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 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. We’ll focus on how they are changing AML best practices today and how they’re likely to influence the management of financial crime risk moving forward.
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
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. Many nations have started enforcing rules that treat some cryptocurrency transmissions with the same gravity as other more traditional forms of ‘money transfer’.
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:
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. You can find a comprehensive list in the OSINT framework.
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. OSD needs to be filtered in ways that allow you to make relevant connections within your investigation. This is a central way in which big data is changing anti-money laundering best practices.
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). The directive extends criminal liability to professionals who fail 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 means that more sophisticated processes to make effective use possible. Investigation processes should be based around more intelligence-led solutions, bringing us directly to trend three.
Taking an intelligence-led approach is about using a wide range of sources and techniques to answer investigation questions. This allows for the more effective use of data for decision-making within any context. Practically speaking, this means mirroring capabilities developed by government and law enforcement intelligence teams to improve AML investigations.
By efficiently focusing investigators on relevant insights within large and complex data sets, an intelligence-led approach changes anti-financial crime processes fundamentally. For example, benefits of this approach in an anti-financial crime context can include:
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 can be used to improve outcomes in both of the examples set out above.
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. 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. This increases 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.
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.
The early stages of the AML investigations cycle, including transaction monitoring and customer screening, can often be highly automated because they follow predictable patterns. The more complex investigations that are triggered following the detection of potentially suspicious activity tend to require a more detailed 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.
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.
Money laundering presents a challenge on a global scale — one that no single country or organisation can hope to overcome unilaterally. A modern and adaptive approach to collaboration is required, including bringing together stakeholders from across the public and private sector.
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.
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. They can also 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:
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.
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:
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.