How to stop ghost-brokering and other emerging threats from haunting financial services
"Institutions also face a perfect storm of challenges: mounting complexity, evolving compliance demands and the race to operationalise AI."
Financial services firms are facing a new breed of fraud. As the risk grows, the industry stands at a crossroads. In a sector built on a labyrinth of relationships between customers, employees, transactions, accounts, and regulators, the old playbook of conventional data models is rapidly losing relevance.
Ghost brokering, once a fringe threat, has become one of the fastest-growing scams in the industry, posing a significant risk to insurance companies. Fraudsters pose as legitimate agents, set up fake comparison sites or AI-generated landing pages, and lure unsuspecting consumers with “too good to be true” policies. Then they vanish, leaving the customer uninsured and the insurer exposed to financial loss and reputational fallout.
What makes ghost brokering so insidious is its invisibility. On the surface, each policy appears legitimate. But beneath lies a hidden network—shared devices, repeated credentials, and recycled behavioural patterns—that legacy systems fail to detect.
Another growing threat is quote manipulation. It may seem harmless, but it’s anything but. More than one in ten adults in the UK believe it is acceptable to lie on an insurance claim to make money, and of those who admitted to doing so, three in five exaggerated a genuine claim to receive a bigger payout. It’s digital gaming of the system, and it’s quietly undermining the foundation of risk-based pricing.
As the threat landscape worsens, institutions are also facing a perfect storm of business challenges: mounting complexity, rising fraud, evolving compliance demands, and the race to operationalise AI. Amid this upheaval, one technology is quietly redefining how to make sense of it all: graph databases.
A new wave of insurance scams
For decades, relational databases were the standard, organising information neatly into rows and columns. But today’s data is dynamic and deeply interconnected. It doesn’t live in silos anymore. That’s where graph databases come in. By mapping relationships between data points like a social network, they reveal patterns, anomalies, and insights that conventional models often miss.
Imagine a digital map revealing in vivid colour where every entity and interaction is connected, making it easier to trace behaviours, spot risks, and uncover opportunities for innovation and competitive differentiation.
Industry leaders are paying attention. Some of the world’s biggest banks — Citigroup, UBS, BNP Paribas — have already integrated graph databases into their core data strategies.
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Even fintech disruptors like Klarna are raising the bar, using graph data models not just to boost efficiency, but to fuel innovation—powering its AI assistant, Kiki, to handle thousands of employee queries every day with speed and precision.
In a world defined by connections, seeing the full picture is no longer optional. This is where graph databases deliver a critical advantage. By connecting disparate data points—devices, IPs, email addresses, quote patterns, and more—graph databases uncover the relational signals that traditional systems miss.
They empower insurers to quickly detect organised fraud networks and stop manipulated quotes before they convert, protecting both reputation and the bottom line for any financial institution. In today’s fast-evolving threat landscape, fighting fraud requires more than rules; it requires relationship-driven intelligence.
Money fraud: Hidden, coordinated schemes
Today’s fraudsters work in tight, fast-moving networks, spinning webs of mule accounts, synthetic IDs, and synchronised transaction flows. Legacy systems, built to analyse isolated transactions, often miss the broader context and allow schemes to go undetected.
Graph databases connect the dots. They map relationships across accounts, devices, and behaviours to expose hidden fraud rings and flag suspicious activity early, before damage is done.
When a suspicious transaction occurs, any related entities and anomalies are quickly identified, revealing hidden networks of fraud before they wreak havoc on any given financial institution.
Regulatory readiness: Unprecedented compliance burdens
Financial institutions are under intense pressure to meet evolving mandates like BCBS 239 and Basel III. The risks of falling short are steep— ranging from regulatory fines, reputational damage, and rising operational costs. UK financial services firms now spend, on average, £188 million each on compliance, equivalent to over £515,000 a day, according to LexisNexis.
Traditional documentation and manual workflows often lack the clarity and agility needed to keep pace. That’s where graph databases offer a smarter approach by creating a connected model of regulations, internal policies, and infrastructure.
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This enables teams to run timely impact assessments, answering critical questions like which systems are affected by a regulatory change or which controls need to be updated.
With a connected view of operations, institutions can identify potential vulnerabilities, improve audit readiness, and respond to regulatory changes with greater speed and accuracy. Graph databases serve as the intelligence layer for building resilience across the enterprise.
Smarter financial services start with connected data
The institutions leading the next era of finance aren’t simply collecting data; they’re understanding it in context. Graph databases offer more than operational efficiency. They unlock a new level of intelligence, one rooted in relationships, patterns, and real-time visibility.
In an industry where every connection matters—between people, transactions, systems, and regulations—seeing the whole picture is no longer a luxury. It’s a competitive necessity.
Graph databases are already delivering measurable results across fraud detection, compliance, and AI enablement. But its real value lies in what it makes possible next: faster decisions, deeper insights, and stronger resilience in an increasingly connected world.
The transformation is already underway. Now’s the time to lead it.
Michael Down is Global Head of Financial Services at Neo4j
