Vall Herard is the CEO of Saifr, a Fidelity Labs company. Vall has previously worked at top banks such as BNY Mellon, BNP Paribas, UBS Investment Bank and more.

As cyber criminals deploy increasingly sophisticated tactics, financial institutions (FIs) must evolve their anti-fraud mechanisms to keep pace. Increasingly, AI is becoming a key tool in this evolution. Far from being just a supportive tool, large language models (LLMs) are completely reshaping the conversation around financial crime, particularly regarding anti-money laundering (AML) and Know Your Customer (KYC) efforts. By augmenting human expertise with machine precision, advanced AI can dramatically boost the efficacy, efficiency, and scale of AML and KYC solutions.

Transforming AML and KYC operations

Artificial intelligence, particularly LLMs, can transform financial crime prevention in three ways. AI tools work 24/7 to process vast amounts of global data from diverse sources. This continuous, high-speed analysis allows for the rapid identification of potential risks that are beyond human analysts’ capabilities. The sheer volume and variety of data that AI can handle — from transaction histories and customer profiles to external databases and news sources— can provide a comprehensive view that was previously unattainable.

Secondly, AI is significantly enhancing risk analysis. Traditional rule-based systems often struggle with the complexity of modern financial crimes, leading to high rates of false positives. LLMs, however, excel at understanding context and identifying subtle patterns. They can significantly reduce false positives and help prioritize true risks. This allows compliance teams to focus their efforts on critical issues first, improving the overall efficiency and effectiveness of AML operations.

Finally, AI is streamlining due diligence processes. Automating risk classification and profiling enables faster, more targeted customer due diligence. AI can quickly analyze a customer's profile, transaction history, and other relevant data to assess risk levels and flag potential issues. This not only speeds up the onboarding process for low-risk customers but also allows for thorough scrutiny of high-risk individuals or entities. The result is a more efficient allocation of resources and a more robust defense against financial crimes.

Challenges and ethical considerations

AI presents significant advantages in the financial space, but its integration also raises ethical considerations. One major concern is the potential for bias in AI systems. If the data used to train these systems is not diverse or adequately representative, it could lead to unfair treatment of certain customer groups. FIs must ensure their AI tools are developed and deployed ethically, with regular audits to check for bias.

Privacy is another critical concern. The vast amount of data processed by AI systems raises questions about data protection and customer privacy. FIs need to strike a delicate balance between leveraging data for effective crime prevention and respecting individual privacy rights.

The future of AI in financial crime prevention

Looking ahead, AI's role in fighting financial crime is set to expand further. As AI technologies continue to evolve, we can expect even more sophisticated applications. For instance, advancements in natural language processing (NLP) could lead to AI systems that can analyze communication patterns associated with financial crimes, potentially uncovering complex, multi-party schemes.

AI, of course, is not a silver bullet. The most effective ways to combat financial crime will likely combine AI tools with human expertise. While AI excels at processing vast amounts of data and identifying patterns, human analysts bring critical thinking skills, industry knowledge, and the ability to understand nuanced contexts that may elude even the most advanced AI systems.

Nonetheless, AI is indeed redefining the conversation around financial crime. By improving data scanning, enhancing risk analysis, and streamlining due diligence, AI is providing financial institutions with powerful new capabilities to thwart bad actors. However, as we embrace these new technologies, we must also be mindful of the challenges they present. With careful implementation and ongoing refinement, AI has the potential to significantly strengthen our defenses against financial crime, creating a safer and more secure financial ecosystem for all.