Fraud detection in medicare claims: A multivariate outlier detection approach

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We apply traditional and customized multivariate outlier detection methods to detect fraud in medicare claims. We use two sets of 11 derived features, and one set of the 22 combined features. The features are defined so that fraudulent medicare providers should tend to have larger features values than non-fraudulent providers. Therefore we have an apriori direction ({open_quotes}large values{close_quotes}) in high dimensional feature space to search for the multivariate outliers. We focus on three issues: (1) outlier masking (Example: the presence of one outlier can make it difficult to detect a second outlier), (2) the impact of having an apriori direction ... continued below

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12 p.

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Burr, T.; Hale, C. & Kantor, M. April 1, 1997.

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Description

We apply traditional and customized multivariate outlier detection methods to detect fraud in medicare claims. We use two sets of 11 derived features, and one set of the 22 combined features. The features are defined so that fraudulent medicare providers should tend to have larger features values than non-fraudulent providers. Therefore we have an apriori direction ({open_quotes}large values{close_quotes}) in high dimensional feature space to search for the multivariate outliers. We focus on three issues: (1) outlier masking (Example: the presence of one outlier can make it difficult to detect a second outlier), (2) the impact of having an apriori direction to search for fraud, and (3) how to compare our detection methods. Traditional methods include Mahalanobis distances, (with and without dimension reduction), k-nearest neighbor, and density estimation methods. Some methods attempt to mitigate the outlier masking problem (for example: minimum volume ellipsoid covariance estimator). Customized methods include ranking methods (such as Spearman rank ordering) that exploit the {open_quotes}large is suspicious{close_quotes} notion. No two methods agree completely which providers are most suspicious so we present ways to compare our methods. One comparison method uses a list of known-fraudulent providers. All comparison methods restrict attention to the most suspicious providers.

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12 p.

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OSTI as DE97005170

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  • Knowledge discovery and data mininig, Newport Beach, CA (United States), 14-17 Aug 1997

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  • Other: DE97005170
  • Report No.: LA-UR--97-1142
  • Report No.: CONF-970837--1
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 503526
  • Archival Resource Key: ark:/67531/metadc696878

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • April 1, 1997

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  • Aug. 14, 2015, 8:43 a.m.

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  • April 21, 2016, 9:49 p.m.

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Burr, T.; Hale, C. & Kantor, M. Fraud detection in medicare claims: A multivariate outlier detection approach, article, April 1, 1997; New Mexico. (digital.library.unt.edu/ark:/67531/metadc696878/: accessed December 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.