Template-type: ReDIF-Article 1.0
Author-Name: Roman Tikhonov
Author-Email: rytikhonov@sberbank.ru
Author-Workplace-Name: Sberbank
Author-Name: Aleksey Masyutin
Author-Email: aamasyutin@sberbank.ru
Author-Workplace-Name: Sberbank
Author-Name: Vadim Anpilogov
Author-Email: anpilogov.v.v@sberbank.ru
Author-Workplace-Name: Sberbank
Title: The Relationship Between the Financial Performance of Banks and the Quality of Credit Scoring Models
Abstract: Model risk in credit scoring can be understood as the bank's losses associated with a model quality deterioration. Deterioration in model quality entails an incorrect assessment of the creditworthiness of borrowers and leads to an increase in potentially defaulting applications in the loan portfolio, as the bank relies on the model performance when making lending decisions. The relationship between model quality and financial performance is embedded in the confusion matrix, where the value of a type I error indicates the bank's lost profit, and the value of a type II error is equivalent to losses in the event of a default. We propose estimating model risk based on the scenario forecast of model quality or the ranking ability of the Gini model over a given time interval. The result of the analysis is the assessment of the bank's net present value for the current and modified models, depending on the approval level. The proposed approach allows us to solve the problem of the optimal choice of a Gini model and answer the question of how model quality affects financial performance.
Classification-JEL: C52, C53, C58, G21
Keywords: model risk, quantitative estimation, bank risk management, credit scoring, machine learning, model, model quality
Journal: Russian Journal of Money and Finance
Pages: 76-95
Volume: 80
Issue: 2
Year: 2021
Month: June
DOI: 10.31477/rjmf.202102.76
File-URL: https://rjmf.econs.online/upload/iblock/4da/Quality_of_Credit_Scoring_Models.pdf 
Handle: RePEc:bkr:journl:v:80:y:2021:i:2:p:76-95