Topic: Predictive Data Analytics and Fraud Detection
Speaker: Dean Abbott, Abbott Analytics
Fraud, waste, and abuse initiatives have skyrocketed in the past several years, including major initiatives by software vendors and consulting houses including IBM, SAS, SAP, Deloitte, and government agencies such as the U.S. Inspector General (OIG), HHS and the IRS. The recent growth has been driven by the adopting of predictive analytics and big data analytics, current buzz-words reflecting the acceptance of advanced statistical and machine learning techniques to find patterns in data. In this seminar, Dean Abbott will explain qualitatively what predictive analytics is and why it provides a leap forward in developing solutions for fraud detection, including several case studies that have seen great operational benefits from using predictive analytics.
Dean Abbott is President of Abbott Analytics, Inc. in San Diego, California. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology. He is also Chief Scientist of SmarterRemarketer, a startup company focusing on behaviorally- and data-driven marketing attribution and web analytics.
Mr. Abbott is a highly regarded and popular speaker at Predictive Analytics and Data Mining conferences, including Predictive Analytics World, Predictive Analytics Summit, the Predictive Analytics Center of Excellence, SAS Institute, DM Radio, and INFORMS.
He has served on the program committees for the KDD Industrial Track and Data Mining Case Studies workshop and is on the Advisory Boards for the UC/Irvine Predictive Analytics Certificate and the UCSD Data Mining Certificate programs. Mr. Abbott has taught applied data mining and text mining courses using IBM SPSS Modeler, Statsoft Statistica, Salford Systems SPM, SAS Enterprise Miner, Tibco Spotfire Miner, IBM Affinium Model, Megaputer Polyanalyst, KNIME, and RapidMiner.