Next I consider at how data analytics can be used to help detect or prevent bribery and corruption where the primary sales force used by a company is third parties. A clear majority of Foreign Corrupt Practices Act (FCPA) violations and related enforcement actions have come from the use of third parties. While sham contracting (i.e. using a third party to conduit the payment of a bribe) has lessened in recent years, there are related data analysis that can be performed to ascertain whether a third party is likely performing legitimate services for your company and is not a sham. There are several more complex analytics that can be run in combination to identify suspicious third parties, and some of the simplest can be to look for duplicate or erroneous payments.
A key to moving from detection to prevention is the frequency of review. It is common for organizations to periodically review a year or more of accounts payable invoices at one time for errors or overpayment. Changing this from a one-time annual or biannual event to something that is done daily or weekly dramatically improves the value of such internal controls. This more frequent, preventative analysis is integral to a foundation of third party audits. While many company perform periodic look-back audits, ongoing monitoring also works to accomplish the same queries on a daily or weekly basis. This allows organizations to find duplicate payments or overpayments after the invoice has been approved but prior to its disbursement. So instead of detecting a payment error three or six months after it is made, you prevent the money from leaving the company altogether.
Duplicate invoices are a favorite mechanism of fraudsters. Consider the following scenario, Invoice No. 955-TX, was paid for $10,597.95. Thirty days later the same vendor re-submitted the same invoice due to non-payment, but it was recorded by the payor organization without the hyphen between 955 and TX, consequently it was not detected by the system of payable controls. The problem is the second invoice had slightly different writing on the face of it, but it was for the same services and hence was a duplicate invoice. On the company side, both invoices were scanned into the company’s imaging system and queued for payment. Data analysis can locate such overpayments and identify a second payment should not be made because it is a match of one that had been previously approved.
Another analysis, which a compliance practitioner could compare using vendor name and other identifying information, for example address, country, data from a watch list such as Politically Exposed Persons (PEP) or Specially Designated National (SDN), to names and other identifying information on your vendor file. An inquiry could also be used to test in other ways such as if a vendor has the same surname as a vendor on the specially designated national terrorist list, or a politically exposed person.
Now suppose they share the same name as an elected official down in Brazil. How do we make sure that our vendor or broker is a different John Doe than the John Doe that is a politically exposed person in that country? It is only upon closer inspection where you can determine that the middle names are different and the ages are different, one of has an address is Brasilia and the other is in Sao Paulo. Without further inspection including other demographic information about your vendors, consultants or third parties and the comparing them to watch list individuals, such red flags are present but not cleared. That is what data analytics is designed to do, is to help you go from tens of thousands of “maybes” to a very small number of potential issues which need to be researched individually.
One of the important functions of any best practices compliance program is to not only follow the money but try to spot where pots of money could be created to pay bribes. Through comparison of invoices for similar items among similar vendors, data analytics uncover overcharges and fraudulent billings. Continual transaction monitoring and data analysis can prove its value through more frequent review, as individuals tend to perform better when they know they are being monitored.
The techniques used in transaction monitoring for suspicious invoices can be easily translated into data analysis for anti-corruption. Software allows a very large aggregation of suspicious payments not only by day or by month, but also by vendor or even by employee who may have keyed the invoices into your system. As these suspicious invoices begin to cluster by market, business unit or person a pattern forms which can be the basis of additional inquiry. That is the value of analytics. Analytics allows a compliance practitioner to sort and resort, combine and aggregate, so that patterns can be investigated more fully.
This final concept, of finding patterns that can be discerned through the aggregation of huge amounts of transactions, is the next step for compliance functions. Yet data analysis does far more than simply allow you to follow the money. It can be a part of your third party ongoing monitoring as well by allowing you to partner the information on third parties who might come into your company where there was no proper compliance vetting. Such capabilities are clearly where you need to be heading.
Three Key Takeaways
This month’s podcast series is sponsored by Opus. Opus helps free your business from the complexity and uncertainty of managing the risks associated with your customers, vendors, and third parties. By combining the most innovative Third-Party Risk Management and Know Your Customer Compliance SaaS platforms with unparalleled data solutions, Opus turns information into action so your business can thrive. Opus solutions include Hiperos 3PM accelerator, the leading platform for third party risk management. To learn more, go to www.opus.com.