Title
Predicting going concern opinion with data miningPredicting going concern opinion with data mining
Author
Faculty/Department
Faculty of Applied Economics
Publication type
article
Publication
Amsterdam,
Subject
Computer. Automation
Source (journal)
Decision support systems. - Amsterdam
Volume/pages
45(2008):4, p. 765-777
ISSN
0167-9236
ISI
000260713900008
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
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 rulebased 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.
E-info
doi:10.1016/j.dss.2008.01.003
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