Meeting/Event Information

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June 27, 2019

Forensic Analytics & Accounting Technology
June 27, 2019
8:30 AM - 4:30 PM
Best Western Plus Hotel
201 Washington Ave.
North Haven, CT 06473
The CT Chapter of NACVA has opened up their upcoming training to any of our members who would be interested in attending. In this workshop, you will learn: How analyze and interpret evidentiary material with particular reference to the telltale patterns

Forensic Analytics & Accounting Technology with Mark J. Nigrini, PH.D. 

 

Topics covered:    Benford’s Law: The mathematical basis of the digit patterns, some fraud examples, and a general awareness of what "suspicious numbers" look like...Special: ATM fraud...Nigrini Cycle and forensic analytics using purchase orders data and purchasing card transactions....Review of The Nigrini Cycle software...Special: Chrysler payment The Nathan Mueller fraud case...The Katherine Harrell scheme...Review of the data analytics software program R....Review of Tableau and other technologies...Special: Pumpkin theft and Circle K...Charlene Corley scheme...Patterns of the numbers in occupational fraud schemes...Fraud case: Susan Thompson of Duluth, MN...Summary and conclusion on the need for strong internal controls and proactive fraud detection activities and much more.
 
In this workshop, you will learn: How analyze and interpret evidentiary material with particular reference to the telltale patterns of fraudulent numbers…The basics of running analytics tests in R…A review of Excel and the Nigrini Cycle, and the use of Excel in forensic analytics…How to apply electronic forensic accounting techniques to small, medium, and large transactional or balances data sets, and,The attributes of various electronic techniques, platforms, and apps that will enhance your productivity and fraud detection effectiveness.
 
About the Instructor:  Mark J. Nigrini is on the Business & Economics faculty at West Virginia University. Nigrini’s research passion for many years has been a phenomenon known as Benford’s Law. The phenomenon relates to the fact that there are predictable patterns to the digits in lists of numbers. The smaller digits (1s, 2s, and 3s) are expected to occur more often in scientific and financial data. Benford’s Law has proved itself to be valuable to auditors in their quest to uncover fraud in corporate data. Nigrini’s current research addresses advanced theoretical work on Benford’s Law, employee fraud, identity theft tax refund fraud, and the use of analytics in forensic accounting.