For insurance companies, the fraud can range from the use of stolen credit cards, collusion and identity theft. According to the Insurance Fraud Bureau (IFB) in the UK, undetected general insurance claims fraud total £2.1billion a year adding on average £50 to the annual costs individual policyholders, on average, each year.

A growing phenomenon is the “Cash for Crash” scam in which fraudsters will target cars or commercial vehicles particularly at busy junctions or slow moving traffic but can be anywhere. The crux of the scam is that by the sudden or unexpected application of their brakes, the innocent driver of the following vehicle will not have sufficient time to react and will inevitably crash into the rear of the fraudster’s vehicle. This is stage one and the fraudsters will proceed to get the cash.

Fraudsters will make a claim against the innocent driver and their insurance company predominantly using third parties such as solicitors or claim management companies. The claims include:
• Compensation for fictitious personal injuries
• Claims for individuals who were not even in the fraudster’s vehicle at the time of the accident
• The cost of recovering the fraudster’s vehicle, even though it was actually driven away
• The cost of repairing the fraudster’s vehicle, including fictitious damage and damage that was already present on their vehicle from previous “Cash from Crash” scams.
• The loss of earnings for the driver and the occupants of the fraudster vehicle.
• The cost of hiring an alternative vehicle until the fraudster’s vehicle has been repaired, despite them driving their vehicle following the accident.

Due to the sheer volume of claims submitted, companies are unable to check individual claims and rely on statistical analysis. Statistical detection does not prove that the claims are fraudulent but merely that they are “red flagged” suspicious claims which require further detailed investigation.

fraXses would afford companies the ability to trawl through combined multiple and disparate data sources and volumes of data in real-time and rapidly and be able to spot trends including easily being able to show the detailed and most granular level of reporting of data such as: repeating claimants details, their addresses, geographical locations of accidents and vehicle information.
Predictive analysis can be used to assist the statistical process to analyse claims and assist the red flagging process.


Ability to perform speedy in-depth and flexible analysis for claim investigations.
• Reduce fraudulent claim pay-outs
• Savings for insurance companies and victims of scams ensuring costs for policyholders are not impacted negatively.
• Ability to share information quickly and easily with law enforcement and regulatory authorities.