The application of data mining techniques in manipulated financial statement classification: The case of turkey

Clicks: 265
ID: 68773
2017
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Predicting financially false statements to detect frauds in companies has an increasing trend in recent studies. The manipulations in financial statements can be discovered by auditors when related financial records and indicators are analyzed in depth together with the experience of auditors in order to create knowledge to develop a decision support system to classify firms. Auditors may annotate the firms’ statements as “correct” or “incorrect” to add their experience, and then these annotations with related indicators can be used for the learning process to generate a model. Once the model is learned and tested for validation, it can be used for new firms to predict their class values. In this research, we attempted to reveal this benefit in the framework of Turkish firms. In this regard, the study aims at classifying financially correct and false statements of Turkish firms listed on Borsa İstanbul, using their particular financial ratios as indicators of a success or a manipulation. The dataset was selected from a particular period after the crisis (2009 to 2013). Commonly used three classification methods in data mining were employed for the classification: decision tree, logistic regression, and artificial neural network, respectively. According to the results, although all three methods are performed well, the latter had the best performance, and it outperforms other two classical methods. The common ground of the selected methods is that they pointed out the Z-score as the first distinctive indicator for classifying financial statements under consideration.
Reference Key
ozdagoglu2017thejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ozdagoglu, G.;Ozdagoglu, A.;Gumus, Y.;Gumus, G. Kurt;
Journal journal of artificial intelligence and data mining
Year 2017
DOI
DOI not found
URL
http://jad.shahroodut.ac.ir/article_664_0152f99d0ceb2f86d0cc6af056b43528.pdf
Keywords
chemistry Biology (General) Engineering (General). Civil engineering (General) Information technology Technology physics environmental sciences geography (general) neurology. diseases of the nervous system business economics as a science electronic computers. computer science hospitality industry. hotels, clubs, restaurants, etc. food service geography. anthropology. recreation computer software information resources (general)

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.