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:
• 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
- Course goes Live: November 3rd 2020
*If you would like an invoice for your payment for reimbursement or related questions on alternative payment methods, please contact firstname.lastname@example.org
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.
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.