A Novel Hybrid Regression Model for Banking Loss Estimation

Year 2024, Volume: 8 Issue: 1, 91 - 105, 27.06.2024

Pınar Karadayı Ataş

https://doi.org/10.33399/biibfad.1391666

Abstract

Given the critical need to identify financial risks in the banking sector early, this study presents a novel approach that uses historical financial ratios from the FDIC database to predict bank failures in the United States. Accurate estimation of potential losses is essential for risk management and decision-making procedures. We present a novel hybrid approach to loss estimation in the context of bank failures in this study. ElasticNet regression and relevant data extraction techniques are combined in our method to improve prediction accuracy. We conducted thorough experiments and evaluated our hybrid approach's performance against that of conventional regression techniques. With a remarkably low Mean Squared Error (MSE) of 0.001, a significantly high R-squared value of 0.98, and an Explained Variance Score of 0.95, our proposed model demonstrates superior performance compared to existing methodologies. The accuracy of our method is further demonstrated by the Mean Absolute Error (MAE) of 1200 units. Our results highlight the potential of our hybrid approach to transform loss estimation in the banking and finance domain, offering superior predictive capabilities and more accurate loss estimations.

Keywords

Financial Risk Analysis, Financial Stability Assessment, Bank Risk Management, Machine Learning.

Bankacılık Zarar Tahmini için Yeni Bir Hibrit Regresyon Modeli

Abstract

Bankacılık sektöründeki finansal risklerin erken dönemde belirlenmesine yönelik kritik ihtiyaç göz önüne alındığında, bu çalışma, Amerika Birleşik Devletleri'ndeki banka başarısızlıklarını tahmin etmek için FDIC veri tabanındaki tarihsel finansal oranları kullanan yeni bir yaklaşım sunmaktadır. Potansiyel kayıpların tahmini önemlidir. Bu çalışmada banka iflasları bağlamında zarar tahminine yönelik yeni bir hibrit yaklaşım sunuyoruz. Tahmin doğruluğunu artırmak için ElasticNet regresyon ve ilgili veri çıkarma teknikleri önerdiğimiz yöntemde birleştirilmiştir. Önerilen hibrit yaklaşımın performansı kapsamlı deneyler yapılarak geleneksel regresyon tekniklerine göre değerlendirilmiştir. 0,001'lik son derece düşük Ortalama Kare Hatası (MSE), 0,98'lik oldukça yüksek R-kare değeri ve 0,95'lik açıklanan varyans skoru ile önerdiğimiz model mevcut metodolojilere kıyasla üstün performans sergilemektedir. Yöntemimizin doğruluğu 1200 birimlik ortalama mutlak hata (MAE) ile gösterilmektedir. Sonuçlarımız, üstün tahmin yetenekleri ve daha doğru kayıp tahminleri sunan, bankacılık ve finans alanında zarar tahminini dönüştürmeye yönelik hibrit yaklaşımımızın potansiyelini vurgulamaktadır.

Keywords

Finansal Risk Analizi, Finansal İstikrar Değerlendirmesi, Banka Risk Yönetimi, Makine Öğrenimi.

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