AML Technology Trends: What You Need To Know in 2023
By Stuart Clarke
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The complex world of anti-money laundering (AML) continues to evolve rapidly. As a result, regulators and authorities are making regular updates to their compliance requirements.
AML regulations are complex, and established players frequently fall foul of the rules. In December 2021, for example, NatWest Bank was fined £264.8 million for failing to comply with AML regulations.
The evolution of technology presents new challenges for AML investigators, but it also provides significant opportunities in the fight against financial crime. Keeping up with new and emerging technologies in AML helps organisations ensure that they are adhering to compliance regulations and preventing money laundering.
In this blog, we’re going to be looking at five key trends in AML technology and examining how they can help your organisation stay within AML regulations.
Suggested reading: If you want to learn more about the alternative approach to AML investigations we have seen emerge in recent years, take a look at our free report — The AML Investigation Revolution.
#1 Artificial Intelligence (AI) and OSINT
Artificial Intelligence is becoming increasingly important within the context of AML. One of its key strengths is in detecting unusual financial transaction data by analysing patterns in large data sets, as well as assessing open source and unstructured data for risk.
How AI transforms the onboarding process
AI can be used throughout AML procedures to intelligently assess customers’ risk levels. This is particularly important during onboarding, when analysts are required to review large or disparate data sets, and high-risk customers may be able to slip through unnoticed.
AI can work alongside existing tools and internal datasets to also assess open source data, carry out adverse media screening, and triage customers based on risk. With high-risk customers flagged early on, analysts are better able to prioritise their workflows and prevent backlogs from developing.
From customer due diligence to transaction monitoring
As much as AI streamlines the onboarding process, it also supports analysts in the transaction monitoring and due diligence stages of AML. If an analyst deems it necessary for an alert or customer to be investigated further, they can use AI to perform OSINT. AI-driven OSINT is especially useful because:
- It ensures transaction monitoring OSINT is ethical and compliant, while effectively reducing false positives.
- It allows investigations to utilise the benefits of OSD, including data on the deep and dark web.
- It provides financial institutions with extensive information about an individual’s connections, risks, and behaviours, without increasing analyst workloads.
Case study: Santander
Santander UK’s £107.7 million fine for AML failures serves as a key example of how AI can benefit AML procedures, particularly during transaction monitoring. Santander’s SAR units faced extensive alert backlogs and significant resourcing pressure, which worsened as their monitoring system failed to collect key customer data. In this kind of scenario, AI-driven OSINT investigation is essential. AI can quickly reveal information regarding their customers’ expected turnover, occupation, and nature of business. Any alerts could then be intelligently prioritised for analyst review, allowing high-risk transactions to be identified more easily.
While AI cannot wholly replace either people or rules-based systems, it can play a critical role in reducing the need for human approval and ensuring monitoring systems are filtering results properly.
To take advantage of AI capabilities fully, investigators should identify the strengths and shortcomings within their processes, picking and choosing where to deploy and where to use other technology or human intelligence. Any AI that is employed must be explainable, meaning that humans are able to understand and interpret the reason for an alert; this ensures that human analysts remain in control of the AML process, and have the flexibility to assess and analyse alerts accordingly.
Cryptocurrencies are an increasingly popular technology that exists digitally and virtually, and uses cryptography, often blockchain technology, to secure transactions. Crucially, within the context of AML, they are often exchanged through peer-to-peer transactions rather than a central hub or clearance process.
As was predicted years ago, cryptocurrencies have made it much easier for bad actors to hide the source and launder the proceeds from criminal activities. Many of the characteristics of digital currencies make them ideal for nefarious purposes, because they:
- Are relatively easy to use
- Can be difficult to track
- Offer anonymity
- Provide the ability to bypass AML regulations designed for traditional transactions
However, cryptocurrency isn’t infallible. In February 2022, the United States Department of Justice announced that it had seized $3.6 billion worth of cryptocurrency stolen during a criminal hack.¹ Two people were arrested for attempting to launder the stolen cryptocurrency.
Even before this, regulations were evolving. The EU 5th Anti-Money Laundering Directive (5AMLD), put into effect in 2020, attempted to address some significant issues in banking industry compliance. This includes anonymity, with the 5AMLD focusing on transparency in company registration and legal arrangements, while clients based in high-risk territories are now subject to compulsory enhanced due diligence.² Cryptocurrency compliance has become even more of a focus following the introduction of the 6th Anti-Money Laundering Directive (6AMLD).
