What you'll learn

Understand Enterprise & Regulatory risk management need for Statistical, Machine Learning and Artificial Intelligence models

Understand the principles for Model Governance and Risk Management

Understand SS1/23 Model Risk Management regulation for Banks in United Kingdom

How to implement an Enterprise Model Governance & Risk Management solution

Requirements

No programming experience or skill is required

A basic understanding of Model Development lifecycle is recommended

A basic of understanding of risk w.r.t IT systems is recommended

Description

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 ManagementModel Governance & Risk Management: Key PrinciplesGovernanceModel Identification and Model Risk ClassificationModel Development, Implementation and UseIndependent Model ValidationModel Risk MitigationImplementationTeam StructureKey functional requirementsLogical architecture for Enterprise Solution Tool Selection for Enterprise SolutionEnroll now to develop a deeper understanding of Enterprise Model Governance and Risk Management!

Overview

Section 1: Introduction & Need for Model Governance and Risk Management

Lecture 1 Introduction

Lecture 2 Enterprise & Regulatory need for Model Governance and Risk Management

Section 2: Model Governance & Risk Management: Key Principles

Lecture 3 Principles Overview

Lecture 4 Governance principle

Lecture 5 Model Identification and Model Risk Classification principle

Lecture 6 Model Development, Implementation and Use principle

Lecture 7 Independent Model Validation principle

Lecture 8 Model Risk Mitigation principle

Section 3: Implementation

Lecture 9 Team Structure

Lecture 10 Key Functional Requirements

Lecture 11 Logical Architecture

Lecture 12 Tool Selection

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