How Big Data Addresses the Issue of Anti-Money Laundering

Anti-Money laundering (AML) is a term mainly used in the financial and legal industries to describe the legal controls that require financial institutions and other regulated entities to prevent, detect, and report money laundering activities. Money laundering activities typically aim to generate income with no regulation to maximize income for as little cash outflow as possible, with no regard for the probable negative economic, political and social implications. These activities also include income-generating actions that aim to raise funds for separate illegal activities.

How to Watch the Money-Flow?

Since money is a limited resource, money accumulated illegally and with no regulation prevents capital to flow into socioeconomically productive industries. The imbalance in money flow also inevitably leads to further printing of money, harming the purchasing power of a country’s currency. If not controlled, this inflation can cripple and erode an economy.

To evade detection of money laundering, launderers have become more sophisticated in their methods, and financial institutions have to remain two steps ahead by deploying advanced analytical and statistical techniques. Criminal and terrorist organizations regularly turn to global merchandise trade to hide the movement of their funds behind the curtain.  It’s a classic needle in a haystack  an $18.3 trillion business formed from a complex web of finance, shipping and insurance interests operating across multiple legal systems and languages[1].

There’s no real way to quantify how much money criminals are invisibly exchanging using the above method  According to Price Waterhouse Coopers (PwC)  Trade-based Money Laundering (TBML) accounts to more than 80 percent of illicit financial flows, ranging from more than $200 billion in 2002 to more than $600 billion in 2011.

How to spot a suspicious transaction?

Both money laundering and fraud can be detected through Big Data searches for suspicious anomalies amongst millions of transactions.  [2]. Companies have taken it upon themselves to spot fraudulent transactions. The result is that they have invested billions in incredibly sophisticated Big Data techniques. A good example to spot a suspicious transaction would be the following:

Suppose at a heritage auction, where collectibles pieces can sell for more than $3 million apiece, the company will run credit risks, and risk score analyses of bidders to detect if they are attempting to make a purchase from a fraudulent account. According to The New York Times[3], high prices art is a growing tool for money transfer by criminals and many times used to launder money which ends up in financing terror and other illegal activities.

Fines and monetary settlements for banks not in compliance with AML regulations are growing and have crossed the $13.4 billion in 2014[4]. As a result, banks are looking to new technologies like Big Data to counter this concern.

What can Big Data actually do?

So how can Big Data and Big Data Analytics help organizations to find illicit transactions in an $18.3 trillion haystack? According to experts, a global one-stop solution for the problem is highly unlikely. However, the following are some of the advanced techniques which can be implemented to counter the concern:

1. Text Analytics: The capability to extract data from text files in an automated fashion can unlock a massive amount of data that can be used for transaction monitoring.
2. Web analytics and Web-crawling:These tools can systematically scan the web to review shipment and custom details and compare them against corresponding documentation.
3. Unit price analysis:This statistic-driven approach uses publicly available data and algorithms to detect if unit prices exceed or fall far below global and regional established thresholds.
4. Unit weight analysis:This technique involves searching for instances where money launderers are attempting to transfer value by overstating or understating the quantity of goods shipped relative to payments.
5. Network (relationship) analysis of trade partners and ports: Enterprise analytics software tools can identify hidden relationships in data between trade partners and ports, and between other participants in the trade life cycle. They can also identify potential shell companies or outlier activity.
6. International trade and country profiling analysis: An analysis of publicly available data may establish profiles of the types of goods that specific countries import and export, flagging outliers that might indicate TBML activity.

Even after implementing the above methods religiously, building effective AML programs is not an easy task because money laundering crimes are well hidden and usually mimic normal behavior. Large data sets and the nature of financial crime present challenges to first-generation, rules-based AML solutions, which rely on pre-defined sets of fixed thresholds. Data quality issues such as missing values, misspellings and abbreviations pose additional challenges. However, discovery and predictive analysis are effective in detecting fraud and money laundering in raw-source data, non-standard and poor quality data. The ability to retrieve interrelated data for e-discovery purposes is of primary importance to compliance[5].

Hexanika: Compliance Made Easy

Hexanika is a FinTech Big Data software company, which has developed an end to end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance.

Hexanika addresses the issue of AML in the following way:

1. Hexanika inbuilt solutions rules identify suspicious transactions for further end user analysis and reporting purpose.

2. This enables the business user to create their own intelligent rules according to their needs to identify AML.

Hexanika helps establish a compliance platform that streamlines the process of data integration, analytics and reporting. Our software platform can develop and clean data to be sourced for reporting and automation, simplifying the processes of data governance and generating timely and accurate reports to be submitted to regulators in the correct formats. Our solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

To know more about our products and solutions, read: https://hexanika.com/company-profile/

Contributor: Akash Marathe

Image Credits: www.gfintegrity.org

[1] Source: http://www.cio.com/article/2871684/big-data/how-big-data-analytics-can-help-track-money-laundering.html

[2] Source: http://blogs.wsj.com/cio/2013/06/03/anti-money-laundering-tools-lag/

[3] Source: http://www.nytimes.com/2013/05/13/arts/design/art-proves-attractive-refuge-for-money-launderers.html?pagewanted=all&_r=0

[4] Source: http://www.financemagnates.com/institutional-forex/bloggers-2/more-money-spent-on-less-money-laundered-the-aml-big-data-story/

[5] Source: www.safe-banking.com/DownloadDocument.ashx?documentID=84