The financial crime landscape in Asia Pacific has undergone a radical shift in recent years, driven by evolving regulatory requirements, technological disruption, and burgeoning wealth in the region. To keep up with this rapid transformation, financial institutions and regulators are turning to advanced technologies to elevate their anti-money laundering capabilities.
The $2.8 billion money laundering case in Singapore last year and the subsequent whole-of-government response are a reflection of the implications of such illicit activities. Money laundering remains a grave concern in financial systems, and anti-money laundering (AML) regimes are no longer just a regulatory checkbox but a critical issue that warrants collective action.
In the wake of heavy fines to prevent money laundering and other financial crimes, financial institutions are spending more resources on compliance and monitoring. However, they continue to rely solely on costly compliance shortcuts. The alarming surge in financial crime fines — a phenomenon known as “fine fatigue” — raises stark questions about the effectiveness of traditional penalties in combating money laundering.
The trend is exacerbating the discrepancy between regulatory intent on paper and real-world implementation between the “say-do gap”, leaving our financial systems vulnerable. However, there is a beacon of hope: AI-enabled AML regimes. By leveraging artificial intelligence’s (AI) analytical power to automate and accelerate common tasks, we can overcome "fine fatigue", address vulnerabilities at their core, and enable financial institutions to not only comply with regulations but also fight against financial crimes.
Start by understanding how generative AI works
The traditional rule-based way of detecting and preventing financial crimes is inadequate. Modern financial criminals and organised crime groups have innovated the infiltration by blending in through sophisticated methods. With criminals mimicking legitimate behaviour, this is akin to finding a specific needle in a haystack of a billion needles. To tackle and stay ahead, organisations need a different kind of tool—one that can adapt and grow as regulations change to keep up with the financial criminal’s sophisticated methods.
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Enter generative AI. With its ability to understand and communicate in natural language and its capacity for adapting to a broad variety of tasks through rapid learning and contextual awareness, generative AI is incredibly well-suited to address challenges in the financial crime space. These characteristics have catapulted it to the forefront of innovative technologies, capturing the imagination of IT professionals and AML experts alike.
Combining predictive and generative AI for the winning edge
Financial crime investigations are plagued by false positive alerts that slow down, distract, and mask genuine risk. Predictive AI technologies to reduce false positives have been available in the market for a number of years now, but have failed to move the needle on operational cost and effectiveness.
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Generative AI technology can supercharge AML operations in multiple ways: Gathering, structuring and presenting disparate data sources to existing predictive AI technologies; accelerating human understanding of risk by mediating the communication between investigators and the AML platform; and providing natural language summaries of AML investigations. The result is an efficient assistant that takes on the manual basics so that human investigators can focus their resources on more strategic, critical tasks.
Simply put, Generative AI helps address the gap in traditional methods with less resources and time. Specialised generative AI-powered solutions offered today are cost-effective and seamlessly integrate with existing infrastructure to enhance capabilities without displacing established processes. By moving from reactive detection to proactive prevention (enabled by generative AI), financial institutions can finally build the agile and robust defences they need to combat evolving modern financial crime.
Striking the balance
Now, with more dynamic AI models, financial institutions can tap into the benefits of generative AI involved in a matter of weeks (compared to years) while mitigating the potential risks involved. It can also free financial institutions from the drudgery of technical heavy lifting and complex upgrades.
That said, as we adopt generative AI and use cases expand, there is always a risk of hallucination or incorrect and made-up non-factual responses. Therefore, we need careful consideration, human oversight, and verified processes. The impact AI decisions have on individuals' lives is profound, and the implications of someone being wrongfully accused of money laundering because of a system error are severe.
As much as generative AI has demonstrated extraordinary ability to enhance processes, human intervention becomes imperative to avoid adverse consequences of false accusations. Human abilities such as accountability, responsibility, fairness, judgment, and critical thinking are crucial for understanding intricate risks and nuanced situations.
From hesitation to harmonisation
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The challenges of the future of financial crime risk continue evolving with bad actors, highlighting the crucial role in AML. As financial institutions seek to rethink strategies, they should perceive generative AI as a breakthrough against effectively combatting modern financial crimes.
As such, the pursuit of harmonised AML regulations throughout the region makes it harder for criminals to exploit weak points and move illicit funds by providing a solid foundation. To truly outsmart today's sophisticated financial criminals, we need a two-pronged approach: Harmonised AML regulations across the region and the synergistic partnership of generative AI and human expertise.
While generative AI’s potential to excel and unlock the complex dynamic nature of financial crime risk is commendable, human expertise and intervention remain indispensable. Seasoned professionals remain crucial in making critical decisions and taking meaningful action based on their insights. Ultimately, it is about how AI empowers human expertise and how human judgment refines AI insights.
Gerard O’Reilly is the managing director for Apac - Financial Services at SymphonyAI
