Brooklyn Quant Experience Lecture Series: Jon Hill

Lecture / Panel
Open to the Public

NYU FRE Brooklyn Quant Lecture Series

The Department of Finance and Risk Engineering welcomes, Jon Hill, NYU Tandon FRE Adjunct Professor, to the BQE Lecture Series.


A Smarter Model Risk Management Discipline Will Follow From Making Smarter Models


What if a financial firm decided to delete its entire set of models and redevelop them from scratch. What might it do differently in the process of rebuilding its entire model eco-system in order to avoid and leverage from some of its previous mistakes? How could such a firm make the Model Risk Management (MRM) platform smarter and less resource-intensive than it was before?

This article describes one forward-looking possibility for making the manually intensive practice of MRM smarter by building models that are smarter in the sense of having a rudimentary level of ‘self-awareness’. Similar to the ways that tech firms have tracked the usage of their smartphones, cars, laptop computers, and printers for many years, active intelligent agents embedded in model source code can support the creation of a dynamic model inventory to serve as a repository of historical data that accurately describes how, when and where a firm’s models are used and to diagram firm-wide inter-dependencies between data and models.

Keywords: model risk management, governance, validation, dynamic model inventory, model usage, transponder function, model-embedded, active intelligent agents, machine learning, big data, SR11-7, OCC2011-16.


Jon leads the New York Chapter of the Model Risk Managers International Association. 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.