Using Data Analysis to Detect Fraudulent Activity
by Kenneth McGovern, Staff Associate II
Posted on July 1, 2015
Preventing and detecting fraud will always be a challenge for any organization, particularly as perpetrators find new and creative ways to commit fraud. The best defense against fraud is a proper “tone at the top” and effective internal control structure. However, even with these mechanisms in place an organization is still susceptible to certain fraud risks. Good internal controls can be further enhanced and strengthened through the use of data analysis to help detect irregularities in financial data.
A few key areas to examine with data analysis include:
- Are transactions being posted after hours or during the weekends?
- Are dormant or closed accounts being used?
- Are certain codes being frequently reversed?
- Are checks for multiple employees being sent to the same address?
- Are employees taking an appropriate amount of leave? (Fraudsters are reluctant to take vacations because they do not want their schemes being uncovered by fill-in employees.)
- Are phantom employees set up in the system?
Accounts payable activity:
- Are vendor payments being sent to employee addresses?
- Are vendor payments being made in the same or similar amounts? (There are specific analytical procedures that can be performed in order to detect non-random payments.)
- Are there irregularities with P.O. or requisition numbers? (e.g., duplicates being used or gaps in sequential numbers)
Targeted test procedures can be developed once an anomaly has been detected. This will allow an organization to more efficiently utilize its resources in an attempt to proactively determine if fraud is being committed. Having a targeted method based on sound evidence is more likely to uncover fraud than just relying on good ole chance!
For more information about how we can help with data analysis at your organization, contact Karin Smith, MBA, CFE at 602-277-9449, x327 or firstname.lastname@example.org or CW Payne, CPA, at 928-774-4201, x204 or email@example.com.