Ebook Validation of Consumer Credit Risk Models
On November 19, 2004, the Payment Cards Center of the Federal Reserve Bank of Philadelphia and the Wharton School’s Financial Institutions Center hosted a “Forum on Validation of Consumer Credit Risk Models.” This one day event brought together experts from industry, academia, and the policy community to discuss challenges surrounding model validation strategies and techniques. The discussions greatly benefited from the diverse perspectives of conference participants and the leadership provided by moderators and program speakers.
Retail lenders, and particularly credit card lenders, use statistical models extensively to guide a wide range of decision processes associated with loan origination, account management, and portfolio performance analysis. The increased sophistication of modeling techniques and the broader application of models have undoubtedly played key roles in the rapid growth of the credit card industry and consumer lending in general. At the same time, the widespread adoption of statistical modeling in these business processes has introduced new risk management challenges. Very simply, how do we know that our credit risk models are working as intended?
The conference discussions focused on two critical types of risk models: credit scoring models commonly used in credit underwriting and loss forecasting models used to predict losses over time at the portfolio level. These two model types differ in a number of ways, but the two modeling processes have strong theoretical links (although they are not often linked in practice).
Credit scoring models used for acquiring accounts are typically built on a static sample of accounts for which credit bureau and of ten other applicant or demographic information is available at the time of application. These data must then be combined with information about how these accounts ultimately performed in their first one to two years after acquisition. Credit scoring models are designed to predict the probability that an individual account will default or, more generally, develop a delinquency status bad enough that the bank would not have booked the account initially had it known this would happen. A number of credit scoring models only use credit bureau data to predict this probability, while others use application or demographic data in addition to credit bureau data.
Loss forecasting models predict dollar losses for a portfolio or sub portfolio, not individual accounts. Some of the most popular loss forecasting models include cumulative loss rate models, which rely on vintage curve analysis, and Markov models, which rely on delinquency analysis of buckets. Loss forecasting models may or may not include segmentation by credit score. Economic data may be explicitly included in the model or implicitly included by using a time series covering an entire business cycle.
Given the economic implications associated with a model’s accuracy and effectiveness, issues concerning model validation are of obvious concern to the industry. Erroneous or misspecified models may lead to lost revenues through poor customer selection (credit risk) or collections management. While academics and other statisticians continue to extend and improve modeling technologies, lenders have to realistically assess the costs and benefits associated with increasing model sophistication and investing in more complex validation techniques. Hence, one of the central issues addressed during the forum was the adequacy of the attention and resources being devoted to validation activities, given these tradeoffs.
The forum also addressed the increasing importance of validation from the regulatory perspective. Bank regulators and policymakers recognize the potential for undue risk that can arise from model misapplication or misspecification. Examining and testing model validation processes are becoming central components in supervisory examinations of banks’ consumer lending businesses.
content
Introduction
Model Validation: Challenging and Increasingly Important
Linking Credit Scoring and Loss Forecasting
Metrics for Model Validation
Incorporating Economic and Market Variables
Conclusion: Art Versus Science.
Appendix A — Institutions Represented at the Conference
Appendix B — Conference Agenda
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