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    Dr. Douglas W. Dwyer

    Douglas W. Dwyer leads Corporate Credit Research in Predictive Analytics. This group produces credit risk metrics of small businesses, medium sized enterprises, large corporations, financial institutions, and sovereigns worldwide. The group’s models are used by banks, asset managers, insurance companies, accounting firms and corporations to measure name specific credit risk for a wide variety of purposes. We measure credit risk using information drawn from financial statements, regulatory filings, security prices, derivative contracts, behavioral and payment information. For each asset class, the methodology is developed based on the available information for each obligor.

    Current projects include developing a climate adjusted probability of default and incorporating ESG factors into credit analytics. We also are developing an approach to produces comparable PDs across asset classes that opportunistically uses whatever information is available.

    Prior to working at Moody’s Analytics, Dr. Dwyer was a Principal at William M. Mercer, Inc., in their Human Capital Strategy practice. Dr. Dwyer earned a Ph.D. in Economics at Columbia University and a B.A. in Economics from Oberlin College.

    Validating and Understanding a Highly Nonlinear Machine Learning Model

    Presentation from Moody’s Analytics Summit 2018

    November 2018

    This presentation provides an overview of the components, aspects, and validation of a highly nonlinear machine learning model.

    Columbia University: PhD, Economics
    Oberlin College: BA, Economics

    Credit Risk Modeling: Moody’s Analytics delivers award-winning credit models and expert advisory services to provide you with best-in-class credit risk modeling solutions.

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    Credit Risk Advisory Services: Moody's Analytics credit risk advisory services enable faster, better informed credit decisions through a holistic and consistent assessment of risk.

    Stress Testing: Moody’s Analytics helps financial institutions develop collaborative, auditable, repeatable, and transparent stress testing programs to meet regulatory demands.

    Published Work