Maximum likelihood continuity mapping for fraud detection

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The author describes a novel time-series analysis technique called maximum likelihood continuity mapping (MALCOM), and focuses on one application of MALCOM: detecting fraud in medical insurance claims. Given a training data set composed of typical sequences, MALCOM creates a stochastic model of sequence generation, called a continuity map (CM). A CM maximizes the probability of sequences in the training set given the model constraints, CMs can be used to estimate the likelihood of sequences not found in the training set, enabling anomaly detection and sequence prediction--important aspects of data mining. Since MALCOM can be used on sequences of categorical data ... continued below

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13 p.

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Hogden, J. May 1, 1997.

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Description

The author describes a novel time-series analysis technique called maximum likelihood continuity mapping (MALCOM), and focuses on one application of MALCOM: detecting fraud in medical insurance claims. Given a training data set composed of typical sequences, MALCOM creates a stochastic model of sequence generation, called a continuity map (CM). A CM maximizes the probability of sequences in the training set given the model constraints, CMs can be used to estimate the likelihood of sequences not found in the training set, enabling anomaly detection and sequence prediction--important aspects of data mining. Since MALCOM can be used on sequences of categorical data (e.g., sequences of words) as well as real valued data, MALCOM is also a potential replacement for database search tools such as N-gram analysis. In a recent experiment, MALCOM was used to evaluate the likelihood of patient medical histories, where ``medical history`` is used to mean the sequence of medical procedures performed on a patient. Physicians whose patients had anomalous medical histories (according to MALCOM) were evaluated for fraud by an independent agency. Of the small sample (12 physicians) that has been evaluated, 92% have been determined fraudulent or abusive. Despite the small sample, these results are encouraging.

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13 p.

Notes

OSTI as DE97005313

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  • Other Information: PBD: [1997]

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  • Other: DE97005313
  • Report No.: LA-UR--97-992
  • Grant Number: W-7405-ENG-36
  • DOI: 10.2172/468619 | External Link
  • Office of Scientific & Technical Information Report Number: 468619
  • Archival Resource Key: ark:/67531/metadc688007

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  • May 1, 1997

Added to The UNT Digital Library

  • July 25, 2015, 2:21 a.m.

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  • April 21, 2016, 9:54 p.m.

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Hogden, J. Maximum likelihood continuity mapping for fraud detection, report, May 1, 1997; New Mexico. (https://digital.library.unt.edu/ark:/67531/metadc688007/: accessed April 21, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.