Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/28049
Title: | Model Risk Management and Ensemble Methods in Credit Risk Modeling |
Authors: | Sexton, Sean |
Advisor: | Racine, Jeffrey Maheu, John Han, Seungjin |
Department: | Economics |
Keywords: | Model Risk Management;Credit Risk;Expected Credit Loss;Basel;CECL;IFRS-9;Boosting;Bagging;Machine Learning;Fractional Response Models;Probability of Default;Loss Given Default |
Publication Date: | 2022 |
Abstract: | The number of statistical and mathematical credit risk models that financial institutions use and manage due to international and domestic regulatory pressures in recent years has steadily increased. This thesis examines the evolution of model risk management and provides some guidance on how to effectively build and manage different bagging and boosting machine learning techniques for estimating expected credit losses. It examines the pros and cons of these machine learning models and benchmarks them against more conventional models used in practice. It also examines methods for improving their interpretability in order to gain comfort and acceptance from auditors and regulators. To the best of this author’s knowledge, there are no academic publications which review, compare, and provide effective model risk management guidance on these machine learning techniques with the purpose of estimating expected credit losses. This thesis is intended for academics, practitioners, auditors, and regulators working in the model risk management and expected credit loss forecasting space. |
URI: | http://hdl.handle.net/11375/28049 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Sexton_Sean_M_202206_PhDEconomics.pdf | 10.29 MB | Adobe PDF | View/Open |
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