Data Science Research Group: Unsupervised Hierarchical Bayesian Methods for Health Care Fraud Detection
Unsupervised Hierarchical Bayesian Methods for Health Care Fraud Detection
Dr. Tahir Ekin
Associate Professor
Texas State University
Abstract:
Fraud instances are seen in a wide range of domains such as finance, telecommunications and health care. In addition to the monetary loss, fraud results in loss of confidence to the governmental systems and diminishes the overall system quality. For instance, in health care, while overpayments are estimated to correspond up to ten percent of expenditures, they also have direct adverse impacts on patient health. Analytical methods have become crucial to handle overpayments in health care systems because of the increasing size and complexity. This talk presents the use of unsupervised data mining approaches as pre-screening tools to aid in health care fraud assessment. The focus will be on Bayesian hierarchical methods, and their use will be illustrated using U.S. Medicare billing and prescription data. These methods are shown to help identify the hidden patterns among providers and medical procedures.
Speaker Bio:
Dr. Tahir Ekin is an Associate Professor of Quantitative Methods in McCoy College of Business, Texas State University. His areas of expertise include statistical applications in health care fraud assessment and decision modeling under uncertainty. His book on health care fraud analytics titled “Statistics and Health Care Fraud: How to Save Billions” is published as part of ASA/CRC series. His scholar work has appeared in a variety of academic journals including Journal of the Royal Statistical Society Series C, International Statistical Review, European Journal of Operational Research and Naval Research Logistics among others. Dr. Ekin holds a Ph.D. in Decision Sciences from The George Washington University, and a B.S. in Industrial Engineering from Bilkent University, Turkey. He is an elected member of International Statistical Institute and currently serves as Vice President of the International Society of Business and Industrial Statistics.
Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.
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