Bankaların Finansal Başarısızlıklarının İncelenmesinde Makine Öğrenme Tekniklerinin Karşılaştırılması

Bankalarda meydana gelecek olan bir finansal başarısızlık sonuçlar bakımından dikkate alındığında ekonomik ve sosyolojik olarak önem arz etmektedir. Makine öğrenme tekniklerinden olan Destek Vektör Makineleri DVM ve Yapay Sinir Ağları YSA finansal başarısızlıklar konusunda erken uyarı sistemi olarak kullanılmıştır. Örnek olay olarak 30 özel sermayeli bankanın beş yıllık finansal oran verilerinden yararlanılmıştır. Yapılan analiz sonuçlarına göre destek vektör makineleri yöntemi yapay sinir ağları yöntemine göre bankalardaki finansal başarısızlıkların değerlendirilmesinde erken uyarı sistemi olarak daha iyi bir sınıflandırıcı olduğu sonucuna ulaşılmıştır

COMPARISON OF MACHINE LEARNING TECHNIQUES FOR ANALYZING BANKS’ FINANCIAL DISTRESS

Analyzing banks’ financial distress has gained great importance due to their importance in national economy and caused sociological and economic results. Support Vector Machines SVM and Neural Networks NN , known as machine learning methods, are applied for classifying banks as an early warning of financial distress. A case study which is taking thirty private equity commercial banks’ five year data and financial ratios, is carried out. As a result SVM obtains better classification ratio than NNs

Keywords:

Financial Distress, Data Mining Support Vector Machines, Financial Ratios,

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