Talking trade: Money laundering and fraud – the silent side of trade finance

 

Trade finance is vital to the international economy. Indeed, The World Trade Organization (WTO) estimates that 80 to 90% of global trade is reliant on it. It is therefore key that the process runs efficiently and is protected from the machinations of fraudsters and money launderers. Agencies are implementing the latest data management and analytics solutions to tackle the threat, but the roll-out of solutions must accelerate if the problem is to be brought under control.

When trade finance fraud occurs, losses can run into millions of dollars. Fraudsters are enticed by the sums involved and money launderers by the ability to hide and disguise their criminal activities, with little fear of discovery. Both are focused on the fragility of the human control processes and the current reliance on manual processes and paper documents. This, coupled with the complexities of trade, different languages and the multitude of organisations involved, provides criminals with the materials they need to carry out fraud and AML abuse.

Today, we’re seeing a growing understanding of the issue, but there isn’t a broad understanding of how to tackle it ...

 

Today, we’re seeing a growing understanding of the issue, but there isn’t a broad understanding of how to tackle it -- not surprising given the current range of trade finance fraud. One of the most prominent and popular types is the double financing approach. This involves importer and exporter organisations collaborating – either to create false turnover to obtain credit or conduct a ‘bust out’ where both receive funding for the same trade and then subsequently disappear.

Other commonly used techniques include falsified accounts, disqualified directors, organisations with risky or shared ownership structures. In terms of money laundering, the typical approach involves over-invoicing, under-invoicing, over-shipping, under-shipping or simply shipping empty cargo containers and using this as a means to transfer funds.

To tackle the fraud and money laundering associated with trade finance, the authorities need to process vast volumes of data, much of which is unstructured and not effectively integrated with other information. The problem is compounded by the need to analyse it in granular detail down to the quantity of goods in each container, as well as routes and times used.

Adding to the challenge, the quality of data in trade finance environments is often mediocre. As a result, most organisations struggle to develop a complete picture of operations within a single country, let alone the global perspective needed to understand an organisation’s full fraud and AML exposure.

Historically, the difficulties involved in tackling fraud and money laundering in this area have been such that few organisations have addressed it effectively. So how can they rectify this? The vital first step is to apply the latest data management and data cleansing solutions to existing data in order to develop a comprehensive picture of all relevant information.

It’s initially a process of exploration and discovery but once relevant data has been captured, data quality methodologies must be applied. Solutions providers have tools that can help this process but that in itself is not sufficient. The banks also need to take responsibility for addressing the issues many of them still have with their data, and put programmes in place to capture better data.

Businesses need to be pragmatic here. They should not wait to get their data perfect. Instead, they should work with what they have right now and use a variety of techniques to improve on their current controls, adding more data and context as new data sources become available to analyse.

Organisations should use technology to enable their compliance and anti-fraud programmes, including visualisation techniques to identify patterns, anomalies and outliers. Money laundering and fraud are all about deception. To combat it effectively, businesses need to make use of third party data sources to gain a complete picture of what’s happening; run a multi-dimensional analysis of their data and start to identify areas of interest. In this context, anchoring data into real-world constructs, ships, ports, goods, companies, directors, owners, etc., helps de-mystify the problems and provide meaningful business insight that can be re-used.

Once they understand what their data looks like, they can start to take the necessary steps to monitor and analyse it; delivering a comprehensive end-to-end monitoring system that effectively connects data internally across the organisation and and externally from third party sources, ensuring full consistent and robust risk coverage. A best practice approach needs to focus on multiple stages in the monitoring and control lifecycle, with the ability to add more contextual information -- all residing within a platform able to ingest and process this additional information efficiently.

For this kind of scenario, big data analytical technologies such as Hadoop combined with high-performance analytics tools, become a must. New features such as dynamic data exploration help investigators analyse and identify the problems. In parallel, data scientists can improve detection quality by using hybrid analytics: incorporating business rules and database searching to pinpoint criminal activity known to have occurred. And they can subsequently bring in anomaly detection, text mining, advanced analytics and social network analytics to identify previously unknown or complex transgressions, and map the organisational links between the fraudsters and money launderers.

With these new technologies, controlling alert volumes becomes easier as more contextual analysis can be conducted. This enables a multi-layered alerting approach and the application of more advanced techniques on both higher risk customers and/or high false positive scenarios, in turn helping to manage alert volumes and apply an effective risk-based approach.

Taken together, the capabilities allow compliance teams to pinpoint behaviour that looks suspicious or lies outside typical ways of doing business, take action to prevent trade finance fraud and money laundering and ultimately bring the perpetrators to justice.


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