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Summary of the Workshop on Credit Card Lending and Payments
The Federal Reserve Bank of Philadelphia’s Supervisory Research Forum (SURF) and Consumer Finance Institute (CFI) held the virtual Workshop on Credit Card Lending and Payments on September 16‒17, 2020. The workshop included sessions on payment systems and financial innovation, the COVID-19 pandemic's impact on consumer finance and credit use, and the industry impact of machine learning and artificial intelligence (ML/AI). This summary offers highlights of keynote speakers, academic paper presentations, and discussion panels.
Working Paper
From Incurred Loss to Current Expected Credit Loss (CECL): A Forensic Analysis of the Allowance for Loan Losses in Unconditionally Cancelable Credit Card Portfolios
The Current Expected Credit Loss (CECL) framework represents a new approach for calculating the allowance for credit losses. Credit cards are the most common form of revolving consumer credit and are likely to present conceptual and modeling challenges during CECL implementation. We look back at nine years of account-level credit card data, starting with 2008, over a time period encompassing the bulk of the Great Recession as well as several years of economic recovery. We analyze the performance of the CECL framework under plausible assumptions about allocations of future payments to existing ...
Working Paper
Credit risk modeling in segmented portfolios: an application to credit cards
The Great Recession offers a unique opportunity to analyze the performance of credit risk models under conditions of economic stress. We focus on the performance of models of credit risk applied to risk-segmented credit card portfolios. Specifically, we focus on models of default and loss and analyze three important sources of model risk: model selection, model specification, and sample selection. Forecast errors can be significant along any of these three model-risk dimensions. Simple linear regression models are not generally outperformed by more complex or stylized models. The impact of ...
Working Paper
Can We Take the “Stress” Out of Stress Testing? Applications of Generalized Structural Equation Modeling to Consumer Finance
Financial firms, and banks in particular, rely heavily on complex suites of interrelated statistical models in their risk management and business reporting infrastructures. Statistical model infrastructures are often developed using a piecemeal approach to model building, in which different components are developed and validated separately. This type of modeling framework has significant limitations at each stage of the model management life cycle, from development and documentation to validation, production, and redevelopment. We propose an empirical framework, spurred by recent developments ...
Working Paper
Who Provides Credit in Times of Crisis? Evidence from the Auto Loan Market
We examine the contribution of different lending channels to the auto loan market in times of crisis. Specifically, we explore lending from traditional banks, credit unions, and finance companies (nonbanks) over the past two decades, with an emphasis on the Great Recession and the COVID-19 pandemic. We find that banks provided weak support during the pandemic, thus losing market share and continuing the trend that emerged following the Great Recession. Nonbank market share during this period grew most significantly for subprime borrowers and in counties with stronger bank dependence. Survey ...
Working Paper
ENDOGENOUS/EXOGENOUS SEGMENTATION IN THE A-IRB FRAMEWORK AND THE PRO-CYCLICALITY OF CAPITAL: AN APPLICATION TO MORTGAGE PORTFOLIOS
This paper investigates the pro-cyclicality of capital in the advanced internal ratings-based (A-IRB) Basel approach for retail portfolios and identifies the fundamental assumptions required for stable A-IRB risk weights over the economic cycle. Specifically, it distinguishes between endogenous and exogenous segmentation risk drivers and, through application to a portfolio of first mortgages, shows that risk weights remain stable over the economic cycle when the segmentation scheme is derived using exogenous risk drivers, while segmentation schemes that include endogenous risk drivers are ...
Working Paper
From Incurred Loss to Current Expected Credit Loss (CECL): Forensic Analysis of the Allowance for Loan Losses in nconditionally Cancelable Credit Card Portfolios
The Current Expected Credit Loss (CECL) framework represents a new approach for calculating the allowance for credit losses. Credit cards are the most common form of revolving consumer credit and are likely to present conceptual and modeling challenges during CECL implementation. We look back at nine years of account level credit card data, starting with 2008, over a time period encompassing the bulk of the Great Recession as well as several years of economic recovery. We analyze the performance of the CECL framework under plausible assumptions about allocations of future payments to existing ...
Working Paper
Consumer risk appetite, the credit cycle, and the housing bubble
We explore the role of consumer risk appetite in the initiation of credit cycles and as an early trigger of the U.S. mortgage crisis. We analyze a panel data set of mortgages originated between the years 2000 and 2009 and follow their performance up to 2014. After controlling for all the usual observable effects, we show that a strong residual vintage effect remains. This vintage effect correlates well with consumer mortgage demand, as measured by the Federal Reserve Board?s Senior Loan Officer Opinion Survey, and correlates well to changes in mortgage pricing at the time the loan was ...
Working Paper
CECL Implementation and Model Risk in Uncertain Times: An Application to Consumer Finance
I examine the challenges of economic forecasting and model misspecification errors confronted by financial institutions implementing the novel current expected credit loss (CECL) allowance methodology and its impact on model risk and bias in CECL projections. We document the increased sensitivity to model and macroeconomic forecasting error of the CECL framework with respect to the incurred loss framework that it replaces. An empirical application illustrates how to leverage simple machine learning (ML) strategies and statistical principles in the design of a nimble and flexible CECL modeling ...
Working Paper
COVID-19 and Auto Loan Origination Trends
We study the impact of the COVID-19 crisis on auto loan origination activity during 2020. We focus on the dynamic impact of the crisis across lending channels, Equifax Risk Score (Risk Score) segments, and relevant geographic characteristics such as urbanization rate. We measure a significant drop in auto loan originations in March‒April followed by a near rebound in May‒June. Originations remain slightly depressed until October and fall again in November‒December. We document the largest drop and the smallest rebound in the subprime segment. We do not find any suggestive evidence that ...