Applying Business Intelligence Concepts to Medicaid Claim Fraud Detection

U.S. governmental agencies are striving to do more with less. Controlling the costs of delivering healthcare services such as Medicaid is especially critical at a time of increasing program enrollment and decreasing state budgets. Fraud is estimated to steal up to ten percent of the taxpayer dollars used to fund governmentally supported healthcare, making it critical for government authorities to find cost effective methods to detect fraudulent transactions. This paper explores the use of a business intelligence system relying on statistical methods to detect fraud in one state’s existing Medicaid claim payment data. This study shows that Medicaid claim transactions that have been collected for payment purposes can be reformatted and analyzed to detect fraud and provide input for decision makers charged with making the best use of available funding. The results illustrate the efficacy of using unsupervised statistical methods to detect fraud in healthcare-related data.

[1]  Sotiris Kotsiantis,et al.  Forecasting Fraudulent Financial Statements using Data Mining , 2007 .

[2]  Carla Wilkin,et al.  IT governance challenges in a large not-for-profit healthcare organization: The role of intranets , 2009, Electron. Commer. Res..

[3]  Pedro A. Ortega,et al.  A Medical Claim Fraud/Abuse Detection System based on Data Mining: A Case Study in Chile , 2006, DMIN.

[4]  T. Wickizer.  Controlling Outpatient Medical Equipment Costs Through Utilization Management , 1995, Medical care.

[5]  M. Nigrini,et al.  The Use of Benford's Law as an Aid in Analytical Procedures , 1997 .

[6]  T. Davenport.  Competing on analytics. , 2006, Harvard business review.

[7]  I. Yeoman.  Competing on analytics: The new science of winning , 2009 .

[8]  Franklin Maxwell Harper.  Data warehousing and the organization of governmental databases , 2004 .

[9]  G. Garson,et al.  Digital Government: Principles and Best Practices , 2003 .

[10]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .

[11]  David L. Olson,et al.  Public sector information system critical success factors , 2008 .

[12]  James G. S. Yang,et al.  Data Mining Techniques for Auditing Attest Function and Fraud Detection , 2009 .

[13]  Solomon Negash.  Business Intelligence , 2011, Lecture Notes in Business Information Processing.

[14]  C. Watrin,et al.  Benford’s Law: an instrument for selecting tax audit targets? , 2008 .

[15]  San-Yih Hwang,et al.  A process-mining framework for the detection of healthcare fraud and abuse , 2006, Expert Syst. Appl..

[16]  Jionghua Jin,et al.  A survey on statistical methods for health care fraud detection , 2008, Health care management science.

[17]  Jirachai Buddhakulsomsiri,et al.  Stratified random sampling for estimating billing accuracy in health care systems , 2008, Health care management science.

[18]  Hongxing He,et al.  Application of neural networks to detection of medical fraud , 1997 .

[19]  J. Vann,et al.  Resistance to Change and the Language of Public Organizations: A Look at “Clashing Grammars” in Large-Scale Information Technology Projects , 2004 .

[20]  Efraim Turban,et al.  Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures , 2013 .

[21]  Peter J. Haug,et al.  Data Preparation Framework for Preprocessing Clinical Data in Data Mining , 2006, AMIA.

[22]  Mohammad Abdollahi Azgomi,et al.  A Taxonomy of Frauds and Fraud Detection Techniques , 2009, ICISTM.

[23]  L Sokol,et al.  Using data mining to find fraud in HCFA health care claims. , 2001, Topics in health information management.

[24]  Seward Wf.  National health expenditures. , 1998 .

[25]  John A. Major,et al.  EFD: A hybrid knowledge/statistical‐based system for the detection of fraud , 1992, Int. J. Intell. Syst..

[26]  Stefan Rüping,et al.  On Integrating Data Mining into Business Processes , 2010, BIS.

[27]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .