The evolution of sanctions screening and transaction monitoring compliance in 2023 witnessed notable trends and events. The ongoing Russia-Ukraine conflict resulted in a surge of sanctions, impacting both individuals and corporations. Compliance teams faced heightened workloads as they had to ensure always up-to-date screening lists, ensuring comprehensive coverage for existing customers and their transactions.
The Economic Crime Act introduced in the UK in 2022 played a significant role in this space by providing clearer guidelines and consequences for law violations. The Act empowered UK sanctions authorities, emphasising a ‘strict liability’ test assessing sanctions breaches against civil liability of ‘balance of probability’ rather than the criminal liability of ‘beyond a reasonable doubt`. Towards the end of 2023, the UK National Crime Agency issued a red alert notice on sanctions, targeting a number of Asian corporate entities and other countries aiding Russians in evading sanctions.
This surge in sanctions complexity prompted financial institutions to increasingly leverage technology, aiming to streamline processes, control costs, and mitigate the risk of sanctions breaches.
On the Transactions Monitoring (TM) front, organisations embraced anomaly detection and network analytics to enhance accuracy, reduce false positives, and identify suspicious activities more effectively.
Financial Institutions are looking to increase the effectiveness of sanctions screening by getting greater intelligence. It is using data intelligence tools and big data to increase the information available on customers and counterparties, to be able to screen them more accurately. Teams are trying to enrich the profiles of customers and counterparties because obviously, sanctioned screenings are only as good as the data you’ve got to screen against. For instance, a richer data set about a customer comprises of name, date of birth, nationality, country of origin, etc., and offers richer hits against sanctions lists. On the opposite, sanctioned individuals try to obfuscate themselves by adding spelling mistakes in their names or changing personal information.
The right technology can perform fuzzy matching to accurately match names even in data that has errors, helping organisations to avoid missing info that can lead to them getting fined. Furthermore, technology can help you be efficient and effective, it can reduce false positives, take away manual effort (especially in investigations that need a lot more resources and screening), and it can help automate processes.
Organisations decide what is the driving force behind adopting a specific technology. When it comes to TM technology, we see many organisations adopting AI and machine learning tools to reduce false positives and speed up investigations, looking more to reducing their costs, to be more efficient, rather than effective. However, this is not the best approach. AI needs to be used also for effectiveness. An investigator has to look deeper into suspicious activity; to detect, investigate, and report it.
Financial institutions need to synchronise their efficiency strategies within their systems and annual risk-based assessments. For AI to be effective, financial crime teams must introduce advanced tools like network analytics and anomaly detection. These technologies are crucial for identifying patterns or behaviours that fall beyond the parameters of the company’s rules-based system, historically undetected by conventional rule-based approaches.
Unfortunately, compared to the last five-six years, the current economic uncertainty pushes down compliance spending, influencing financials to opt more for a let’s be more efficient than effective mindset.
With the widespread acknowledgement that AI plays a crucial role in financial crime prevention and compliance in the financial services sector, both regulators and organisations are actively endorsing its adoption. AI has become indispensable; organisations are compelled to integrate it, considering it the industry norm. Even if an organisation relies on manual processes, the prevailing trend involves the implementation of AI across the sector. In the regulatory landscape, the focus is primarily on meeting the stipulated requirements. The use of AI is not mandated but recommended for maintaining industry standards.
Efficiency in compliance processes is generally acceptable from a regulatory standpoint. If an organisation aligns with the established regulations, the specific approach to achieving compliance, whether manual or automated, is considered compliant. However, challenges arise in the long term when high-profile investigations reveal lapses in compliance effectiveness. While regulatory fines can motivate a shift toward effectiveness, it often takes reputational damage or exposure in the form of a significant scandal or fines or supervisory penalties to prompt serious investment in enhancing compliance effectiveness.
Various use cases distinguish between the applications of technology in sanctions and transaction monitoring (TM) within the financial crime landscape.
On the sanctions side, the primary focus is on minimising false positives to prevent legitimate customers from being ensnared in a sanction’s investigation, particularly concerning payment transactions. Blocking payments erroneously identified as sanctions hit can lead to customer friction, an undesirable outcome. Screening extends beyond sanctions to encompass adverse media, politically exposed persons (PEPs), and more.
In TM, the objective is to ensure that alerts are triggered only for genuinely suspicious activities, ultimately leading to the reporting of such activities to law enforcement authorities. Achieving this goal involves the implementation of systems capable of identifying the right activities and providing accurate information to law enforcement.
While AI and machine learning are commonly discussed, rules-based systems still play a crucial role. These systems serve as checks and balances, acting as benchmarks for AI-only systems. The integration of AI should augment rules, not replace them. Anomaly detection, along with rule systems featuring thresholds, becomes essential for detecting additional suspicious activities.
Network analytics, although not new, has gained prominence by extensively examining relationships, transaction flows, and connections between parties to determine potential suspicious links. This approach adds an extra layer to detection capabilities.
The emerging trend of generative AI is anticipated to be significant in the coming year, particularly in generating Suspicious Activity Reports (SARs). The technology can automatically compile information gathered, summarise it, and create a narrative for SARs, potentially streamlining the reporting process. However, human involvement remains crucial to reviewing, editing, and ensuring the final quality of SAR narratives.
Another noteworthy development is the growing emphasis on information-sharing systems between organisations. The introduction of regulatory frameworks, such as the Economic Crime and Corporate Transparency Act in the UK, facilitates secure and legal information sharing among entities. This trend is expected to see increased adoption and innovation in information-sharing technologies soon.
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This interview was originally published in The Paypers` Global Payments and Fintech Trends Report 2024. The report compiles insights and expertise from leaders representing companies across the financial services spectrum and it delves into the latest innovations and trends in payments and fintech across key markets worldwide.
Adam McLaughlin is the Global Head of AML Strategy and Marketing and the AML Subject Matter Expert at NICE Actimize. Adam possesses several years of operational experience in identifying and mitigating financial crime risks. Prior to joining the company, he spent many years managing financial crime teams and mitigating financial crime risk in financial institutions. Adam was also a Police Detective in the U.K. for 10 years, managing a Financial Crime investigation team in the City of London Police, the U.K.’s national lead force for Economic Crime for three of those years.
NICE Actimize, the leading provider of financial crime solutions, offers innovative technology to protect global financial institutions and regulators. Specializing in anti-money laundering, real-time fraud prevention, and trading surveillance, the company addresses concerns like payment fraud, cybercrime, sanctions monitoring, and insider trading. Explore more at www.niceactimize.com or Nasdaq: NICE.
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