From an insurance perspective, my work has convinced me that sustainable growth of the automobile market in China depends on rebuilding the risk analysis and pricing system for new-energy products. In particular, the electric vehicle has become the central challenge for insurers because it replaces fuel combustion with a high-voltage battery system, changes driving behavior, creates new failure modes, and introduces different usage patterns. In this article, I intend to summarize the risk factors of electric vehicle insurance based on recent investigations, construct a predictive model from 521 claim records, and propose practical optimization strategies.
1. Introduction
During China’s Fourteenth Five-Year Plan period, policies explicitly encourage the development of the electric vehicle industry and promote a green and low-carbon transformation. Tax reductions, purchase subsidies, and charging infrastructure programs stimulated widespread adoption. The national development plan for the electric vehicle industry further required a safe, reliable, and technologically advanced industrial system. However, risk assessment and protection mechanisms in electric vehicle insurance have not kept up with the pace of adoption.
In January 2025, several Chinese regulators jointly issued guidance on deepening reforms to promote high-quality development of new-energy vehicle insurance. The disclosed loss data in 2024 are alarming. Insurers underwrote about 31.05 million electric vehicles, received RMB 140.9 billion in premiums, provided RMB 106 trillion in risk protection, and suffered an underwriting loss of RMB 5.7 billion. Among 2,795 vehicle series covered by the industry, 137 series had loss ratios above 100%, excluding daily operating expenses of property insurers. The table below summarizes the distribution across passenger and freight vehicles.
| Segment | Covered vehicle series | Covered units | High-loss series (>100%) |
|---|---|---|---|
| All vehicles | 2,795 | 31.05 million | 137 |
| Passenger vehicles | 1,654 | 29.82 million | 99 |
| Freight vehicles | 1,141 | 1.23 million | 38 |
These figures demonstrate that traditional automobile insurance methods are not always suitable for the electric vehicle. The core business difficulty is no longer a simple claim-frequency adjustment; rather, the entire risk chain, from battery chemistry and repair technology to usage intensity and charging behavior, must be re-examined. Therefore, I believe that optimizing insurance mechanisms for the electric vehicle is a critical task for both the insurance industry and the whole industrial ecosystem.
2. Industrial and Risk Environment of Electric Vehicle Insurance
2.1 Development of the electric vehicle industry
The Chinese electric vehicle market has grown quickly due to government subsidies, traditional automakers entering the market, advances in battery technology, and expansion of charging infrastructure. In 2023, the national output of electric vehicles reached 9.587 million units, an increase of 35.8% year-on-year. In 2024, production reached 12.888 million units, up 34.4%, and sales reached 12.866 million units, up 35.5%. This explosive growth represents a huge opportunity for insurers, but it also increases the urgency of managing electric vehicle insurance risks.
The expansion of the electric vehicle fleet is not a simple substitution of powertrains. Electric vehicles have distinct operational characteristics: silent acceleration, heavier bodies due to batteries, high torque, and intensive use in ride-hailing and freight applications. Since the cost of electricity per kilometer is lower than that of gasoline, many electric vehicle owners participate in shared mobility or commercial delivery, exposing themselves to higher road usage. In addition, the architecture of the electric vehicle dramatically influences the consequences of a crash: a low-speed impact can damage expensive battery modules, and repair work often requires qualified electric-vehicle specialists. Insurers must therefore distinguish fleet-level and individual-level risk profiles more carefully than in the era of the internal-combustion vehicle.
2.2 PEST analysis of electric vehicle insurance
The macro environment for electric vehicle insurance can be analyzed by political, economic, social, and technological forces. Each of these forces affects the frequency and severity of electric vehicle insurance claims.
| PEST dimension | Effect on electric vehicle insurance |
|---|---|
| Political | Carbon neutrality targets, purchase subsidies, registration advantages, safety mandates, and data regulation |
| Economic | Rising household income, changing total cost of ownership, interest rates, and increasing repair inflation |
| Social | Higher environmental awareness, growth of shared mobility, acceptance of autonomous features, and younger buyers |
| Technological | Battery performance, over-the-air updates, advanced driver assistance systems, telematics, and repair technology |
Politically, the Chinese government promotes the electric vehicle as a strategic emerging industry. Purchase tax exemptions and subsidies increase the volume of insured electric vehicles. At the same time, regulators require stronger supervision of insurance pricing and claims, which compels insurers to be more transparent about loss experience. The social dimension is equally important because environmental awareness raises demand for green mobility. Many consumers now regard the electric vehicle as a responsible choice, and this cultural shift enlarges the market for premium insurance products. Technologically, the rapid iteration of battery management systems and automated driving features raises questions about liability allocation and reparability. Insurers must understand whether software defects or hardware failures cause accidents; otherwise, loss ratios for the electric vehicle will remain unpredictable.
2.3 Risk investigation in insurance operations
In my own investigation at a provincial property insurance branch, I compared recent claims data of electric vehicles with those of conventional vehicles. The branch’s business results were consistent with the national trend. The loss ratio of electric vehicle policies reached 79.3% in 2024, which was 15 percentage points higher than the loss ratio for conventional vehicle policies. Such a gap is not explained only by expensive spare parts. It also arises from higher claim frequency and from the branch’s insufficient experience in damage assessment of electric vehicles.
