To supervise or to self-supervise: A machine learning based comparison on credit supervision - José Américo Pereira Antunes (Financial Innovation, 2021)
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: C45; G21; G28; M48; 650; Bank supervision; Loan loss provisions; Machine learning; On-site credit supervision