Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

Dallas Thornton, Mannes Poel, Jos van Hillegersberg, Roland Mueller, Guido C. van Capelleveen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    21 Citations (Scopus)
    1423 Downloads (Pure)


    Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.
    Original languageEnglish
    Title of host publicationProceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014)
    Number of pages11
    ISBN (Print)978-989-758-028-4
    Publication statusPublished - Apr 2014
    Event16th International Conference on Enterprise Information Systems, ICEIS 2014 - Lisbon, Portugal
    Duration: 27 Apr 201430 Apr 2014
    Conference number: 16


    Conference16th International Conference on Enterprise Information Systems, ICEIS 2014
    Abbreviated titleICEIS


    • Fraud detection
    • Medicaid
    • Healthcare fraud
    • Anomaly detection
    • Data mining
    • Outlier detection


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