Loan Participations, CECL, Stress Testing, Loan Modeling for Banks and Credit Unions

Santa Fe, NM  |   (505) 670-7670  |  info@deepfutureanalytics.com

February 2022

Quantifying model selection risk

PRESENTED BY
Dr. Nikolay Dobrinov, Senior Data Scientist, Deep Future Analytics

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ABOUT THE PRESENTER

Nikolay Dobrinov

Dr. Nikolay Dobrinov has over 11 years of experience in credit risk analytics and specializes in forecasting and stress testing the performance of retail and commercial loan/line portfolios. Nikolay has developed numerous credit risk models, as well as performed independent reviews of such models, for both internal planning and regulatorypurposes (CCAR, DFAST, CECL) for banks and fintech companiesof all sizes.

WEBINAR OVERVIEW

How can we quantify model selection risk and what are it’s impacts on loss reserves and economic capital?

A large set of model specifications, that performs similarly in-sample, but may have distinct paths out of sample, may be available to choose from even within a single modeling framework. Dr. Dobrinov shows our latest research into various measures to quantify the model selection risk, its magnitude in credit risk modeling and its impact on loss reserves.The results highlight two sources of risk: a systematicdifference between the expectation value of the lifetime loss forecast and the medianof its simulated distribution that should be incorporated in the calculation of the loss reserves, and the 95% confidence interval from thedistribution which should contribute to the model risk component ofthe economic capital.

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ABOUT US

At Deep Future Analytics, we arm financial institution decision makers with best-in-class loan modeling tools and guide them to healthy and sustainable growth.

Quantifying Model Selection Risk