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.
Learning Objectives
Upon completion of this course, you will be able to:
• Role of Machine Learning and AI in financial services
• Model Risk Management challenges and best practices for machine learning models
• Validating machine learning models: Quantifying risk, best practices and templates
• Regulatory guidance and the future
• Practical case studies with sample code
Delivery:
- Session: 1.5 hours/session
- Duration: 5 weeks + Optional Guided Exercise (2 weeks)
- Case study + Labs using the Qu.Academy
- Course goes Live: July 2nd week
Who should attend?
- Model Risk professionals, Model validators, Regulators and Financial professionals new to data-driven methodologies
- Quantitative analysts, investment professionals, Machine learning enthusiasts interested in understanding model risk and governance aspects in fintech, insurance and financial organizations
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 rare 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.
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 & Machine Learning APIs (Google,
Comprehend, Watson)
ML on the cloud vs On-prem
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
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
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
*NEW*
Managing models in the day of Covid19 - Perspectives on point-forecasts, validation and fat-tails!
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?
Guided Exercise, Part 1: Scoping and design
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.
Course instructor:
Sri Krishnamurthy, CFA
Chief Data Scientist, QuantUniversity
Sri Krishnamurthy is the founder of www.quantuniversity.com, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than two decades of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications.
Prior to starting QuantUniversity, Sri has worked at Citigroup, Endeca, MathWorks and with more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School.
Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College.
QuantUniversity (www.quantuniversity.com) is a quantitative analytics and machine learning advisory based in Boston, Massachusetts. QuantUniversity runs various data science and machine learning workshops in Boston, New York, Chicago, San Francisco and online. The company offers an Analytics Certificate Program and the Fintech Certificate program along with multiple workshops in its Explore-Experience-Excel series. Contact us at info@qusandbox.com
The Professional Risk Managers International Association (PRMIA) is a professional organization focused on the "promotion of sound risk management standards and practices globally", and "the integration of practice and theory".It provides certification and credentialing for professional risk managers, as well as other educational programs and resources.
Past Attendees of QuantUniversity workshops include Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..
