Regulators around the globe have been stepping up anti-corruption compliance efforts. The past few years have seen a marked uptick in both formal inquiries and legal actions related to money laundering and bribery, with regulators demanding increased access to company records. Given their global scope, the costs of responding can be enormous. For example, the global retailer Walmart predicts that its anti-bribery compliance-related costs for this year alone will be upward of $180 million[1].This is not atypical for companies with a large global footprint. Earlier this year, Olympus resolved a $22.8 million Foreign Corrupt Practices Act (FCPA) enforcement action concerning alleged misconduct in Brazil, Bolivia, Colombia, Argentina, Mexico and Costa Rica. Managers at an Olympus factory in China were also tied to related company investigations.[2] Separately, Olympus Corporation of the Americas agreed to pay $612 million plus interest to resolve parallel criminal and civil investigations into alleged violations of the Anti-Kickback Statute and the False Claims Act[3].The primary costs are typically pre-enforcement action professional fees and expenses, with the bulk of these expended on information collection and analysis. This information typically comes from a myriad of sources and locations and in an equally diverse number of formats. It often includes company records, such as emails and text messages, invoices, contracts, memos, wire transfer and other financial records, accounting ledgers, spreadsheets, purchase and sales records, and other transactional information, as well as external information from outside sources, such as bank records, trade and customs data. Continually, the ever increasing sophistication of bad actors compounds the problem. Investigators, are constantly struggling to find smaller needles in larger haystacks, often with the needle hidden inside a piece of straw or disguised as an umbrella.
So how can data analytics help organizations find illicit transactions in a multitrillion dollar haystack? Unlike their traditional counterparts, newer analytics systems based on big data technologies, predictive analytics and artificial intelligence are not bound by upfront data transformation and normalization requirements, nor are they bogged down by the large data volumes that can choke a relational database. These more powerful and intelligent systems can also do a lot more of the routine heavy lifting with spreadsheets and other documents, freeing up human resources to focus on the more dynamic aspects of investigations. For example, in using predictive text analytics and concept-based search technologies, legal teams can greatly reduce the amount of time and costs associated with the review of documents and communications. Instead, machine learning may be leveraged to help bring the most critical and relevant communications to the top of the review pile, even where actors are using coded terms to hide illicit activities.
Similarly, new data analytics platforms allow for considerably more flexibility and agility with regard to the types of data they handle and the speed at which they do so. Newer tools often offer single visual workflow interfaces that enable the integration of a variety of data sources on the fly and adjustments to business logic applied to them. Users can twist and turn data easily, blending it from different sources to create custom analytics that yield highly accurate results, all without the need for extensive upfront modeling or a preconceived overarching data model. Here again, the tools take the manual hunting burden off the forensic accounting teams, allowing for quicker identification of the most critical transactions and their comparison across other data sources for validation and highlighting, without the need to pour over books and records line by line and document by document. In the end, the sea of information generated by illicit commercial activities – the invoices, bills of lading, insurance certificates, inspection documents, bank transactions, emails, and more – that makes it difficult to see what’s truly happening may also be the point of vulnerability when combined with newer analytics tools, which not only have the ability to identify patterns and anomalies but can also greatly reduce compliance costs.
The opinions expressed are those of the author and do not necessarily reflect the views of AlixPartners, LLP, its affiliates, or any of its or their respective professionals or clients.
Walmart 4th Quarter FY2015 Management Earnings Call Transcript, pg 38 at
[1] http://s2.q4cdn.com/056532643/files/doc_financials/q4-2015/FY15_Q4_earni…
[2]http://www.nytimes.com/2016/04/10/business/international/olympus-investi…
[3]See http://www.fcpablog.com/blog/2016/3/1/olympus-pays-doj-228-million-to-re…
David White is a director at AlixPartners LLP, where he advises clients on information governance, information security and electronic discovery.
You can reach the author at dwhite@alixpartners.com with questions about the article.
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