As per the U.S. National Highway Traffic Safety Administration (NHTSA), there has been a decrease of about 3.3% in the number of fatalities in the first three months of 2023 compared to the same time in 2022, with the estimated fatality rate decreasing to 1.24 fatalities per 100 million vehicle miles travelled (VMT) compared to a higher fatality rate of 1.32 during the same time in 2022¹. The fatality rate for 2022 decreased to 1.35 fatalities per 100 million VMT, down from the reported rate of 1.37 fatalities per 100 million VMT in 2021².
As per the U.S. car accident statistics for 2023, about 35% of fatal motor vehicle accidents occurred due to intoxicated drivers driving under influence (DUI) and about 29% of deadly car accidents occurred due to over speeding³. The data also shows that male casualties in these accidents are almost twice the female casualties, which in turn drives the demographic factor during insurance calculation.
Around 350 million connected vehicles are currently on the roads generating large amount of data such as a) driving behaviour (aggressive or average or conservative), b) sudden acceleration/braking, c) night-time driving pattern, d) average speed and acceleration, especially in speed restricted zones and sharp curves, insurance providers are able to more accurately calculate the insurance premiums based on behaviour-based (pay-how-you-drive) and usage-based (pay-as-you-drive) patterns⁴. The car insurance costs are on the rise in the USA with almost 15% year-on-year (y-o-y) increase and more than 45% increase between 2013 and 2022⁵. As per a McKinsey report⁶, four core technologies, coupled with Artificial Intelligence (AI), will reshape the insurance industry, viz, 1) increased availability of data from connected vehicles, 2) increased prevalence of robotics and (semi) autonomous features, 3) open-source code and data, and 4) advances in cognitive technologies. In a recently published report by Innovate UK⁷, the impact of increased electrification of vehicles on the insurance industry is presented in detail.
As part of the basic governing structure, it is important to address any bias in the input data that may reflect in the AI model output because the underlying algorithm of AI may not be transparent in many use cases, leading to in-built biases either intentionally or unintentionally⁸. The process must incorporate checks and balances so any potential bias or unintended output from the AI model should call for human intervention to double check and fix any anomalies to ensure fairness in the insurance premium calculation and policy underwriting. Some researchers have proposed the idea of “explainable AI” so the model could respond to the queries on “why” it has reached certain conclusions and arrived at certain decisions. Some industry experts and insurance regulators have also mooted the possibility of “supervisory analysis” of the AI model output wherein the supervisors at the insurance providers could actively monitor the output and look for trends to ensure that there are no instances of bias or discriminatory results.
II. Impact of Electrification on Insurance & Repair Industry
As the share of Battery Electric Vehicles (BEVs) in the mobility industry increases year-over-year with advancing technologies in the batteries and the powertrain, augmented by embedded connectivity, ADAS, and autonomous features, it has brought significant disruption in the insurance and repair industry. Many of the vehicle insurance companies have been traditionally basing their underwriting and pricing practices and claims handling on internal combustion engine (ICE) vehicles. There has been a long history of vehicle experience in the market for ICE vehicles, which is still in a nascent stage for the BEVs, posing a challenge for insurers to adapt to the increased electrification.
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Differences in claims process between ICE vehicles and BEVs
As detailed out in the report in Ref [7], there is a significant difference in the insurance claims process for a BEV in comparison to an ICE vehicle as multiple factors add complexity and cost into the insurance policy & premium for BEVs along with the corresponding post-accident repair and replacement of vehicle parts, electric powertrain, and battery. The factors which bring in additional complexity include a) nature and damage of the accidents, b) damage to e-powertrain especially battery, c) rescue and transportation after the accident, d) quarantine of BEVs during repair, e) battery handling/storage, f) complexity of the repair and the skills required, g) key-to-key time, h) decision on repair vs total loss, and i) resale value of BEV vis-à-vis battery replacement cost.
As the percentage of BEVs compared to ICE vehicles is still small, most of the insurers continue to use the existing systems, software, and processes for a) First Notification of Loss (FNOL) questions, b) vehicle triaging, c) damage assessment, and d) repair vs salvage decision, with little or no change to account for different requirements to handle the BEV damage and repair estimation.
