Metaheuristic Algorithms and Modern Credit Classification Methods: A Systematic Review

Abstract

Number of proposed advanced analysis methods, which try to successfully predict if applicants are going to default in credit applications show an increasing pattern, especially after the Global Financial Crisis. Alternative to conventional statistical classification methods, machine learning methods arrive on the scene; they have capability to reveal information from the data independently from constraints and assumptions. Along with machine learning methods, metaheuristic algorithms that substantially improves classification performances take part in studies. Combined usages of learning methods and metaheuristic algorithms aim to benefit from the contemporary data storage and process capacities at the highest level and greatly contribute to credit risk assessment field. In this review study, credit classification studies that adopt metaheuristic algorithms in the analyses are examined with a systematic process, for the period after 2000. By forming a general framework, classification methods, metaheuristic algorithm implementations, algorithms’ intended uses and performance assessment criteria are addressed. Examination showed that there is a growing interest, nevertheless method preferences are concentrated over a limited option. It is necessary to incorporate more novel metaheuristics and/or hybrid and combined usages to the studies. It is possible to say that progressive works parallel to the developments in computer and mathematical sciences will continuously contribute to the literature.

Keywords

• Credit risk
• Credit scoring
• Credit assessment
• Machine learning
• Metaheuristics algorithms

Modern Kredi Sınıflandırma Çalışmaları ve Metasezgisel Algoritma Uygulamaları: Sistematik Bir Derleme

Abstract

Öz Kredi başvurularında, başvuranların temerrüde düşüp düşmeyeceklerinin başarılı şekilde tahmin edilmesi amacıyla önerilen gelişmiş analiz yöntemlerinin sayısı, özellikle Küresel Finans Krizi sonrası dönemde önemli bir artış göstermiştir. Geleneksel istatistiksel sınıflandırma yöntemlerine alternatif olarak bilgiyi, kısıtlar ve varsayımlardan bağımsız olarak doğrudan veri kümelerinden ortaya çıkarma yeteneğine sahip makine öğrenme yöntemleri kullanılmaya başlanmıştır. Bu yöntemlerin yanı sıra, sınıflandırma performansları üzerinde çok büyük iyileştirmeler sağlayan metasezgisel algoritmalar da yazında kendilerine fazlaca yer bulmaya başlamıştır. Veri saklama ve işleme kapasitelerinde yaşanan artıştan en üst düzeyde faydalanmaya yönelik olarak öğrenme yöntemleri ile metasezgisel algoritmaların birlikte kullanımları, kredi risk değerlendirme alanına büyük katkılar sağlamaktadır. Bu derleme kapsamında 2000 sonrası dönemde yazına sunulmuş olan ve metasezgisel algoritmaların yer aldığı kredi sınıflandırma çalışmaları sistematik bir süreç ile incelenmiştir. Yazında karşılaşılan sınıflandırma yöntemleri, uygulanan metasezgisel algoritmalar ile kullanım amaçları ve sınıflandırma performans değerlendirme kriterleri ele alınmış ve mevcut duruma ilişkin genel bir çerçeve oluşturulmuştur. İnceleme, metasezgisel algoritmalar ile makine öğrenme yöntemlerine yönelik artan bir ilgi olduğunu ortaya koymaktadır ancak yöntem tercihleri birkaç alternatif üzerine yoğunlaşmış durumdadır. Yeni geliştirilen metasezgisel algoritmaların ve/veya hibrit ve birlikte kullanımların alanda daha fazla yer alması gerekmektedir. Bilgisayar ve matematik bilimlerinde yaşanan gelişmeler ile paralel olarak ilerleyecek çalışmaların, yazına sürekli katkı sunmaya devam edeceğini söylemek mümkündür.

Keywords

• Kredi riski
• Kredi skoru
• Kredi değerlendirme
• Makine öğrenme
• Metasezgisel algoritmalar

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