Current Expected Credit Loss (CECL) a New Paradigm for Captives, Too

The ramifications of CECL on Financial Institutions has in large part focused on Banks, but as we addressed in a recent paper, “Current Expected Credit Loss: Why the Expectations Are Different,” this new accounting treatment extends to a much larger universe.  An example of this are the captives that finance American’s love affair with cars; their portfolios of leases and loans have become much larger and the implications of CECL more significant.

As with other institutions, data, platforms, and modeling make up the challenges that captives will have to address.  But unlike other types of institutions captives have more concentrated portfolios, which may aid in “pooling” exercises, but may be inadvertently affected by scenario modeling.  A basic tenet for all institutions is the life-of-loan estimate and the use of reasonable and supportable forecasts.  While some institutions may have had “challenger” models in the past that moved in this direction, captives have not tended to utilize this type of approach in the past.

The growth of captives portfolios and the correlation to a number of macro-economic factors (e.g. interest rates, commodity prices, tariffs, etc.) call for data and scenarios that require a different level of modeling and forecasting.  Because FASB does not provide template methodologies or calculations it will be necessary to develop these scenarios with the mindset of the “reasonable and supportable” requirement.  While different approaches will likely be adopted, those that utilize transaction level data have the ability to provide a higher level of accuracy over time, resulting in the goals laid out in the new guidelines.  As might be imagined the ability to leverage experience in the development and deployment of these types of models can’t be overemphasized.

We have found that having the ability to manage the following functional components of the platform are critical to building a flexible platform that can manage the changing needs of the users:

  • Scenario Management
  • Input Data Mapping and Registry
  • Configuration Management
  • Model Management

Experience has taught that there are significant considerations in implementing CECL, but there are also some improvements that can be realized for institutions that develop a well-structured plan. Captives are advised to use this as an opportunity to realize efficiencies, primarily in technology and existing models. Considerations around data, platforms, and the models themselves should leverage available resources to ensure that investments made to address this change provide as much benefit as possible, both now and into the future.

IFRS 9: Modeling Challenges

Calculating expected credit losses under IFRS 9 is easy. It requires little more than high school algebra to determine the aggregate present value of future cash flows. But it is not easy to ascertain the key components that are used by the basic equation—regardless whether the approach taken is “advanced”  (i.e., where PD, LGD, and EAD are modeled) or ”simplified” (also called “intermediate”). The forward-looking stance mandated by IFRS 9 makes the inherently difficult process of specifying these variables all the more complex.

For the sake of brevity, let’s consider only the advanced approach for this discussion. There are two immediate impacts on PD model estimation: the point-in-time requirements and the length of the forecast horizon.

PD estimates need to reflect point-in-time (PIT) rather than through-the-cycle (TTC) values. What this means is that PDs are expected to represent the current period’s economic conditions instead of some average through an economic cycle. Bank risk managers will have to decide whether they can adapt a CCAR (or other regulatory) model to this purpose, determine a way to convert a TTC PD to a PIT PD, or build an entirely new model.

The length of the forecast horizon has two repercussions. First, one must consider how many models to build for estimating PDs throughout the forecast. For example, it may be determined that a portfolio warrants one model for year 1, a second model for years 2 to 3, and a third model for years 3+. Second, one should consider how far into the forecast horizon to use models. Given the impacts of model risk, along with onus of maintaining multiple models, perhaps PDs for a horizon greater than seven years would be better estimated by drawing a value from some percentile of an empirical distribution.

Comparatively speaking, bank risk managers may find it somewhat less difficult to estimate LGDs, especially if collateral values are routinely updated and historical recovery rates for comparable assets are readily available in the internal accounting systems. That said, IFRS 9 requires an accounting LGD, so models will need to be developed to accommodate this, or a process will have to be defined to convert an economic LGD into an accounting one.

