2017 saw several of the UK’s top tier banks get accused of inadvertently facilitating the laundering of enormous sums of money thought to be linked to crime rings and corrupt officials. Much of the media furore surrounding the scandals criticized the UK for being a target for international criminals looking to turn the profits of crime into ostensibly legitimate assets, on account of what authorities claim are it’s largely inefficient Anti-Money Laundering procedures.
Paradoxically, it is often the failings of the very AML systems that have been put in place to monitor for suspicious activity that are facilitating this illicit activity without detection. The systems that are in place in many top-tier UK banks were instated somewhat hastily as a response to increasing regulatory pressures and, as such, are not fit for purpose.
These AML systems analyse transactions according to pre-established rules that dictate what is and is not considered to be suspicious activity. The problem is that the analytics are basic entry-level methods of anomaly detection that are not tailored to the monitoring that they are doing. This means that they are extremely prone to triggering false positives and herein lies the essence of the problem.
Why the System is Failing to Stop Money Launderers
The ineffectiveness of these systems is inadvertently facilitating illicit financial flows for two main reasons. Firstly, the elementary nature of these systems means that criminals are able to systematically test the parameters of the rules on which they are based and tailor illicit activity accordingly. Once a criminal organisation has established certain variables that are likely to get flagged by the system – like transaction size or certain geographical region – they are able to plan a laundering strategy that avoids triggering any sort of alert.
Secondly, and perhaps more importantly, inadequate development means that these systems make inaccurate connections between various activities which are misinterpreted as suspicious. This subsequently triggers an alert which is found to be false more than 90% of the time. Alerts often occur at transaction level and without any relevant contextual information which means investigating each one is costly in terms of both time and resources. This is further exacerbated by the fact that regulatory expectations require that every alert be investigated fully. Essentially, analysts are completely overloaded by alerts that they are legally obliged to investigate, despite the fact that the vast majority of them were erroneously produced.
How can we improve AML systems?
The functionality of the AML systems that are contributing to these problems can be easily improved. Several top tier banks in the UK are using technologies known as contextual monitoring to replace the legacy systems they know to have been inefficient and ineffective. This process involves the application of sophisticated analytics to large data sets, contextualising each transaction within a network of related activity and thus, making it far easier to identify suspicious activity.
However, the issue of regulation is one that cannot be solved as simply. In light of several high-profile scandals that have emerged over the past decade, AML regulations are unsurprisingly strict. AML teams within banks must be careful because they otherwise risk personal liability and reputational damage. This means that analysts are inclined to file defensively, in case they are at risk of being found to have ever inadvertently incorrectly closed a case that was later found to be associated with illicit activity. This sort of defensive filing further worsens the problem by inundating internal teams as well as external Financial Intelligence Units and law enforcement encouraging a greater likelihood that genuinely illicit activities will go undetected.
Institutions additionally face the problem of having clients with multiple Suspicious Activity Reports (SARs). Continuing to do business could be seen as facilitating illicit activity, and further defensive filings (i.e. where are no reasonable ground to suspect money laundering) are not protected by the safe harbour provisions in the same way that true SARs are. Interestingly, financial institutions tend to find very high-risk activity more effectively using various data mining and analytic techniques, as well as by using historical internal and external data. Technological advancements have started to emerge which can transform the way we look for suspicious activity, meaning the quantity of alerts is smaller and the quality of alerts is higher.
AML experts at banks, regulators at policy level, and investigators on the ground all share a common goal in allowing the UK to take full control of its banks and the activity that goes on within them. There does, however, exist a discrepancy between the way that all three parties approach this as a target. For any sort of genuine change to be made, there needs to be a more cohesive and mutual understanding amongst all involved parties in how they can work together to address this growing issue.
Featured in the Global Banking & Finance Review
You may be interested in…
Why You Need A Holistic View of Integrity Risks Within The Supply Chain
Businesses that support organizations through their supply chain face a growing number of risks. Learn how Decision Intelligence can help.
How to Achieve Growth & Manage Risk with Real-Time Customer Insights
Discover how forward-thinking organizations are deploying new technologies to create a dynamic, contextual understanding of their customers.
The Quantexa Community: Where Customers and Partners Can Learn, Share and Collaborate
Find out more about The Quantexa Community, a global professional network built to bring Quantexa users together.
Quantexa Opens State-of-the-Art Technology & Analytics Hub in Malaga
Quantexa has opened the doors to its Technology & Analytics Hub, set in the very heart of Malaga’s thriving Tech Park. Learn more today.
It’s Time To Upgrade Your Early Warning Signal Systems – Here’s How
Find out why risk managers are turning to CDI technology to provide more accurate early warning signals that anticipate risk changes earlier.
How to Build Additional Context into Your Machine Learning Algorithm
Learn how building additional context into your machine learning algorithm can help your organization detect risks at speed.
Reduce the tax gap, identify fraud and non-compliance, and operate as efficiently as possible with limited resources.
Reveal hidden risks and detect criminal activity faster. Reduce false positives to manage the cost of compliance. And improve investigations to make faster and more consistent decisions at scale.
Customs Agencies & Border Control
Contextual Decision Intelligence enables faster decisions, increased revenue collection and enhanced compliance. The Quantexa platform enables Customs and Border agency teams to analyze data successfully, automate and accelerate decision-making, and achieve improved results.
Identify potentially fraudulent activity by looking at people or transactions in isolation. Understand the context surrounding the organizations you do business with to make fast, accurate decisions.
Fraud, Waste & Abuse
Empower your team with the best tools available for today’s challenges to identify and prevent fraud, waste and abuse with contextual decision intelligence software.
Understand your customers, their business structures and supply chains. Make better lending decisions, faster. And support digital risk transformation.
Revolutionize Your Financial Crime and Fraud Detection
Enhance the efficiency, effectiveness and consistency of your operational and complex investigations to empower your teams to expose and understand risk faster.
Master Data Management
Connect all data—internal and third party—to create a joined-up, contextual view of all the relationships between your customers and every other domain.
See how we help to reduce costs and improve coverage for financial crime compliance.
See how our platform uses contextual analysis to turn data into a high value asset.
See how our platform uses financial crime technology to enhance your existing IT ecosystem.
Unified CRM Solution
Know Your Customer
Reduce significant manual effort across onboarding, refreshes and remediation. Automate checks, implement continuous monitoring, and focus on contextual decision making.