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Article: Detection of financial statement fraud and feature selection using data mining techniques

TitleDetection of financial statement fraud and feature selection using data mining techniques
Authors
KeywordsData mining
Feature selection
Financial fraud detection
GP
Neural networks
SVM
T-statistic
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/dss
Citation
Decision Support Systems, 2011, v. 50 n. 2, p. 491-500 How to Cite?
AbstractRecently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. © 2010 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/139827
ISSN
2015 Impact Factor: 2.604
2015 SCImago Journal Rankings: 2.262
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorRavisankar, Pen_HK
dc.contributor.authorRavi, Ven_HK
dc.contributor.authorRaghava Rao, Gen_HK
dc.contributor.authorBose, Ien_HK
dc.date.accessioned2011-09-23T05:57:08Z-
dc.date.available2011-09-23T05:57:08Z-
dc.date.issued2011en_HK
dc.identifier.citationDecision Support Systems, 2011, v. 50 n. 2, p. 491-500en_HK
dc.identifier.issn0167-9236en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139827-
dc.description.abstractRecently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. © 2010 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/dssen_HK
dc.relation.ispartofDecision Support Systemsen_HK
dc.subjectData miningen_HK
dc.subjectFeature selectionen_HK
dc.subjectFinancial fraud detectionen_HK
dc.subjectGPen_HK
dc.subjectNeural networksen_HK
dc.subjectSVMen_HK
dc.subjectT-statisticen_HK
dc.titleDetection of financial statement fraud and feature selection using data mining techniquesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9236&volume=50&issue=2&spage=491&epage=500&date=2011&atitle=Detection+of+financial+statement+fraud+and+feature+selection+using+data+mining+techniques-
dc.identifier.emailBose, I: bose@business.hku.hken_HK
dc.identifier.authorityBose, I=rp01041en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.dss.2010.11.006en_HK
dc.identifier.scopuseid_2-s2.0-78650176118en_HK
dc.identifier.hkuros193214en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78650176118&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume50en_HK
dc.identifier.issue2en_HK
dc.identifier.spage491en_HK
dc.identifier.epage500en_HK
dc.identifier.isiWOS:000286851300013-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridRavisankar, P=35307820600en_HK
dc.identifier.scopusauthoridRavi, V=15770237000en_HK
dc.identifier.scopusauthoridRaghava Rao, G=36651435000en_HK
dc.identifier.scopusauthoridBose, I=7003751502en_HK
dc.identifier.citeulike8298032-

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