Projecting EADs is similarly challenging. Loan amortization schedules generally provide a valid starting point, but unfortunately they are only useful for installment loans. How does one treat a revolving exposure? Can one leverage, and tweak, the same rules used for CCAR? In addition, embedded options have to be taken into account. There’s no avoiding it: estimating EADs calls for advanced financial modeling.

As mentioned above, there are differences between the requirements of IFRS 9 and those of other regulatory requirements (e.g., CCAR). As a result, the models that banks use for stress testing or other regulatory functions cannot be used as-is for IFRS 9 reporting. Bank risk managers will have to decide, then, whether their CCAR models can be adapted with relatively minor modifications. In many cases they may conclude that it makes more sense to develop new models. Then all the protocols and practices of sound model design and implementation come into play.

Of course, it is also important to explain the conceptual basis and present the supporting evidence for PD, LGD, and EAD estimates to senior management—and to have the documentation on hand in case independent auditors or regulatory authorities ask to see it.

In short, given PD, LGD, and EAD, it’s a trivial matter to calculate expected credit losses. But preparing to comply with the IFRS 9 standard is serious business. It’s time to marshal your resources.

Managing Model Risk

The Federal Reserve and the OCC define model risk as “the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports.”[1]  Statistical models are the core of stress testing and credit analysis, but banks are increasingly using them in strategic planning. And the more banks integrate model outputs into their decision making, the greater their exposure to model risk.

Regulators have singled out model risk for supervisory attention;[2] managers who have primary responsibility for their bank’s model development and implementation processes should be no less vigilant. This article summarizes the principles and procedures we follow to mitigate model risk on behalf of our clients.

The first source of model risk is basing decisions on incorrect output.  Sound judgment in the design stage and procedural discipline in the development phase are the best defenses against this eventuality. The key steps in designing a model to meet a given business need are determining the approach, settling on the model structure, and articulating the assumptions.

  • Selecting the approach means choosing the optimal level of granularity (for example, should the model be built at the loan or segment level).
  • Deciding on the structure means identifying the most suitable quantitative techniques (for example, should a decision tree, multinomial logistic, or deep learning model be used).
  • Stating the assumptions means describing both those that are related to the model structure (for instance, distribution of error terms) and those pertaining to the methodology (such as default expectations and the persistence of historical relationships over the forecast horizon).

Once the model is defined, the developers can progressively refine the model, critically subjecting it to rounds of robust testing both in and out of sample. They will make further adjustments until the model reliably produces plausible results.

Additionally, independent model validation teams provide a second opinion on the efficacy of the model.  Further model refinement might be required.  This helps to reduce the risk of confirmation bias on the part of the model developer.

This iterative design, development, and validation process reduces the first kind of risk by improving the likelihood that the final version will give decision makers solid information.

The second kind of model risk, misusing the outputs, can be addressed in the implementation phase. Risk managers learned the hard way in the financial crisis of 2007-2008 that it is vitally important for decision makers to understand—not just intellectually but viscerally—that mathematical modeling is an art and models are subject to limitations. The future may be unlike the past.  Understanding the limitations can help reduce the “unknown unknowns” and inhibit the misuse of model outputs.

Being aware of the potential for model risk is the first step. Acting to reduce it is the second. What hedges can you put in place to mitigate the risk?

First, design, develop, and test models in an open environment which welcomes objective opinions and rewards critical thinking.  Give yourself enough time to complete multiple cycles of the process to refine the model.

Second, describe each model’s inherent limitations, as well as the underlying assumptions and design choices, in plain language that makes sense to business executives and risk managers who may not be quantitatively or technologically sophisticated.

Finally, consider engaging an independent third party with the expertise to review your model documentation, audit your modeling process, and validate your models.

For information on how FRG can help you defend your firm against model risk, please click here.

[1] Federal Reserve and OCC, “Supervisory Guidance on Model Risk Management,” Attachment to SR Letter 11-07 (April 4, 2011), page 3. Emphasis added.

[2] See for example the Federal Reserve’s SR letters 15-8 and 12-17.

Subscribe to our blog!