What is the current and future nature, scale, prevalence and cost of AI-enabled fraud?
Background
Many economic crimes are by their nature hidden, occur internationally and are typically under-identified and under-reported. It is therefore challenging to accurately measure the scale or prevalence of different types of economic crime.
It should be noted that in terms of measuring the scale of fraud, the landscape is more mature compared to other types of economic crime; for example, the CSEW regularly measures the scale and nature of fraud against individuals. The ARIs in this group focus on particular areas of the scale of the problem and measuring the threat on which further evidence would be helpful and extend our pre-existing evidence base
Next steps
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Source
This question was published as part of the set of ARIs in this document:
Economic Crime Areas of Research Interest ARI report July 2025 1
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Related UKRI funded projects
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Financial crime vaccines
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Why might this be relevant?
Partially relevant as it addresses financial crime analytics using trustworthy AI concepts.
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FRAUDSIM: A fraud control optimisation tool for readjustment to the new normal
The importance for our society to reduce criminal profit and deter future generations from finding financial crime as a rewarding lifestyle is one of the key aspects that we are envisioning to contribute with this projec...
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Why might this be relevant?
Partially relevant as it focuses on fraud control optimization using advanced financial simulation.
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Fraud During a Pandemic: Identifying and Appraising New Challenges for the Criminal Justice Response in England and Wales
The global Covid-19 pandemic has presented new opportunities for those looking to commit fraud, capitalising on new vulnerabilities (of systems, organisations, and members of the population), propelled by sophisticated c...
Funded by: COVID
Lead research organisation: University of Bristol
Why might this be relevant?
The project specifically focuses on identifying and appraising new challenges related to fraud during the pandemic, including the nature, scale, prevalence, and cost of AI-enabled fraud.