The COVID-19 saga has caused real difficulties for risk modelers. Loss projections made using pre-pandemic models soared in mid-2020, as global economic data spiraled downward. Portfolio performance, however, has held up very well under extremely difficult circumstances.
When it comes to both the present and the future, which financial institutions are in a better position: those that rely on traditional, structural credit risk models or those that lean toward machine-learning models? Accuracy, intuitiveness, adaptability and behavioral shifts are among the factors that should be evaluated when answering this question – but we must first consider how different types of models have performed in 2020.
2020 has been a challenging year for risk modelers, rife with uncertainty. What have we learned about the effectiveness of existing stress tests and the scenarios that drive them?
Looking in the rearview mirror, it’s usually easy to see whether previously accepted risks were adversely realized. The moment you pay back the $20 I loaned you last month, for example, I know that the credit risk associated with our transaction has cured.
What is a forward-looking model?
During the 2008/09 global financial crisis, loan-loss accounting methods were unable to provide timely, accurate information to investors about the quality of loans held by banks. CECL and IFRS 9 were introduced to address these concerns but, for different reasons, have failed to transmit useful signals in the COVID-19 economy.
In 2020, for the first time in 11 years, a U.S. stress test was conducted while the economy was actually in recession. The test has been criticized by detractors who have argued that it did not take full account of COVID-19 scenarios and therefore should have been postponed. But are these concerns valid?