The financial services sector in the United Kingdom is actively exploring enhancements in economic crime detection, emphasizing the potential of artificial intelligence (AI) in these efforts. As outlined by UK Finance chair, Robert Wigley, economic crime, including fraud, represents a significant proportion of total crime within the UK, prompting a critical focus on detection and prevention within the industry.
According to UK Finance, fraud constitutes approximately 40% of all reported crime in the UK, prompting initiatives to bolster defenses against this predominant threat. Despite only a small fraction of police resources being allocated towards combating fraud, the financial and banking sector has taken substantial steps to curb this issue, reportedly preventing two-thirds of attempted fraudulent activities through their interventions. Technology, particularly AI and machine learning, plays a central role in the strategy to combat financial crime. These tools are employed for various tasks, such as analyzing new client data using natural language processing to identify potential risks, and monitoring financial transactions for signs of money laundering or fraudulent behavior.
The sector has produced numerous alerts over the past year, which has led to a significant number of Suspicious Activity Report (SAR) submissions to the National Crime Agency. The large volume of alerts, many pertaining to lower-level activities, has prompted discussions within UK Finance about optimizing focus towards more substantial and organized economic crimes. AI is also being leveraged to detect so-called mule accounts by observing unusual transaction patterns that may indicate misuse. For instance, Mastercard’s VocaLink utilizes the Mule Insights Tactical Solution (MITS) to scrutinize UK bank accounts for activity that suggests mule behavior, albeit within the constraints of current data protection laws.
UK Finance is advocating for legislative changes to facilitate greater collaboration and information-sharing among banks in cases of suspected illegal activities. This is seen as a critical step towards reducing the prevalence of economic crimes.
Ensuring fairness and avoiding bias in algorithmic decision-making is another challenge acknowledged by the financial services industry. UK Finance has responded by developing a set of data ethics principles and establishing a Data Ethics Working Group, aiming to replicate best practices seen in companies like Visa.
Engagement with regulatory bodies such as the Financial Conduct Authority (FCA) and the Bank of England continues to be a priority for UK Finance, particularly concerning the future of AI regulation. These discussions are part of a broader collaborative effort, including a public-private forum and close coordination with the Information Commissioner’s Office (ICO).
In the ongoing fight against economic crime, UK Finance perceives current information-sharing limitations as a significant hindrance, advocating for legislative progress to empower the financial sector with more robust tools for this battle.
