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Authors: Dallas Thornton 1 ; Guido van Capelleveen 2 ; Mannes Poel 3 ; Jos van Hillegersberg 3 and Roland M. Mueller 4

Affiliations: 1 University of California, United States ; 2 University of California and San Diego, United States ; 3 University of Twente, Netherlands ; 4 Berlin School of Economics and Law, Germany

ISBN: 978-989-758-028-4

Keyword(s): Fraud Detection, Medicaid, Healthcare Fraud, Outlier Detection, Anomaly Detection.

Abstract: 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 o ther specialties and enable its use by fraud analysts. (More)

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Paper citation in several formats:
Thornton D., van Capelleveen G., Poel M., van Hillegersberg J. and Mueller R. (2014). Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data.In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ISS, (ICEIS 2014) ISBN 978-989-758-028-4, pages 684-694. DOI: 10.5220/0004986106840694

@conference{iss14,
author={Dallas Thornton and Guido van Capelleveen and Mannes Poel and Jos van Hillegersberg and Roland M. Mueller},
title={Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ISS, (ICEIS 2014)},
year={2014},
pages={684-694},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004986106840694},
isbn={978-989-758-028-4},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ISS, (ICEIS 2014)
TI - Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data
SN - 978-989-758-028-4
AU - Thornton D.
AU - van Capelleveen G.
AU - Poel M.
AU - van Hillegersberg J.
AU - Mueller R.
PY - 2014
SP - 684
EP - 694
DO - 10.5220/0004986106840694

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