Maryam Mahboob June 19, 2025 8:15 am Data is driving the future of insurance. But what does it mean for everyday motorists when vehicles start talking directly to insurers? In the UK, the rise of connected vehicles—those capable of collecting and transmitting data—has revolutionised everything from real-time navigation to predictive maintenance. However, its greatest impact may be seen in the insurance sector, particularly in how motor claims are processed and approved. At Crystal Claims Management, we help drivers navigate the often-complex world of vehicle-related insurance claims. While we do not offer personal injury services or legal referrals, we focus on assisting claimants with disputes, liability queries, and delays in claim resolution. As connected technology evolves, we are helping clients adapt to new systems that promise faster, more efficient outcomes. What Is Vehicle-Insurer Data Sharing? Vehicle-insurer data sharing refers to the seamless exchange of accident, usage, and driving behaviour data between a vehicle’s on-board systems and insurance providers. This real-time or post-incident data transmission enables insurers to: Understand how an incident occurred Verify liability Approve or deny claims faster Prevent fraud through accurate digital records Why Is This Important Now? As of 2024, over 55% of new vehicles sold in the UK are equipped with telematics systems capable of recording speed, braking patterns, GPS locations, and crash data. With government support for connected and autonomous vehicles (CAVs) under the Centre for Connected and Autonomous Vehicles (CCAV) initiative, the integration of smart data into insurance decision-making is accelerating. Key Vehicle Data Points Shared with Insurers   Data Type Description Purpose in Claims Speed & Braking How fast the car was going and how quickly it stopped Assess impact severity and fault GPS Location Exact location and route of the vehicle Verify scene of accident and position Time & Date Stamp Precise timing of the incident Correlate event to reports or traffic conditions Sensor-Detected Impact Which parts of the vehicle sustained force Match to damage reports Driver Behaviour Lane changes, acceleration, mobile use (if monitored) Establish driver attentiveness and decision-making How It Impacts Claim Approvals 1. Faster Fault Determination Traditionally, claim approval can take weeks due to back-and-forth communication and manual evidence collection. With vehicle-sourced data, insurers can determine liability in hours, not days. 2. Reduction in Fraud Fraudulent claims cost UK insurers over £1.1 billion annually (ABI, 2023). With objective data on hand, insurers can better detect inconsistencies, reducing false claims and protecting honest drivers. 3. Automated Claim Handling Machine learning and AI systems can now analyse driving data to process low-value claims automatically. This speeds up approvals for minor repairs and reduces administrative overhead. 4. Improved Customer Experience Policyholders benefit from quicker resolutions and fewer disputes. When data supports the claim narrative, trust in the system increases, improving overall satisfaction. Who Owns the Driving Data?  Autonomous vehicles collect extensive data through cameras, radar, GPS, and on-board sensors. This data is essential in accident investigations—but who owns it?  The manufacturer?  The software provider?  The vehicle owner?  In many cases, this data is encrypted and inaccessible without the manufacturer’s approval, making claims more difficult to assess and prolonging disputes.  How the UK’s Legal System Is Responding  The Automated and Electric Vehicles Act 2018 aims to address some of these complexities. Key takeaways:  Insurers must pay compensation if a self-driving car causes an accident, even when no human is driving.  Insurers may recover costs from the manufacturer or software provider if fault lies in design or programming.  Drivers must still maintain certain levels of awareness unless the vehicle is certified as fully autonomous.  While this law is a step forward, it doesn’t yet solve all ethical or practical concerns. Traditional Claims vs. Data-Driven Claims Claim Stage Traditional Model Data-Driven Model Accident Reporting Manual, reliant on driver recollection Instant, pulled from vehicle sensors Damage Assessment Visual inspections, third-party reports Sensor-detected impact zones analysed remotely Fault Determination He said/she said; police reports Speed, GPS, and timing cross-checked digitally Time to Resolution 10–21 days average 3–5 days possible with connected data Risk of Dispute High if details are contested Lower due to objective data insights Challenges in Data Sharing Despite its benefits, data sharing isn’t without complications: 1. Privacy Concerns Many drivers are concerned about how their personal and location data is stored and shared. UK GDPR regulations mandate strict controls, and insurers must clearly communicate: What data is collected How long it is stored Who has access to it What it will be used for 2. Consent and Transparency Drivers must consent to telematics sharing, often through policy terms. However, the depth and implications of this data sharing are not always well understood. 3. Inconsistent Manufacturer Systems Different vehicle brands use various data protocols. A lack of standardisation makes it harder for insurers to access or interpret data consistently across the market. 4. Ethical Use of Data There’s an ongoing debate about whether data should be used to adjust premiums retroactively or penalise drivers post-claim based on behaviour, even if it didn’t cause the accident. Future-Proofing Insurance: What’s Next? The next decade will likely see even greater integration between vehicles, insurers, and claims handlers. Anticipated developments include: Real-Time Claims Triggering – A crash could instantly notify insurers, start documentation, and schedule vehicle recovery. Blockchain-Verified Evidence Chains – Accident data may be securely timestamped using blockchain to prevent tampering. Unified Telematics Standards – The UK government may enforce universal data-sharing frameworks across manufacturers and insurers. AI-Driven Risk Profiles – Behavioural data may be used to build dynamic insurance pricing models based on real-world risk rather than static demographics. Conclusion  Data sharing between vehicles and insurers is reshaping the way claims are made, evaluated, and approved in the UK. With real-time accident data, advanced analytics, and automated workflows, claim decisions are becoming faster, more accurate, and less reliant on subjective testimony. However, transparency, consent, and fairness must remain at the forefront as the insurance sector embraces this technological shift. At Crystal Claims Management, we are committed to helping everyday drivers navigate this evolving space. Whether you’re confused