An evaluation of support vector machines in consumer credit analysis

This thesis examines a support vector machine approach for determining consumer credit. The support vector machine using a radial basis function (RBF) kernel is compared to a previous implementation of a decision tree machine learning model. The dataset used for evaluation was provided by a large bank and includes relevant consumer-level data, including transactions and credit-bureau data. The results suggest that a support vector machine offers similar performance to decision trees, but the parameters specifying the soft-margin constraint and the inverse-width used in the RBF kernel could significantly affect its performance.

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

2013

Author dc:creator

• Mattocks, Benjamin A

Advisor dc:contributor.advisor

• Andrew W. Lo.

• 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/1721.1/85446

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/85446