2026-09-19T12:54:50Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1006142026-06-06T00:55:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrew W. Lo and John Guttag.
Yuan, Danny
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-01-04T19:58:49Z
2016-01-04T19:58:49Z
2015
2015
http://hdl.handle.net/1721.1/100614
932622145
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 65-66).
Current credit bureau analytics, such as credit scores, are based on slowly varying consumer characteristics, and thus, they are not adaptable to changes in customers behaviors and market conditions over time. In this paper, we would like to apply machine-learning techniques to construct forecasting models of consumer credit risk. By aggregating credit accounts, credit bureau, and customer data given to us from a major commercial bank (which we will call the Bank, as per confidentiality agreement), we expect to be able to construct out-of-sample forecasts. The resulting models would be able to tackle common challenges faced by chief risk officers and policymakers, such as deciding when and how much to cut individuals account credit lines, evaluating the credit score for current and prospective customers, and forecasting aggregate consumer credit defaults and delinquencies for the purpose of enterprise-wide and macroprudential risk management.
by Danny Yuan.
M.Eng.
66 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Applications of machine learning : consumer credit risk analysis
Thesis
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<Abstract>Current credit bureau analytics, such as credit scores, are based on slowly varying consumer characteristics, and thus, they are not adaptable to changes in customers behaviors and market conditions over time. In this paper, we would like to apply machine-learning techniques to construct forecasting models of consumer credit risk. By aggregating credit accounts, credit bureau, and customer data given to us from a major commercial bank (which we will call the Bank, as per confidentiality agreement), we expect to be able to construct out-of-sample forecasts. The resulting models would be able to tackle common challenges faced by chief risk officers and policymakers, such as deciding when and how much to cut individuals account credit lines, evaluating the credit score for current and prospective customers, and forecasting aggregate consumer credit defaults and delinquencies for the purpose of enterprise-wide and macroprudential risk management.</Abstract>
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