Motor insurance fraud is on the rise, with the Association of British Insurers reporting that more than 84,000 cases were identified in 2023 alone, costing insurers over £1bn.
But with the rise of AI in both onboard vehicle technology, and in post-incident claims screening – could the tide be about to turn for ‘crash for cash’, exaggerated damage, and other common types of fraud?
For over a decade, Activate Group has worked with some of the UK’s best-known insurers, brokers, and MGAs to deliver technology-first accident management solutions. From incident reporting, to intelligent damage triage, engineering, and vehicle repair – we oversee hundreds of thousands of claims per year – with a focus on reducing risk, controlling costs, and ensuring market-leading outcomes for both insurers and policyholders.
Here, we explore how AI could eliminate some of the most common types of motor insurance fraud – protecting policyholders from fraudulent incidents, and reducing unnecessary costs for insurers:
Common types of fraudulent motor claims, and how AI could stop them
Motor insurance fraud is a complex topic. Fraudulent claimants are known to employ a number of tactics to defraud both insurers and non-fault third-parties, and make a profit in the process.
Here are some of the most common types of motor claims fraud on UK roads, and how AI could help to prevent them:
1 – ‘Crash for Cash’ & Staged Incidents
‘Crash for cash’ is one of the most widely publicised types of motor insurance fraud. It involves a driver – either the policyholder, a third party, or both – purposefully staging an incident to secure a payout from an insurer.
This might involve one driver braking sharply in front of another’s vehicle, or even two parties staging an incident between themselves, then submitting a claim to either party’s insurer.
How AI could stop it
AI applications, both in the vehicle through telematics & dashcams, or within insurers’ systems, could help them spot common signs of staged incidents, and flag them as they’re reported.
Telematics & camera integrations
Dashcams and onboard telematics systems are becoming smarter, with AI applications being used to spot patterns in driver behaviour, and triage incident footage for common fraud markers.
This makes it much easier, and much less time consuming, for insurers to analyse incident data & footage in-depth, and spot suspicious activity with high accuracy – based on comparisons with historic cases of fraud.
AI claims screening
AI screening tools are fast being integrated into insurers’ claims management systems. They can review incident reports as they’re submitted either electronically or over the phone, spot inconsistent or suspicious details, and flag the claim for further analysis.
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2 – Phantom Accidents
‘Phantom accidents’ involve fraudulent claimants reporting an incident that never occurred – often to secure a payout or repair for accidental damage which is not covered by their policy, or even for damage that hasn’t occurred at all.
For example, a policyholder might damage their vehicle by reversing into a bollard, then later claim they were involved in a hit-and-run, or crashed into by an unidentified third-party.
Or, they could fabricate an incident entirely, and hope that the insurer proceeds for payout without requesting evidence.
How AI could stop it
AI claims screening, and visual damage assessment applications, could help insurers to spot the signs of phantom accidents, and differentiate them from genuine non-fault claims.
AI claims screening
Using predictive AI to screen claims at incident reporting stage could help insurers spot the signs of phantom accidents, by highlighting common markers found in historic cases.
This might include inconsistent or unclear details of how the incident occurred, delays in when the report is submitted, or suspicious statements – or even tone of voice – from the policyholder.
AI-driven damage assessment
Visual AI tools are increasingly being used to assess vehicle damage through images & video, and compare it directly with the circumstances reported by the policyholder.
With insurers increasingly requesting images or footage of damage before progressing claims any further, AI could help to spot inconsistencies between reported causation, and the appearance of the damage – flagging cases of misrepresentation with high accuracy.
3 – Exaggerated Damage
Exaggerated damage fraud happens when a policyholder misrepresents the scale of the damage caused to their vehicle during a genuine incident, in order to secure a higher payout or more comprehensive repair.
Unlike a phantom accident, an incident has actually occurred, but the actual scale of the damage caused is less significant than the policyholder claims.
