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

Abstract This study investigates the need for credit supervision as conducted by on-site banking supervisors. It builds on a real bank examination to compare performance of hypothetical self-supervision approach, in which banks themselves assess their loan portfolios without external intervention, with approach Central Bank Brazil. The experiment develops two machine learning classification models: first model is based good and bad ratings informed banks, second past portfolio examinations supervision. findings show that overall consistently higher than justifying Bank.