Industry News Details

Model Risk Management in the Age of AI Posted on : Jun 30 - 2020

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.

The Criticality of ModelOps

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.

Most of the resources financial service organizations spend on AI initiatives specifically support model development. This isn’t to say organizations are completely ignoring ModelOps. Instead, they appear to be treating ModelOps as an afterthought rather than a continuous cycle of deployment, governance and monitoring that serves as the keystone of the AI/ML model lifecycle. View More