Automation in Model Risk Management

Automation in model risk management has been gaining traction, however, despite the benefits automation offers, banks still resist adopting it. Read on to learn why automation will be critical to the future success of MRM teams, and how you can embrace it now.

The Model Risk Management Challenge

With the widespread model usage in banks, there’s been a dramatic increase in the number of models being developed. Financial institutions rely heavily on these models for decision-making across most of their day-to-day activities.

However, the risks of model misuse or defective models are immense, and the consequences are dire. Model malfunctioning was an unexpected result of the pandemic, elevating the importance of model risk management (MRM).

MRM teams are crucial in mitigating these risks, but the overwhelming number of models in use is draining MRM resources. With increasing inventory, model validation delays are escalating as teams are unable to keep up with the demand. The growing development of AI and ML models is adding layers of complexity, further complicating model governance. 

Firms need to add capacity to their teams to keep up with demand, but adding resources is costly. Furthermore, each additional team member added will only minimally increase capacity, slightly easing pressure, but not solving the problem.

How Automation Can Help

Massive team expansion is unrealistic given the substantial budget required. So, how can firms increase their capacity in a way that will allow them to scale as demand continues to grow?

For financial institutions to truly be able to scale their MRM operations, they need to turn to automation. By automating repetitive, manual tasks, firms can significantly reduce process turnaround time, freeing highly paid talent to focus on higher-value work. Automation in model risk management can also enhance regulatory compliance by improving accuracy and reducing the risks of human errors.

One of the main hurdles to adopting new technology is the assumption that it will disrupt business and require extensive time and resources to deploy, however, this does not have to be the case. It is possible to find platforms that are easy to implement and require minimal change management. Let us show you:

How to Get Started

So, how can you implement automation and start saving time NOW?

  1. Start by taking a look at your current processes and identifying the most time-consuming, manual processes.
  2. Evaluate whether any of these processes be automated. The answer is often
  3. Achieve quick wins by implementing tools that are built for MRM and easily fit into your existing processes.

Model documentation is one area that is a major headache for MRM leaders. An important aspect of model governance is making sure that every step of the process, from model development, to validation, to periodic review is thoroughly documented in reports that typically consist of hundreds of pages. Documentation automation has attracted a lot of attention in the risk industry given the time savings it can create.


Evalueserve’s latest tool, MRMraptor, automates test results interpretation and documentation across the entire MRM value chain.

  1. It runs alongside your existing testing infrastructure, so it’s quick to deploy and nonintrusive, and
  2. MRMraptor runs on domain-specific AI, it was made specifically for MRM teams by MRM experts.

The MRM team at a top Nordic retail bank was struggling with manually managing a large volume of models. They attempted to automate the documentation process internally but ran into multiple challenges, so they came to Evalueserve for help. Evalueserve was able to quickly implement MRMraptor to immediately scale its team’s capacity and shorten reviews from months to weeks.

To learn more about how automation in model risk management can scale your team’s capacity, talk to one of our experts

Allison Cornett
Marketing Manager Posts

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