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
- Session: 1.5 hours/session
- Duration: 5 weeks + Optional Guided Exercise (2 weeks)
- Case study + Labs using the Qu.Academy
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
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.
Febraury 14th 2021
5 weeks + Optional Guided Exercise (2 weeks)
Case study + Labs using the QuAcademy
If you would like an invoice for your payment for reimbursement or related questions on alternative payment methods, please contact email@example.com
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
- Models redefined: Data, Modeling environment, Modeling tools, Modeling process
Model Risk Management for Machine Learning Models - Part 1
- ML Life cycle management
- Metadata management
The Decalogue: Ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models:
- Models redefined: It’s not just input, process and output
- Governing the Machine Learning process
- Model Verification and Validation for Machine Learning Models
- Performance Metrics and Evaluation criteria
- 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):
- Integrating Data Governance and Model Governance
- Development Models vs Production Models
- Fairness, Reproducibility, Auditability, Explainability, Interpretability & Bias
- How do we objectively measure these?
- Review of the Apple-Goldman Sachs credit card debacle
- Machine Learning options and considerations
- AutoML (Data Robot, H20.ai, etc.), ML as a service (Google, Comprehend, Watson) and home-cooked custom models
- ML and Governance: Roles and Responsibilities redefined
- Perspectives on point-forecasts, validation and fat-tails!
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
Hands-on Case study
Learn from the past: How does Supervised machine learning work?
- 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.
Guided Exercise, part 2: Demonstrate your skills
Participants will demonstrate their findings to the class and obtain feedback from instructors and industry participants.
Preview the Class
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..