The IFORS Distinguished Lecture – Credit Scoring and Financial Crisis
The IFORS Distinguished Lecture was given by Dr. Lyn Thomas from University of Southampton. The lecture was both insightful and interesting. Dr. Thomas first introduced the history of credit scoring, which uses operations research and statistical models to assess default risk, for consumer lending. The fact that San Francisco is the birth place of credit scoring is a happy coincidence. Among the different approaches have been used in credit scoring, logistic regression is the most common one being used now. Besides logistic regression, classification trees and ensemble models are currently in use as well. Credit scoring for subprime mortgages played an important role in the financial crisis. There were two credit models mainly used in the financial crisis: bureau scoring models and rating agencies’ models. The objective of the Fannie Mae, Freddie Mac, and rating agencies such as Standard & Poor’s was to acquire loans and securitize them. Therefore, they strongly tended to give subprime loans very high scores so that they can sell the products to investors. Dr. Thomas suggested seven lessons for O.R. in credit scoring, and I quote:
1. Ensure model objective relevant to decision maker.
- Know if static model is sufficient or if situation dynamics needs to be included.
- Making models “public” means model will be gamed.
- Verify data used in model.
- If updated data becomes available, build model to use the updated data.
- Model extrapolation can be dangerous. Credit rating agencies used corporate model for consumer CDOs.
- If model disagrees with common sense, think before using the model.