Michael Down, Global Head of Financial Services, Neo4j
Popular TV shows like Industry have brought the drama of risk and misconduct inside financial institutions into the spotlight. While the media tends to frame fraud as something driven by personal ambition, secrecy, and power, the reality facing banks is far more complex.
Fraud is rarely the result of a single actor but a sophisticated network that is highly coordinated and engineered specifically to bypass traditional monitoring systems. As fraud becomes more technologically advanced – primarily shaped by AI – financial institutions need a new way to detect what traditional systems miss.
The network effect of modern fraud
In the UK, fraud now accounts for more than 44% of all crime, making it the single largest category of criminal activity. In the first half of 2025 alone, £629.3 million was lost to payment fraud and scams, highlighting the economic burden it places on society.
It’s the complex web that underpins modern fraud which makes it difficult to detect. Increasingly, it presents itself as coordinated and multi-layered activities; mule accounts are linked to synthetic identities, compromised credentials are reused across platforms, and shared devices connect profiles that appear unrelated. What’s more, funds are deliberately routed through layered transaction chains so that no single step appears unusual. Criminal groups deliberately fragment their operations across institutions, products, and jurisdictions, exploiting the seams between banking, payments, and insurance systems.
In this environment, the challenge for banks is no longer simply identifying suspicious transactions but understanding how it all fits together as part of a wider illicit network.
Fraud has evolved; detection must too
To make matters more complex, AI is enhancing traditional fraud techniques, increasing their scale, sophistication, and speed. As it stands, more than half of fraud cases now involve AI-enabled tactics, such as deepfakes, synthetic identity generation, and AI-powered phishing scams.
This means that legacy fraud detection systems which rely on relational databases to analyse account data in isolation, stored in rows and columns, are increasingly outmatched by the power of AI. While these systems were designed with rule-based controls to detect individual anomalies, they are unable to interpret fluid, AI-fraud networks that are constantly advancing and adapting.
This makes graph databases imperative for banks that want to protect themselves against fraud. By mapping data relationships, graph intelligence technology allows banks and financial institutions to better understand the intricacies of how accounts, transactions, devices, and identities interact. It exposes clusters and hidden linkages that would otherwise remain buried across siloed systems.
This approach is already making an impact, too, as global institutions use this technology to strengthen their fraud processes. BNP Paribas Personal Finance, for instance, has unified its customer, account, and device data with a graph-based detection model and witnessed a drop in fraud-related losses as well as time spent on investigations as a result.
Connected data is the foundation of strong financial systems
In financial services, the value of graph intelligence not only underpins fraud prevention but supports AI-driven decision making too. It can help institutions improve the quality, explainability, and reliability of their technology in areas such as risk management, compliance, and customer experience.
Banks rely on vast amounts of customer, operational, and regulatory data to operate effectively, but that information is often fragmented across siloed systems and business functions. This means that when they introduce AI tools, they often lack the appropriate, robust context to deliver meaningful, accurate outputs. Instead, it can hallucinate – providing biased, false, or misleading insights – and cause unnecessary delays or inefficiencies. In cases of fraud, this can lead to compliance officers expending resources reviewing false alerts, and in more extreme cases, to an inability to spot criminals taking advantage of their payments network.
But the importance of graph intelligence extends to regulatory compliance as well. Banks operate in an industry with zero tolerance for AI error. So much so that a failure to show an audit trail, practice robust governance, or deliver reliable models can lead to compliance pressures, including financial penalties – not to mention loss of customer trust and reputational damage. In this environment, graph models provide the structure and context to data that can reliably inform AI’s outputs, offering a credible source of truth for decision making.
Addressing the AI – and data – problem
As fraud evolves so quickly, the future of prevention rests upon banks having a robust data strategy that informs AI decisioning. Too many still operate with fragmented systems and outdated detection models built decades ago that are no longer fit for purpose. In this environment, it’s impossible to discover the complex connections between different fraudulent events and ultimately identify and stop criminals.
In an industry where trust is paramount, be it through a customer, partner, or regulatory lens, banks have no room for error. Connected data matters because it not only delivers context, explainability, and transparency, but it also supports faster, smarter, and better decision making to address the fraud threat and help the institution to scale sustainably. Banks must continue to invest in the right technologies to gain a competitive advantage and build resilient financial systems that the AI era demands.
