As Default Probabilities Rise, Which U.S. Banks Are Most Likely To Fail?

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This is a reader-driven sequel to our recent post "As Default Probabilities Rise, How Many U.S. Banks Will Fail?" We address the question "who is most likely to fail?".

We present the highest cumulative default probabilities at one-year and three-year horizons among the 5,909 FDIC-insured banks in the US. We do the same for the CCAR banks.

The one-year outlook for defaults is benign (averaging 14 failures), but the average three-year failure rate of 184 banks will likely include both large and small banks.

Yesterday, we had some interesting reactions to the note "As default probabilities rise, how many U.S. banks will fail?" The graph above shows the distribution of outcomes in a 10,000 scenario simulation for one year using KRIS default probabilities for all 5,909 banks insured by the FDIC. The range was from 3 to 30 banks, with a median of 14 and an average of 14.3 banks failing. The average for a three-year horizon was 184.5 banks failing. How many U.S. banks will fail in the years ahead? Readers made a few consistent points:

  • Which banks are most likely to fail?
  • Isn't it just insignificant banks which will fail, a totally inconsequential outcome?
  • Are such default probabilities really available on a daily basis?

We address each of these questions in this brief note.

Which banks are most likely to fail?

This is an easy question to answer using the Kamakura Risk Information Service U.S. Bank model, which provides default probabilities for all of the 5,909 banks insured by the FDIC. See the Appendix for details on the KRIS U.S. Bank model. From a statistical point of view, when the scenario count is large, the banks that are most likely to fail are obviously those with the highest cumulative default probability over the one-year and three-year time horizons. The banks with the highest one-year cumulative default probabilities out of the 5,909 insured banks are shown here:

The same chart for the highest three-year cumulative default risk is given here:

It would be a mistake to conclude that only small banks are at risk. As we show below, that is not the case.

Isn't it just insignificant banks which will fail, a totally inconsequential outcome?

In this section, we analyze the banks operating in the United States (regardless of headquarters location) which were subject to the Federal Reserve's CCAR stress testing in 2016. The banks with the highest three-year cumulative default probabilities are shown in this table:

Note that this chart uses the KRIS public firm default probabilities for the parent companies, not the default probabilities of the U.S. bank subsidiaries. Ally Financial (NYSE:ALLY), the highest ranked "pure" American organization, has a 3.17% three-year cumulative default rate. On average, we would expect Ally to be tallied a defaulter in 317 of the 10,000 scenarios. Ten other American organizations have a three-year cumulative default probability of 1 percent or more, meaning they would show as defaulters in at least 100 of the 10,000 scenarios. These numbers come from an economy that on March 9, 2017, was at the 82nd percentile level for the period 1990-2017, with 100 being "best." When the economy worsens, of course, default probabilities will be higher.

My conclusion may differ from others, but that's normal. My reading of the numbers is that a fair amount of reasonably large institutions have a moderate chance of being among the defaulters as the time horizon extends beyond the first year.


Robert Jarrow, David Lando, and Fan Yu, "Default Risk and Diversification: Theory and Applications," Mathematical Finance, January 2005, pp. 1-26


The Kamakura U.S. Bank Model (abbreviated "KDP-BK1" for Kamakura Default Probability, Bank Model Version 1.0) was launched in 2014 after three years of development by Kamakura Risk Information Services. The model was developed using the insights of Prof. Robert A. Jarrow, Kamakura Managing Director of Research and former Senior Fellow at the Federal Deposit Insurance Corporation. In his role at the Federal Deposit Insurance Corporation, Prof. Jarrow co-authored the FDIC's 2003 Loss Distribution Model, which correctly forecast that the FDIC deposit insurance fund was significantly under-funded. The new model uses more than 2.3 million monthly observations of U.S. commercial banks and a time period that spans the full credit crisis experience for maximum accuracy. The KDP-BK1 model is even more accurate than the widely respected Kamakura public firm models. The Kamakura U.S. Bank Model is a modern reduced form model developed using the insights gleaned from Kamakura's public firm models, non-public firm models, and sovereign models.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.