Data Mining techniques for the detection of fraudulent financial statements
Manolopoulos, Yannis/ Spathis, Charalambos/ Kirkos, Efstathios/ Μανωλόπουλος, Γιάννης/ Σπαθής, Χαράλαμπος/ Κύρκος, Ευστάθιος
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Manolopoulos, Yannis | el |
| dc.contributor.author | Spathis, Charalambos | el |
| dc.contributor.author | Kirkos, Efstathios | el |
| dc.contributor.other | Μανωλόπουλος, Γιάννης | el |
| dc.contributor.other | Σπαθής, Χαράλαμπος | el |
| dc.contributor.other | Κύρκος, Ευστάθιος | el |
| dc.date.accessioned | 2015-06-30T10:45:00Z | el |
| dc.date.accessioned | 2018-02-27T18:49:58Z | - |
| dc.date.available | 2015-06-30T10:45:00Z | el |
| dc.date.available | 2018-02-27T18:49:58Z | - |
| dc.date.issued | 2007-05 | el |
| dc.identifier | http://www.sciencedirect.com/science/article/pii/S0957417406000765# | el |
| dc.identifier | 10.1016/j.eswa.2006.02.016 | el |
| dc.identifier.citation | Kirkos, E., Spathis, C., Manolopoulos, Y. (3 Μαρτίου 2006). Data mining techniques for the detection of fraudulent financial statements. Expert systems with spplications 32, (4). Διαθέσιμο σε: http://www.sciencedirect.com/science/article/pii/S0957417406000765# (Ανακτήθηκε 30 Ιουνίου 2015). | el |
| dc.identifier.citation | Journal: Expert Systems with Applications, vol. 32, no. 4, 2007 | el |
| dc.identifier.issn | 0957-4174 | el |
| dc.identifier.uri | http://195.251.240.227/jspui/handle/123456789/5340 | - |
| dc.description | Δημοσιεύσεις μελών--ΣΔΟ--Τμήμα Λογιστικής, 2007 | el |
| dc.description.abstract | This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances. | el |
| dc.language.iso | en | el |
| dc.publisher | Elsevier | el |
| dc.rights | This item is probably protected by Copyright Legislation | el |
| dc.rights | Το τεκμήριο πιθανώς υπόκειται σε σχετική με τα Πνευματικά Δικαιώματα νομοθεσία | el |
| dc.source.uri | http://www.sciencedirect.com/science/journal/09574174/32/4 | el |
| dc.subject | Auditing | el |
| dc.subject | Data Mining | el |
| dc.subject | Management fraud | el |
| dc.subject | Fraudulent financial statements | el |
| dc.subject | Greece | el |
| dc.title | Data Mining techniques for the detection of fraudulent financial statements | el |
| dc.type | Article | el |
| heal.type | other | el |
| heal.type.en | Other | en |
| heal.dateAvailable | 2018-02-27T18:50:58Z | - |
| heal.language | el | el |
| heal.access | free | el |
| heal.recordProvider | ΤΕΙ Θεσσαλονίκης | el |
| heal.fullTextAvailability | false | el |
| heal.type.el | Άλλο | el |
| Appears in Collections: | Δημοσιεύσεις σε Περιοδικά | |
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http://195.251.240.227/jspui/handle/123456789/5340
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