AI-driven synthetic fraud a growing threat to UK financial institutions

We recently shared some insights highlighting a critical issue for the financial services industry: as UK consumers increasingly rely on digital platforms, synthetic fraud is on the rise. Our latest data has shown a staggering 60% increase in synthetic identity fraud cases in 2024 compared to the previous year, with these cases now constituting nearly a third (29%) of all identity fraud.

What this underscores is the evolving tactics of fraudsters, who are leveraging advanced technologies like generative AI to create convincing fake identities. As these fraudulent activities become more sophisticated, financial institutions are having to find ways to better safeguard themselves and their consumers against new threats.

To effectively combat this escalating issue, financial institutions must prioritise two key strategies: deploying cutting-edge technologies and fostering collaborative efforts. By embracing innovative solutions and working together, they can enhance their defences and ensure robust protection against the ever-changing landscape of fraud.

Understanding synthetic fraud

Historically, creating new identities to apply for financial products involved combining an individual's sensitive information, such as national insurance numbers or dates of birth, with either different identities or fake personally identifiable information. This process was time-consuming, but with generative AI, synthetic fraud can take place in a matter of minutes. Some criminals go as far as to fabricate entire social media accounts to make their fake identities feel more legitimate.

Detecting synthetic fraud is considerably more challenging than traditional identity fraud. Because synthetic identities are not linked to real individuals, there is no person monitoring the credit file who might raise the alarm. As a result, fraudulent accounts or lines of credit can go unnoticed for extended periods. Unlike identity theft, where the real person might notice and report unfamiliar accounts, synthetic fraud lacks this layer of detection, making it harder to spot.

Generative AI also aids fraudsters in altering voices and producing convincing fake identity documents to bypass security screenings. It is believed that the number of fake passports generated through AI could now exceed digitally altered physical documents for the first time.

The role of artificial intelligence

Fortunately, AI solutions are at the forefront of the solving the problem too. These advanced systems can analyse vast amounts of data in real-time, identifying patterns and anomalies that may indicate fraudulent activity. The three most prevalent use cases are:

1. Real-time fraud detection: AI algorithms can monitor transactions as they occur, flagging suspicious activities for further investigation. This real-time analysis is crucial in preventing fraud before it happens, rather than reacting after the fact.
2. Behavioural analytics: AI can analyse user behaviour and detect deviations from normal patterns that may suggest fraudulent activity. For example, if a customer’s spending habits suddenly change, the system can alert the financial institution to investigate further. This approach can also help remove good customers from referral queues, reducing friction in their journey.
3. Identity verification: Adopt AI-powered systems can enhance identity verification processes by cross-referencing user information with multiple data sources. This ensures that the person making a transaction is who they say they are, thereby reducing the risk of identity fraud.

Ultimately, the integration of AI and other advanced technologies has had a significant impact on fraud prevention in the UK. According to UK Finance, financial services companies prevented £710 million of unauthorised fraud in the first half of the year. This success is largely due to the sophisticated fraud-prevention technologies now in place.

The role of data sharing

Data sharing also plays a crucial role in preventing synthetic? fraud by fostering collaboration and information exchange among industry players. It enables banks, insurance companies, and other financial firms to share data on fraudulent activities, suspicious transactions, and emerging threats in real-time, creating a robust mechanism against fraud.

One of the main benefits of data sharing is the ability to identify and mitigate fraud patterns more effectively. By pooling data from multiple sources, financial institutions can detect abnormalities and patterns that may indicate fraudulent activity. This allows for quicker identification of fraud schemes that might go unnoticed if companies were operating in isolation.

Moreover, data sharing enhances the speed and accuracy of fraud detection. When a suspicious transaction is flagged by one company, the information can be rapidly shared, alerting other members to potential threats.

Staying ahead

What is interesting is that additional research by Experian, which surveyed more than 500 financial service companies, found that only a quarter (25%) feel confident in addressing the threat posed by synthetic identity fraud. Additionally, just 23% feel equipped to deal successfully with AI and deepfake fraud. This highlights the critical need for businesses to take action now. While the fight against fraud is an ongoing battle and criminals continue to develop new methods, the key for financial institutions is to remain vigilant and proactive in updating their strategies against preventing financial crime. By leveraging the latest AI and data-sharing technologies, and fostering industry collaboration, they can stay ahead of emerging threats and safeguard their customers.