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Artificial Intelligence and Machine Learning (AI/ML) have been in the news again with the release of OpenAI’s chat bot, ChatGPT. This tool uses elements of artificial intelligence to communicate with people in a human-like way and has fascinated the public with its swift and detailed responses and potentially wide-ranging uses.
However, ChatGPT is merely the latest public development in the long-established field of AI/ML, which the financial sector has been exploring for numerous years. AI/ML has the potential to transform the financial industry through rapid analytical tools and enhanced data processing capacity. For example, huge volumes of data can be analysed more efficiently to improve trading strategies or to optimise capital models.
Financial regulatory authorities around the globe have equally been paying close attention to AI/ML, as they acknowledge it has many conceivable uses within the sector. The EU is currently developing an AI Act, while the UK has released a discussion paper on AI/ML, to which AFME responded. A key message was the need for more dialogue with supervisors on areas where firms may find it more challenging to innovate, such as financial crime compliance. This is an area with huge potential for the use of AL/ML, although barriers still exist to its deployment.
The Potential for AI/ML in Financial Crime Compliance
AI/ML could prove especially transformative in the prevention and detection of financial crime, including anti-money laundering and combatting the financing of terrorism (AML-CFT). By taking advantage of the advances in data analytics derived from AI/ML, financial institutions can more effectively use their client, communications and transactional data created by their products and services.
Financial institutions’ surveillance systems could benefit from more sophisticated incorporation of unstructured data into datasets (for example, to contextualise transaction data with current affairs) or better oversight of internal and client communications using natural language processing capabilities to detect misconduct. The incorporation of AI/ML will also aid in improving the accuracy and relevance of the alerts that are generated by these surveillance systems, for both financial institutions and their supervisors. For instance, Europol estimates that currently just 10% of suspicious transaction reports submitted lead to further investigation by competent authorities.
Barriers to Deployment
Integrating AI/ML solutions into financial crime compliance, however, is not simple. To ensure a suitable baseline standard, the relevant regulatory frameworks tend to be rule-based, rather than principles-based. For example, these frameworks mandate analysis by specific risk indicators or variables, with minimal scope for a highly tailored approach or innovative technological solutions.
A key benefit of AI/ML as a technology, on the other hand, is its ability to find new solutions for the task it is set, incorporating different data sources and finding new patterns. Given the mismatch between regulatory requirements and the adaptive nature of AI, FIs interested in implementing AI/ML applications in financial crime compliance therefore cannot use them to retire their current systems, even where a new approach can produce more effective results. ...
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