ACCT 572 Week 3b Fraud Detection Discussion
Fraud Detection (graded)
The Institute of Internal Auditors (IIA) has taken many proactive steps to educate professionals on how to detect fraud. What is the IIA all about? How is it going to help us detect fraud?
(an instructor response)
RE: Fraud Detection
excellent information, thank you!
(an instructor response)
Chapter 6, Case 8
1. Class, please read Chapter 6, Case 8, on page 194. What is Benfords Law? How does it aid in detecting fraud? Could fraud be occurring in this organization?
Case 8
1. Compare the first-digit frequency in the transactions in this table below with Benfords Law. What are the results?
2. Could fraud be occurring in this organization?
AMOUNT DESCRIPTION CHECK. NO.
$235.65 Payment to U.S. West for phone bill 2001
$654.36 Johns Heating and Cooling for fixing A/C in December 2002
$4,987.36 Sharkys Used Car Dealership for Yugo truck 2003
$339.13 Salt River Project for power in December 2004
$475.98 Arizona Department of Internal Revenue for taxes 2005
$254.14 Grainger Corp. for power tools 2006
$504.17 Home Depot for outdoor carport 2007
$171.54 Steelins Consulting for help with computer network 2008
$326.45 Payment to U.S. West for phone bill in January 2009
$477.67 Bank of America for loan payment 2010
(an instructor response)
Class, please read Chapter 6, Case 11, on pages 195 and 196. Do you detect any fraud here?
Case 11
By examining first digits, Company XXX suspects fraud. You are asked to review the sample of invoices 195196shown on the previous page to see if they make sense. You are familiar with several fraud detection methods and are eager to try out Benfords Law.
CASE 11
INVOICE AMOUNTS
$ 149,200.00 $ 19,489.00 $ 1,134.00
1,444.00 12,485.00 446.00
1,756.00 26,995.00 678.00
91.59 235,535.00 456.00
2,250.00 59,155.00 341.00
38,005.00 109,995.00 890.00
45,465.00 212,536.00 402.00
112,495.00 685.00 467.00
137,500.00 765.00 465.00
37,300.00 234.00 1,516.00
36,231.00 435.00 375.00
26,695.00 1,045.00 679.00
1. Do you suspect possible fraud? Why?
(an instructor response)
R
What is the data-driven fraud detection approach? Can you give an example of how this works?
(an instructor response)