Webinar: AI Explainability and Fairness by MRMIA

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About This Webinar

In-depth discussion around issues of explainability and fairness in AI and machine learning models

Presenters

Joseph Breeden

Dr. Breeden has been designing and deploying risk management systems for loan portfolios since 1996. He founded Deep Future Analytics in 2011, which focuses on portfolio and loan-level forecasting solutions for pricing, account management, stress testing, and CECL; serving credit unions, banks, and finance companies. He is also the owner of auctionforecast.com, which predicts the values of fine wines using a proprietary database with over 2.5 million auction prices.

He is member of the board of directors of Upgrade, a San Francisco-based FinTech; an Associate Editor for the Journal of Credit Risk, the Journal of Risk Model Validation, and the Journal of Risk and Financial Management; and President of the Model Risk Managers’ International Association (mrmia.org).

Dr. Breeden earned a Ph.D. in physics, and has published over 80 academic articles, 8 patents, and 4 books. His upcoming book, Creating Artificial General Intelligence and Preventing the AI Apocalypse, will be published in Summer 2022.

Peter Quell

Head of Portfolio Analytics at DZ Bank

Dr. Peter Quell is Head of the Portfolio Analytics Team for Market and Credit Risk in the Risk Controlling Unit of DZ BANK AG in Frankfurt. He is responsible for methodological aspects of Internal Risk Models, Economic Capital and Model Risk. He holds a MSc. in Mathematical Finance from Oxford University and a PhD in Mathematics.

Peter is member of the editorial board of the Journal of Risk Model Validation and a founding board member of the Model Risk Managers’ International Association (mrmia.org).

Peter is member of the editorial board of the Journal of Risk Model Validation and a founding board member of the Model Risk Managers’ International Association (mrmia.org).

Agus Sudjianto

Executive Vice President, Head of Model Risk and a Member of the Management Committee at Wells Fargo

Agus Sudjianto is an executive vice president, head of Model Risk and a member of the Management Committee at 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 an executive and head of Quantitative Risk at Bank of America. Prior to his career in banking, he was a product design manager in the Powertrain Division of Ford Motor Company. 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.

Nicholas Schmidt

CEO of SolasAI

Nicholas Schmidt is the CEO of SolasAI, a compliance-focused AI software platform that identifies and mitigates bias and discrimination in algorithmic decisioning. He is also the Artificial Intelligence Practice Leader at BLDS, LLC, where he provides expert guidance in the application of economics and statistics to questions of law and regulation. As head of the AI practice, Nick focuses on algorithmic fairness, explainable AI, and ensuring robust model governance practices. In addition to working with many of the largest U.S. lenders, FinTechs, and insurance companies, Nick regularly advises regulatory agencies in addressing questions relating to discrimination and innovation in AI.

Ulf Menzler

Manager at d-fine

Dr. Ulf Menzler is a manager in the field of applied AI at the consulting firm d-fine. Since studying theoretical astrophysics at Ruhr-Uni Bochum, and joining d-fine's risk management practice, he has been involved in the development, validation and implementation of data-intensive applications and IT architectures, esp. in the cloud, for over seven years. A special focus of his work is on operationalization of ML and NLP solutions (MLOps), in which domain he has accompanied numerous use case and platform projects.

David Asermely

Global Lead of Model Risk Management at SAS

David Asermely is the Global Lead of Model Risk Management at SAS, responsible for product design, support, partner strategy and more. Passionate about translating data into actionable intelligence, David combines the best technologies and design principles to help financial services organizations improve modeling efficiency and quality.

David holds two master’s degrees from the University of Massachusetts Amherst. Prior to joining SAS, he managed the Bank of New York Mellon’s Global Performance and Risk Analytics product set.