Model Risk Management in the Age of AI

Stu Bailey, insideBIGDATA – June 30, 2020

In this article, Stu Bailey, Co-Founder and Chief AI Architect of ModelOp, discusses how financial services companies can easily validate multiple AI/ML models and reduce ML project costs by 30% through automation. ModelOps refers to the process of enabling data scientists, data engineers, and IT operations teams to collaborate and scale models across an organization. This drives business value by getting models into production faster and with greater visibility, accountability and control.

77% of financial services industry professionals expect AI/ML to be extremely important to their business by 2022, while only 16% currently employ AI/ML models. Clearly financial services organizations possess the impetus to take advantage of AI and ML capabilities, and yet models still aren’t being deployed– which exposes a quagmire in the process of model deployment. Could it be they’re focusing too much on the development aspect and ignoring the criticality of ModelOps?

Model validation is required across all regulated industries, but FinServ institutions especially face significant regulatory compliance mandates from the federal government – placing yet another roadblock on their path to AI success. Given these same institutions leverage thousands of models per day, they must typically staff large teams across their model risk management program, including spinning up large teams of model validators.

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