Evaluating model concordance in a multi-model binary scoring framework: feature-specific and sampling-driven agreement analyses using Kendall’s tau rank correlation for credit risk assessment

Fuat Kaan Mirza; Arif Selçuk Öğrencı; Ayşe Hümeyra Bilge; Aslı Togan; Chifaa Kardous; Btissam Hajjaoui; Berkay Çiçek; Burak Yüksel; Özden Gebizlioğlu Özvural; Talat Kasımoğlu

doi:10.1007/s00521-025-11806-1

Evaluating model concordance in a multi-model binary scoring framework: feature-specific and sampling-driven agreement analyses using Kendall’s tau rank correlation for credit risk assessment

Neural Computing and Applications 2026

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Authors: Fuat Kaan Mirza, Arif Selçuk Öğrencı, Ayşe Hümeyra Bilge, Aslı Togan, Chifaa Kardous, Btissam Hajjaoui, Berkay Çiçek, Burak Yüksel, Özden Gebizlioğlu Özvural, Talat Kasımoğlu

DOI: 10.1007/s00521-025-11806-1

Cited by: 2

References: 61

Fields of Study & Subjects

Financial Distress and Bankruptcy Prediction Reliability and Agreement in Measurement Statistical Methods and Bayesian Inference

Publication details

Journal / source: Neural Computing and Applications
Publisher: Springer Science and Business Media LLC
Volume: 38
Issue: 7
Publication date: 26 March 2026
Language: EN

References 61