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Title: Maximum likelihood continuity mapping for fraud detection

Abstract

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.

Authors:
Publication Date:
Research Org.:
Los Alamos National Lab., NM (United States)
Sponsoring Org.:
Department of Defense, Washington, DC (United States)
OSTI Identifier:
468619
Report Number(s):
LA-UR-97-992
ON: DE97005313; TRN: AHC29710%%115
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: [1997]
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; FRAUD; TIME-SERIES ANALYSIS; DETECTION; MAXIMUM-LIKELIHOOD FIT; USES; MEDICINE; ALGORITHMS

Citation Formats

Hogden, J. Maximum likelihood continuity mapping for fraud detection. United States: N. p., 1997. Web. doi:10.2172/468619.
Hogden, J. Maximum likelihood continuity mapping for fraud detection. United States. doi:10.2172/468619.
Hogden, J. Thu . "Maximum likelihood continuity mapping for fraud detection". United States. doi:10.2172/468619. https://www.osti.gov/servlets/purl/468619.
@article{osti_468619,
title = {Maximum likelihood continuity mapping for fraud detection},
author = {Hogden, J.},
abstractNote = {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.},
doi = {10.2172/468619},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu May 01 00:00:00 EDT 1997},
month = {Thu May 01 00:00:00 EDT 1997}
}

Technical Report:

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