Enterprise Model (AI) Governance & Risk Management

Model Governance and Risk Management is now essential for any organization leveraging predictive models. The inherent risks associated with these models – where predictions significantly diverge from reality – can lead to severe consequences for both the organization and its customers. Therefore, robust, proactive, and reactive guardrails are crucial throughout the entire model development lifecycle.

While organizations have addressed model risk management in various ways, a comprehensive framework has solidified in recent years. A pivotal moment was the US Federal Reserve's SR 11-7 Supervisory Guidance on Model Risk Management (April 2011). Now, with rapid advancements in Machine Learning (ML), Artificial Intelligence (AI), and the emergence of generative AI like GPT-4 and DALL·E, regulatory scrutiny is intensifying globally. The Bank of England's SS 1/23 Model Risk Management principles (May 2023) for UK banks, covering both traditional and AI/ML models, exemplifies this trend.

This course provides a comprehensive overview of Model Governance and Risk Management principles, offering a high-level guide to implement or strengthen your organization's processes. We utilize the UK's SS 1/23 regulations as a practical example, but the implementation framework is designed to be industry-agnostic and geographically flexible.

What you'll learn:

• Enterprise & Regulatory Need: Understand the business and regulatory drivers for effective Model Risk Management.
• Model Governance & Risk Management: Key Principles: Explore the core principles underpinning sound model risk management.
• Governance: Establish effective model governance structures.
• Model Identification & Risk Classification: Learn how to identify and categorize models based on their associated risks.
• Model Development, Implementation & Use: Best practices for managing models throughout their lifecycle.
• Independent Model Validation: Implement robust model validation processes.
• Model Risk Mitigation: Strategies for minimizing and controlling model risk.
• Implementation: Practical steps for implementing a model risk management framework.
• Team Structure: Define the necessary roles and responsibilities within a model risk management team.
• Logical Architecture: Understand the logical architecture for an enterprise model risk management solution.
• Tool Selection: Learn how to choose appropriate tools for supporting your model risk management program.

Enroll now to gain a deeper understanding of Enterprise Model Governance and Risk Management and ensure responsible AI and ML adoption!