Uncovering accounts payable fraud

Welcome to a fuzzy world

By Christine L. Warner ;Bruce G. Dubinsky, CFE, MST, CPA, CVA


 Even if you're already using fuzzy matching and algorithms, here are some ways to utilize the classic methods and avoid some common pitfalls of accounts payable fraud.   

While conducting a duplicate payment audit for a medium-sized health-care product manufacturer, we noticed multiple $40,000 payments made out to one person on the same day. This made us curious, so we ran a mathematical algorithm to identify above-average payments per vendor and the same payments floated to the top of the report. The person turned out to be an employee who typically received a bimonthly paycheck of between $500 and $1,000. Indeed, $40,000 seemed unusual - especially three payments made on or near the same day! It was also highly unusual that there were no invoice numbers on these high-dollar payments. Most accounts payable systems won't accept an invoice entry without an invoice number: in fact, many A/P clerks are coached to enter the date if no invoice number is provided. Well, we got lucky in this situation because the client had given us its check register (instead of an A/P extract), which listed all checks issued, including checks with missing invoice numbers. Using cutting-edge data mining technology, we were able to import the electronic version of the check register and create a database from it. Then using this database, we were able to implement time-tested fraud detection algorithms and uncover the fraud. (We used SAS to complete the task, but there are other text-mining software packages such as Monarch that can be utilized).

After speaking with the new controller of the company, we found that the employee had already left. The new controller mentioned a legal mishap, but she didn't know about the $40,000 checks.

Do you want to stop this type of fraud before your employees run away with the cash? If so, here we'll introduce some approaches to identifying potential accounts payable duplicate payments and fraud using "fuzzy matching" and algorithms.




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