Normalizing Pandemic Data for Credit Scoring

Joseph L. Breeden ()
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Joseph L. Breeden: Deep Future Analytics LLM, Santa Fe, NM 87505, USA

JRFM, 2025, vol. 18, issue 11, 1-17

Abstract: The COVID-19 pandemic created abnormal credit risk conditions that did not align well with pre-2020 credit scores. Since the pandemic, most organizations have either excluded the period 2020–2021 from their modeling or included it without adjustment, leaving it as noise in the data. Model validators and examiners have been divided about requiring one of these approaches or defaulting to model developer judgment. None of this is ideal from a model development perspective. This paper presents a unique technical solution that allows for the inclusion of pandemic data while constructing credit scores and actually produces scores that perform better and have long-term stability across the entire economic cycle. This result negates the common belief that credit scores must be frequently rebuilt in order to maintain rank order accuracy. This analysis uses lifecycle and environment outputs from an Age-Period-Cohort analysis as fixed offsets to credit score development. Panel data is used, so the credit score is developed with a discrete time survival model approach. Logistic regression and stochastic gradient boosted regression trees were tested as estimators with the panel data and APC inputs.

Keywords: credit risk modeling; panel logistic regression; discrete time survival analysis; Age-Period-Cohort (APC) modeling; pandemic effects; stochastic gradient boosted regression trees (SGBRTs) (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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