Explainable models of credit losses

João Bastos and Sara M. Matos

No 2021/0161, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa

Abstract: Credit risk management is an area where regulators expect banks to have trans-parent and auditable risk models, which would preclude the use of more accurate black-box models. Furthermore, the opaqueness of these models may hide unknownbiases that may lead to unfair lending decisions. In this study, we show that banksdo not have to sacrifice prediction accuracy at the cost of model transparency tobe 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: Credit risk; Loss given default; Recovery rates; Explainable machine learning; Forecasting (search for similar items in EconPapers)
JEL-codes: C14 C52 G21 G33 (search for similar items in EconPapers)
Date: 2021-02
New Economics Papers: this item is included in nep-ban and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Journal Article: Explainable models of credit losses (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ise:remwps:wp01612021

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