Download
1 / 18
180 likes | 189 Views
Credit Scoring Update. CAS November 14, 2007 John Wilson. My Topics. The Federal Reserve Board’s Credit Scoring Study – the companion to the FTC’s Study Observations From a Published Paper on Why Credit Scoring Works as Presented at the Recent CAS Predictive Modeling Seminar. The FRB’s Task.
E N D
Credit Scoring Update CAS November 14, 2007 John Wilson
My Topics • The Federal Reserve Board’s Credit Scoring Study – the companion to the FTC’s Study • Observations From a Published Paper on Why Credit Scoring Works as Presented at the Recent CAS Predictive Modeling Seminar
The FRB’s Task • Report on the Effect of Credit Scoring on the Availability and Affordability of Financial Products to Consumers • Determine if the Relationship Between Credit Scoring and Performance Vary Across Demographic Groups • and
The FRB’s Task (continued) • Determine if credit scores or credit factors lead to differential treatment of specific subpopulations, and if so • Determine if the differential treatment could be mitigated by changing the model development process
Some Differences • Thousands of credit scoring models are used in consumer financial services • Many of those models (ex. Mortgage) incorporate factors other than those contained in credit report data • The most common type of scoring contrasts “good” accounts with “bad” accounts based on delinquency
Key Findings • Limited direct evidence but substantial indirect evidence shows that credit has become more available as credit scoring use has grown • This increased availability applies to the consumer population as a whole, including different races and ethnicities
Key Findings • Credit scoring has likely contributed to improved affordability as well, due to cost and time savings as well as consistency in application
Key Findings • Credit history scores evaluated by the FRB, including generic industry models and FRB-derived models, were found to be predictive of default risk for the consumer population as a whole • This held true for all major demographic groups as well
Key Findings • Credit attributes do not serve as proxies for race, ethnicity, or gender • However, some credit attributes serve as limited proxies for age • Dropping attributes which serve as limited proxies for age would weaken results, since they have predictive power above their age relationship.
Key Findings • Credit scores differed substantially across demographic groups such as race and ethnicity • For given score ranges, loan performance and availability and affordability differed across demographic groups • Many of the observed differences could be mitigated by controlling for demographic factors, but some differences remained.
FRB Study Contrasts to FTC • FRB used a nationally representative random sample of roughly 285,000 consumer credit reports examined at two points in time 18 months apart • The FTC study was much larger in terms of record volumes, but was also limited to a small number of carriers
FRB Study Contrasts to FTC • FRB developed several “scorecards” in their model building process, using “thin file,” “major-derogatory,” and “clean file” sub-populations • The FTC model building process did not attempt to develop multiple sub-population scorecards • How might sub-populations have impacted the observed results?
Impact Comparison • Based on the studies, can we determine how much difference there is in impact to demographic groups across the two fields (insurance / consumer finance) from the use of credit scoring? • Direct comparisons are difficult to make, and may be somewhat academic.
Impact Observations • FTC Table 4 shows the change in mean predicted insurance risk from using credit scoring by demographic group
Impact Observations • FRB Figure O-1 shows the mean TransRisk score by demographic group normalized so that 50 equals the median
Impact Observations • FRB Figure 2A and FTC Figure 8 show very similar distribution patterns by demographic group across score deciles, suggesting similar economic impacts across the two fields.
Impact Observations • Insurers know from their own rating plans how much premium variation can be generated based on where a risk scores • Financial Institutions know from their own risk-based pricing programs, how much more will be charged over the life of a loan based on where a risk scores • To provide one example, per FRB figure 10-B, Auto Loan interest rates for the worst decile can exceed 15% while rates for the best decile are close to 6%.
