AI Model Risk Management


Incorporate AI MRM into your existing processes to improve efficiency, accelerate model to market and increase ROI on your AI investments

GOVERNANCE | RISK | COMPLIANCE...with efficiency, speed, and accuracy
What Is AI Model Risk Management?

The function of Model Risk Managment (MRM) is to provide assurance that models are well controlled and fit for purpose.  It is an established practice for Financial Institutions around the world.

FAIRLY is leading the charge in incorporating best practices and regulatory requirements for AI Model Risk Management into the existing MRM framework for Financial Institutions in a rapidly-changing regulatory environment. At the same time, we are bringing this established MRM practice to other highly regulated industries such as healthcare, education and hiring to reap the benefits of Model Risk Management for AI.

Standardize Testing

In addition to routine regulatory compliance testing, we incorporate additional tests that are unique to AI models to help your model validation teams ease into the world of AI model validation.

Manage Inventory

Are you still using spreadsheets to track your models? If so, keeping up with your AI model inventory will be a nightmare. FAIRLY provides an intuitive platform to manage your statistical and AI models together or separately, per your needs.

Ongoing Monitoring & Reporting

Existing AI monitoring and explanability tools are reactive debugging tools built for data scientists. FAIRLY's monitoring and reporting solution is built for your model validation team for proactive regulatory compliance.

One Platform, Three Engines

Keep track of and seamlessly generate reports about your AI development and production systems for  regulators, partners, or auditors — all with the click of a button.


Manage and streamline your responsible AI and compliance workflows, engaging teams and fostering collaboration across business and technical stakeholders.


Our industry-first AI Model Analytical engine provides insights for business decision makers on the health of the AI they approve to build.