Credit Risk

Latest Credit Risk

This study analyzes five-year credit outcomes for consumers who participate in credit counseling and in some cases enroll in debt management plans (DMPs) using data from a national initiative by the National Foundation for Credit Counseling.
This paper examines the role of racial bias in contributing to disparities in consumer bankruptcy outcomes. Using a deep learning model trained on voter registration data to impute race in their analysis of bankruptcy filings between 1990 and 2022, the authors find that Black filers are more likely to have their cases dismissed without any debt relief under both Chapter 13 and Chapter 7 than White filers.
This paper finds that mortgage refinancing benefits from lower interest rates during the pandemic have not been shared equally among racial and ethnic groups. Based on a sample of 5 million mortgages, the authors estimate that only 6% of Black borrowers and 9% of Hispanic borrowers refinanced between January and October 2020, compared with almost 12% of White borrowers. Among those who experienced distress during the peak months of May and June 2020, the percent who were still behind on their mortgage payments as of February 2021 was 9 percentage points higher among Black borrowers and 2.2 percentage points higher among Hispanic borrowers as compared to White borrowers.
This post examines consumers who remained in forbearance one year after the pandemic lockdowns started. The authors found that 13% of all mortgage borrowers were in forbearance for at least one month during the past year and that 35% of those participants were still in forbearance as of March 2021. More than 70% of consumers still in forbearance were not making any payments in March, suggesting that they are relatively vulnerable to serious delinquency as forbearance programs end.
This report analyzes mortgage default using information taken from the JPMorgan Chase Institute housing finance research to evaluate the relationship between liquidity, equity, income level, and payment burden and default. Across all four groups, the report finds that liquidity may be more predictive for determining the likelihood of mortgage default particularly among borrowers with little post-closing liquidity and little liquidity but high equity. Overall, the report determines that alternative underwriting standards incorporating a minimum amount of post-closing liquidity may be a more effective way to prevent mortgage default compared to using DTI thresholds at origination.
This paper provides a nontechnical overview of common machine learning algorithms used in underwriting credit. It provides a review – including strengths and weakness – of common machine learning techniques in credit underwriting, including tree-based models, support vector machines, and neural networks. The paper also considers the financial inclusion implications of machine learning, nontraditional data, and fintech.
This paper examines geographic patterns to assess the extent to which where one resides is correlated with one’s likelihood of remaining credit invisible. The paper explores the following topics: credit deserts, credit invisibility in rural and urban areas, entry products by geography, and credit invisibility. The Bureau of Consumer Financial Protection’s Office of Research
This paper evaluates users’ digital footprints or the information that people leave online by accessing or registering a website to predict consumers’ likelihood of default. Using more than 250,000 observations, the authors show that information gleaned from people’s digital footprint can be equal to or even exceed the information content of credit bureau scores. FDIC Center for Financial Research Working Paper No. 2018-04
This paper uses various types of machine learning models to predict credit risk using historical mortgage data. It finds gains in predictiveness that would likely lead to an increase in approvals across all demographic groups, but that average prices could increase for African-American and Hispanic borrowers due to differences in risk calculations.