Can Big Data Analytics be used to Track Money Laundering and Terrorist Funding Activities?

Establishing the cross-border anti-money laundering units (AML) has been the focus of many governments over the last decade. The reason is that many of the world’s terrorist and organized criminal organizations are using international trade as a way to transfer funds across borders illegally.

So far, they have succeeded, leading to the shift from financial smuggling to global trade. Big data analytics can provide the missing piece in the puzzle, and help detect these movements once and for all. Laundering funds for criminal activities today has become more unobtrusive, enabling financiers to skirt international sanctions and other regulatory laws.

To evade detection, these groups have increased their level of sophistication, and the only way to catch up with and overtake them is employing highly sophisticated big data statistical/analytical techniques. This has become the focus of financial institutions with global reach as well as different governments.

Global trade provides these terrorist groups the perfect setting, in a needle-in-a-haystack way. Global trade is an 18.3T USD undertaking made up of a web of complex relations between shipping, financing and insurance all working across numerous legal systems, languages, currencies, custom processing and regional/national procedures and norms.

Some of these practices have been around for centuries, but the interplay between the many variables makes tracking activity difficult, at least not with the current systems.

Examining the flow of money

To date, there isn’t an accurate way to quantify how much these criminal groups are transferring using the global trade system, but according to Global Financial Integrity estimates, about 80% of illegal funds flowing across developing countries do so through trade-based laundering activities.

Trade finance is ideal because, given the complexity of the system and the multiple variables, practices that have been determined to work hardly ever change. While many other industries have upgraded their technologies, trade financing remains largely paper-based and document-intensive, being built on tried and tested systems, practices, tools and instruments.

Unfortunately, this also makes the system relatively opaque, making AML monitoring activities difficult. The environment being so paper-based, AML efforts remain largely manual and hence subject to the same shortfalls of manual systems: cumbersome, slow and susceptible to human error.

Why information sharing is necessary

These challenges become worse by the fact that no reliable systems exist to promote data transfer and sharing between tax, legal, customs authorities. Most of them rely on the AML to identify illicit fund transfer and misuse of financial systems.

Instead, all governmental authorities would be better served by establishing their own trade-based laundering detection and response teams. These efforts should focus more on data and text sharing as well as big data analytics. However, what do these teams need to look out for?

Below are some common practices of trade-based laundering:

  1. Under-invoicing – the exporter invoice trade goods below fair market value (FMV) by the exporter. This allows the exporter to add value to the importer, who will then resell the goods at the higher open market price, having paid a significantly lower price for the goods.
  2. Overinvoicing – works in the same way, but now the value transfers from the importer to the exporter, who has invoiced his goods at a price higher than the FMV.
  3. Multiple invoices – here, the criminal activity financier or money launderer will give more than one invoice for a single global trade transaction. The fact that payments can be from multiple financial institutions adds to the complexity. If discovered, they are chalked up under valid justifications like payment terms amendments, penalties and payments of late fees among others.
  4. Over- or under-shipment – in the same way as the invoicing systems, exporters can over- or under-state actual quantities shipped vis-à-vis payments received. In extreme cases, no goods are shipped at all, a phenomenon called “phantom shipping”. However, processing of shipping, payment and custom documents is in the usual way.
  5. Falsifying goods descriptions – the exporter/money launderer misrepresents the type or quality of goods shipped. For instance, an expensive collectible may be described and paid for but in actual sense a lesser quality product is shipped and vice versa, depending on direction of value transfer.
  6. Informal moneytransfer systems (IMTS) – the best example of these are the Black Market Peso Exchange (BMPE) system used in Colombia. Criminal groups commonly mastermind such networks. The BMPE for instance was created to circumvent the country’s stringent forex policies. A user sells dollars to a broker for pesos. The broker will in turn resell the dollars to a legitimate business owner that needs US dollars to make imports. Similar systems exist in other countries.

Further, such criminal activity financing organizations use additional walls including shell/fictitious companies, account funneling, barter trading among others, especially when transacting in carefully monitored jurisdictions.

Using Big Data to change the game

Given a haystack worth USD 18.3 trillion, how can big data help?

While there isn’t a single solution that can fit the whole globe, one effective solution suggested by PwC is establishment of some compliance requirements for every organization involved in international trade, much like what banks have. This may create much-needed transparency, but comes at a cost – a huge layer of red tape, which would negatively impact trading between legitimate parties.

More targeted responses are quite difficult because of the fact that trade-based laundering still remains largely undetected and hence not quantifiable. This would also make it difficult to justify implementation of the aforementioned system. Global regulation failing, perhaps global analytics might jump to the rescue, according to PwC.

Global analytics involves automation of as many systems as possible, including the AML monitoring activities. For this to work, however, the program must be correctly aligned to the main business areas and include as many advanced techniques as can be had e.g.

  • Web crawling and analytics – use advanced tools to scan the web, reviewing custom and shipment data and weigh them against the existing documentation.
  • Unit price and weight analysis – use statistical methods and data available in the public domain, algorithms are set up to determine if unit prices of goods go far above/below FMV prices. Similarly, examination of shipments can determine whether weights have been over-/under-stated vis-à-vis existing information on payments.
  • Text analytics – techniques towards automated extraction of valuable data from text files to provide data against which to gauge monitoring activities.
  • Networking/relationship analysis – using enterprise software/analytics tools, it will be possible identify hidden relationships/networks between merchants, ports and other contributors in the global trade system. The tools can also help to identify outlier activities for further monitoring.
  • Trade profiling and analytics – using publicly available data, analysis of countries can identify common patterns of import and export activity. Outliers could be indicative of trade-based laundering.

While these largely remain theoretical, we believe that big data analytics is the only way to attack this vicious activity by working from the inside out.

Author bio: Jack Dawson is a web developer and UI/UX specialist at BigDropInc.com. He works at a design, branding and marketing firm, having founded the same firm 9 years ago. He likes to share knowledge and points of view with other developers and consumers on platforms.

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