New rules of the game in the AML field?
The COVID-19 pandemic is affecting virtually all areas of the economy. Fraudulent players are also adapting to the new situation. Can anti-money laundering (AML) systems effectively counter them?
With each passing year, criminals come up with smarter methods of introducing illegally obtained money into the economy. Market regulators try to follow their actions and place ever-changing demands on financial institutions. For banks, this means the necessity to constantly adapt procedures to the growing number of regulations and an increase in operating costs in the AML area.
According to the LexisNexis study from 2017, financial institutions in Germany alone spend over $46 billion in costs annually to comply with AML regulations.
Change of the traditional rules of the game
Traditional AML systems are rule-based. In their case, the system creates alerts about suspicious transactions based on pre-developed algorithms. But do the old rules remain effective in the times of pandemic? The dishonest players also had to find themselves in the new reality – not only by adapting their existing methods, but also by looking for new opportunities.
According to a recent KPMG report on the money laundering prevention during the pandemic, new challenges for the existing prevention efforts may arise from, among other things:
- development of new money laundering methods linked to the current epidemiological situation (methods so far unknown to banks and their systems);
- changed consumer behaviour (increasing the number of false positive alerts and the workload for analysts);
- remote work of analysts (which may impede due diligence and timely handling of alerts);
- regulators’ expectation to quickly mitigate new risks and introduce preventive measures.
For traditional systems, the pandemic may also have come as a surprise. Rules can only be adjusted by a manual change, and it is difficult to define yet unknown activities. As the previously written rules didn’t take into account the new challenges, the current performance of these systems may be less precise, burdened with greater error rate, reducing the efficiency of analysts’ work. This may, in turn, lead to reduced effectiveness in detecting money laundering attempts.
An ace up the sleeve
AML systems can effectively identify new risks when supported by artificial intelligence. In this case, first, AML specialists verify alerts created by the rule-based system. Next, during the learning phase, artificial intelligence (AI) algorithms learn based on that data and datasets which contain information such as KYC and transactional data. This way, the system is trained to properly analyse new data in the future. AML analysts then receive alerts with relevant information. Alerts with a value below a predetermined threshold are treated as very low-risk threats and can then be discarded or analyzed as part of a considerably simplified procedure. If the alert value exceeds the set threshold, it is sent to analysts for further verification.
What is of great importance to meet the challenges of pandemic times is that AI can find anomalies. This will allow them to identify criminals much faster, close their accounts and escalate the problem, thereby reducing the activity of criminals fraudulently using their banks.
Know the players better
AML procedures can also be improved by robotising processes in the know your customer (KYC) area. It reduces the number of necessary but repetitive manual tasks. The system is able to automatically collect data from multiple internal and external sources and creates clear reports based on this data. In addition, the system automatically scans media news for information on money laundering, corruption, drug trafficking, etc. With these enhancements, analysts can get straight down to risk assessment without wasting time on data collection.
Double the stake
The fight against money laundering is a continuous process that requires constant improvement. However, it is also worth making these efforts to avoid high penalties for non-compliance with AML regulations, the amounts of which are growing every year.
In 2018, global enforcement institutions imposed more than $4 billion in fines. In 2019, this amount doubled. In 2020, by October, it amounted already to over $13 billion. Therefore, the benefits of using AI and robotisation are not only faster and more precise transaction risk assessment. They also include decreased operating costs, increased analyst job satisfaction, and a chance to avoid high penalties.
By Katarzyna Boniecka, business development manager, Comarch Finance
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