Data analytics in procurement fraud prevention
Approved for public release; distribution is unlimited === The purpose of this research project is to explore the viability of detecting anomalies through using data analytics software as a tool in procurement fraud prevention and to analyze its potential policy implications on federal procurement s...
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Monterey, California: Naval Postgraduate School
2014
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ndltd-nps.edu-oai-calhoun.nps.edu-10945-427082015-05-06T03:58:55Z Data analytics in procurement fraud prevention Phillips, Thurman B. Lanclos, Raymond J. Rendon, Rene G. Rendon, Juanita M. Eger, Robert J. Graduate School of Business & Public Policy (GSBPP) Approved for public release; distribution is unlimited The purpose of this research project is to explore the viability of detecting anomalies through using data analytics software as a tool in procurement fraud prevention and to analyze its potential policy implications on federal procurement stakeholders. According to a survey conducted in 2012 by the Association of Certified Fraud Examiners, organizations lose an estimated 5% of their revenues to fraud each year. In order to relate this estimate to the Department of Defense (DOD), this estimated percentage was applied to the requested DOD FY 2013 budget of $613.9 billion outlined in the Fiscal Year 2013 Budget Overview, resulting in a projected total fraud loss of $30.7 billion. The use of data analytics software has the potential to not only detect fraudulent procurements, but also to help deter fraudulent activities before they occur. The results of this research study will be a recommendation on the use of data analytics as a tool to detect anomalies that may indicate procurement fraud in DOD organizations. 2014-08-13T20:17:56Z 2014-08-13T20:17:56Z 2014-06 Thesis http://hdl.handle.net/10945/42708 This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted. Monterey, California: Naval Postgraduate School |
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Approved for public release; distribution is unlimited === The purpose of this research project is to explore the viability of detecting anomalies through using data analytics software as a tool in procurement fraud prevention and to analyze its potential policy implications on federal procurement stakeholders. According to a survey conducted in 2012 by the Association of Certified Fraud Examiners, organizations lose an estimated 5% of their revenues to fraud each year. In order to relate this estimate to the Department of Defense (DOD), this estimated percentage was applied to the requested DOD FY 2013 budget of $613.9 billion outlined in the Fiscal Year 2013 Budget Overview, resulting in a projected total fraud loss of $30.7 billion. The use of data analytics software has the potential to not only detect fraudulent procurements, but also to help deter fraudulent activities before they occur. The results of this research study will be a recommendation on the use of data analytics as a tool to detect anomalies that may indicate procurement fraud in DOD organizations. |
author2 |
Rendon, Rene G. |
author_facet |
Rendon, Rene G. Phillips, Thurman B. Lanclos, Raymond J. |
author |
Phillips, Thurman B. Lanclos, Raymond J. |
spellingShingle |
Phillips, Thurman B. Lanclos, Raymond J. Data analytics in procurement fraud prevention |
author_sort |
Phillips, Thurman B. |
title |
Data analytics in procurement fraud prevention |
title_short |
Data analytics in procurement fraud prevention |
title_full |
Data analytics in procurement fraud prevention |
title_fullStr |
Data analytics in procurement fraud prevention |
title_full_unstemmed |
Data analytics in procurement fraud prevention |
title_sort |
data analytics in procurement fraud prevention |
publisher |
Monterey, California: Naval Postgraduate School |
publishDate |
2014 |
url |
http://hdl.handle.net/10945/42708 |
work_keys_str_mv |
AT phillipsthurmanb dataanalyticsinprocurementfraudprevention AT lanclosraymondj dataanalyticsinprocurementfraudprevention |
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