How to Choose a Model Validator: A Guide for Banks and Credit Unions
As of this writing, regulatory expectations for model validation may be loosening; however, the underlying risks remain and will not change.
In DCG’s experience, a well-performed model validation can reveal significant strategic opportunities when it’s not treated as just a checkbox to tick. This then raises the questions: How do you choose a model validator? And how do you evaluate the firms that you speak with? We’ll answer both below.
What to Look for in a Model Validator
Because model validation is critical to strategy, choosing the right model validator is also a strategic decision. That’s because model validation can help improve risk positions, identify assumptions and deficiencies in data, and strengthen the institution’s modeling.
Therefore, take care when choosing the right provider. Here’s a helpful framework for what to look for when evaluating model validators:
Data accuracy: The foundation of effective modeling is accurate data. Look for a firm that verifies the integrity, accuracy, and completeness of the data by not only reconciling the files but also benchmarking cash flows to ensure files were loaded correctly into the model.
Depth of review: A traditional “audit-like” validation may not be sufficient to generate meaningful value. A good model validator asks yes-or-no questions to confirm you have a model in place. A great validator asks probing questions to assess the adequacy of the model, the institution's policies, documentation, control processes, inputs/outputs, and procedures.
Effective Challenge: The biggest differentiator in model validators is the ability to incorporate effective challenge into the validation. This requires an understanding of how models should perform, their limitations, and an ability to foster change through educational and persuasive communication with key stakeholders. Effective challenge is considered the most technically sound approach to validating models to ensure they continue to perform optimally and as intended.
Focus on assumptions: Assumptions must be supported both qualitatively and quantitatively and be specific to the institution. A strong model validator regularly evaluates the assumptions development process, including sensitivity and stress testing and ongoing monitoring, to ensure assumptions reflect current business trends.
Model validation is an opportunity to improve decision-making and increase confidence in your models, so take care to thoroughly evaluate providers to choose the right one.
Red Flags to Watch Out for When Choosing a Validator
We covered the areas to focus on when evaluating a model validator, so now let’s review the red flags to watch out for.
The top red flags DCG recommends looking out for are:
What do they mean by “data?”: Not all data validations are created equal. Make sure to confirm the depth of the data review and validation. Does the validator perform a reconciliation, or just review your reconciliation? Do they look at the detailed files for completeness, or just review column headings to make sure the right fields are included (but do not confirm whether they are accurate)? Do they perform a benchmarking of the data by reading the files into an independent model to confirm cashflows are similar, thus providing confidence that the data was properly loaded into the model? These are important questions to understand when someone says, “We look at data.”
The high cost of low-cost validations: It makes sense to get the most value for your investment. If the pricing seems too good to be true or significantly less than other quotes, the level/rigor and expertise may not be commensurate with best practices. Expert validators can identify model deficiencies and errors quickly and know what kind of testing needs to be performed. The less experienced may have lower fees, but could miss something that ends up costing much more if the model produces incorrect information.
“We’re wide open!”: If the validator tells you that you can name your time because their calendar is “wide open,” it may be a troublesome sign. Consider why the provider’s schedule may have so much room. Is it because previous clients had bad experiences and won’t re-hire them? Open calendars may lead to lower prices to try to fill up space, which may initially be appealing to bankers who are looking for a quick/low-cost way to get the validation done. But that option could end up costing much more in the long run if the validator misses something (due to lack of experience/expertise), and you will be the one left to answer for it.
There are substantial potential negative consequences to choosing the wrong validation provider. While regulatory requirements may be changing, the underlying risks to institutions remain unchanged. A validator that doesn't dig deep enough into the data and effectively challenge the model may miss threats and blind spots that could ultimately cause meaningful harm.
Questions to Ask a Model Validator
Red flags may not reveal themselves until a model validation is already underway. By that point, it may be too late.
Below are the top four questions we recommend asking a model validator before you begin an engagement to understand how they will approach your institution.
What Type of Data Review Is Included in the Validation?
Most validators will say a data review is included in the validation, but it’s critical to understand exactly what they mean by “review.”
Some validators may only confirm that the files reconcile to the general ledger and perhaps review column headings to ensure downloads include the proper fields. That alone is not nearly enough to ensure the model is cashflowing correctly. It’s critical to confirm what steps the validator will take to ensure the foundation of the model is solid.
How Do You Confirm Customer Behaviors Are Reasonably Reflected in the Model?
Assumptions development is critical to reliable model output, yet it is fraught with challenges and pitfalls that may lead to wrong decision-making. Understand the processes your validator will go through to ensure assumptions are developed, supported, and tested appropriately and approved as required.
How Do You Help Ensure Models Continue to Perform as Intended?
Over time, model performance can deteriorate, whether through environmental challenges, technology not keeping pace, or turnover in modeling personnel.
Effective validation should confirm that models are properly back-tested to ensure model output remains consistent with actual results and reasonable compared to peer groups or an alternative analysis. Performance monitoring and outcomes analysis are key to ensuring models provide reliable results, so ensuring a validation strengthens these processes will build confidence.
How Is Your Validation Different?
This can pull back the curtain on your validator’s business. If they focus on being a low-cost option that can get it done fast, then they are driven by winning business and rapid engagements (not necessarily quality business results). Those who provide effective challenge signal that they are more willing to explain what issues were found, why they are important to address, and the potential exposures if they are left unremedied. A validator who takes time to educate their client will better form an ongoing relationship that benefits both parties, with a goal of becoming a trusted resource to count on when future challenges arise.
Learn more in DCG’s guide to the questions you’re not asking a model validator, but should be.
Ready to Start Your Model Validation?
Model validation remains a critical component of an institution’s strategic process. While the wrong validation can miss potential gaps and pitfalls, the right validation provider can truly make a difference with better model performance that leads to better decisions and a stronger bottom line.
When you choose to work with DCG, you aren’t just choosing a validator; you’re choosing a strategic collaborator that has your best interest in mind at all times!
“We really appreciated the willingness to discuss not just the core functions of the model being validated, but also the broader context around best practices and other options and approaches.” – Chief Risk Officer, $23B Credit Union
For over 40 years, Darling Consulting Group has helped banks and credit unions think differently. With a transformed, data-driven perspective, our clients can manage their entire balance sheet effectively and rely on their risk management models with confidence. Every year, over 650 institutions – ranging from de novo to $100B – seek DCG's independent counsel honed from broad-based expertise in asset/liability management and enterprise risk management modeling.
Do you need an outside perspective on your models? Schedule a call with Darling Consulting Group today.
ABOUT THE AUTHOR
Mark Haberland is a Managing Director at Darling Consulting Group. Mark has over 25 years providing balance sheet and model risk management education and consulting to the community and mid-size banking space. A frequent author and top-rated speaker on a wide array of risk management topics, Mark facilitates educational programs and workshops for numerous financial institutions, industry and state trade associations, and regulatory agencies.
Contact Mark Haberland: mhaberland@darlingconsulting.com or 978-499-8152 to learn more about DCG's approach to model validations and Model Risk Management.
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