To supervise or to self-supervise: a machine learning based comparison on credit supervision

José Américo Pereira Antunes ()
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José Américo Pereira Antunes: Central Bank of Brazil

Financial Innovation, 2021, vol. 7, issue 1, 1-21

Abstract: 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.

Keywords: Bank supervision; Machine learning; Loan loss provisions; On-site credit supervision (search for similar items in EconPapers)
JEL-codes: C45 G21 G28 M48 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1186/s40854-021-00242-4

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