Editorial: The use of artificial intelligence in fighting financial crime, for better or worse?

Published date18 April 2023
Date18 April 2023
Subject MatterAccounting & finance,Financial risk/company failure,Financial compliance/regulation,Financial crime
AuthorChris Stears,Joshua Deeks
Editorial: The use of articial
intelligence in ghting nancial
crime, for better or worse?
The general use of articial intelligence is on the rise
The general adoption of articial intelligence (AI) has been around for far longer than
most would regard. In 2023, AI is often benchmarked against sophisticated tools, such as
ChatGPT, capable of taking a simple user inputand churning out a complex, well-informed
response even going so far as being capable of designing entire websites and generating
full code scripts. But AI has been around for a long time already in more basic forms such as
Amazons Alexa and Apples Siri. In fact, it is estimated that 97% of mobile users are
already using AI-poweredvoice search and voice action tools [1]. It is not hard to understand
why AI has risen stratospherically in the past six months given that OpenAIs ChatGPT
advanced autoregressive large language model crossed 100 million users in January 2023
and now sees 13 million individual users daily [2]. But the jury remains divided on how
advances in the capabilities of AI could change the landscape of professional and nancial
AI in the nancial services sector
The nancial services sector hasseen a remarkable transformation in the past decade. And
nancial services rms have been investing in deploying AI through their tech stack with
differing levelsof intensity and in different places throughout theiroperations.
For example, JP Morgan Chase deploys AI in fraud detection and prevention, and they
have developed a chatbot known as COiN to help service their customers in a faster and
more efcient way.And its not just banks, but the vendors that serve them thathave done a
good job so far of integrating AI into theirown products. A good example of this is Palantir
which deploys AI throughout its ontology core, the notably so in their dynamic layerin
which a client can create data models from which it canrun advanced simulations and test
automated AI-baseddecisions.
As AI use cases increase, so too has regulatory interest. Notably, in the Autumn of last
year, the UK Regulators published a joint Discussion Paper [3] exploring the questions
around whether (and if so, how) the potential benetsof, as well as the novel challenges and
risks posed by AI, can be managed within theexisting regulatory framework, or whether in
fact, a new approach is needed. It is worth briey trailing some of the observations that
might be made on AI in this regulatorycontext, through a nancial crime lens, specically.
The case for AI in ghting nancial crime
When it comes to identifyingpotential money laundering activities, AI can prove invaluable
due to its ability to processvast amounts of data quickly and accurately. AI-driven analytics
can provide rms with deep insights into customer behaviour that would not be possible
with traditional methods (or at least would be very time-consuming and complex by
comparison). AI-based systems can be trained to detect and alert anomalous behaviour
simultaneously at the portfolio, segmentation and individual levels and alert the nancial
institution, allowingthem to act before any money is laundered.
Journalof Money Laundering
Vol.26 No. 3, 2023
pp. 433-435
© Emerald Publishing Limited
DOI 10.1108/JMLC-05-2023-174

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