Fraud costs are on the rise…again. More than 23% of occupational fraud cases resulted in a loss of at least $1 million. When owners or executives committed fraud, the median damage was more than 10 times worse than when employees were the perpetrators. 1
Most frauds are detected through tips, rather than internal or external auditors. There is probably a good explanation for that, considering expanding data volumes and the complexity of information that needs to be analyzed. Regardless, there is little tolerance when errors and fraud go undetected.
Using the right tools and knowing where to look are critical. Relevant information resides in financial files within the organization. The larger and more detailed the data files are, the more useful data analysis becomes.
The median loss from a single occupational fraud in the U.S. is $120,000.
Source: Association of Certified Fraud Examiners (ACFE)
2016 Report to the Nations
IDEA 10 has some built-in features to help sift through vast amounts of data to uncover anomalies and outliers that may indicate fraud. If you haven’t upgraded to IDEA 10, you are missing out on some key audit intelligence features that are now available including:
Discover – Identifies trends, patterns and outliers, and creates a dashboard to further refine the data based on your specific needs
Visualize – Helps interpret and monitor data trends in a single or multiple databases
The visualization of the data is key for those that aren’t in the IT world and may not understand how databases are set up. Numbers don’t speak to them as they do to others. Seeing things visually is invaluable.
Aaron Boor, CISA, IT Audit & Project Automation Manager, Donegal Insurance Group
Advanced Fuzzy Duplicate – Identifies multiple similar records using up to 3 character fields, then groups them based on the degree of similarity to detect data entry errors, multiple data conventions for recording information and fraud
Fraud Risk Assessment Steps
Start where the fraudsters start – where the money is. Here are some key areas and analytics to use to identify commonly-used fraud schemes:
Payroll Fraud Schemes
While most payroll frauds are found by accident, data analysis can be used on a regular basis to analyze payments and search for outliers simply by matching payments to the payroll master file. Often fictitious or “ghost” employees are set up on a salary system to receive automatic payments.
Data Analysis Tests to Perform:
Purchase frauds are prevalent, mainly because there are so many ways a potential fraudster can work the system to their advantage. Dummy invoices, reuse of valid invoices and withholding of credit notes are just a few examples of purchasing frauds. Many frauds involve the manipulation of the payments information on personal accounts within the AP system. Examples of this include:
Miscellaneous accounts are particularly vulnerable, and don’t overlook frauds perpetuated on a genuine suppliers account without their knowledge. Accounts with high levels of transactions are susceptible to fraud because fictitious items can easily be buried.
Complex purchasing systems with automatic reordering capabilities are also a target. Once a supplier has been set up, or a requisition is input, payments are processed automatically. IDEA can be used on multiple files to test for fraud including
Supplier Master File
Many of these tests can be automated, and if you need assistance with creating a script, contact us at firstname.lastname@example.org. We also welcome your questions and provide live, step-by-step assistance at no cost to supported IDEA users – simply contact the IDEA Help Desk at 888.641.2800 and select option 4 or email us at email@example.com.
Source:1 2016 Global Fraud Study, Report to the Nations on Occupational Fraud and Abuse, Association of Certified Fraud Examiners
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