The cost of fraud to the UK economy is staggering, estimated at £219 billion annually. These stolen funds fuel other forms of crime, creating a ripple effect of harm. In 2024 alone, a total of £11.4 billion was stolen from consumers. The number of fraud filings to the Cifas National Fraud Database (NFD) also reached an all-time high, with over 420,000 cases recorded to the NFD last year – a 13% rise compared to 2023.

The rapid advance of technology is supercharging the reach and capabilities of criminals. AI is revolutionising the way they operate, enabling them to harvest data at scale, generate near-flawless fake documents within seconds, and execute attacks on networks with unprecedented efficiency.

The use of deepfakes and AI-generated voices has made impersonation fraud more convincing than ever, allowing criminals to pose as consumers, business leaders, and authority figures to steal sensitive data and life savings. As a result, fraud is not only harder to detect but also more pressing and urgent than ever before.

From our most recent data, Cifas members recorded a fraud-risk case to the NFD every two minutes, and together we prevented over £2.1 billion in fraud losses in 2024. Here is what our latest insight tells us.

Identity fraud is the dominant fraud type recorded to the NFD. This has been the case for many years. Criminals rely on well-established impersonation tactics, luring victims into fraudulent transactions through scam calls promising fake handset upgrades and discounts.

Those aged 61 are the most targeted demographic highlighting a need for increased education and protective measures to safeguard these consumers.

Facility (account) takeovers surged in 2024. Mobile phone accounts were the primary target as online retail platforms remained a hotspot, with criminals frequently altering account details or redirecting deliveries to fraudulent addresses.

False applications increased by 10% in 2024. AI-driven fraud is also playing a critical role in this trend. Criminals are using sophisticated generative technologies to create high-quality fake documents that can evade traditional verification checks.

Individuals under thirty years old accounted for 61% of money mule cases, reinforcing the need for continued education and proactive prevention strategies to deter young people from being recruited into illicit financial schemes.

Employees who misuse their access to company systems remain a critical risk according to filings to the Cifas Insider Threat Database, often exploiting their knowledge of internal security measures to evade detection.

Our latest fraud-risk data serves as a stark warning: fraud is evolving at an unprecedented pace, and no sector is immune. The rapid rise of AI-driven fraud tactics, coupled with increasing facility takeovers and identity fraud, poses a growing challenge for businesses and consumers alike.

To combat these threats effectively, cross-sector collaboration, intelligence sharing, and continued investment in fraud prevention technologies and training are essential. Organisations must stay ahead of emerging fraud tactics, leveraging data, intelligence and AI to detect and prevent fraud before it occurs.

Additionally, consumer education remains a critical pillar in fraud prevention. Raising awareness about common fraud tactics – particularly among vulnerable demographics such as older individuals and young people targeted as money mules – can help mitigate risks.

As criminals continue to refine their methods, staying vigilant, adaptive, and proactive will be the key to protecting both organisations and individuals from financial crime in 2025 and beyond.

Visit www.fraudscape.co.uk to discover more about the UK’s latest fraud trends and emerging threats

1. Verify customer identity
   Use multi-layered identity verification methods such as government-issued IDs, biometric authentication, and credit history checks to confirm a customer’s legitimacy before extending credit.
2. Monitor transactions for red flags
   Use real-time fraud detection systems to flag suspicious transactions, such as unusual spending patterns, multiple applications from the same IP address, or inconsistencies in personal details.
3. Implement strong data security measures
   Protect customer data with encryption, secure access controls, and regular cybersecurity audits to prevent identity theft and fraud.
4. Educate employees and customers
   Train staff on fraud detection and prevention strategies and educate customers on recognising and reporting suspicious activity related to their credit accounts.
5. Use AI and automation for fraud prevention
   Leverage AI-driven fraud detection tools that analyse behaviour patterns, detect anomalies, and prevent fraudulent credit applications in real time.