.png){kind=icon}
Return
Predicting going concern opinion with data mining
.png){kind=icon}
PRE
AI
DOI：10.1016/j.dss.2008.01.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2008 Elsevier B.V. All rights reserved.
Keywords:
Going concern opinion
Audit
Data Mining
Classification
Journal
IF:
6.8
Papers:
3.8K
Citations:
1.5W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveShareSave
View more