期刊
FINANCIAL INNOVATION
卷 7, 期 1, 页码 -出版社
SPRINGER
DOI: 10.1186/s40854-021-00242-4
关键词
Bank supervision; Machine learning; Loan loss provisions; On-site credit supervision
The study finds that the on-site banking supervision approach consistently outperforms the self-supervision approach in assessing loan portfolios, justifying the need for on-site credit portfolio examination conducted by the Central Bank of Brazil.
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.
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