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On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003
Slides and video at: https://academy.qusandbox.com/#/market/5f467c5499aa4a24691da707
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Lecture 2: Rapid Prototyping Quant Research ML Models for Algorithmic Auditing using the QuSandbox
Unlike traditional quant models, ML models require constant iteration, tweaking, testing, monitoring and retuning. Without a rigorous process for facilitating these Agile workflows for machine learning, Quants are destined to be tied up in a brittle process that is not agile nor scalable OR build models without any process encumbrances incurring major model risks in their workflow.As the scale of ML model adoption increases within the enterprise, a controlled process that enables Quants to be creative and explore tools and datasets of their choice is needed. In this talk, we will illustrate, through a case study on why a Sandbox based approach to building machine learning models is warranted.
