Model Governance and Risk Management is an integral part of Model Development Lifecycle.

Due to predictive nature of models, there is an inherent risk associated with them. If the model predictions deviate significantly from real world scenarios, it could have catastrophic results for both an organization and its customers. In such a scenario, it becomes extremely important to have well defined, preventive and detective guardrails around model development and use.

Organizations have done model risk management in one form or the other, but the overarching principles and framework has started shaping in the last decade. In April 2011, the US Board of Governors of the Federal Reserve System published the Supervisory Guidance on Model Risk Management (SR 11-7). With the recent advances in Machine Learning & Artificial Intelligence and the introduction of generative AI like GPT-4 and DALL·E, government and regulatory bodies around the world are showing tremendous interest in strengthening existing regulations or introducing new ones. On 17th May 2023, the Prudential Regulatory Authority of Bank of England published SS 1/23 Model Risk Management principles for banks in UK covering traditional banking models as well as Machine Learning and AI models.

This course gives an overview of Model Governance and Risk Management principles and can serve as a high level guide to implement or harden model governance and risk management processes for your organization or clients. We have taken the regulation SS1/23 Model Risk Management principles for Banks in UK as an example. Though we are using this regulatory example, the implementation framework discussed in this course is industry and geography agnostic.

What is covered in this course?

• Enterprise & Regulatory need for Model Governance and Risk Management

• Model Governance & Risk Management: Key Principles

  • Governance

  • Model Identification and Model Risk Classification

  • Model Development, Implementation and Use

  • Independent Model Validation

  • Model Risk Mitigation

• Implementation

  • Team Structure

  • Key functional requirements

  • Logical architecture for Enterprise Solution

  • Tool Selection for Enterprise Solution

Enroll now to develop a deeper understanding of Enterprise Model Governance and Risk Management!

Who this course is for:

• Data Scientists and Machine Learning Engineers who want to understand Model Risk Management principles and processes
• Model Validators and Model Risk professionals who want to implement/harden Model Risk Management solution
• Product Owners & Managers (who are working on Machine Learning & AI products)
• Anyone who wants a deeper understanding of Enterprise Model Governance and Risk Management