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Topic

Kubeflow-based ML Model Risk Management with streamlined SR11-7 compliance

Abstract

The lack of updated compliance reporting for machine learning models frequently blocks profitable models from being deployed. Financial organizations need a process to produce reports and artifacts for governmental regulators, such as those defined in the Federal Reserve’s SR11-7 Guidance on Model Risk Management. In this workshop, attendees will receive a valuable briefing of Model Risk Management’s background, requirements and benefits as well as how AI ethics apply to Financial organizations. The second section of the workshop will provide an update on Kubeflow, which is a popular open source MiP framework for machine learning on Kubernetes. The final section will review a demonstration of Fairly’s Model Risk Management system, which leverages Kubeflow and provides a streamlined user experience for deploying models and producing compliance reports that will satisfy SR-117 requirements.
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Profile

Dr Jon Hill, Ph.D. Adjunct Professor of Model Risk NYU Tandon Financial Risk Engineering With over twenty years of experience in diverse areas of quantitative finance, Jon is recognized as a subject matter expert in model risk management, governance and validation and is the author of numerous publications on these topics. Jon is also an adjunct professor in NYU’s Financial Risk Engineering Dept. where he teaches a graduate course in Advanced Model Risk Management, Governance and Validation. Jon holds a Ph.D. in Biophysics from the University of Utah. He is a frequent speaker and chairperson at model risk conferences throughout the US and Europe.