Data Analysis 2I end my review of the types of data analysis that can be used to help detect or prevent bribery through the case studies from Joe Oringel, co-founder and Managing Director of Visual Risk IQ, a firm that helps audit and compliance people see and understand their data. Having previously discussed Visual Risk IQ case studies involving review of employee expenses and duplicate payments to vendors, today we reflect on an area not usually considered; that being rebates and other adjustments to customer revenues that can be a source to create a pot of money to pay bribes. Finally, I consider why data analysis has become a best practice going forward.

Adjustments to revenue (returns, rebates, and discounts) are quite different from the duplicate invoice situation we previously explored, where someone has provided services and then overbills or bills multiple times for those services. A similar mechanism was used by Hewlett-Packard’s (HP’s) German subsidiary to pay bribes where the business unit made fraudulent sales to corrupt distributors, booked the revenue then a couple of quarters later repurchased the equipment at a higher price and pooled those price differences to pay bribes. A similar scheme was used to fund to fund bribes paid to senior level Petrobras employees where corrupt companies would provide a discount to Petrobras yet the money was not rebated or credited to Petrobras but diverted to Swiss bank accounts.

Oringel introduced the techniques that one would use to identify what accountants call a contra-revenue account (CRA), which is generally recognized as the account in which you might record a discount or a rebate. He further explained these are ways through which gross revenue gets reduced and becomes net revenue. This is yet another way a pot of money can be developed from which bribes can paid.

Oringel and his team have tackled this issue when performing data analytics around rebates. Visual Risk IQ is located in North Carolina, which is a state where there continues to be a large domestic furniture manufacturing industry. This is an industry where rebates, particularly in the form of advertising allowances, are fairly common. He explained the fact pattern similar to the following, a “furniture manufacturer sells an independent dealer a mattress with a wholesale cost of $1,000; and if that mattress brand is advertised and promoted during the 4th quarter, and that mattress sells during the 4th quarter, then the dealer can claim an additional $100 discount to be used for that advertising, yielding them a net wholesale price of only $900 for the mattress.”

Visual Risk IQ was asked by a client to use data analysis to help determine whether there were improper or suspicious claims for advertising allowances by the channel partners of a furniture manufacturer. After comparing the relative discounts between dealers, based on both percentage and absolute dollar amount, the team also began to compare orders by month to advertising allowances claimed. These analyses were used to select dealers for additional scrutiny as part of their advertising allowance rebate program, but this approach was different from prior reviews, which were primarily accomplished using statistical sampling. Oringel built an analysis that compared order size by month with prior claims for advertising allowances for each of the various dealers that were buying the furniture from this manufacturer. By comparing order size to advertising allowance claims, the team identified dealers that were claiming disproportionate allowances relative to orders and expected on hand inventory. Indeed, certain dealers were claiming to have significantly negative on-hand inventory balances during the holiday selling season, based on their past orders and the timing of these large allowance claims.

Oringel further explained, “by identifying what was estimated to be an expected and minimum on-hand inventory, based on dealer size and prior order history, a forecasted allowance was computed. The additional scrutiny devoted to dealers whose claims yielded unusually low levels of inventory resulted in disallowed rebates and allowances after additional customer sales documentation was not provided as requested.” Visual Risk IQ and the client team “found, as you might expect, since this was the first time that advertising rebates were ever audited with such a data analytics approach, that there were many dealers and channel partners that appeared to be following the rules, but there were also several that really did appear to be problematic.”

I asked Oringel if he could provide any examples where he found issues involving the client’s channel partners. He stated an “example of one of those problematic channel partners was a dealer that had sold almost a year’s worth of furniture in a single quarter. To help put some numbers on this. They had purchased, in the preceding 12 months, about 400 units with order size varying between 25 and 50 units each month. Yet nearly all of these 400 units were claimed as Q4 sales, which was the quarter with the largest advertising allowance.”

The Visual Risk IQ team asked some thoughtful follow-up questions when they compared the pattern of purchases with the sales claimed to be related to the advertising allowances. “Do these orders make sense? Why did they keep ordering February, March, April, 40 pieces, 30 pieces, 40 pieces all year, if they were not selling any of them in Q1 and Q2?” Finally he added, “And how did they get from 400 to nearly zero in Q4?” Using these and other questions together with the data analytics, the company was able to successfully challenge some of the advertising allowance monies claimed by certain dealers.

These CRA’s are similar to customer rebates, which can be fraught for abuse. Improper accounting of customer rebates can be used to create a pot of money to pay a bribe. However, a Chief Compliance Officer (CCO) may not consider these for review. Oringel’s example shows the power of data analytics for a wide variety of transactions which could be used to pay bribes.

If there has been one consistent message the Department of Justice (DOJ) has communicated since at least the Lanny Breuer days, it is that a compliance program should evolve, both in terms of how your company’s business evolves but also as standards and technology evolves. Data analysis is moving towards the forefront in the realm of best practices. Moreover, use of data analysis can be the only way a CCO or compliance practitioner can have visibility into a large amount of data to determine trends and issues. Finally, it is through the use of data analytics that a CCO can move the compliance practice from detection to preventative to proscriptive so that your program can spot and then stop issues and trends from becoming Foreign Corrupt Practices Act (FCPA) violations.

Joe has more than twenty-five years of experience in internal auditing, fraud detection, and forensics, including ten years of Big Four assurance and risk advisory services. His corporate roles included information security, compliance and internal auditing responsibilities in highly-regulated industries such as energy, pharmaceuticals, and financial services. He has a BS in Accounting from Louisiana State University, and an MBA from the Wharton School at the University of Pennsylvania.

Joe Oringel can be reached at joe.oringel@visualriskiq.com.

This publication contains general information only and is based on the experiences and research of the author. The author is not, by means of this publication, rendering business, legal advice, or other professional advice or services. This publication is not a substitute for such legal advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified legal advisor. The author, his affiliates, and related entities shall not be responsible for any loss sustained by any person or entity that relies on this publication. The Author gives his permission to link, post, distribute, or reference this article for any lawful purpose, provided attribution is made to the author. The author can be reached at tfox@tfoxlaw.com.

© Thomas R. Fox, 2016

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