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Abstracts prior to volume 5(1) have been archived!

Issue 5(1), October 2010 -- Paper Abstracts
Girard  (p. 9-22)
Cooper (p. 23-32)
Kunz-Osborne (p. 33-41)
Coulmas-Law (p.42-46)
Stasio (p. 47-56)
Albert-Valette-Florence (p.57-63)
Zhang-Rauch (p. 64-70)
Alam-Yasin (p. 71-78)
Mattare-Monahan-Shah (p. 79-94)
Nonis-Hudson-Hunt (p. 95-106) 



JOURNAL OF ACCOUNTING AND FINANCE 


The Tricare Fraud: A Case Study in Data Analytics for Healthcare Fraud Detection



Author(s): Devon Baranek, Maria H. Sanchez

Citation: Devon Baranek, Maria H. Sanchez, (2018) "The Tricare Fraud: A Case Study in Data Analytics for Healthcare Fraud Detection",  Journal of Accounting and Finance, Vol. 18, ss. 8, pp. 10-17

Article Type: Research paper

Publisher: North American Business Press

Abstract:

Healthcare fraud is a huge problem in the United States and around the world. In fiscal year 2016, the
U.S. Department of Justice recovered $2.5 billion in settlements and judgments from civil cases involving fraud and false claims related to the healthcare industry (DOJ, 2016a). Government agencies as well as insurers can use data analytics to detect health care fraud. The Tricare case is a recent example of healthcare fraud, and involved a dozen doctors, pharmacy owners and marketing professionals who were indicted over a complex scheme involving a fake medical study. Over $100 million was stolen from Tricare (the federal health program for veterans and their families). This case allows students to learn about health care fraud and its prevention, the role of the auditor, and the increasing use of data analytics in fraud prevention and detection.