For example, the claimant might attempt to include damage that was present before the incident in their claim to the insurer – such as scratches or dents they’ve caused themselves, or have previously gone unreported.
How AI could stop it
Similar to phantom accidents, the key to identifying exaggerated damage with AI lies in both visual assessment, and real-time screening as the incident report is filed.
AI Damage Assessment
Just like with phantom accidents involving misrepresented damage, visual AI can be used to compare incident circumstances directly with images/footage of the damage.
Thanks to the wealth of visual data the AI is trained on, it can often detect inconsistencies or suspicious damage patterns with higher accuracy than a human engineer – increasing the chance of intercepting fraud.
AI FNOL screening
By using real-time AI screening at incident reporting (FNOL) stage, insurers can spot signs of inconsistencies even without visual evidence.
By reviewing the policyholder’s description of the damage, and comparing this with the incident circumstances, the AI can flag any reported damage which is unlikely to have been caused during the incident.
4 – Vehicle Dumping & False Theft
Vehicle dumping is another common type of motor insurance fraud. This usually involves the policyholder ‘dumping’ or hiding a vehicle, and claiming it has been stolen in order to secure a payout.
Insurers usually label a vehicle as a ‘total loss’ (write-off) after a set amount of time after it has been lost or stolen, usually prompting a payout for comprehensive policies. This means, as well as securing a payout, fraudsters may even retain the vehicle, and sell it on to an unsuspecting buyer for a further profit.
How AI could stop it
Both OEM and insurer-fitted vehicle tracking systems are making vehicles more ‘connected’, and easier to track and locate. Pairing this with more qualitative vehicle data, insurers can reduce the risk of false theft claims, and ensure these vehicles are identified if sold on, or an attempt is made to reinsure them.
Smarter telematics & vehicle tracking
Onboard vehicle tracking systems are becoming increasingly common amongst manufacturers – often meaning the vehicle doesn’t need to be fitted with an insurers’ own telematics system.
Coupled with AI driver monitoring, this could help to spot ‘red flags’ of vehicle dumping, or false theft, in real-time – making it harder for fraudsters to succeed in their claims.
AI-enhanced vehicle checks
Checks for stolen vehicles have been around for a long time, and widely used by insurers, the authorities, and dealers. However, AI and in-vehicle technology could make this analysis even smarter – relying not only on the vehicle’s registration plate and VIN (which can commonly be changed by fraudsters during private sale), but also in-vehicle systems and asset-identifying technology.
The case for human oversight with AI fraud detection
While AI presents a strong case for assisting insurers in fraud detection – it isn’t a silver bullet in itself. Human input is not only essential for reducing the risk of false positives, but also providing further training for AI & machine learning systems to increase their accuracy long-term.
Historical data is a powerful tool for supporting these learnings, but it has its limitations. The types and complexities of insurance fraud change with time, as false claimants find new ways to surpass advancing claims technology.
Human expertise in engineering, critical analysis of claims circumstances, and incident triage will therefore always be an essential part of maintaining accuracy in accident management.
In summary: AI Fraud Detection in Motor Claims
From dashcam footage analysis and telematics monitoring, to AI-driven claims screening and visual damage assessment, artificial intelligence offers insurers a powerful toolkit for tackling fraud.
These technologies can spot behavioural red flags, cross-reference multiple data sources, and detect inconsistencies between reported events and actual damage with remarkable speed and accuracy.
Common types of motor insurance fraud – including ‘crash for cash’ incidents, vehicle dumping, phantom accidents, and exaggerated damage – can all be intercepted earlier when AI is embedded into both vehicle technology, and post-incident validation processes.
The result of this could mean lower costs for insurers due to fewer fraudulent payouts, and greater protection for genuine policyholders.
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To see how this works in practice, explore SafetyNet by sopp+sopp, Activate Group’s specialist fleet division. SafetyNet uses real-time FNOL validation to enhance accuracy, automatically identify liability and causation, and flag potential fraud before it becomes a costly problem.
