Title: Credit rating prediction using ant colony optimization
Authors: Martens, David ×
Van Gestel, Tony
De Backer, Manu
Haesen, Raf
Vanthienen, Jan
Mues, Christophe
Baesens, Bart #
Issue Date: Apr-2010
Publisher: Published by Pergamon Press for Operational Research Society
Series Title: Journal of the Operational Research Society vol:61 issue:4 pages:561-573
Abstract: The introduction of the Basel II Capital Accord has encouraged financial institutions to build internal rating systems assessing the credit risk of their various credit
portfolios. One of the key outputs of an internal rating system is the probability of default (PD), which reflects the likelihood that a counterparty will default on his/her financial obligation. Since the PD modeling problem basically boils down to a discrimination problem (defaulter or not), one may rely on the myriad of classification techniques that have been suggested in the literature. However, since the credit risk models will be subject to supervisory review and evaluation, they must be easy to understand and transparent. Hence, techniques such as neural networks or support vector machines are less suitable due to their black box nature. Building upon previous research, we will use AntMiner+ to build internal rating systems for credit risk. AntMiner+ allows to infer a propositional rule set from a given data set hereby using the principles from Ant Colony Optimization. Experiments will be conducted using various types of credit data sets (retail, small- and medium-sized enterprises (SMEs) and banks). It will be shown that the extracted rule sets are both powerful in terms of discriminatory power, and comprehensibility. Furthermore, a framework will be presented describing how AntMiner+ fits into a global Basel II credit risk management system.
ISSN: 0160-5682
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
× corresponding author
# (joint) last author

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