 A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
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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.
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Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
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All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Dr.Agus Sudjianto is an executive vice president and head of Corporate Model Risk for Wells Fargo, where he is responsible for enterprise model risk management.
Prior to his current position, Agus was the modeling and analytics director and chief model risk officer at Lloyds Banking Group in the United Kingdom. Before joining Lloyds, he was a senior credit risk executive and head of Quantitative Risk at Bank of America.
Agus holds several U.S. patents in both finance and engineering. He has published numerous technical papers and is a co-author of Design and Modeling for Computer Experiments. His technical expertise and interests include quantitative risk, particularly credit risk modeling, machine learning and computational statistics.
He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.
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Sri Krishnamurthy, CFA is the Founder and CEO of QuantUniversity. Prior to that, Sri has worked at Citigroup, Endeca, MathWorks and with more than 25 customers in the financial services. Sri is the creator of QuSandbox, a platform for experimenting analytical and machine learning solutions for enterprises prior to adoption.
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Sri teaches classes at QuAcademy (www.qu.academy)Â and teaches graduate courses in Machine Learning and AI at Northeastern University.
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Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA from Babson College.
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QuantUniversity is a quantitative analytics and machine learning advisory based in Boston, Massachusetts. QuantUniversity runs various programs and workshops in Boston, New York, Chicago, and online. The company offers online programs in Machine Learning and AI for Financial services
Contact us at info@qusandbox.com to know more.
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