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dc.contributor.advisorKoyak, Robert A.
dc.contributor.advisorAlt, Jonathan K.
dc.contributor.authorLee, Nicholas L.
dc.date.accessioned2019-05-15T19:37:54Z
dc.date.available2019-05-15T19:37:54Z
dc.date.issued2019-03
dc.identifier.urihttp://hdl.handle.net/10945/62268
dc.descriptionApproved for public release. distribution is unlimiteden_US
dc.description.abstractThe Federal Funding Accountability and Transparency Act (FFATA) of 2006 makes available to public view information on all federal contracts beginning in 2006. This transparency presents an opportunity to examine large volumes of procurement data, in particular to infer whether anomalies or irregularities are present. In this thesis, we examine direct-order purchases made by the U.S. Army between calendar years 2013 and 2017. A total of 73,570 direct-order contracts were issued by the Army during this period, with a total obligation value of over $36 billion. We use supervised machine learning techniques to detect trends regarding levels of competition, set-aside programs used, sole sourcing, and monies spent both in individual contracts and in the awarding offices that issued the contracts. We also identify specific contracts that warrant further inspection. The suite of analytical tools that we develop can be applied generally to direct-order contracts issued by other DoD service branches. Application of these tools would allow an investigator to identify DoD contracts that warrant further scrutiny, and would allow contracting activities to be monitored with respect to criteria that are identified with best spending practices.en_US
dc.description.urihttp://archive.org/details/utilizationofmac1094562268
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleUTILIZATION OF MACHINE LEARNING TECHNIQUES TO DETECT ANOMALIES IN DEPARTMENT OF DEFENSE CONTRACT DATAen_US
dc.typeThesisen_US
dc.contributor.secondreaderSmith, Anthony D.
dc.contributor.departmentOperations Research (OR)
dc.subject.authoranomaly detectionen_US
dc.subject.authormachine learningen_US
dc.subject.authorprocurementen_US
dc.subject.authorcontract dataen_US
dc.subject.authorfinancial dataen_US
dc.subject.authorspendingen_US
dc.description.serviceMajor, United States Armyen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.identifier.thesisid30428


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