 Explainable models of credit lossesJoão Bastos and Sara M. Matos European Journal of Operational Research, 2022, vol. 301, issue 1, 386-394 Abstract: Credit risk management is an area where regulators expect banks to have transparent and auditable risk models, which would preclude the use of more accurate black-box models. Furthermore, the opaqueness of these models may hide unknown biases that may lead to unfair lending decisions. In this study, we show that banks do not have to sacrifice prediction accuracy at the cost of model transparency to be compliant with regulatory requirements. We illustrate this by showing that the predictions of credit losses given by a black-box model can be easily explained in terms of their inputs. Because black-box models are better at uncovering complex patterns in the data, banks should consider the determinants of credit losses suggested by these models in lending decisions and pricing of credit exposures. Keywords: Risk management; Loss given default; Recovery rates; Explainable machine learning; Forecasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:301:y:2022:i:1:p:386-394 DOI: 10.1016/j.ejor.2021.11.009 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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