Build value and insight from model validation activities

Ralph D. Wright, Caroline Curley, Beatriz Rincόn Young
Build value and insight from model validation activities

Model validation is a requirement, but some extra effort can help your organization find benefits beyond checking boxes.

Model validation is a regulatory necessity. But for many financial services organizations – especially smaller ones – the road to efficient model validation is not always smooth.

Internal and external factors can cause gaps, inefficiencies, and frustration in an organization’s model validation process. Such challenges can be technical, logistical, or structural – but a common thread runs through them all.

Understanding and addressing the barriers to model validation processes can reduce the number of functional headaches and provide benefits beyond checking the regulatory boxes.

Reliable models can inform more effective decisions and provide a clearer picture of risk in existing customer and product portfolios. Higher model reliability can also help direct an organization’s new product and service pursuits. Decisions that once seemed risky might lie within an organization’s risk appetite when viewed through a more dependable lens.

But to take the most effective steps toward problem-solving and opportunities, organizations must first identify their barriers to model reliability.

Need insight into navigating the regulatory gray areas between models and tools?

Potential causes of interference in model validation processes

Potential causes of interference in model validation processes

Financial services organizations can experience multiple challenges in their model validation and risk monitoring goals. Following are some common model validation challenges:

Resources and team experience

Success can be difficult for departments if their staffing levels aren’t sufficient to handle consistent model validation and risk monitoring.

Because of the COVID-19 pandemic, some organizations have had to manage with reduced department sizes or increased workloads without new hiring. Additionally, staff familiar with the model validation process might have moved on, resulting in critical knowledge gaps.

Risk-based testing

Applications of tools and models can vary across organizations. The risk-based testing deployed on each tool or model also varies depending on each organization’s decisions.

Organizations should establish confidence in the definitions and validation procedures associated with their model validation programs. They should not hesitate to discuss their approach with regulators or consultants with extensive experience in model validation.

A policy of waiting for advice or recommendations from regulators can contribute to issues in models being discovered later than they could have been.

Vendor collaboration

Vendor collaboration

Regulators continue to have high expectations for use and independent testing of vendor models. As a result, financial services organizations maintain the responsibility of understanding their vendor models’ functions and the outputs received from them. As model technology evolves in areas such as artificial intelligence and machine learning, the need to stay updated grows more urgent.

When a model owner or vendor is not fully engaged, the resulting gaps in knowledge and protocols could lead to confusion and difficulty meeting regulatory demands down the road.


Many model validations identify the need for more thorough documentation. Proper documentation of model inputs, user access, model output, and the approach to ongoing monitoring is critical to detecting foundational problems with models and making informed decisions about how to resolve them.

Ideally, an organization should not wait until a model is fully implemented to build a monitoring and validation plan. Policies for documentation should be established as soon as reasonably possible.

New acquisitions and partnerships

New acquisitions and partnerships

When financial services organizations take on new relationships, the responsibilities for models might fall under them. This situation can be especially common with fintech partnerships.

Fintechs and regulated financial services organizations don’t share the same requirements for model validation. For example, when a fintech arrives as part of an acquisition or partnership, the responsibility for its models often shifts to the financial services organization. Therefore, an organization must determine what risks it is – or could be – taking on with the new business relationship.

A more reliable model validation plan

Financial services organizations can build sustainable model validation programs while protecting their investments.

Incorporating consistent monitoring and collaboration between model teams and vendors can increase the dependability of models and ease some of the burdens experienced with model validations. But working toward more effective model validations and maintenance requires addressing issues at multiple levels.

That’s where Crowe consultants can help make a difference. Our specialists have extensive experience in the product and banking sides of models. They stay up to date with what regulators are looking for and create sustainable monitoring and testing processes for your organization.

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Crowe specialists know models from all angles

Contact us for a consultation. We’d love to show you how model validation is more than a check-the-box exercise and how it can be the means to more powerful decisions.
Ralph D. Wright
Principal, Financial Services Consulting
Bea Young
Beatriz Rincόn Young
Director, Financial Services Consulting
Caroline Curley
Financial Services Consulting