Finding Fraud in Revenue: Cash Larceny
by Alycia Watt & Leah Wietholter
The term Data Sleuth is a trademark developed by Workman Forensics to describe our unique services based on the combination of forensic accounting procedures and data analytics. A lot of our concepts are based on a Venn diagram whether it be for investigations, forensic accounting, damages, or calculations. Everything in this world usually boils down to what happened versus what should have happened. We are going to analyze the data, then we are going to combine our analytics with forensic accounting procedures and fraud investigation, which gives us Data Sleuthing. Data is the foundation of everything we do.
First, we take “what actually happened”, what data supports this? For example, we see that an employee opened a bank account at the same bank where their employer has a bank account. We have a list of transactions showing that the employee transferred funds out of their employer’s bank account, to their personal bank account. Now that we know what happened, we are going to compare it to what should have happened. We look at all the bank transfers that the company made and identify the known bank accounts, this helps us determine any unknown bank accounts. We would also take a list of transfers made by the employee and compare that to a list of all approved transfers by the company , this results in any unapproved transfers made by the employee. The unapproved transfers will typically contain your loss.
When money comes in, there are only so many access points that somebody can steal money. If we can identify those access points , then we can monitor these areas for fraud . Money coming in should represent the revenue generated by the primary sales or services generated by the business, sometimes known as operating revenue.. Miscellaneous income is a catch-all category which includes varied sources of income not usually thought of as revenue, essentially income from non-core business activities. Examples of miscellaneous income are scrap income, interest income, and gains on the sale of assets or investments.
We are going to use a clothing store as our example., We will call it Clothing Store LLC. Clothing Store LLC is a retail store with two related employees, the general manager and the clerk. The owner knew that there was a problem, later on we realized there were lots of problems. One of the first things we started looking at was how the company received revenue. They accepted cash, credit cards, checks, and in addition they used a square account for trade shows. We've got our point-of-sale system data exported and we're going to look at that a little closer.
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The first thing that we do is add a few fields to our exports, columns B through H, those came from the point-of-sale system. We tend to add line numbers to our information so that we can sort them. Then we add something called a year/month column, add the columns that have been highlighted in green to pull out the year from the date column and then also just pull out the year and month in the date column. That makes it where we can summarize on a year month basis and we can compare things on a monthly basis by year that gives us a column that we can summarize by year as well.
We're going to take these point-of-sale records and then we're going to summarize this information by our year month column to see how much cash was received according to the point-of-sale during this time period. What this formula does is return the summarized amount, for example July 2012, just on one line and then you can filter that and we've now summarized by year month, how many cash payments we received in the point of sale.
Then we're going to do essentially the same thing over on the bank statement schedule. What we're wanting to do is identify all of the cash deposits. Then we're going to do the exact same formula only this time we're looking at the words “cash deposit.” Our next step is to take that summarized information and compare them on a monthly basis.If you notice, $95,000 did not make it to the bank in cash. In this particular case the attorneys were able to subpoena the bank accounts for our subjects and we scheduled all of the subjects bank accounts and combined them into one spreadsheet. We also did something called a lifestyle analysis where we discovered that if this subject hadn't deposited cash she wouldn't have been able to pay her bills, meaning there was extra cash coming from somewhere. We had all her bank accounts, her payroll accounts, her husband's accounts, they were all combined. We filtered by cash deposit and then used this formula to sum our cash deposits, and it was $23,508. Which helped law enforcement understand our case.
One of the things related to fraud is that you have to have intent and you have to have benefit. Fraud investigators are primarily looking at who benefited and how much did they benefit so that's where tracing takes that to the next step, to look at the subjects bank accounts, and see what those deposits consisted of and finding that cash, that’s where it is really important.
To learn more about finding fraud in your revenue watch the Finding Fraud in Revenue Webinar Replay.