|About This Course
MEET OUR TRAINING PARTNER
This course is being delivered by a PRMIA Training Partner, QuantUniversity. All registrations, payments, and course operations will be managed exclusively by QuantUniversity. When registering for the course, you will leave PRMIA.org and use QuantUniversity's registration system, proprietary learning management system, and www.qu.academy for labs. See QuantUniversity course policy below.
Lecture & hands-on lab work!
The financial industry has been adopting AI and machine learning at a rapid pace. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms exploring novel modeling methods to augment their traditional investment and decision workflows. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling models. While there is significant enthusiasm, model risk professionals and risk managers are concerned about the onslaught of new technologies, programming languages, and data sets that are entering the enterprise. With little formal guidance from regulators on how to validate models and quantify model risk, organizations are developing their own home-cooked methods to address model risk management challenges.
In this course, we aim to bring clarity on some of the model risk management and validation challenges with data science and machine learning models in the enterprise. We will discuss key drivers of model risk in today’s environment and how the scope of model risk management is changing. We will introduce key concepts and discuss aspects to be considered when developing a model risk management framework incorporating data science techniques and machine learning methodologies in a pragmatic way.
Upon completion of this course, you will be able to:
• Understand the role of machine learning and AI in financial services
• Identify model risk management challenges with machine learning models
• Validate machine learning models: Quantifying risk, best practices and templates
• Translate regulatory guidance
• Apply lessons learned from practical case studies with sample code
Join Sri for an optional Guided Exercise, August 18 - 25, 2020
Participants will go through a guided exercise to perform model validation on a chosen machine learning model of their choice. Guidance will be provided in scoping and implementing the project. You will then have the opportunity to demonstrate your findings and receive feedback. This is a great opportunity to apply what you have learned in a test environment and receive feedback to ensure your understanding. Subsequently, this will allow you to immediately apply new skills to your role/position.
How It Works
This course is delivered by QuantUniversity. Shortly before the course start date, you will be provided with login credentials and a link to the course lecture and labs. Each Tuesday a lesson is launched. Although this is a self-paced course, we highly recommend you attend weekly classes and participate in labs during the week scheduled to get the most out of the course and support from the faculty.
| Lesson 1
July 14, 2020
||Machine Learning and AI: A Model Risk Perspective
- Drivers of Model Risk in the age of data science and AI
- Machine Learning vs Traditional quant models: How has the world changed?
- A tour of Machine Learning and AI methods
- Supervised vs Unsupervised Learning (Regression, Neural Networks, XGBoost, PCA, Clustering)
- Deep Learning & Reinforcement Learning (Keras, Tensorflow, PyTorch)
- Automatic Machine Learning (Data Robot, H20.ai etc.) & Machine Learning APIs (Google,
- ML on the cloud vs On-prem
| Lesson 2
July 21, 2020
||Model Risk Management for Machine Learning Models - Part 1
- Models redefined: Data, Modeling environment, Modeling tools, Modeling process
- The Decalogue: Ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models:
1. Models redefined: It’s not just input, process and output
2. Governing the Machine Learning process
3. Model Verification and Validation for Machine Learning Models
4. Performance Metrics and Evaluation criteria
5. Model Inventory and tracking
| Lesson 3
July 28, 2020
||Model Risk Management for Machine Learning Models – Part 2
- The Decalogue: Ten things to think about when developing your model governance framework when integrating Machine Learning models (cont’d):
6. Integrating Data Governance and Model Governance
7. Development Models vs Production Models
8. Fairness, Reproducibility, Auditability, Explainability, Interpretability & Bias
a. How do we objectively measure these?
b. Review of the Apple-Goldman Sachs credit card debacle
9. Machine Learning options and considerations
a. AutoML (Data Robot, H20.ai, etc.), ML as a service (Google, Comprehend, Watson) and home-cooked custom models
10. ML and Governance: Roles and Responsibilities redefined
| Lesson 4
August 4, 2020
|| Pragmatic Model Risk Management for AI/ML models
- Challenges and best practices for pragmatic model management within the enterprise
- Working with open source projects
- Working with vendor models and machine learning APIs
- Quantifying model risk for machine learning models
- Model risk management for deep-learning models
- Validation criteria and best practices
- Templates for Model Validation for machine learning models
- Use of Synthetic datasets
| Lesson 5
August 11, 2020
|| Hands-on Case study
Validating a Credit Risk Machine Learning Model:
- A case study illustrating a model validation of a credit risk model involving machine learning
- Working with Regression, Neural Networks, and Random Forest models
- Development models vs Production models
- Sample templates and worksheets will be provided
- Roadmap for the MRM team to upskill and keep abreast of changes in the AI and ML landscape
- Training, education, and expectation setting
- Future outlook: Regulation, Sandboxes, Frameworks
- Review of recent regulatory efforts
- How should companies proactively plan for changes and the future?
August 18, 2020
||Guided Exercise, Part 1: Scope and Implement
Put your newly learned skills to practice while being mentored through the process. Participants will go through a guided exercise to perform model validation on a machine learning model of their choice. Guidance will be provided in scoping and implementing the project.
August 25, 2020
||Guided Exercise, Part 2: Demonstrate Your Skills
Participants will demonstrate their findings to the class and obtain feedback from instructors and industry participants.