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. We have found that a technical solution is available. Our analysis uses lifecycle and environment outputs from an Age-Period-Cohort analysis as fixed offsets to the credit score development. Panel data is used, so the credit score is developed with a discrete time survival model approach. We tested logistic regression and stochastic gradient boosted regression trees as estimators with the panel data and APC inputs. For this research, we used Fannie Mae data. The APC model was estimated on the full available history, from 2005 through 2024. The origination scores were estimated on two-year periods from 2016 through 2024 and tested on all other periods, including a score that was developed on the full period. All models were also tested on comparably prepared data from Freddie Mac for cross-validation.

In order to stress test loan portfolios for the impacts of climate change, historical events need to be analyzed to create templates to stress test for future events. Using the 2012 Midwestern US drought as an example, this work creates a stress-testing template for future droughts. The analysis connects weather and crop yield data to impacts on local macroeconomic conditions by comparing drought-impacted agricultural counties with nearby urban counties. After measuring the net macroeconomic impacts of the drought, this was used as an overlay with existing macroeconomic stress models to stress test a lender in a different part of the US for possible drought impacts. Having a library of such climate events would allow lenders to stress test their portfolios for a wide range of possible impacts.

Macroeconomic Adverse Selection in Machine Learning Models of Credit Risk

Classical and quantum computing methods for estimating loan-level risk distributions