Applications of machine learning : consumer credit risk analysis

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Yuan, Danny

Advisor dc:contributor.advisor

• Andrew W. Lo and John Guttag.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/100614

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100614