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    Douglas Dwyer

    Credit risk modeling expert; machine learning researcher; quantitative and statistical data analyst

    Douglas Dwyer heads Single Obligor Research, a group that produces credit risk metrics of small businesses, medium-sized enterprises, large corporations, financial institutions, and sovereigns worldwide. Banks, asset managers, insurance companies, accounting firms, and corporations use the group’s models to measure credit risk for a variety of purposes. Doug and his group are researching machine learning-based techniques for credit risk modeling.

    Columbia University
    PhD, Economics
    Oberlin College
    BA, Economics
    Moody's Analytics | Credit Modeling

    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.

    Moody's Analytics | Operational Risk Audit Compliance Training

    Portfolio Optimization: Quantify diversification benefits across portfolios and define risk types that inform risk management and active asset allocation decisions.


    Asset Valuation: Process of determining the fair market or present value of assets using book values.

    Economic Risk Assessment: Quantitative economic assessment to help you understand the impact of forward-looking changes on the performance of your business and portfolios.

    Portfolio Models: Models that enable portfolio managers to assess and optimize portfolio risk.

    Representative Project

    Developed a method to convert a rating into a point-in-time term structure of PDs that supports IFRS 9 and CECL calculations.

    Published Work

    Sovereign & Size-Adjusted EDF-Implied Rating Template (for Private Firms)

    RiskCalc™ EDF™ (Expected Default Frequency) values and agency ratings are widely used credit risk measures. RiskCalc EDF values typically measure default risk for private companies, while agency ratings are only available for rated companies. A RiskCalc EDF value measures a company's standalone credit risk based on financial statement information, while an agency rating considers qualitative factors such as Business Profile, Financial Policy, external support, and country-related risks. Moody's Analytics new Sovereign & Size-Adjusted EDF-Implied Rating Template combines RiskCalc EDF values with additional factors to provide a rating comparable to agency ratings for private companies. The new template applies to RiskCalc EDF values across numerous geographies and regulatory environments. With the new template, users can generate a rating more comparable to an agency rating than RiskCalc EDF values or EDF-implied ratings. Analyzing data from 3,900+ companies in 60+ countries, we find that sovereign rating and total asset size, in addition to EDF value, have a statistically significant impact on an agency rating — our quantitative template incorporating these three variables reliably estimates agency ratings in a robust fashion.

    December 2018

    Validating and Understanding a Highly Nonlinear Machine Learning Model

    In validating a highly nonlinear model, a traditional nonlinear model provides a useful reference.

    November 2018

    Identifying At-Risk Firms in Your Private Firm Portfolio

    Identifying At-Risk Firms in Your Private Firm Portfolio

    October 2018

    Features of a Lifetime PD Model: Evidence from Public, Private, and Rated Firms

    With the new CECL and IFRS 9 requirements, this document formally investigates and summarizes the term structure properties consistently seen across public, private, and rated firms.

    May 2018

    Combining Financial and Behavioral Information to Predict Defaults for Small and Medium-Sized Enterprises: A Dynamic Weighting Approach

    This note presents the first tool that assesses borrowers' credit risk using a scientific method that leverages both financial and behavioral information.

    September 2017

    Combining Information to Better Assess the Credit Risk of Small Firms and Medium-Sized Enterprises

    In this article, we combine financial information with behavioral factors to more accurately assess credit risk for small firms and medium-sized enterprises.

    July 2017