Can Big Data & Analytics come to the rescue of TBML violations?
We've penned numerous blog posts on one of the biggest challenges in KYC/AML processes, that of catching money laundering transactions. A robust KYC process with deep technology-driven link analysis (of transaction data) has been the primary technique used to catch launderers.
However, there is one approach to money laundering that has become very difficult to catch; We're talking about Trade Based Money Laundering (TBML).
According to research published by PwC, Global Financial Integrity (GFI) Research and Advocacy Organization believes that over 80 per cent of illegal financial transactions happens through trade-based mechanisms. The numbers are staggering. GFI believes that in 2012 alone, over US $101 billion was smuggled into China through over-invoicing techniques. Overall, volume of TBML increased 6 times between 2007 and 2012.
So, how exactly does TBML work? Here are some of the common techniques:
3. Misrepresenting description of Goods
4. Modifying shipping data and generating fake shipping reports
5. Money Exchange Fraud
Simply put, all 5 techniques mentioned above point to one approach – creating a mismatch between the value of goods actually traded and money transferred for the same.
At Cenza, we believe next-generation KYC Analytics is a critical need to reduce and eventually end TBML. In this blog, we list out specific big data techniques that are crucial to the KYC process, especially to nab TBML fraud.
Trend Analytics: Irrespective of the TBML method used, the common aspect here is that the value of goods shipped is misrepresented. Using big data techniques to run comparisons between trade transaction data and trend reports on market value of goods shipped, currency conversion data, shipping data and such, can play a critical role in raising red flags.
Text Analytics: The key to implementing a high quality big data process is ensuring quality of input data. Conversion of text documents to data formats that can be read by statistical tools is paramount. Transaction Information, including SWIFT Transfer reports, Shipping Documents and Invoices will have to be crawled and automatically converted into inputs to the analytics process.
Profiling Analytics: Big data around profile of companies engaged in trade, country-specific risk and profile of customers can be used to build a risk profile of entities engaged in trade.
Rule-based Analytics: It'd help to create rule engines, to report and raise red flags around risky geographies. Either based on country of trade or risk profile of customers, or even nature of transaction data, drawing insights based on preset rules will help categorize transactions in various buckets.
Broadly speaking, TBML techniques are among the most difficult to catch and any KYC process will be incomplete without a framework to track TBML fraud.
Over the years at Cenza, we bring to the fore experience, expertise and a high-quality talent pool to deliver a robust KYC/AML compliance process for our clients. Would you like an additional pair of eyes to evaluate and enhance your KYC compliance approach? Would you like to speak to our resident expert on KYC Compliance? Please do reach out to us. At Cenza, we always stay ahead of the curve and have developed robust frameworks to help banks and FIs run a smooth, efficient, seamless KYC process. Click here to contact us.