5 AML Compliance Trends in 2022

By Blackdot Solutions

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    The threats posed by financial crime have continued to evolve rapidly in recent years. Increased reliance on mobile and digital banking has only increased the number of avenues that financial criminals have to forward a malicious agenda. Money laundering activities already cost 2-5% of the world’s GDP, highlighting the critical nature of these challenges.1

    Financial institutions must stay abreast of trends in financial crime. In part, this is because of pressure from regulators. Examples of this include warnings from the Financial Conduct Authority (FCA) surrounding AML (anti-money laundering) compliance, and the growing potential for personal liability if risks aren’t met head-on.2 However, simply from an internal best practice standpoint, taking a proactive stance regarding AML should be a priority.

    In this article, we will explore five different trends in AML compliance, focusing on how they are changing AML best practices today, and how they are likely to influence the management of financial crime risk moving forward.

    Trend #1: Cryptocurrency

    Owned by 9.8 million Britons, cryptocurrencies are virtual currencies secured by cryptography.3 There are currently 10,000 different types of crypto,4 but with 60% of the market, which is valued at $2.48 trillion, Bitcoin is by far the most prevalent.5

    How does this impact AML best practices?

    Cryptocurrencies effectively function as digital cash, making them very popular among criminals in money laundering and black market transactions. In fact, 63% of banking industry respondents perceive the unregulated peer-to-peer transaction of cryptocurrencies as a risk rather than an opportunity.6

    Over the last few years, governments around the world have started to regulate the cryptocurrency industry more aggressively. In 2018 and 2019, authorities in Australia, the UK, the EU and the USA all initiated measures to start enforcing rules that treated some cryptocurrency transmissions with the same gravity as other more traditional forms of ‘money transfer’.

    What does this mean for AML compliance?

    As cryptocurrency regulations continue to tighten, AML compliance must take into account this previously unconsidered aspect of financial operations. If cryptocurrencies are to retain their value and be better integrated with the global financial system, exchanges of virtual currency will be required to meet the four stages of KYC/AML compliance, which include: 

    1. Who can trade and in what amounts?
    2. Whether customers meet requirements established in customer acceptance policies (CAPs).
    3. Monitoring of transactions and reporting of any suspicious activity.
    4. Implementation of ongoing risk management (e.g. data analytics, continuous monitoring, key risk indicators).7 

    Trend #2: Using open source data

    Open source data (OSD) is publicly available or publicly licensable information. This includes a broad and diverse range of sources, such as public social media, corporate data sources, government data sources, and crime statistics. For more information, a comprehensive list can be found in the OSINT framework.

    How does this impact AML best practices?

    OSD provides invaluable context for AML investigations. It can be used to uncover previously missed connections and associations between individuals, groups and institutions. This enhances risk recognition, and can improve KYC and due diligence outcomes. 

    The effective use of OSD requires targeted and relevant data capture, and the filtering of OSD in ways that allow you to make relevant connections regarding your investigation. Fundamentally, this is a central way in which big data is changing anti-money laundering best practices.

    What does this mean for AML compliance?

    Increasingly, financial institutions will be expected to make use of OSD. This expectation is demonstrated by the European Union’s 6th anti-money laundering directive (6AMLD), which extends criminal liability to professionals who fail to achieve the necessary levels of oversight, referring specifically to the responsibility to act on all readily evident risks in real-time.8

    Sorting, cataloguing and categorising data is central to any data analysis process. However, the sheer volume of OSD available requires the development of more sophisticated processes to make its effective use possible. To put it another way, investigatory processes should be based around more intelligence-led solutions, bringing us directly to trend three. 

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    Trend #3: An intelligence-led approach 

    Taking an intelligence-led approach is about applying processes, techniques and technology to your investigations that keep you focused on finding answers to relevant questions based on an agreed set of priorities. This ensures the alignment of data processing with the goals of your investigation, and allows for the more effective use of data for decision-making within any context. Practically speaking, this means mirroring capabilities developed by government, law enforcement and military intelligence teams to flexibly engage with AML investigations. 

    How does this impact AML best practices?

    By efficiently focusing investigators on relevant insights within large and complex data sets, an intelligence-led approach changes both back-of-house processes and how financial crimes are directly addressed. For example, benefits of this approach in an anti-financial crime context can include: 

    • Customer due diligence: Being able to investigate outside of a traditional, ‘tick-box’ process gives analysts increased ability to check and verify customer identities and associated risks.
    • In-depth ML investigations: Investigators have the ability to make use of more and different data, giving them higher levels of relevant information that brings key insights and better understanding of risk actors within touching distance. 

    The key to implementing an effective intelligence-led approach is to utilise the five stages of the Intelligence Cycle (Direction, Collection, Processing, Analysis, and Dissemination). This process directs investigators to set goals and then flexibly sort and analyse collected information in ways aligned with those goals. This can be used to improve outcomes in both of the examples set out above.

    What does this mean for AML compliance?

    The increased application of intelligence-led techniques puts more pressure on financial institutions to efficiently recognise and act upon risks. One answer to this comes in the form of open source intelligence (OSINT).

    OSINT is the use of intelligence processes for the analysis of OSD. By leveraging the information available from OSD, OSINT solutions focus on targeted collection and analysis in order to gain new insights, improve decision-making, and, ultimately, mitigate risks. Effective OSINT tools have the ability to cross-check data sources, and visualise networks, thereby making hidden connections and patterns easier to identify — and increasing the likelihood of suspicious activity being detected, ensuring better AML compliance. 

