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Demystifying AI in financial crime compliance

AI's prominence in AML isn't just a passing trend; it's a response to criminals using advanced technology in an evolving financial landscape. However, it's imperative that we do not rely on AI as a silver bullet, and that we do not conflate myths surrounding it as fact.

Janet Bastiman
October 24, 2023

AI's prominence in AML isn't just a passing trend; it's a response to criminals using advanced technology in an evolving financial landscape. As we navigate a world filled with increasingly sophisticated financial crime typologies and money laundering schemes, there's an urgent need for advanced tools that can keep pace with these challenges. AI, with its ability to process vast amounts of data, detect subtle patterns, and adapt to new threats, is a game-changer.  

However, it's imperative that we do not rely on AI as a silver bullet, and that we do not conflate myths surrounding it as fact. At Napier’s recent Disrupt Fincrime Forum in New York, we delved into AI for AML, and demystified some of its biggest misconceptions in the financial crime compliance space.  

Is AI just a hot topic?

The recent surge in interest can be attributed to breakthroughs in generative AI and large language models, which have expanded the possibilities for AI applications in AML. AI isn't just a buzzword; it's a powerful tool that can enhance AML efforts and safeguard the integrity of the financial system.

Demystifying AI

  1. A common myth is that the goal of implementing AI is to eliminate all false positives. While reducing false positives is a worthy objective, it's essential to strike a balance. Overemphasizing this goal can lead to overlooking genuine threats, compromising the effectiveness of AML efforts.  

  1. Another prevalent misconception is the notion that AI will take over the compliance function in AML strategies. In reality, compliance remains paramount, and AI should be harnessed to enhance and augment these efforts, not replace them.  

  1. Moreover, there's a risk of conflating metrics and inflating project achievements, leading to unrealistic expectations of what AI can accomplish. It's crucial to approach AI implementation in AML with a clear understanding of its capabilities, recognising that compliance, transparency, and adaptability are fundamental pillars of a successful integration.  

By dispelling these myths, we pave the way for a more realistic and effective utilisation of AI in AML processes. Emphasising the need to feed AI with the right data and understanding the role of explainability in a compliance strategy were crucial aspects of the conversation at Disrupt Fincrime.

Regulatory requirements and the role of explainability

Regulators are increasingly focusing on explainability – the ability to understand why AI-driven decisions are made. With a lens on the US, the Algorithmic Accountability Act was introduced in 2022, aiming to bring transparency and oversight to systems being used to make automated decisions. The bill requires firms to conduct impact assessments for bias, effectiveness and other factors. This is particularly relevant to firms using automated decision-making with customer data, such as customer onboarding. This shift in focus compels regulated institutions to take a closer look at the dos and don'ts of AI implementation in AML.

Learn more about how authorities are approaching regulating the use of AI in financial crime compliance

The vital role of data

When it comes to AI strategies, data is the linchpin. Ensuring data quality, format, and relevance is crucial, especially in the context of varying requirements across different sectors. AI can help soften the impact of stringent regulations, but it must be built on a foundation of clean and compliant data.

As institutions transition from legacy systems to modernised solutions, the state of their data is under scrutiny. Emphasising the importance of using data in a legal and appropriate manner, especially during onboarding stages, is crucial. Data issues are universal and will persist even in automated systems, we just have to find the right strategies for AI to continue to learn and adapt from it.  

Learn more about how data is the critical ingredient to detecting financial crime typologies here

AI for AML holds immense promise, but it must be approached with caution, attention to data quality, and regulatory compliance. As we continue to decode AI for AML, it's crucial that we stay abreast of regulatory changes and industry best practices to maximise the benefits of innovative technologies while maintaining the highest standards of integrity and transparency.

Learn about the role of AI in turning financial crime compliance from a burden into a strategic asset

Photo by on Unsplash

Chair of the Royal Statistical Society’s Data Science and AI Section and member of FCA’s newly created Synthetic Data group, Janet started coding in 1984 and discovered a passion for technology. She holds degrees in both Molecular Biochemistry and Mathematics and has a PhD in Computational Neuroscience. Janet has helped both start-ups and established businesses implement and improve their AI offering prior to applying her expertise as Head of Analytics to Napier. Janet regularly speaks at conferences on topics in AI including explainability, testing, efficiency, and ethics.
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