On-line evolving clustering for financial statements' anomalies detection
This document proposes an approach for financial statements' anomalies detection by using on-line evolving clustering. Official records of the financial activities of a business are called financial statements and they are recorded in journals and general ledger in a supervised process. Anomali...
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| creator | Omanovic, S. Avdagic, Z. Konjicija, S. |
| description | This document proposes an approach for financial statements' anomalies detection by using on-line evolving clustering. Official records of the financial activities of a business are called financial statements and they are recorded in journals and general ledger in a supervised process. Anomalies in financial statements are caused by human mistakes during forming of financial statements, or as a result of changes in the software that produced un-expected errors, or as possible financial fraud. |
| doi_str_mv | 10.1109/ICAT.2009.5348416 |
| format | Conference Proceeding |
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Official records of the financial activities of a business are called financial statements and they are recorded in journals and general ledger in a supervised process. Anomalies in financial statements are caused by human mistakes during forming of financial statements, or as a result of changes in the software that produced un-expected errors, or as possible financial fraud.</description><subject>anomalies detection</subject><subject>Authentication</subject><subject>Data security</subject><subject>evolving clustering</subject><subject>fraud detection</subject><subject>Information security</subject><subject>Performance analysis</subject><subject>Privacy</subject><subject>Product codes</subject><subject>Radio frequency</subject><subject>Radiofrequency identification</subject><subject>Scalability</subject><isbn>9781424442201</isbn><isbn>1424442206</isbn><isbn>9781424442218</isbn><isbn>1424442214</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUMFKAzEUjEhBrfsB4mVvnnZ9L8luNkcpWguFXvZekuyLRLZZ2cSCf2-LvTiXmYFhGIaxB4QaEfTzZvXS1xxA142QncT2ihVadSi5lJJz7K7_ecAFuzvHNaCWcMOKlD7hBNlwAfyWbXaxGkOkko7TeAzxo3Tjd8o0n6Wf5tKHaKILZixTNpkOFHN6Kk2cDmYMlMqBMrkcpnjPFt6MiYoLL1n_9tqv3qvtbn1ava2Chlxpj0pxDcLzVkgu9Glaqzji0BnrrFZC2rYxxBVa7QdvnQPrEKBrSSmrxJI9_tUGItp_zeFg5p_95QzxC7juUBo</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Omanovic, S.</creator><creator>Avdagic, Z.</creator><creator>Konjicija, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200910</creationdate><title>On-line evolving clustering for financial statements' anomalies detection</title><author>Omanovic, S. ; Avdagic, Z. ; Konjicija, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-9f1772903f263423909967211d8abcb9734b65ae271b9fdfbcc0bc10086e77b73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>anomalies detection</topic><topic>Authentication</topic><topic>Data security</topic><topic>evolving clustering</topic><topic>fraud detection</topic><topic>Information security</topic><topic>Performance analysis</topic><topic>Privacy</topic><topic>Product codes</topic><topic>Radio frequency</topic><topic>Radiofrequency identification</topic><topic>Scalability</topic><toplevel>online_resources</toplevel><creatorcontrib>Omanovic, S.</creatorcontrib><creatorcontrib>Avdagic, Z.</creatorcontrib><creatorcontrib>Konjicija, S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Omanovic, S.</au><au>Avdagic, Z.</au><au>Konjicija, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>On-line evolving clustering for financial statements' anomalies detection</atitle><btitle>2009 XXII International Symposium on Information, Communication and Automation Technologies</btitle><stitle>ICAT</stitle><date>2009-10</date><risdate>2009</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><isbn>9781424442201</isbn><isbn>1424442206</isbn><eisbn>9781424442218</eisbn><eisbn>1424442214</eisbn><abstract>This document proposes an approach for financial statements' anomalies detection by using on-line evolving clustering. Official records of the financial activities of a business are called financial statements and they are recorded in journals and general ledger in a supervised process. Anomalies in financial statements are caused by human mistakes during forming of financial statements, or as a result of changes in the software that produced un-expected errors, or as possible financial fraud.</abstract><pub>IEEE</pub><doi>10.1109/ICAT.2009.5348416</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2009 XXII International Symposium on Information, Communication and Automation Technologies, 2009, p.1-4 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | anomalies detection Authentication Data security evolving clustering fraud detection Information security Performance analysis Privacy Product codes Radio frequency Radiofrequency identification Scalability |
| title | On-line evolving clustering for financial statements' anomalies detection |
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