Tony Hughes and Michael Vogan share valuable insights for managing your auto lending business more effectively.
In this presentation, we explore how vehicle choice can help predict probability of default, even after controlling for financial terms and borrower credit score. Our analysis suggests that vehicle information, including residual price forecasts, lifts the ability of traditional scores to classify borrowers from most likely to default to least likely. We combine data on consumer credit performance with vehicle information forecasts under a range of economic scenarios to answer:
- Can vehicle information help assess the creditworthiness of borrowers?
- What vehicle types are associated with better credit performance?
- How economically significant is vehicle information?
- How do borrowers act under stressed economic conditions?
Can Vehicle Residual Forecasts Lift Auto Credit Scores?
Tony Hughes | April 2018
We look at climate risk and consider how a heating planet might impact a bank's performance
Expanding Roles of Artificial Intelligence and Machine Learning in Lending and Credit Risk Management
With ever-expanding and improving AI and Machine Learning available, we explore how a lending officer can make good decisions faster and cheaper through AI. Will AI/ML refine existing processes? Or lead to completely new approaches? Or Both? What is the promise? And what is the risk?
When banks manage risk, conservatism is a virtue. We, as citizens, want banks to hold slightly more capital than strictly necessary and to make, at the margin, more provisions for potential loan losses. Moreover, we want them to be generally cautious in their underwriting. But what is the best way to arrive at these conservative calculations?
The traditional build-and-validate modeling approach is expensive and taxing. A more positive and productive validation experience entails competing models developed by independent teams.
The industry is currently a hive of CECL-related activity. Many banks are busily testing their systems or finalizing their preparations for the go-live date, which is either in January 2020 or somewhat later, depending on the organization. Some are still making plans for implementation, and the rest are worried that they should be.
The theory that banks are now safer because of CCAR, though, has not yet been tested.
Loan-loss provisioning models must take a variety of economic and client factors into account, but, with the right approach, banks can develop sensible loss forecasts that are more accurate and less susceptible to volatility.
As evidence of climate change builds and threats materialize,data will be invaluable in creating a framework for making future credit decisions.
In recent years, attention has increasingly turned to the promise of artificial intelligence (AI) to further increase credit availability and to improve the profitability of banks and other lenders. But what is AI?
Good-quality CECL projections can be developed using high-quality data that is available free of charge.