Publication
Title
Predicting going concern opinion with data mining
Author
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.
Language
English
Source (journal)
Decision support systems. - Amsterdam
Publication
Amsterdam : 2008
ISSN
0167-9236
Volume/pages
45:4(2008), p. 765-777
ISI
000260713900008
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
E-info
Web of Science
Record
Identification
Creation 12.09.2011
Last edited 25.06.2017