Cost-Effective Classification for Credit Decision-Making

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ConferenceProceedings of the Third International Conference on Artificial Intelligence Applications on Wall Street (AIAW'95), June 7-9, 1995., New York, New York, USA
Subjectcredit scoring; reinforcement learning; apprentissage par renforcement
AbstractThere is an increasing need for credit decision making systems that can dynamically analyze historical data and learn complex relations among the most important attributes for loan evaluation. In this paper we propose the application of a new machine learning algorithm, QLC, to the credit analysis of consumer loans. The algorithm learns how to classify a loan by minimizing the expected cost due to both credit investigation expenses and possible misclassification. QLC is built upon reinforcement learning. A dataset of actual consumer loans issued for evaluating the algorithm. The experiments reported show that QLC performs better than other cost-sensitive algorithms on this dataset.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number38385
NPARC number5764328
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Record identifier4075460a-b62b-4be8-a3d6-e8f0cb28aecb
Record created2009-03-29
Record modified2016-05-09
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