• Document: CONTINUOUS FRAUD MONITORING AND DETECTION VIA ADVANCED ANALYTICS. SCOTT MONGEAU, PH.D. Manager Deloitte Netherlands
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CONTINUOUS FRAUD MONITORING AND DETECTION VIA ADVANCED ANALYTICS Business analytics and big data are highly publicised terms, but what is the core value proposition? The advent of systems automation, enhanced by advanced analytics, has established the capacity for continuous fraud monitoring and detection solutions. This session will provide an overview of the main advanced analytics techniques available to tackle the problem of fraud, and a practical case will be examined in order to demonstrate the principles at work for a hands- on perspective. SCOTT MONGEAU, PH.D. Manager Deloitte Netherlands Scott Mongeau has more than 20 years of experience in project-focused analytics functions in a range of industries. He is an active university researcher; lecturer; conference presenter; and writer in the areas of business analytics management, decision analysis, decision model management, model risk, organizational management, social network analysis (SNA), evidence- based decision-making, and analytics methodologies. Previously he owned and operated his own independent analytics consulting company, SARK7. “Association of Certified Fraud Examiners,” “Certified Fraud Examiner,” “CFE,” “ACFE,” and the ACFE Logo are trademarks owned by the Association of Certified Fraud Examiners, Inc. The contents of this paper may not be transmitted, re-published, modified, reproduced, distributed, copied, or sold without the prior consent of the author. ©2014 24th Annual ACFE Global Fraud Conference S. Mongeau – smongeau@deloitte.nl Amsterdam, Netherlands – March 24th, 2014 Fraud Detection and Mitigation via Advanced Analytics: Trends and Directions Scott Allen Mongeau Deloitte Netherlands smongeau@deloitte.nl Nyenrode Business University s.mongeau@edp1.nyenrode.nl ABSTRACT This paper seeks to summarize trends and implications associated with business analytics and Big Data for the benefit of practitioners working in the field of fraud detection and mitigation. A brief definition of business analytics is offered, followed by an examination of existing and emerging applications to the problem of fraud. It is proposed that the emerging state-of-the-art is a hybridization of advanced analytics methods, increasingly powerful IT hardware, and software systems which manage human workflow related both to forensics and detection model management. Combined with growing market demand, packaged, integrated software frameworks are appearing to fulfill a need for enterprise fraud detection and mitigation solutions. Although yet in the early stages, it is proposed that these solutions will ultimately fuse with 3rd party data providers to serve both established enterprise markets and to provide cloud-based solutions (software as a service) for smaller customers in need of real time credit, risk, and transaction validation services across a range of industries. Keywords Fraud, forensics, continuous monitoring, advanced analytics, business analytics, data analytics Disciplines: Financial / risk managers, investigators, fraud experts, analytics experts Industries: Financial Services, Credit Provision, Banking, Insurance, Law Enforcement, Public Sector Finance, Risk Management, Corporate Risk Management Page 1 of 10 24th Annual ACFE Global Fraud Conference S. Mongeau – smongeau@deloitte.nl Amsterdam, Netherlands – March 24th, 2014 INTRODUCTION Business analytics and Big Data are highly publicized terms, their prolific use leading to confusion concerning their fundamentals. Fraud detection and mitigation is often cited as a use- case for advanced business analytics. The advent of the ability to store and analyze large data sets introduces the prospect of enterprise-scale fraud detection and suggests real-time prevention. This paper proposes a definition for fraud analytics, examines advanced applications, and explores emerging trends. but what is the core value proposition? This targeted presentation will drill-in on specifics concerning the use of advanced analytics for fraud detection and mitigation. The advent of systems automation enhanced by advanced analytics has established the capacity for continuous fraud monitoring and detection solutions. Advanced analytics approaches such as machine learning allow for cyclical fraud detection improvements. An overview of the main advanced analytics techniques available to tackle the problem of fraud will be reviewed. Specific attention will be focused on

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