In March 2022, in light of new sanctions against Russia following the conflict in Ukraine, enforcement requirements and recommendations to comply with these measures in the UK were made more explicit.³ This guidance includes a list of red flags to watch out for in relation to crypto and virtual assets.⁴ However, it should be noted that this isn’t an entirely new development, as the US Office of Foreign Assets Control (OFAC) listed crypto addresses in sanctions lists back in 2018.⁵
More than ever, AML investigators need to be sure that they understand these red flags and have processes in place to identify and investigate cryptocurrency-related financial crime. This could include everything from employing an expert cryptocurrency investigator, to utilising AI risk scoring technologies to identify risky clients and transactions involved in such crimes.
#3 Cloud computing
Cloud computing is now used across various business operations, including AML activities. Analysts frequently need to interact with multiple systems and information sources, and the cloud can help rationalise and consolidate data from disparate sources.
Cloud computing services offer several other benefits too. For example:
- Natural language processing (NLP) and artificial intelligence (AI) capabilities can aid in research and analysis, especially in the calculation and assignment of risk scoring
- Easier or more efficient deployment
Historic security concerns related to the remote storage of private AML data have been alleviated with the growth of modern, sophisticated cloud computing security architecture.
Financial institutions should implement capabilities such as NLP and AI as part of their onboarding, transaction monitoring, and risk-scoring processes. When procuring new software, they should be ready to consider cloud-based deployment options to reduce both friction and costs.
#4 Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is a subset of software technology wherein software robots are built, deployed, and managed to emulate human interactions with digital systems, usually as a way of automating repetitive and clear, rule-based processes.
These automated solutions can help save time and money when it comes to routine AML tasks, which means that the financial services sector is increasingly deploying RPA technology. The technology is suitable for numerous high-volume tasks, including all or parts of:
- Data entry and transaction processing
- Suspicious activity investigations
- Customer onboarding processing
- Client screening
To best take advantage of RPA, financial Institutions should consider where employees are having to undertake significant manual work that could be automated.
By introducing RPA technologies here, they can free up capacity for work in other vital areas of AML operations where automation technology is not so effective, and where the workload would be better suited to AI tools or human analysts. These areas might include the analytical parts of more complex investigations, or more basic qualitative tasks such as communicating with clients and stakeholders.
#5 Intelligent Automation (IA)
Intelligent Automation (IA) can be defined in different ways, but generally uses ML and AI to simulate parts of human intelligence. At Blackdot, we use IA to allow a system to make reasoned judgments, reach well-informed decisions, and carry out analysis based on real-world information.
Using IA boosts investigators’ potential while minimising the risk of missing insights or important data points. IA also enhances the ability of RPA to make small-scale decisions, while leaving critical decisions in the hands of human experts. For example, when performing background checks, investigators need to draw much of the information from various databases. In this situation, identifying which data belongs to the subject, rather than those with similar names, can be incredibly time consuming for analysts. IA can help identify similar names and contextualise the name with other criteria, like date of birth and occupation.
Some traditional AI-driven approaches might conduct this process and decide if the duplicates are the same person or two different people with the same name, without providing clarity on how the decision was made. An IA system is clear on the steps that have been taken automatically, and leaves the confirmation that the names are indeed the same person (or not) to an investigator. For important decisions, the IA-driven process can give investigators and stakeholders more confidence that the right judgement has been made.
IA platforms can therefore bring a host of benefits to the AML process by making it possible to:
- Undertake efficient data collection that means investigators spend less time doing manual searches
- Make faster decisions due to the classification of data types based on predefined rules
- Carry out more in-depth investigations by automating large parts of processing and some analysis across multiple data sources
IA is a key technology that financial institutions should invest in to increase efficiency while preserving transparency around decision-making.
Use Videris to stay AML compliant and up to date
Given the impact of advanced AML technology and the increasing information volumes available to investigators, it’s vital to take advantage of cutting-edge tools in your AML compliance. Cloud computing, IA functionality, AI risk scoring tools, and RPA processes should all be incorporated into AML workflows to increase efficiency.
At Blackdot, we built Videris to help investigators undertake effective AML investigations. The platform delivers a single solution for the collection, analysis, and visualisation of open source and internal data, and includes cloud hosting and standalone corporate network capabilities. Videris also utilises IA to boost the power of investigations, keeping human investigators in control while reducing the errors that emerge from AI-driven processes.
Videris cuts through the noise that sometimes overwhelms investigators, delivering a single, integrated solution. Its powerful functionality makes it possible to undertake open source intelligence (OSINT) investigations in a central location, which is crucial given that AML information is often siloed…
AML software looks set to play an important role in investigations moving forward, and Videris provides a platform that helps ensure accuracy and efficiency. Book a demo today, and see for yourself how Videris can enhance your AML investigations.