Figure 1. Vehicle damage patterns in accidents involving ICE vehicles and BEVs (Ref [7])
Figure 2. HV battery damage probability estimates relative to BEV impact areas (Ref [7])
The vehicle damage patterns in accidents involving BEVs, new age ICE vehicles, and other vehicles are captured in Figure 1 (Ref [7]) and show similarities between BEVs and ICE vehicles. The key differentiating factor in damage or repair estimates between BEVs and ICE vehicles is the “battery”. As shown in Figure 2 (Ref [7]), major potential for battery damage result from underbody damage and mechanical, interior, or roof damage, with severity of damage determined by the number of zones that may be affected.
Figure 3. – Quarantine Arrangement of a storage area of which the boundaries have no surrounding infrastructure (orange zones represent quarantine radius) (Ref [7])
Unlike ICE vehicles, regulations mandate that extra care be taken in handling BEVs after accidents. These precautions include checking for 1) vehicle structural damage, 2) electrical isolation through Low Voltage (LV) disconnect and Manual Service disconnect (MSD), 3) sharp object penetration into or puncturing of the battery pack, if any, 4) chemical smell from battery leakage, 5) sounds due to intermittent sparking or gas hissing, and 6) excessive heat from the battery. Additionally, it is highly recommended that the damaged BEVs not be towed on their driven wheels as most of the BEVs have regenerative power capabilities due to electric powertrain which may cause damage to the High Voltage (HV) systems on board.
In Figure 3 (Ref [7]), the quarantine guidelines for a damaged BEV that may be parked in a repair shop are shown wherein the orange zones represent the quarantine radius, which is typically around 12 meters for an average sized vehicle. Additional costs associated with the quarantine guidelines and safe handling of damaged batteries by skilled technicians trained in handling HV safety systems need to be accounted for by insurance companies during the underwriting of a policy.
Owing to the higher technological complexity of electric powertrains and batteries in BEVs, they are expected to be handled by technicians with special skills and training, leading to increased costs. Further, the cost of replacement batteries being relatively high and constant over a period of 5 years, the lack of adequate battery diagnosis and repair methods, and the challenges associated with recycling/salvage of batteries account for a majority of the total loss rate/claims costs.
Figure 4. Average key-to-key & Median Repair Cost Comparison – ICE Vehicles vs BEVs (Ref [7])
The average key-to-key times for repair and average repair cost comparison between ICE and BEVs are shown in Figure 4 (Ref [7]). The key-to-key times for BEVs are about 15% higher than for ICE vehicles, impacting the insurance premium. As indicated in Figure 4, the average repair costs are about 16% higher for BEVs than ICE vehicles that are less than 2 years old and are about 40% higher for BEVs for vehicles that are 3 to 5 years old.
Figure 5. Battery cost vs Vehicle Depreciation cost for BEVs (Ref [7])
In Figure 5, the average repair costs, vehicle value, and battery costs are shown along with the vehicle depreciation cost for BEVs (Ref [7]). It is evident that the battery cost (orange bar graph) remains relatively constant over a period of 5 years, but the vehicle value depreciates significantly during this period. The cost of a replacement battery is more than the used price of the vehicle after only 1 year, posing a significant challenge for BEV owners and insurance companies in getting a fair resale value of the BEV.
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It is imperative that the insurance companies that have been still following the legacy processes and systems designed for ICE vehicles pay close attention to the specific issues and costs associated with BEVs shown in Figures 3-5 and take these differences into account while underwriting insurance policies for BEVs.
Conclusions
The article touched upon the impact of increased electrification on the repair and insurance industry in terms of frequency and severity, through more accurate and comprehensive vehicle information and effective elimination of input data bias. As most new vehicles come with embedded connectivity features, more accurate insurance premium calculations are being obtained through Usage-based-insurance (UBI) and driver-based-insurance (DBI) features associated with real-time connectivity features in the vehicles.
