Enhancing the role of data and analytics in the fight against trade-based money laundering

Trade growth in Asia – Boon or Bane

Asia is the world’s largest trade-finance market today. In 2013, Emerging Asia accounted for approximately one-fourth of the value of global exports. This is expected to become 40 percent by 2030[1]. However the growth in trade is becoming a double-edged sword for the region as it is also more susceptible to trade-based money laundering (TBML).

TBML is one of the most common methods of moving money around illegally. Governments believe it is the primary method of financing terrorist activity[2]. According to an analysis from Global Financial Integrity, over 83 percent of illicit money flow could stem from trade activities. This means that approximately $400 billion could have been lost in Asia to TBML in 2013[2].

Asia must take the lead in fighting TBML

If TBML persists, it could have devastating effects on Asia’s economy over the next decade. The region already faces a trade finance gap of approximately $693 billion[3], which is crucial to mitigating developmental slowdown. However, because of money laundering scandals, banks may face hefty fines for breaching anti-money laundering rules and refuse to lend money leading to liquidity issues. In 2014, BNP Paribas paid the world’s largest fine to the US government, $8.9 billion, for violating US trade sanctions[4]. The recent BSI Bank shutdown case[5] and setting up of a dedicated department by MAS to strengthen enforcement and combat money laundering[6] is a wakeup call for banks in Asia to invest proactively in their compliance regime across people, process and technologies. 

With its leading role in trade, Asia needs to play an active role in directing efforts to combat TBML. Developed countries such as Singapore and Hong Kong have recently issued updated Guidance on countering TBML, but these are still not enforceable. While such Guidance may have legal implications in the future, the financial industry must take added measures today. This is crucial especially for many of Asia’s emerging economies which do not have proper systems in place to deal with the threat of TBML. With ever increasing attention from regulators on AML/CFT compliance, the approach of de-risking is becoming prevalent in trade finance business.  With de-risking leading to broader issues for the regulatory in terms of lack of oversight, with non-banking players entering the market and with legitimate customers being denied services in high risk jurisdictions, it is essential that banks change their approach to risk and compliance and tackle this compliance challenge head-on.

Why current monitoring measures are still inadequate

While banks should take on greater responsibility to mitigate cases of TBML, they are faced with a daunting task. The fact is, trade-finance involves multiple moving parts and majority of the process is still over-reliant on document-centric records as well as manual screening. Furthermore, a lot of information is available to be harnessed from data sources external to the bank. This results in a lot of unstructured and semi-structured information being available.

The industry recognizes there needs to be a shift towards automation of TBML monitoring, but banks themselves are facing various challenges. These include the lack of specialized and consolidated anti-money laundering and sanctions screening services, cost of automation, limited internal expertise of trade finance professionals in compliance teams and the suspected high volume of false positives, which lead to increased screening time. All these have impact on customer service and banks find themselves in a catch 22 situation. They do not want to turn away business by making processes difficult for customers but are simultaneously faced with possible penalties for enabling money laundering (sometimes unknowingly).

Despite governmental bodies investing in national trade infrastructures[7] and banks working on a robust KYC framework, there is still a gap in maturity levels on anti-money laundering transaction monitoring capabilities.  While banks are having existing transaction screening systems which typically look for unusual behaviours in structured data, there is no system that can holistically analyze the various interactions and information involved in trade finance.

A framework for Data consolidation and analysis

So what needs to change? If the industry is serious about enhancing anti-money laundering capabilities, I believe it needs to focus on two things.

Firstly, more emphasis should be placed on data analysis in monitoring and this means banks rethinking the way information is recorded. Developing an accurate data preparation foundation is essential and this requires collaboration between business owners of the systems and processes and relevant IT teams supporting trades. This is especially crucial since there is a wide variety and formats of information sources involved in trade based money laundering monitoring, available within and outside the banks.

Once this data assessment and preparation is complete, it will pave the way to enhance the data to enable effective screening. A five-step data governance framework could help in acquiring and improvising the screening data.

1. Identify – Identify red flags and corresponding key fields required for automating the AML rules to generate alerts
2. Source – Identify the source for retrieving/deriving key fields from internal applications, transactions, SWIFT messages, as well as documents and data sources from internal and external entities, and establish data lake by consolidating raw extracts from these variety of sources
3. Assess - Assess data quality, retrieve, normalize, parse and consolidate trade finance data from various sources to derive structured information
4. Enhance – Enhance completeness, accuracy and consistency of trade finance data along with functionality to minimize false positive alerts.
5. Monitor: Apply data governance tools to monitor and resolve any data quality exceptions arising in the screening process.

Secondly, banks will need to leverage a host of analytical techniques, both for identifying TBML red flags as well as for investigative analytics once such red flags are identified in a customer, contract or transaction information. These could include application of traditional and non-traditional AML monitoring techniques such as

1. Text Analytics – Deriving high quality information from free text
2. Big Data Discovery – Using big data tools to analyze raw data and turn these into actionable insights
3. Link Analysis – Discovering and analyzing relationships across two or more entities to derive a network (this may identify hidden relationships involved in the trade-finance deal)
4. Rule Engines – Creating parameters which analyze volume, velocity and variance of trades
5. Profiling and trend analysis – Analyzing existing data sources to provide statistics and summary information about data
6. Sequence mining – Finding statistically relevant patterns between data examples delivered in sequence
7. Statistical Analytics – Using statistical techniques to create mathematical models to help inform better decisions
8. Visual and Predictive Analytics – Using predictive models to anticipate possible future outcomes and take immediate actions
9. Web Analytics – Measuring, collecting and analyzing web data to enhance decision making

With the growth of global trade and digitization fuelling the next leg of trade finance business, banks must raise their game. It is vital, especially here in Asia where TBML is becoming major issue, that banks relook at how they combine their processes, controls, data modeling and technologies so that they can create a more effective monitoring system.

I’d love to hear what else you think banks can do to enhance their TBML monitoring standards.

References

[1] Anderson, Kym; Strutt, Anna. 2011. Asia’s Changing Role in World Trade: Prospects for South-South Trade Growth to 2030. © Asian Development Bank; Accenture Analysis.

[2] Illicit Financial Flows from Developing Countries: 2004-2013, Global Financial Integrity

[3] 2015 Trade Finance Gaps, Growth and Jobs Survey, Asian Development Bank

[4] 30 Jun 2014, Washington Post: France’s BNP Paribas to pay $8.9 billion to U.S. for sanctions violations
https://www.washingtonpost.com/business/economy/frances-bnp-paribas-to-pay-89-billion-to-us-for-money-laundering/2014/06/30/6d99d174-fc76-11e3-b1f4-8e77c632c07b_story.html

[5]  24 May 2016, MAS directs BSI Bank to shut down in Singapore http://www.mas.gov.sg/News-and-Publications/Media-Releases/2016/MAS-directs-BSI-Bank-to-shut-down-in-Singapore.aspx

[6] 13 June 2016, MAS Sets Up Dedicated Departments to Combat Money Laundering and Strengthen Enforcement
http://www.mas.gov.sg/News-and-Publications/Media-Releases/2016/MAS-Sets-Up-Dedicated-Departments-to-Combat-Money-Laundering-and-Strengthen-Enforcement.aspx

[7] National Trade Infrastructure Factsheet, Singapore Customs, https://www.ida.gov.sg/~/media/Files/About%20Us/Newsroom/Media%20Releases/2015/0527_ib2015/Annex11.pdf