Redesigning Credit Risk Modeling to Achieve Profit and Volatility Targets (eBook, ePUB)
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Why does performance by bureau score change so radially through the credit cycle? Why do we have cut-off scores? Why do machine learning models degrade so fast when deployed, and do they need to? What is the real purpose of alternate data? What are the true dimensions of borrower behavior that we need to understand? Why isn't forecast uncertainty included in underwriting? Why do applications of Modern Portfolio Theory fail for loan portfolios? These questions and many more are answered in this integrated approach to credit risk analytics. Credit risk analysts are not tapping the real power of ...
Why does performance by bureau score change so radially through the credit cycle? Why do we have cut-off scores? Why do machine learning models degrade so fast when deployed, and do they need to? What is the real purpose of alternate data? What are the true dimensions of borrower behavior that we need to understand? Why isn't forecast uncertainty included in underwriting? Why do applications of Modern Portfolio Theory fail for loan portfolios? These questions and many more are answered in this integrated approach to credit risk analytics. Credit risk analysts are not tapping the real power of machine learning and alternate data, because their models are built in a 1960s scoring architecture. Changing the architecture not only solves problems of overfitting and out-of-time degradation, but it also turns machine learning models into cash flow forecasters that integrate directly with yield and NPV models in finance. When account-level forecasts directly predict yield, underwriting decisions can be based on financial targets directly, rather than judgmental, backward-looking score cutoffs. The material is presented conceptually with a focus on analytic methods with business value. To solve these decades-long mysteries, the industry must break free of the 1960s mindset of how analytics should be used in credit risk, and this book lights the way. CEO, Deep Future Analytics LLC (Deepfutureanalytics.com) President, Model Risk Managers' International Assocation (MRMIA.org) 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 banks, credit unions, and finance companies. He is also the owner of auctionforecast.com, which predicts the values of fine wines using a proprietary database with over 4.5 million auction prices. He is a 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, the Journal of Risk and Financial Management and the journal AI and Ethics; and President of the Model Risk Managers' International Association (mrmia.org). Dr. Breeden invented vintage analytics for lending in 1997 and created credit risk models through the 1995 Mexican Peso Crisis, the 1997 Asian Economic Crisis, the 2001 Global Recession, the 2003 Hong Kong SARS Recession, the 2007-2009 US Mortgage Crisis and Global Financial Crisis, and the COVID-19 Pandemic. These crises have provided Dr. Breeden with a rare perspective on crisis management and the analytics needs of executives for strategic decision-making. In 2018 Dr. Breeden invented Multihorizon Survival modeling, combining vintage analytics with behavior scoring using logistic regression or machine learning. Dr. Breeden earned a Ph.D. in physics, and has published over 90 academic articles, 8 patents, and 6 books
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