How AI could change the AML landscape - Part Two

AML - A Few Big Buts…

Part one available here

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AI is a powerful tool that can be used to automate a wide range of tasks, but it's important to reiterate that AI is essentially applied statistics. This means that it relies on algorithms to identify, extract and generate patterns and trends in data. Furthermore, with statistics thee are always outliers. 

While AI can be effective for certain aspects of due diligence - see part three -  it's not always suitable for more stringent regulatory needs.

There are a few general reasons why AI isn't a complete solution for regulatory due diligence. As discussed, AI algorithms are only as good as the data they're trained on. If the data is incomplete or inaccurate, the AI algorithm will not be able to produce accurate results. AI algorithms can be biased, which can lead to inaccurate results. AI algorithms can be difficult to explain and audit, which can make it difficult to ensure that they're being used fairly and ethically. 

There are also a few specific regulatory reasons why AI in the AML - or any other regulatory - space needs to be used with great care. These are mainly related to human understanding and oversight and the handling of sensitive and highly confidential information. 

Firstly, AI struggles with the nuances of human language and behaviour or the context of financial transactions. AML regulatory due diligence often involves understanding complex and nuanced information, such as business rationale and motivations of individuals and the purposes and relationships between different legal entities and natural persons. Transaction monitoring can necessitate human review and clearance of the most complicated money flows. AI algorithms are not yet capable of understanding and verifying this type of information in the same way that experienced humans can.

Secondly, AI is limited with regard to making complex judgments that require common sense and ethical reasoning. AML regulatory due diligence often requires making complex judgments about the risk of money laundering and terrorist financing within the context of the customer, the transactions, and the broader financial system - a very broad and complex set of factors.

Thirdly, and very importantly, AI may not be able to explain, and cannot be held accountable for, its decisions. If an AI algorithm makes a mistake, it can be difficult or impossible to determine why the mistake was made and how to prevent it from happening again. This is because AI algorithms are often complex, unaudited and difficult to understand.

Lastly, many AI models are not yet transparent or secure enough to be trusted with sensitive data. AML regulatory due diligence often involves processing highly sensitive and confidential data about individuals and businesses. It is important to be able to trust that the systems used to process this data are transparent and secure and that the information is not reused (e.g. as a learning (Large Language) model) outside of the controlling organisation.

Of course, despite these limitations, AI can still be a valuable tool for AML regulatory due diligence. AI can be used to automate tasks such as data collection and analysis, which can free up human analysts to focus on more complex tasks. AI can also be used to identify patterns and trends in data that would be difficult or impossible for humans to identify on their own.

Yes, AI is a powerful tool that can be used to improve AML regulatory due diligence. However, it is important to remember that AI cannot replace human analysts entirely. Human analysts are still needed to define policies, design processes and rules, provide oversight, handle expectations, make complex judgments and accept the accountability for the systems and decisions made.

It remains an important truism that you can outsource the work - but you can’t outsource the accountability… or the fine!.

Next up on Friday: AI opportunities in the AML & KYC space