Evaluation of fraud detection data mining used in the auditing process of the Defense Finance And Accounting Service
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Authors
Jenkins, Donald J.
Subjects
Advisors
Buttrey, Samuel E.
Date of Issue
2002-06
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
The Defense Finance and Accounting Service (DFAS) uses data mining to analyze millions of vendor transactions each year in an effort to combat fraud. The long timeline required to investigate potential fraud precludes DFAS from using fraud as a supervised modeling performance measure, so instead it uses the conditions needing improvement (CNI) found during site audits. To verify this method, a thorough literature review is conducted which demonstrates a clear relationship between fraud and CNIs. Then recent site audits are analyzed to prove that supervised modeling is detecting CNIs at a higher rate than random record selection. The next phase of the research evaluates recent models to determine if models are improving with each new audit. Finally, to enhance the upervised modeling process, four initiatives are proposed: a revised model scoring implementation, a knowledge base of audit results, alternative model streams for record selection and a recommended modeling process for the CNI knowledge base. The goal of the proposed enhancements is to improve an already successful program so that the data-mining efforts will further reduce taxpayer losses through fraud, error or misappropriation of funds.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xviii, 108 p. : col. ill. ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This 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.