The global fight against financial crime continues to pit fraudsters against financial institutions in a high-stakes game of cat and mouse. Operating as sophisticated and organised crime networks, fraudsters relentlessly probe for system weaknesses, refining strategies and openly sharing knowledge and tactics.
Staying ahead of these threats is a constant challenge for financial institutions, but the relatively recent emergence of collaborative fraud intelligence networks could move the goal posts and tip the balance back in banks’ favour.
Why sharing fraud intelligence matters
Fraudsters’ success can be at least partly attributed to their willingness to operate as a collective; sharing strategies allows them to coordinate attacks, in turn maximising their overall effectiveness. Financial institutions, by contrast, have historically worked in silos, driven by operational constraints, competition, and compliance concerns. However, this is now changing.
Banks and payment providers are increasingly recognising that sharing risk insights can transform how fraud is detected and mitigated. Thanks to technologies like homomorphic encryption, organisations can exchange anonymised intelligence about suspicious behaviours, such as flagged email addresses, devices or transactions, without compromising personal data. To be clear, this is not future gazing. Eight of the top 10 UK banks are already doing it, as are many hundreds of other financial institutions globally.
The result is a living, growing network of shared intelligence that has already exceeded 100 billion annual transactions, making it a formidable tool in any organisation’s fraud arsenal. By leveraging the collective knowledge of its constituent parts, the network empowers member organisations with 20-20 vision of fraud in real time, allowing for instant mitigation. Not only that, it helps reduces false positives and enables faster authentication and a better experience for trusted returning customers.
How it works in practice
Collaborative networks widen the scope of risk assessment significantly. A bank operating in isolation can only assess risks seen within its own customer base. By joining a global intelligence network, the same bank gains access to the shared experiences of many other organisations. The practical benefit of this is that when one bank detects suspicious activity, for example a SIM-swap on an erstwhile trusted customer device, this information can be used by other network members to immediately identify potential high-risk interactions within their transaction environment.
Analysis by LexisNexis® Risk Solutions illustrates the remarkable value of this broader perspective. If a single device related to a transaction exhibits suspicious behaviour, the likelihood of fraud increases fivefold compared to the same transaction with no associated risk. Add additional suspect behaviours – such as a flagged email address – and the risk multiplies to eight times. These insights allow banks to build and act upon significantly richer risk assessments in real time and act pre-emptively to stop fraud before it happens.
In practice, this can deliver measurable results. In 2024 one major US bank saw a whopping 1,700 per cent increase in detection of high-risk fraud events using collaborative data. Similarly, a leading US card issuer improved high-risk transaction detection by a factor of 23 using broader risk signals. In the UK, Metro Bank used collaborative risk insights to identify over £2.5 million in fraudulent transactions within six months – a 105 per cent improvement over previous efforts. Beyond fraud detection, these efforts invariably benefit consumers too, by providing streamlined, secure digital interactions.
Barriers to adoption
Despite its advantages, many financial institutions still do not fully utilise collaborative networks. A recent report revealed that only 27 per cent of financial services providers and online retailers in the EMEA region engage in risk intelligence sharing as part of their fraud prevention strategies. Globally, adoption varies even more widely.
Some of this can be attributed to operational challenges, with fears about competition, scalability and system latency common among them. Here, advanced technologies like AI-driven federated learning can address concerns by analysing data directly on user devices, rather than centrally, preserving system performance and allowing owners to retain agency of their data.
Fraudsters operate as interconnected groups, sharing resources and knowledge to evade detection. To effectively counter their efforts, financial institutions must adopt a similar collaborative mindset. Pooling data, technology, and expertise strengthens the collective defence, creating a robust global network that mitigates fraud risks.
With advanced encryption and AI technologies ensuring ethical and compliant data sharing, fraud detection can shift from being reactive to proactive. By participating in these networks, organisations gain 360-degree visibility of risk and trust across the digital ecosystem, ultimately benefiting billions of legitimate consumers worldwide.
Shared intelligence is no longer optional – it’s essential for staying ahead in the fight against fraud.
Rob Woods is fraud and identity director at LexisNexis® Risk Solutions.
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