In consumer finance and insurance, businesses have been building, using, and governing models for decades. The adoption of unstructured data and advanced computational techniques is adding new layers of complexity that affect model deployment and the associated governance and risk mechanisms. Many enterprises are finding that existing governance and risk management practices need to be strengthened to allow these AI/ML models to be used.
Some of the emerging challenges associated with governing AI/ML models are:
- Incomplete or inaccurate model inventories due to the increased use of AI models across business lines
- The lack of insight into the predictive factors of AI/ML models, hindering interpretability and explainability
- The increased frequency at which AI/ML models must be monitored due to the use of real-time data and model decision-making in high frequency digital channels
Enterprises are sorting through the complexities of operationalizing and governing AI/ML models, identifying the gaps that exist. Fortunately, new tools for managing, monitoring and governing AI/ML models are helping AI leaders find the right mix tools, process and organizational change to deliver reliable, compliant AI decision making.
This ebook is based on a discussion with two experts: Stu Bailey, co-founder of ModelOp and expert in model operations and data analytics, and Shrikant Dash, a financial services executive and risk management expert.