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
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 firstname.lastname@example.org
Machine Learning and AI: A Model Risk Perspective
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:
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
Managing models in the day of Covid19
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
Synthetic data for Model Risk Management
Hands-on Case study
Learn from the past: How does Supervised machine learning work?
Guided Exercise, Part 1: Scoping and design
Guided Exercise, part 2: Demonstrate your skills
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..