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?
Smaller lending institutions face a dilemma. The primary motivation behind the Current Expected Credit Loss (CECL) standard is to provide investors with enhanced forward-looking information about the state of the lending book. Producing high-quality/ low-volatility forward estimates, however, is difficult and can be expensive.
Stress testing, up until now, has basically been a theoretical exercise. Growth has been slow but steady and the imbalances that can trigger recessions have largely been absent. However, with many now calling for a 2020 U.S. recession – and with Brexit looming – we may soon find out whether stress tests actually work when applied in the real world.
For banks, one of the more onerous aspects of regulatory supervision is documentation. Regulators demand detailed descriptions of the models being used and banks have responded by the bucket-load.
Suppose I have two competing forecasting methods, each designed for CECL or IRFS 9 loss provisioning. Both would pass muster with regulators and auditors. How do I decide which is better?
Society benefits greatly from bank stress testing. However, stress testing also costs society a bundle.
We can have a vigorous debate about whether the societal benefits outweigh the costs. I think they do, by a margin, but I will understand if we disagree on this point.
A few years ago, I was lucky to hire an excellent summer intern from a leading economics PhD program in Europe. At the time, Lending Club made their historical performance data public and they included in the file a brief written request (likely penned by the prospective borrower), urging investors to fund their loan. I asked my intern to explore whether a quantitative treatment of the text would be useful in assessing the subsequent credit risk of the observed consumers.
IFRS 9 and CECL were designed with two outcomes in mind: to ensure sufficient reserves on the eve of a recession and to prevent restricted lending from curtailing a nascent recovery.
Let’s say I recently ventured to a Himalayan mountaintop. There I met a strange hermit who bestowed on me a precious gift: the specification of the true model for predicting credit card default.
In recent weeks, public debate in the banking industry has centered on loosening stress testing rules for the largest banks. Democrats in Congress have discussed the prospect of removing the burden of Comprehensive Capital Analysis and Review (CCAR) for all banks with assets under $250 billion. Treasury Secretary Steven Mnuchin has gone further, suggesting that banks with assets between $10 billion and $50 billion be freed from all regulatory stress test scrutiny.