Though not its intent, model validation can be disruptive to model owners and others seeking to carry out their day-to-day work. We have performed enough model validations over the past decade to have learned how cumbersome the process can be to business unit model owners and others we inconvenience with what at times must feel like an endless barrage of touch-point meetings, documentation requests and other questions relating to modeling inputs, outputs, and procedures.
We recognize that the only thing these business units did to deserve this inconvenience was to devise or procure a methodology for systematically improving how something gets estimated. In some cases, the business owner of an application tagged for validation may view it simply as a calculator or other tool, and not as a "model." And in some cases we agree with the business owner. But in every case, the system under review has been designated as a model requiring validation either by an independent risk management department within the institution or (worse) by a regulator, and so, the validation project must be completed.
As with so many things in life, when it comes to model validation preparation, an ounce of prevention goes a long way. Here are some ideas model owners might consider for making their next model validation a little less stressful.
Overall Model Documentation
Among the first questions we ask at the beginning of a model validation is whether the model has been validated before. In reality, however, we can make a fairly reliable guess about the model's validation history simply by reading the model owner's documentation. A comprehensive set of documentation that clearly articulates the model's purpose, its inputs' sources, how it works, what happens to the outputs and how the outputs are monitored is an almost sure sign that the model in question has been validated multiple times.
In contrast, it's generally apparent that the model is being validated for the first time when our initial request for documentation yields one or more of the following:
- An 800-page user guide from the model’s vendor, but no internally developed documentation or procedures
- Incomplete (or absent) lists of model inputs with little or no discussion of how inputs and assumptions are obtained, verified, or used in the model
- No discussion of the model’s limitations
- Perfunctory monitoring procedures, such as, "The outputs are reviewed by an analyst for reasonableness"
- Vague (or absent) descriptions of the model's outputs and how they are used
- Change logs with just one or two entries
No one likes to write model documentation. There never seems to be enough time to write model documentation. Compounding this challenge is the fact that model validations frequently seem to occur at the most inopportune moments for model owners. A bank's DFAST models, for example, often undergo validation while the business owners who use them are busy preparing the bank's DFAST submission. This is not the best time to be tweaking documentation and assembling data for validators.
Documentation would ideally be prepared during periods of lower operational stress. Model owners can accomplish this by predicting and staying in front of requests from model risk management by independently generating documentation for all their models that satisfies the following basic criteria:
- Identifies the model's purpose, including its business and functional requirements, and who is responsible for using and maintaining the model
- Comprehensively lists and justifies of the model's inputs and assumptions
- Describes the model's overall theory and approach, i.e., how the model goes about transforming the inputs and assumptions into reliable outputs (including VBA or other computer code if the model was developed in house)
- Lays out the developmental evidence supporting the model
- Identifies the limitations of the model
- Explains how the model is controlled—who can access it, who can change it, what sorts of approvals are required for different types of changes
- Comprehensively identifies and describes the model’s outputs, how they are used, and how they are tested
Any investment of time beforehand to incorporate the items above into the model’s documentation will pay dividends when the model validation begins. Being able to simply hand this information over to the validators will likely save model owners hours of attending follow-up meetings and fielding requests. Additional suggestions for getting the model’s inputs and outputs in order follow below.
All of the model’s inputs and assumptions need to be explicitly spelled out, as well as their relevance to the model, their source(s), and any processes used to determine their reliability. Simply emailing an Excel file containing the model and referring the validator to the ‘Inputs’ tab is probably going to result in more meetings, more questions, and more time siphoned out of the model owner’s workday by the validation team.
A useful approach for consolidating inputs and assumptions that might be scattered around different areas of the model involves the creation of a simple table that captures everything a validator is likely to ask about each of the model’s inputs and assumptions.
Systematically capturing all of the model’s inputs and assumptions in this way enables the validators to quickly take inventory of what needs to be tested without having to subject the model owner to a time-consuming battery of questions designed to make sure they haven’t missed anything.
Being prepared to explain to the validator all the model’s outputs individually and how each is used in reporting and downstream applications greatly facilitates the validation process. Accounting for all the uses of every output becomes more complicated when they are used outside the business unit, including as inputs to another model. At the discretion of the institution’s model risk management group, it may be sufficient to limit this exercise only to uses within the model owner’s purview and to reports provided to management. As with inputs, this can be facilitated by a table:
Outputs that impact directly on financial statements are especially important. Model validators are likely to give these outputs particular scrutiny and model owners would do well to be prepared to explain not only how such outputs are computed and verified, but how the audit trails surrounding them are maintained, as well.
To the extent that outputs are subjected to regular benchmarking, back-testing, or sensitivity analyses, these should be gathered as well.
A Series of Small Investments
A model owner might look at these suggestions and conclude that they seem like a lot of work just to get ready for a model validation. We agree. Bear in mind, however, that the model validator is almost certain to ask for these things at some point during the validation, when, chances are, a model owner is likely to wish she had the flexibility to do her real job. Making a series of small time investments to assemble these items well in advance of the validator's arrival not only will make the validation more tolerable for model owners, but will likely improve the overall modeling process as well.
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