A framework for detecting financial statement fraud through multiple data sources
This project deals with how to detect fraud and non-compliance in financial statements in the present day in one of the biggest economies in the world, the U.S. Since it is mainly public companies that release detailed financial infor-mation, they are the focus. This project focuses on the top five...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2009
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/14389 |
| _version_ | 1848748610038005760 |
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| author | Dillon, Darshan Hadzic, Maja |
| author2 | Okyay Kaynak |
| author_facet | Okyay Kaynak Dillon, Darshan Hadzic, Maja |
| author_sort | Dillon, Darshan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This project deals with how to detect fraud and non-compliance in financial statements in the present day in one of the biggest economies in the world, the U.S. Since it is mainly public companies that release detailed financial infor-mation, they are the focus. This project focuses on the top five market sectors where fraud is most common. It focuses on a variety of fraud types, but not on cases of deception that do not constitute fraud. A framework will be proposed which ac-counts for both structured data (the numbers in the balance sheet, income statement and cash flow statement) and unstruc-tured data (the footnotes in these financial statements). It uses ontology-driven data mining techniques to do so. |
| first_indexed | 2025-11-14T07:07:46Z |
| format | Conference Paper |
| id | curtin-20.500.11937-14389 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:07:46Z |
| publishDate | 2009 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-143892022-12-09T05:23:42Z A framework for detecting financial statement fraud through multiple data sources Dillon, Darshan Hadzic, Maja Okyay Kaynak Mukesh Mohania data mining public companies revenue recognition ontology financial statement fraud This project deals with how to detect fraud and non-compliance in financial statements in the present day in one of the biggest economies in the world, the U.S. Since it is mainly public companies that release detailed financial infor-mation, they are the focus. This project focuses on the top five market sectors where fraud is most common. It focuses on a variety of fraud types, but not on cases of deception that do not constitute fraud. A framework will be proposed which ac-counts for both structured data (the numbers in the balance sheet, income statement and cash flow statement) and unstruc-tured data (the footnotes in these financial statements). It uses ontology-driven data mining techniques to do so. 2009 Conference Paper http://hdl.handle.net/20.500.11937/14389 IEEE fulltext |
| spellingShingle | data mining public companies revenue recognition ontology financial statement fraud Dillon, Darshan Hadzic, Maja A framework for detecting financial statement fraud through multiple data sources |
| title | A framework for detecting financial statement fraud through multiple data sources |
| title_full | A framework for detecting financial statement fraud through multiple data sources |
| title_fullStr | A framework for detecting financial statement fraud through multiple data sources |
| title_full_unstemmed | A framework for detecting financial statement fraud through multiple data sources |
| title_short | A framework for detecting financial statement fraud through multiple data sources |
| title_sort | framework for detecting financial statement fraud through multiple data sources |
| topic | data mining public companies revenue recognition ontology financial statement fraud |
| url | http://hdl.handle.net/20.500.11937/14389 |