Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/237257 
Year of Publication: 
2021
Citation: 
[Journal:] Financial Innovation [ISSN:] 2199-4730 [Volume:] 7 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2021 [Pages:] 1-21
Publisher: 
Springer, Heidelberg
Abstract: 
This study investigates the need for credit supervision as conducted by on-site banking supervisors. It builds on a real bank on-site credit examination to compare the performance of a hypothetical self-supervision approach, in which banks themselves assess their loan portfolios without external intervention, with the on-site banking supervision approach of the Central Bank of Brazil. The experiment develops two machine learning classification models: the first model is based on good and bad ratings informed by banks, and the second model is based on past on-site credit portfolio examinations conducted by banking supervision. The findings show that the overall performance of the on-site supervision approach is consistently higher than the performance of the self-supervision approach, justifying the need for on-site credit portfolio examination as conducted by the Central Bank.
Subjects: 
Bank supervision
Loan loss provisions
Machine learning
On-site credit supervision
JEL: 
C45
G21
G28
M48
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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