As the electric vehicles (EVs) are being increasingly introduced and gaining market share across different automotive markets of the world, it is observed that much of the insurance and repair industry is still slow to adapt to the challenges and implications of new/additional features, technologies, upskilling, training, circular economy, and sustainability. In fact, many consumers in USA, UK, and EU have been complaining about the lack of awareness and even outright refusal by even renowned insurance companies to underwrite an insurance policy for EVs, even when consumers have accepted to pay much higher premiums for the EVs. A potential complacency for the insurance and repair industry arises from the fact that the EVs still account for a small portion of the overall automotive industry though these numbers are rising rapidly.
Future Work
It is imperative for the insurance and repair industry to collaborate more closely with the automotive manufacturers, third-party service providers for vehicle input data, autonomous software and over the air (OTA) software update service companies, battery cell/pack suppliers, battery management system (BMS) software vendors and ADAS equipment suppliers to better understand the details/causes of a crash, discern the role of the driver and/or autonomous technologies, assess the battery state of health (SOH), diagnostics, and remaining useful life (RUL) and input this information to better calculate accident repair/replacement costs and the impact on vehicle insurance and life insurance premiums.
From the perspective of motor vehicle insurance companies, as the market share of EVs increases, they need to have a better understanding of the insurance calculation model in situations where a significant number of batteries may need to be repaired or replaced after accurate assessment of the SOH and RUL of the batteries. The investments and infrastructure for battery circular economy, including a more detailed battery digital passport, are still in nascent stages even in some developed markets, resulting in a disproportionate loss of value of the batteries in the overall assessment of repair/replacement costs by the insurance companies. The incentives from the governments across the world and tax breaks on investments are expected to improve the battery circular economy.
With the advent of Level 2+ (L2+) and Level 3 (L3) autonomous vehicles, the complexity of insurance underwriting will only increase. A few insurance companies such as Liberty Mutual have already taken the lead and partnered with Edge Case Research and Carnegie Mellon University (CMU) to better understand the risks associated with autonomous vehicles, especially in the edge cases, and utilize this knowledge for underwriting⁹.
References
1. US NHTSA, “NHTSA estimates traffic fatalities dropped in the first three months of 2023,” June 21, 2023, https://www.nhtsa.gov/press-releases/nhtsa-traffic-fatalities-estimates-first-quarter 2023#:~:text=The%20projected%20decrease%20occurred%20alongside,the%20same%20time%20in%202022
2. US NHTSA, “Early estimate of motor vehicle traffic fatalities in 2022,” April 2023,
https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813428
3. Bieber, C. and Ramirez, A. “Car accident statistics for 2023,” 23 January 2023, https://www.forbes.com/advisor/legal/car-accident-statistics/
4. Ksycinsky, M. “How more connected vehicles on the road will impact the insurance industry,” 15 June 2022, https://grapeup.com/blog/connected-vehicles-impact-the-insurance-industry/
5. Martin, E. and Masterson, L. “Car insurance facts and statistics 2023,” 17 August 2023, https://www.forbes.com/advisor/car-insurance/car-insurance-facts-and-statistics/#:~:text=Car%20insurance%20costs%20are%20on,difference%20of%20%241%2C136%20a%20year
6. Balasubramanian, R. et. al, “Insurance 2030 – the impact of AI on the future of insurance,” March 2021, https://www.mckinsey.com/industries/financial-services/our-insights/insurance-2030-the-impact-of-ai-on-the-future-of-insurance
7. “Impact of BEV adoption on the repair and insurance sectors,” Innovate UK Thatcham Research, 2023, https://www.thatcham.org/wp-content/uploads/2023/07/Impact-of-BEV-Adoption-on-the-Repair-and-Insurance-Sectors-report-Innovate-UK-and-Thatcham-Research-FINAL.pdf
8. OECD Report, “The impact of big data and Artificial Intelligence (AI) in the Insurance Sector,” 2020, http://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.htm
9. Liberty Mutual Insurance, “Liberty Mutual partners with edge case research to better understand and underwrite autonomous vehicle risks,” https://www.libertymutualgroup.com/about-lm/news/articles/liberty-mutual-partners-edge-case-research-better-understand-and-underwrite-autonomous-vehicle-risks
Views expressed by Dr. Arunkumar M. Sampath, Principal Consultant, Tata Consultancy Services (TCS)
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