Applications of machine learning : consumer credit risk analysis
Name
932622145-MIT.pdf
Description
Full printable version
Size
653.39 KB
Format
Adobe PDF
Checksum (MD5)
817b7e3b8a28a5dd0c7a1149303bed2a
Author(s)
Yuan, Danny
Advisor(s)
Andrew W. Lo and John Guttag.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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