Monday, June 18 | 3:35-4:50 PM
Recommended Prerequisite: General understanding of modern data analytic methods (machine learning, etc.) used in decision-making
Field of Study: Auditing
Data analytics provide an excellent set of fraud detection tools that can identify outliers in data that could represent fraud, flagging them for investigation and recovery. However, you might be missing opportunities to use the data you have collected to develop fraud profiles that include the characteristics of a fraud event. Such a profile can be used to predict when fraud might occur and provide the opportunity to stop it prior to the loss. This session will explore this technique in further detail, providing examples of how to use historical data to better understand how fraud manifests itself within an organization, and how to use this understanding to predict and prevent a future fraud event.
You Will Learn How To:
Director, Grant Thornton LLP
Linda Miller leads Grant Thornton’s fraud risk practice in the commercial and public sector. Prior to joining Grant Thornton, Miller spent 10 years with the Government Accountability Office (GAO), most recently as an Assistant Director with GAO’s Forensic Audits and Investigative Services team. She was the principle author of GAO’s recently issued Framework for Managing Fraud Risks in Federal Programs, which describes leading practices that agency managers can use to develop a fraud risk management program.
Principal, Grant Thornton LLP
Paul Seckar is a Principal within Grant Thornton’s Public Sector Practice and is responsible for leading the Decision Analytics service line. He has more than 25 years of consulting experience solving complex business problems with a variety of analytics, including technology- based solutions and applications as well as decision-analytic studies and engagements that deliver implementation recommendations for improved performance, streamlined operations and strategic decision making. Prior to working for Grant Thornton, Seckar led the Predictive Analytics service area within IBM’s Public Sector Strategy and Analytics practice where he was responsible for designing and leading efforts to address client challenges through the use of advanced mathematical techniques including data mining, predictive modeling, forecasting, statistical analysis, cost analysis and simulation.
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