Title: Predicting going concern opinion with data mining
Authors: Martens, David ×
Bruynseels, Liesbeth
Baesens, Bart
Willekens, Marleen
Vanthienen, Jan #
Issue Date: Nov-2008
Series Title: Decision Support Systems vol:45 issue:4 pages:765-777
Abstract: The auditor is required to evaluate whether substantial doubt exists about the client entity's ability to continue as a going concern. Accounting debacles in recent years
have shown the importance of proper and thorough audit analysis. Since the 80s, many studies have applied statistical techniques, mainly logistic regression, as an automated tool to guide the going concern opinion formulation. In this paper, we introduce more advanced data mining techniques, such as support vector machines and rule-based classifiers, and empirically investigate the ongoing discussion concerning the sampling methodology. To provide specific audit guidelines, we infer rules with the state-of-the-art classification technique AntMiner+, which are subsequently converted into a decision table allowing for truly easy and user-friendly consultation in every day audit business practices.
ISSN: 0167-9236
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Administrative and Support Services, Faculty of Economics and Business, Leuven - miscellaneous (-)
Research Center for Management Informatics (LIRIS), Leuven
Research Center Accountancy, Leuven
× corresponding author
# (joint) last author

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