The Current Landscape of AML
The financial sector is confronting significant challenges in combating money laundering, exacerbated by the rapid increase in non-cash transactions, which reached 1,3 trillion globally in 2023 (1) and are projected to reach 2.3 trillion by 2027, with a growing rate of 15% annually. This surge is driven by the proliferation of digital payments and innovative financial services. Concurrently, money laundering schemes have become more sophisticated, with criminals exploiting advanced technologies and complex financial instruments to obscure illicit activities. Traditional rule-based AML systems are increasingly inadequate, generating high false-positive rates that burden compliance teams with resource-intensive investigations and failing to adapt to evolving laundering techniques. These inefficiencies underscore the urgent need for innovative approaches, such as AI-driven solutions(2), to effectively combat global financial crime.
The General Role of AI in Modernising AML
Traditional AML systems face significant challenges due to their reliance on static, rule-based mechanisms and manual processes. These systems often struggle to keep pace with the evolving sophistication of illicit financial activities, such as layering and structuring, which are designed to evade detection. This static approach results in high false-positive rates, overwhelming compliance teams with alerts that require extensive human effort to investigate, thereby diverting resources from genuine threats (3). Moreover, the cost-benefit imbalance is stark; financial institutions incur substantial compliance costs, yet the recovery rates of illicit funds remain low, highlighting the inefficiency of traditional AML frameworks (4). These limitations underscore the urgent need for more dynamic and adaptive AML solutions that can effectively address the complexities of modern financial crime.
Key Innovations Brought by AI
AI is transforming AML frameworks by integrating advanced technologies that collectively tackle the complexities of modern financial crime. At the core of this transformation is Machine Learning (ML), which enhances anomaly detection through adaptive algorithms capable of learning from vast, diverse datasets. By identifying subtle patterns and behaviours, ML significantly minimises false positives, enabling compliance teams to concentrate on genuine risks rather than expending resources on irrelevant alerts.
Graph analytics further strengthens detection capabilities by facilitating the analysis of intricate transaction networks. This method uncovers hidden connections and suspicious clusters that might otherwise remain undetected. Notably, graph analytics can reveal “unknown unknowns”—new and unexpected financial crime patterns—allowing institutions to identify and mitigate emerging threats before they escalate.
Underpinning these advancements is High-Performance Computing (HPC), which ensures AI systems can scale to process billions of data points efficiently. HPC provides the speed and computational power needed to analyse large-scale, complex transaction data in real-time, allowing institutions to monitor and respond to financial crime as it unfolds.
The Broader Impact
The integration of AI into AML frameworks brings about transformative changes that go far beyond immediate operational gains. By automating intricate detection processes and streamlining investigative workflows, AI dramatically improves efficiency, cutting down both investigation time and associated costs. This enables compliance teams to allocate their efforts to high-priority cases, enhancing overall accuracy and effectiveness.
AI’s capacity to analyse financial activities in real time introduces continuous monitoring and facilitates the detection of emerging money laundering patterns. This proactive approach allows institutions to anticipate and address evolving criminal tactics, rather than merely reacting after they occur.
Moreover, standardised and scalable AI tools foster greater global collaboration. By ensuring consistency and interoperability across institutions and jurisdictions, these technologies enable seamless information sharing and coordinated efforts to combat money laundering on a global scale.
Mopso’s Innovative Contribution to AI-Driven AML
Andrea Danielli, CEO and Founder of Mopso (5) , presents their solution:
Combining Social Network Analysis and AI for Continuous Risk Monitoring
Mopso, in collaboration with the LIST, developed the project PAMLA (Performant Anti Money Laundering Analytics), which has been granted a HPC bridge from the Ministry of Economy. We aim at improving network analysis in the transaction monitoring domain, through the development of techniques for identifying specific crime-related traits in the topology of the network and associated attributes. The software will identify and characterise clusters looking for network motifs and other features that map to established crime patterns, as defined by the authorities. Secondly, if we can dispose of real banking data, we will look for the unknown-unknown, i.e., we will test many machine learning techniques like graph neural networks and deep neural networks, to explore unknown connections’ patterns not yet addressed by the actual transaction monitoring systems.
Centralising Data for a Comprehensive View of Customer Activity
Mopso uses semantic web technologies to integrate a very large number of information sources that can be of different nature: internal to the financial institution, coming from open data, from data providers and from open-source intelligence. This means that, for every customer, the solution finds and combines up to 200 data sources, comparing data in between different time intervals. Once elaborated, this data could produce specific alerts, called “scenarios”, which create the customer’s individual money laundering risk profile. Then the technology is able to combine individual risk profiles into a bigger picture, thanks to network analysis, making it possible to spot profiles that, at first glance, seem legitimate.
Prefilled SARs and More
We use different AI techniques in our software: the exploratory part is linked to fully explainable rule-based algorithms; on top of the analysis results we then use a layer of LLM to summarise the results or guide analysts in the necessary insights. The combination of the solutions allows to intercept, analyse and summarise the activities at risk of money laundering.
Transforming AML Workflows
Some preliminary results, prior to the PAMLA project, allowed us to identify as suspicious some operations that no transaction monitoring system had intercepted. In one exemplary case, we succeeded because we identified patterns that involved several subjects, both Italian and foreign, all traceable to the same pivotal subject, sometimes implicated as an administrator, sometimes as a partner. We noted that this technology is very strong in combating the so-called shell companies, widely used in the field of money laundering as they are easy to open, operate and allow the laundering of huge amounts of money.
Conclusion
The financial sector stands on the cusp of a transformative era, as AI offers groundbreaking tools to combat money laundering. Traditional methods, constrained by static rules and plagued by high false-positive rates, struggle to keep pace with increasingly sophisticated financial crimes. AI-powered solutions enable real-time monitoring, reveal hidden patterns, and craft comprehensive customer risk profiles, ushering in a proactive approach to tackling financial crime.
The moment for decisive action is here. As the industry turns to AI, financial institutions have an opportunity to lead by adopting these transformative technologies, enabling a more transparent and resilient financial ecosystem. Embracing cutting-edge innovations helps strengthen compliance, and positions organisations as frontrunners in the fight against financial crime in this new age of AI.
Footnotes:
Image: Midjourney
(1) Capgemini (14 Sep 2023) “Global non-cash transaction volumes set to reach 1.3 trillion in 2023” https://www.capgemini.com/news/press-releases/global-non-cash-transaction-volumes-set-to-reach-1-3-trillion-in-2023
(2) European Institute of Management and Finance (EIMF) “The Impact of Artificial Intelligence in Anti-Money Laundering” https://eimf.eu/the-impact-of-artificial-intelligence-in-anti-money-laundering
(3) Team Sanction Scanner (19 July 2024) “The Future of Anti-Money Laundering: Trends and Technologies” https://www.sanctionscanner.com/blog/the-future-of-aml-trends-and-technologies-917
(4) Raditio Ghifiardi (November 9, 2024) “The Urgency of AI in Anti-Money Laundering and Counter-Terrorism Financing: A Global Imperative” https://moderndiplomacy.eu/2024/11/09/the-urgency-of-ai-in-anti-money-laundering-and-counter-terrorism-financing-a-global-imperative
(5) https://www.mopso.eu/
