How network visualisation allows fraud to leap from the screen

Network visualisation is a game changer because fraud is rarely committed in isolation. The real power of network visualisation is to reveal hidden relationships, shared entities, and behavioural patterns that aren’t obvious when looking at claims or policies in isolation.

Traditional fraud detection methods (rules engines, scoring models, etc.) focus on individual transactions or policies, for example “Does this claim look suspicious on its own?”

“The real power of network visualisation is to reveal hidden relationships, shared entities, and behavioural patterns that aren’t obvious when looking at claims or policies in isolation.”

However, most serious fraud, especially the high-value or organised crime, happens when multiple people, policies, or entities are connected in subtle ways:

The same phone number used on several unrelated claims
A repair garage appearing in multiple suspicious motor claims
The same bank account linked to dozens of small injury settlements
A ghost broker using one address to register many fake policies

it’s incredibly hard to detect this fraud by looking at rows of data.

Network visualisation turns data points into entities and relationships

Each person, address, vehicle, phone number, or policy becomes a node; each shared connection becomes an edge.

Nodes: people, policies, claims, vehicles, addresses, phone numbers, companies
Edges: (e.g. “same phone number”, “same address”, “same payment card”)

Figure 1 - A network visualisation of a real non-fraudulent claim. Labels have been removed for data protection purposes.

Once visualised, fraud rings and anomalies often leap off the screen. A non-fraudulent claim will generally look quite boring, a small succinct network.

A fraudulent claim (on the other hand) can look quite different as data points re-occur and bigger networks present themselves.

Figure 2 - A network visualisation of a real fraudulent claim network. Labels have been removed for data protection purposes.

Introducing GRAPHT

GRAPHT allows intelligence teams to view more of these networks (100 times more than legacy network visualisation products)¹ , allowing these fraudulent networks to come to the forefront in 3D visuals with analytical features that simply aren’t available anywhere else

It’s estimated that undetected fraud costs a large UK insurer between 100 - 120 million a year.²

Can your organisation afford to not use GRAPHT?

Estimated from 10,000 items being usably visualised on a modern PC using legacy network visualisation software. With GRAPHT usably visualising 1,,000,000 items on the same machine through the browser.

Detected insurance fraud in the UK was estimated at £1.1 billion in 2022 (ABI). Studies suggest a similar amount of fraud goes undetected each year, implying total UK fraud of roughly £2.2 billion. Large insurers with 10% of the UK market would therefore face a total fraud exposure of £220 million annually, of which approximately half (£100–120 million) is detected, with a similar amount (£100–120 million) estimated to be undetected.