Model Risk Validation: What Makes a Strong Provider?

The financial industry is becoming increasingly data-driven, with institutions relying on sophisticated models to interpret and process this data. These models are being used for a variety of reasons – from fraud detection to credit risk and cyber vulnerability analysis.  

With advancing technology, models are growing more complex, particularly those incorporating machine learning or artificial intelligence (AI). While these tools offer powerful insights, they also introduce new risks. Periodic model validation is essential for managing those risks, and selecting the right validation provider is just as important as the validation itself. 

Common Model Issues You May Face 

Whether you just implemented a new model, or have been using the same one for years, there may be underlying issues that you’re not seeing during day-to-day operations, including: 

• Data import or quality issues  
• Default out of the box assumptions that may not be appropriate for your credit union 
• Inaccurate internal procedures 
• Setting misconfigurations  

Identifying these issues sooner rather than later is crucial. Waiting until the model underperforms or fails could expose your institution to regulatory penalties, financial losses, or even reputational damage. A thorough model validation review is designed to uncover these risks – before they escalate.  

How to Choose a Strong Model Risk Validation Provider 

Choosing a model validation provider is not a one-size-fits-all decision. While there are many firms offering these services, their capabilities and expertise can vary widely. The right partner should bring a deep understanding of your specific model and the regulatory landscape, along with a proven track record. Here are a few essential qualities to look for: 

• Expertise in the specific model being validated 
• Regulatory knowledge of SR 11-7 and related model governance, including ASC 326-20 
• Thorough validation procedures that address all aspects of the model, including data inputs, recreation of model components, and evaluation of outputs 

Most importantly, the provider should have appropriate tenure. Firms with a strong history of model validation are better positioned to stay ahead of the rapidly evolving model environment. Their experience enables them to identify emerging patterns, potential risks, and catch blind spots that less seasoned providers might overlook. 

Effective Model Validation Hinges on the Right Partner 

Models are becoming more sophisticated and central to decision-making – and the risks tied to them are increasing. Regular validation will not only help your organization meet regulatory expectations, but catch issues before they impact performance, compliance, or reputation.  

The right validation provider plays a key role in this process. Look for a partner with deep industry expertise and regulatory understanding, and proven experience. Ask for case studies and references while evaluating partners. 

A strong validation partner doesn’t just check boxes – they provide the insight you need to keep your models reliable, accurate, and aligned with your institution’s risk appetite. Choosing your provider carefully is key. 

Fintech and financial institution regulators are expecting more from your model risk management framework. Learn about the evolution of model risk regulations, understand the model risk process, and read a real-life case study on model validation in our new ebook. Download The Model Risk Playbook: Essentials for Banks & Fintechs today.