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The High Cost of Fraud

Tips for Testing Common Schemes Using IDEA

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:

  1. Build a profile of potential frauds to be tested
  2. Analyze data for possible indicators of fraud
  3. Automate the detection process through continuous
    auditing/monitoring of high-risk business functions to improve controls
  4. Investigate and drill down into emerging patterns
  5. Expand scope and repeat as necessary
  6. Report

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 to Gather:

  • Payroll master file with cumulative totals and static data
  • Monthly transactions file
  • Employee data including Social Security numbers, address, employee number


Data Analysis Tests to Perform


  • Test for duplicate employees on the entire payroll file (appending or joining payroll files if necessary) using the employees’ SSNs as a unique employee identifier
  • Check for duplicate bank accounts [Note: False positives may include family accounts where more than one family member is employed by the organization]
  • Identify employee accounts with excessive credit memos, or large deposits
  • Match master information from the payroll file with the organization’s personnel file to determine whether there are “ghost” employees on the payroll
  • Compare the payroll file using two dates (beginning and end of the month) to determine whether new hires and terminations are represented as expected, and if any employees have received unusually large salary increases
  • View employee salaries by minimum and maximum by position and/or level. Also test allowances by position and level
  • Check for excessive overtime and allowance claims
  • Compare holidays/vacation and sick leave against limits by position/level
  • Match termination dates against the final few paychecks – look for scheme where extra checks were issued and diverted to the clerk’s account


Purchase Frauds


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:

  • Creation of a fictitious supplier in the general ledger
  • Creation of a fictitious branch within a genuine supplier
  • Reactivating a dormant account

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.

Data to Gather:

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
  • Purchase ledger
  • Payments history
  • Purchase invoices


Supplier Master File


  • Using the first 5-6 characters of the name, match supplier names against a list of employee surnames from the payroll or personnel file
  • Test for accounts without VAT numbers or duplicate VAT numbers
  • Examine purchase ledger transactions for entries at or just below the management approval level – if the system finds the approving authority for a transaction, examine the value distribution for each manager
  • Test to see if amounts are being approved just above or below break points in authority level by a value distribution across the whole ledger
  • Search for split invoices to enable approvals by an individual
  • Extract all invoices within 90% of an approved limit and search for all invoices from that supplier. Next, sort by approving manager, department, and date to identify possible split invoices or summarize payments by invoice number to determine how many partial payments have been made for each invoice.
  • Test for duplicate invoices using value and supplier codes as key fields for one test, and purchase order number for another. The 2nd processing of invoices can be used to establish a value on the purchase ledger to make a fraudulent payment.




  • Compare employee home addresses, SSNs, telephone numbers and bank routing/account numbers to the vendor master file
  • Identify invoices without a valid purchase order or from unapproved vendors
  • Find invoices with more than one purchase order authorization
  • Identify multiple invoices with the same item description
  • Extract vendors with duplicate invoice numbers
  • Find invoice payments issued on non-business days, such as weekends or holidays
  • Identify multiple invoices just under approval cut-off levels




  • Search the payments file for payees without “Inc”, “LLC” and LTD” in their names to identify payments to individuals
  • Stratify the size of payments to extract any exceptionally high payments
  • If payments are made by electronic transfers, extract lists of bank codes and account numbers from both the P/L payments files and the payroll – compare to see if any accounts match
  • Compare voucher or invoices posted against purchase order amounts

Many of these tests can be automated, and if you need assistance with creating a script, contact us at [email protected]. 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 protected].

Source:1 2016 Global Fraud Study, Report to the Nations on Occupational Fraud and Abuse, Association of Certified Fraud Examiners

CaseWare IDEA , News

Posted By

By Sarah Palombo
Sarah Palombo founded Avery Public Relations in 2007 and took on Audimation Services as her first client. She has more than 20 years of experience developing communications programs and creating content.

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