Türkiye-Kazakistan Bankalarının Çok Kriterli Karar Verme Yöntemleriyle Karşılaştırılması ve Yapay Sinir Ağlarıyla Analizi

Abstract

Bankaların mevcut mali tablo verileri ve bu verilerin kullanılması sonucunda oluşturulan finansal analizler, artan rekabet koşullarında bankaların performans derecesini belirlemek konusunda önem arz etmektedir. Bu çalışmanın amacı, Borsa İstanbul (BIST) 50 endeksinde işlem gören bankaların ve Kazakistan Borsasında yer alan 5 bankanın 2013-2023 yıllarındaki finansal performanslarının Çok Kriterli Karar Verme (ÇKKV) yöntemlerinden Standart Deviation (SD) ve Combinative Distance-based Assessment (CODAS) yöntemleri kullanarak değerlendirmesidir. Bu amaçla söz konusu sıralama sonuçlarına etki eden faktörlerin etki dereceleri Weka programı kullanılarak belirlenmiş ve rastgele seçilen bir bankanın 2024-2025 yılsonu finansal performans sıralama tahmini yapılmıştır. SD ve CODAS yöntemlerinin uygulanması sonucunda elde edilen sıralamalara bakıldığında her iki ülke bankaları arasında M&T Bankın çalışmaya konu olan yıllarda genel olarak ilk sıralarda yer aldığı görülmüştür. Yapay sinir ağları yöntemine göre elde edilen sonuçlar değerlendirildiğinde CODAS değerlendirme sıralamalarında, her iki ülkede de toplam yükümlülük verilerinin daha etkili olduğu anlaşılmıştır.

Keywords

• Bankacılık sektörü
• çok kriterli karar verme
• finansal performans analizi
• Kazakistan
• Türk dünyası
• Türkiye
• yapay sinir ağları

Comparison of Turkish and Kazakh Banks Using Multi-Criteria Decision-Making Methods and Analysis with Artificial Neural Networks

Abstract

The financial statement data of banks and the economic analyses generated using these data play a crucial role in assessing the performance rankings of banks amid intensifying competitive conditions. This study aims to evaluate the economic performance of banks listed in the Borsa Istanbul (BIST) 50 index and five banks listed on the Kazakhstan Stock Exchange for the period 2013–2023, employing Multi-Criteria Decision-Making (MCDM) methods such as Standard Deviation (SD) and Combinative Distance-based Assessment (CODAS). The significance levels of factors affecting ranking outcomes were determined using the Weka program, and a financial performance ranking forecast for 2024–2025 was conducted for a randomly selected bank. Upon examining the rankings obtained from SD and CODAS methods, M&T Bank consistently ranked among the top banks across both countries throughout the study period. Additionally, an analysis based on artificial neural networks revealed that, within CODAS ranking evaluations, total liability data proved to be the most influential determinant in both Turkish and Kazakh banking sectors.

Keywords

• Banking sector
• multi-criteria decision-making
• financial performance analysis
• Kazakhstan
• Turkic world
• Türkiye
• artificial neural networks

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