Predicting fraudulent financial statements with machine learning techniques
| dc.contributor.author | Κουμανάκος, Ευάγγελος | el |
| dc.contributor.author | Kotsiantis, S. | en |
| dc.contributor.author | Tzelepis, D. | en |
| dc.contributor.author | Tampakas, V. | en |
| dc.date.accessioned | 2015-11-24T17:05:43Z | |
| dc.date.available | 2015-11-24T17:05:43Z | |
| dc.identifier.issn | 0302-9743 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11367 | |
| dc.rights | Default Licence | - |
| dc.title | Predicting fraudulent financial statements with machine learning techniques | en |
| heal.abstract | This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. This study indicates that a decision tree can be successfully used in the identification of FFS and underline the importance of financial ratios. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.secondary | <Go to ISI>://000238053100061 | - |
| heal.journalName | Advances in Artificial Intelligence | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2006 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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