    Fundamentally, these are all critical open source investigation techniques that need to be considered within an AML context. As discussed in the previous section, OSD (and, therefore, OSINT) are an increasingly important consideration for organisations looking to modernise their compliance functions. 

    Suggested reading: For more on how anti-money laundering best practices are evolving, take a look at The AML Investigation Revolution report by Matthew Redhead.

    Trend #4: Intelligent automation and greater use of technology

    Intelligent automation (IA) utilises robotic process automation (RPA) to automate repetitive tasks at scale while simultaneously flagging key decisions to human operators. This enables the use of automation without relying on artificial intelligence (AI) or machine learning (ML) to make critical decisions regarding areas as sensitive and complex as financial crime. 

    How does this impact AML best practices?

    The early stages of the AML investigations cycle, including transaction monitoring and customer screening, can often be highly automated because they follow predictable patterns, so it’s possible to take a rules-based approach. The more complex investigations that are triggered, following the detection of potentially suspicious activity, tend to require a more forensic investigation that relies on human expertise. 

    As the use of data within AML compliance functions continues to grow, solutions that combine tech-enabled automation alongside expert human investigators are increasingly important. This is exactly what IA delivers. By reducing the manual data wrangling, IA frees up investigators to spend more time drawing insights from the data and disseminating reports to relevant stakeholders.

    What does this mean for AML compliance?

    As well as transforming investigation efficiency, IA’s human-led focus is an increasingly fundamental aspect of AML compliance. IA helps investigators identify and understand complex risks more quickly without needing to rely on fully automated solutions that are not yet capable of accommodating the intricacies of complex AML investigations. 

    In short, IA puts the right information in the hands of (experienced) AML investigators, at the right time, leading to more actionable outcomes. 

    Trend #5: Innovation and collaboration

    Money laundering presents a challenge on a global scale — one that no single country or organisation can hope to overcome unilaterally. As a result, a modern and adaptive approach to collaboration is required, including bringing together stakeholders from across the public and private sector. 

    How does this impact AML best practices?

    The UK’s Joint Money Laundering Intelligence Taskforce (JMLIT), Australia’s Fintel Alliance and the Europol Financial Intelligence Public Private Partnership (EIFPPP) are just three examples of the innovative financial collaborations that can now be seen across Europe and beyond. 

    This increased sharing of strategic and tactical-level intelligence is producing a number of positive outcomes. It is allowing FIs to better calibrate their risk frameworks, based on the latest understanding of financial crime typologies, and is also allowing investigative resources to be directed toward areas that are of greater priority for government authorities.

    What does this mean for AML compliance?

    As the Financial Action Task Force (FATF), government authorities and other prominent voices within the anti-financial crime community call for ever-closer collaboration, FIs should consider adopting innovative technologies that enhance the effectiveness of these partnerships. By utilising tools that draw on readily available and largely internet-based OSD, FIs can both improve their understanding of the threats and risks they face at a strategic level, as well as develop a more contextual understanding of the risks associated with specific customers, transactions and counterparties.

    To take these collaborative efforts further, financial institutions should look for specific features in an OSINT solution, including: 

    • Access to disparate data sets (corporate records, news media, social media, dark web)
    • Network mapping and analysis capabilities
    • Permission-based access
    • Cross-matching

    Adopting such technologies will ultimately lead to FIs being able to provide more useful intelligence back to law enforcement in the form of higher quality SARs.

    Using an OSINT platform to prepare for the future

    The future of anti-money laundering is increasingly uncertain as financial services expand across digital landscapes. Anti-financial crime control frameworks will have to adapt. This means taking a holistic approach to innovation that includes the adoption of an intelligence-led, and risk-based processes and policies, as well as effectiveness-enhancing technologies.

    Finding ways to better utilise OSD can enable your organisation to adopt a range of the trends discussed above. Specifically, an intelligence-led approach using OSINT can enable cultural change within investigation teams.

    At Blackdot, we’ve developed a platform called Videris. Originally developed for military and government use, Videris enables complex investigations across both private and public sector implementations. Core features include: 

    • Automated Analysis: Intelligent automation makes it far easier to sieve through large data sets, and identify valuable information that analysts can turn into actionable insights.
    • Targeted data collection: Simplifies and speeds up AML investigations and due diligence.
    • Secure browsing: Ensures the anonymity and efficiency of AML investigations.
    • Multiple use cases: Can be applied across a wide range of activities from AML investigations, network mapping and complex look back exercises, fraud investigations and more.

    With features built specifically to enable financial crime investigations, Videris can improve your AML outcomes and future proof the effectiveness of your anti-financial crime controls. Book a demo today and find out what Videris can do for you.

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    1. 27 Informative Money Laundering Statistics in 2021
    2. Anti-money laundering moves intensify in the UK and worldwide
    3. Cryptocurrency Statistics UK
    4. List of all cryptocurrencies and tokens
    5. Cryptocurrency’s Value Surges to $45 Billion One Day After Its Debut
    6. How Cryptocurrencies May Impact the Banking Industry
    7. The Paradox of AML Compliance and Cryptocurrency Regulation
    8. DIRECTIVE (EU) 2018/1673 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 October 2018 on combating money laundering by criminal law

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