Operational Risk Capital Proposal: Time to Hit the Pause Button
The so-called Basel III Endgame could lead to significant amendments in operational loss estimation at big banks. But these recommended regulatory revisions could have unintended consequences, and all parties involved would be wise to reconsider B3E’s operational risk impact before pushing forward.
Friday, November 17, 2023
By Clifford Rossi
The largest banks in the U.S. are now facing major changes to the way they calculate capital operational risk. But the proposed modifications to regulatory capital are misguided and not reflective of the underlying drivers of operational loss events. They would, moreover, likely place a drag on economic growth.
Known as the Basel III Endgame (B3E) and developed by a regulatory coalition comprising the Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation and the Office of the Comptroller of the Currency, the proposal calls for firms to shift away from internal models and toward a standardized approach for operational risk loss estimation. Comments about B3E (which also includes revisions to market risk, credit risk and financial derivative risk) are due on November 30, and large banks would have to start transitioning to the new framework by January 1, 2025.
But until they perform a more thorough assessment of how operational losses manifest at large banks, regulators would be wise to reconsider the proposed B3E modification to bank capital requirements.
Flaws in B3E Operational Risk Capital Methodology
The largest U.S. banks (i.e., designated Category I and II banks) have in the past been able to use internal models to develop estimates of operational risk capital (ORC) under the advanced measurement approach (AMA) to regulatory capital requirements. However, under B3E, the use of internal models will be eliminated in favor of a standardized approach for operational risk (SA-OR).
Regulators argue that this revision is necessary because there is simply too much variability in model results among the banks applying their own models and data to measuring operational risk. That decision is probably right, but for the wrong reason.
Statistical modeling of operational loss distributions remains one of the greatest challenges in risk measurement, because of the scarcity of data to reliably generate an estimate of tail risk outcomes needed for determining ORC. Though there are many academic papers on advanced modeling techniques for developing operational loss distributions, this literature is fraught with a host of measurement issues.
The methodology underlying SA-OR, however, is even more problematic. SA-OR attempts to bring a measure of pseudo-precision into the determination of ORC through a set of formulas relating ORC to a business indicator component (BIC) and to an internal loss multiplier (ILM). But the BIC is determined by historical estimates of income, expenses, and revenues from various banking sources, such as lending, investment, services and trading activities. And it is crude, at best, to use income, expenses and revenue to scale operational loss, because the use of these factors to calculate ORC lacks a credible theoretical and empirical foundation.
Indeed, aside from limiting the upper and lower boundaries of ORC outcomes, the B3E’s ORC methodology features a set of parameters that curiously have very little empirical support. These parameters include the BIC coefficients (weights assigned to banks based on their BI levels), a floor on the ILM and a dampener on ILM. The ILM floor is particularly problematic, as it provides no incentive for U.S. banks to improve their operational risk capabilities.
The Fed’s stress loss models are overly simplistic representations of operational loss outcomes. One of these models relates historical operational losses to a set of macroeconomic factors, sans any kind of model validation.
Blending the Fed’s stress test losses with B3E to set large bank ORC is inherently flawed, theoretically and analytically. It seems driven more by data expediency than by sound theory.
How to Improve Operational Risk Capital Calculations
Undoubtedly, more theoretical and empirical work needs to be conducted to better understand operational risk. The ramifications of higher ORC charges are simply too important to rely on crude and unfounded methods.
Designing a better operational risk capital framework starts by developing a robust theory for what drives operational losses. There are five factors that have direct bearing on each of the seven operational loss event types under Basel capital standards: (1) operational and organizational complexity; (2) risk profile; (3) asset growth rate; (4) scale of operations; and (5) investment in operational capabilities.
Banking operations could benefit from some cross-industry operational complexity analysis. Nonfinancial firms like industrial manufacturers and logistics companies have, for example, delved into measuring complexity in their supply chains and other processes.
As it has done in years past, the Fed could gather data on operational losses from large financial institutions. This information would shed more light on operational loss dynamics, and firms could eventually use it to build a more robust ORC framework.
Of course, that will take time. In the interim, the Fed should extend the use of internal models for ORC determination and provide a credit toward ORC for operational loss charges from annual stress tests. While not a perfect solution, it would at least maintain the status quo until a more thoughtful approach to ORC is developed.
The regulatory community was clearly caught off-guard by this year’s U.S. regional banking crisis. But the proposed B3E operational risk framework feels a bit rushed – and is far from an ideal solution.
The old standby regulatory response to a banking crisis to raise capital is ineffective and, in the specific case of B3E ORC, misses the mark entirely. It actually will demotivate banks to invest in operational capabilities, pushing more banking activity to less regulated nonbank entities. What’s more, it will impose higher costs on consumers without having any empirical basis on which to support ORC charges.
Let’s hit the pause button on changing the B3E ORC methodology until better data, frameworks and analysis come to light.
Clifford Rossi (PhD) is the Director of the Smith Enterprise Risk Consortium at the University of Maryland (UMD) and a Professor-of-the-Practice and Executive-in-Residence at UMD’s Robert H. Smith School of Business. Before joining academia, he spent 25-plus years in the financial sector, as both a C-level risk executive at several top financial institutions and a federal banking regulator. He is the former managing director and CRO of Citigroup’s Consumer Lending Group.