Dusting Your Data for Fraud’s Fingerprints


August 31, 2021 | 2:00 p.m. ET
Duration: 100 Minutes
CPE Credits: 2

Fraudsters think that they won’t get caught because they are unique and smart and because nobody’s looking. However, case after case shows that they use the same types of schemes over and over. Whether anybody's looking is under your control.   

Focusing on occupational fraud and bribery (and a dash of financial statement fraud), this webinar will dissect the six common number patterns that fraudsters unknowingly use in their deceptions. These patterns include round numbers, rapidly rising numbers, threshold numbers, non-Benford numbers, duplicate numbers and outlier numbers. For each number pattern type, we will review one or more fraud cases that used that pattern, and how the pattern could be detected in real data using Excel and another forensic analytics program widely known and used.   

All of the examples will be from recently prosecuted cases including the college admissions scandal and the COVID-related frauds currently being prosecuted.  


You Will Learn How To: 

  • Identify the round numbers from a table of transactions 
  • Differentiate between a normal Benford result and one with excessive control threshold activity 
  • Inspect a table of duplicate numbers for the red flags of fraud 
  • Appraise a data set for outliers with fraud fingerprints. 
  • Determine the validity of a vendor with rapidly rising numbers. 
  • Deduce a red flag when there are excessive numbers under a control threshold. 

Speaker:

User-added image

Mark J. Nigrini, Ph.D.

Accounting Faculty, College of Business & Economics
West Virginia University
 


Field of Study: Auditing
Course Level: Intermediate
Prerequisite: A working knowledge of Excel and familiarity with basic statistics
Advanced Prep: None
Delivery Format: Group Internet Based




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Please note: To be eligible for CPE credit for the Online Archive webinar, you must complete the final exam within one year of purchase date. You may only claim CPE credit for a course once.