Today I consider a fraud audit by using data analytics to help detect or prevent bribery and corruption where the primary sales force used by a company are its FCPA and Chinese domestic law, involved China based employees defrauding their company by using false expense reports to create a pot of money to use as a slush fund to pay bribes. Here you can think back to the Eli Lilly FCPA enforcement action from 2012 up to the 2014 GlaxoSmithKline Plc (GSK) problems as examples of where employees used their expense accounts not for personal use but for greater corporate malfeasance.

Joe Oringel, co-Founder and co-Principal of Visual Risk IQ, related case studies where his organization used data analysis to review employee expense reports and how that experience can be used to formulate the same type of fraud analysis for a CCO or compliance practitioner. Also of this can be used as ongoing monitoring to facilitate continuous improvement of your compliance program.

One common technique fraudsters use is to split larger purchases across multiple smaller transactions, so their organization has designed their data analytics queries to detect such split transactions. An example might be where procurement cards (P-cards) are used for certain low dollar-value expenses. If a company has a procurement card limit for employees in their organization, which is $3,000 for a single transaction and $10,000 in aggregate spend for a single month; it would want to identify any use of P-cards for larger dollar transactions used for inappropriate or illegal purchases.

Contrast this with the problem of split payments. This is the situation where a single invoice is divided and the full amount of the payment is made in two or more simultaneous transactions, all done by different types of internal corporate payments. The key is to understand where the invoices are coming from and if only one vendor or supplier, investigate who is splitting the payments and why.

Another area to focus on using data analytics is gift, travel and entertainment (GTE), to identify out-of-policy expense reports and out-of-compliance expenses. Here the biggest issue is “double dipping”. This means an expense is recorded once on a T&E report and then a second time on another expense report or a P-card charge or other type of expense. These are examples that can be uncovered with data with analytics and from there you can move to determine if they might be an intentional, as opposed to an unintentional, mistake.

In the case of double dipping, a key is to look for the same airfare or hotel or meals, perhaps being reported on multiple employees’ T&E expense reports. An example might be where an employee takes another employee out for a business meal; they pay for the meal on one expense report. Then separately a coworker records the meal, same day, same city, and claims that employee as one of their attendees. We find these sorts of situations with our analytics, and these are clear examples of suspicious transactions that ought to be discussed with both employees”

Other examples of double dipping include duplicate transactions between meals and per diem allowances, or mileage and company vehicles or rental cars. These are all things that can be identified with data analytics that are very difficult for an individual approver to see on a single expense report. The reason is that when you are tasked with approving an employee’s expense report, the reviewer most often has single report in front of themselves for review. This makes it difficult to recall who would have submitted a report one or two months ago, and it’s very possible that somebody submitted an airplane ticket when the ticket was purchased, and then six weeks later when they took the trip, that air expense could be reported a second time.

This same issue could arise with P-card purchases if you have an approver considering a single $2,500 purchase who approves that purchase on Monday and then again on Friday. Yet had those two transactions been on the same day, more than the employee’s spending limit, the approver might not have approved both, but because they were submitted on different dates, it may well appear to the approver they were two separate transactions. With data analytics, you can aggregate those multiple trip or P-card reports into a single report, to help a reviewer or an approver determine whether the transactions meet employees’ policies, both individually and in the aggregate.

This double dipping technique led to two anti-bribery compliance enforcement actions. One in the US involving Eli Lily and a second in China involving the US pharmaceutical entity GSK. So the risk is real and by using ongoing data monitoring you might not only get ahead of the legal violation but you would have a much more efficient business process going forward.

Three Key Takeaways

  1. The typical fraud audit will get down into the weeds with data analytics.
  2. Split dollar expenses are key metric.
  3. Double-dipping can lead to larger problems.


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