Maximum likelihood continuity mapping for fraud detection Metadata

Metadata describes a digital item, providing (if known) such information as creator, publisher, contents, size, relationship to other resources, and more. Metadata may also contain "preservation" components that help us to maintain the integrity of digital files over time.

Title

  • Main Title Maximum likelihood continuity mapping for fraud detection

Creator

  • Author: Hogden, J.
    Creator Type: Personal

Contributor

  • Sponsor: United States. Department of Defense.
    Contributor Type: Organization
    Contributor Info: Department of Defense, Washington, DC (United States)

Publisher

  • Name: Los Alamos National Laboratory
    Place of Publication: New Mexico
    Additional Info: Los Alamos National Lab., NM (United States)

Date

  • Creation: 1997-05-01

Language

  • English

Description

  • Content 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.
  • Physical Description: 13 p.

Subject

  • Keyword: Fraud
  • Keyword: Maximum-Likelihood Fit
  • STI Subject Categories: 99 Mathematics, Computers, Information Science, Management, Law, Miscellaneous
  • Keyword: Detection
  • Keyword: Uses
  • Keyword: Medicine
  • Keyword: Time-Series Analysis
  • Keyword: Algorithms

Source

  • Other Information: PBD: [1997]

Collection

  • Name: Office of Scientific & Technical Information Technical Reports
    Code: OSTI

Institution

  • Name: UNT Libraries Government Documents Department
    Code: UNTGD

Resource Type

  • Report

Format

  • Text

Identifier

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

Note

  • Display Note: OSTI as DE97005313