustrates the framework's capacity to augment credit risk assessment, curtail defaults, and facilitate decision-making, providing financial institutions with a robust instrument for operational efficiency and financial stability.

Özet

Bu çalışma, kredi onayı tahmini için geleneksel süreçlerin doğasında bulunan verimsizlikleri ele alan çok aşamalı kademeli bir makine öğrenimi çerçevesi sunmaktadır. Çerçeve, her aşamada tahminleri iyileştirmek için Gradient Boosting, Destek Vektör Makinesi ve XGBoost'u birleştirmektedir. Bu yaklaşım, çerçevenin karmaşık ilişkileri ve dengesiz verileri ele almak için her tekniğin güçlü yönlerinden yararlanmasını sağlar. Çerçeve, Gradient Boosting'in ve ev sahipliği gibi geleneksel özelliklerin tahmin doğruluğunu artırmadaki kritik rolünü gösteren kapsamlı bir finansal veri kümesi üzerinde test edilmiştir. Araştırma, çerçevenin kredi riski değerlendirmesini artırma, temerrütleri azaltma ve karar vermeyi kolaylaştırma kapasitesini göstermekte ve böylece finansal kurumlara operasyonel verimlilik ve finansal istikrar için sağlam bir araç sağlamaktadır.

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Cite This Article

Akusta, A. (2026). Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt: 21 Sayı: 1, 258–278. https://doi.org/10.17153/oguiibf.1596734

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