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Financial crime, especially money laundering, remains a complex issue for financial institutions to tackle. All banks have Anti-Money Laundering (AML) systems in place, yet global money laundering transactions are still estimated at 2 to 5 per cent of global GDP – US$800 million and US$2 trillion – but only 1 per cent are seized by authorities.
Over 25 per cent of financial services firms have not conducted AML/CFT (Combating the Financing of Terrorism) risk assessments across their global footprint – so, it is no surprise that criminals are continuing to find loop holes. To add to this, current systems are repurposed instead of bespoke, inefficiency breeds from being inundated with false positives, and a new influx of data is contributing to the continued vulnerability of banks to money laundering.
Nevertheless, according to Wealth Insight, global AML spending is predicted to rise from US$5.9 billion in 2013 to US$8.2 billion in 2017 – promising a new opportunity for banks to create stronger barriers to fight against these criminals.
Banks across the sector installed their current AML systems as a quick, short term reaction to increasing regulatory pressure. Subsequently, most financial institutions have attempted to repurpose retail bank AML or market abuse system models from a different era that aren’t sophisticated enough for current requirements. These models offer a limited understanding of risk exposures that bank AML systems face, and don’t have the capacity to identify and act on suspicious behaviour causing laundered money to become increasingly unrecognised.
Remove the blinkers
Money laundering today involves a complex web of companies, individuals, trades, settlements and payments organised by low level individuals who deposit cash into the banking system in low volumes, which then is moved around the world in large volumes. With traditional methods, basic detection occurs at a transaction or account level in isolation, without an understanding of the wider context surrounding individual activity.
As a result, sophisticated money launderers are escaping being identified. Banks and financial institutions must act and understand the wider picture of individual activity in order to reduce their vulnerability to illegal activity. This web is being made more complex by the increase in connected devices; by 2020, experts predict that there will be more than 50 billion connected devices across the world, raising cause for concern for banks as criminals will be able to communicate and hide their activity. Companies must use this data for their benefit, to understand the complex web that facilitates financial criminals.
The colossal number of false positives flagged by current AML systems is arguably the biggest drawback for banks in their efforts to combat money laundering. Layered company structures, usually across international networks, makes it incredibly difficult for banks to define certain AML transaction monitoring systems (TMS) requirements that identify risk at an acceptable level of false positives. In an attempt to avoid missing any potential criminal activity, current TMS flag tenuous links that aren’t comprehensively connected, ranging from two people living at the same address, the same school or the same name.
Currently 90 to 95 per cent of alerts are false positives, yet analysts are legally obliged to investigate, regardless of legitimacy, due to the fear of eyewatering fines. These investigations are labour and cost-intensive, keeping banks in a vulnerable position as they continue to waste time investigating false positives and making it more difficult to spot cases of true illegal activity.
A new approach
In order to address these vulnerabilities, banks need to take a fresh, modern approach to their AML systems in order to combat fraudulent activity. It is clear that banks are treating launderers as individual transactions, rather than a web of individuals. Understanding the network and its capabilities is the first step into reducing false positives and becoming more efficient in the fight against criminal activity.
However, new practices are becoming easily accessible. Contextual monitoring uses entity and network analysis techniques, in combination with advanced analytical methods to detect anomalous and suspect activity. Taking a holistic approach allows banks to risk assess all networks and entities of connected parties and provide an aggregated view of the risk it poses across the data. As a result, the number of false positives is drastically reduced, eliminating weak links and focusing on high risk business, networks and individuals.
Money laundering continues to remain a large-scale issue for banks and financial institutions alike. As the criminals get smarter, current stubborn AML systems remain in the dark ages. To make use of the infinite data now made accessible to banks, they need to adopt new compliance technologies and understand criminal networks as an entity, rather than a single transaction. Only then, will banks and financial institutions alike reduce their vulnerability to money laundering.