Several causes explain the high claim frequency of electric vehicles. On the one hand, electric vehicles are often used as commercial vehicles, such as ride-hailing cars and delivery trucks, which have long daily mileage and frequent stops. On the other hand, inexperienced younger drivers are over-represented among electric vehicle owners because the electric vehicle is perceived as a fashionable and low-cost car. Without proper training, those drivers may not be familiar with the strong acceleration and silent running characteristics of an electric vehicle, increasing the chance of low-speed collisions and parking mishaps. In freight segments, some electric trucks are heavier and wider, and their lane-changing and braking behavior differs from that of conventional trucks, leading to severe losses once an accident occurs.
Damage assessment of the electric vehicle also raises a set of technical difficulties. Battery packs are installed on the chassis, and even minor underbody impacts can deform the watertight enclosure. Many insurers cannot quickly determine whether the battery cells are still safe or whether the whole pack must be replaced. As a result, claim leakage becomes common. In some cases, an insurer may pay for a full replacement battery even though the battery management system only requires repair; in other cases, a hidden internal short circuit leads to a post-repair fire. This uncertainty directly affects pricing accuracy and reserves for the electric vehicle.
3. Empirical Analysis of Electric Vehicle Insurance Risks
3.1 Data preprocessing and variable encoding
To understand the risk structure of electric vehicle insurance, I selected claim records from a property insurance company’s database. After removing invalid and null records, 521 valid electric vehicle claim files were retained. The dependent variable is the number of claim occurrences, which I measure as the number of reported accidents covered by a single insurance term. Independent variables include vehicle attributes, driver attributes, and policy attributes. The following table shows the encoding methods used in this study.
| Variable name | Treatment | Coding scheme |
|---|---|---|
| Claim frequency | Ordinal encoding | 0, 1, 2, 3, 4 |
| Vehicle age | Ordinal encoding | 0 for <1 year; 1 for 1–2; 2 for 2–3; 3 for 3–4; 4 for 4–5; 5 for ≥5 |
| Usage nature | One-hot encoding | 0 for commercial; 1 for non-commercial |
| Individual/group attribute | One-hot encoding | 0 for individual; 1 for group |
| Powertrain type | One-hot encoding | 0 for pure battery electric; 1 for hybrid; 2 for fuel cell |
| Battery energy density | Ordinal encoding | 0 for <50 Wh/kg; 1 for 50–100; 2 for 100–150; 3 for 150–200; 4 for ≥200 |
| Battery type | One-hot encoding | 0 for lithium iron phosphate; 1 for ternary lithium; 2 for other |
| Curb weight | Ordinal encoding | 0 for ≤1.5 t; 1 for 1.5–2 t; 2 for ≥2 t |
| Vehicle value | Ordinal encoding | 0 for ≤¥100k; 1 for ¥100–200k; 2 for ¥200–300k; 3 for ¥300–400k; 4 for ¥400–500k; 5 for ≥¥500k |
| Driver gender | One-hot encoding | 0 for female; 1 for male |
| Driver age | Ordinal encoding | 0 for 18–25; 1 for 26–35; 2 for 36–45; 3 for 46–55; 4 for 56–65; 5 for >65 |
| Driving experience | Ordinal encoding | 0 for <10 yr; 1 for 11–20 yr; 2 for >20 yr |
| New/renew/transfer marker | One-hot encoding | 0 for new; 1 for renewal; 2 for transfer |
| Channel | One-hot encoding | 0 for agency; 1 for broker; 2 for direct; 3 for other |
3.2 Descriptive statistics
After encoding, I performed descriptive statistics. The sample includes both individual electric vehicles and fleet-owned electric vehicles. Commercial usage accounted for only 6.3% of the sample, but this small proportion showed a much higher average claim count. Approximately 43% of the sampled electric vehicles recorded one claim during the observation period, while 25.3% recorded two claims, 20.9% recorded three, 7.7% recorded four, and 3.1% recorded five claims. These frequencies are higher than those of conventional vehicles in the same branch, confirming that the electric vehicle has a genuine risk-concentration problem.
| Risk attribute | Category | Percentage (%) |
|---|---|---|
| Claim counts | 1, 2, 3, 4, 5 | 43.0, 25.3, 20.9, 7.7, 3.1 |
| Powertrain type | Battery electric | 66.0 |
| Hybrid | 34.0 | |
| Battery type | Lithium iron phosphate | 56.0 |
| Ternary lithium | 39.2 | |
| Other | 4.2 | |
| Curb weight | ≤1.5 t | 12.3 |
| 1.5–2 t | 59.9 | |
| ≥2 t | 27.8 | |
| Energy density | 100–150 Wh/kg | 66.0 |
| 150–200 Wh/kg | 34.0 | |
| Usage nature | Commercial | 6.3 |
| Non-commercial | 93.7 | |
| Vehicle age | <1 year | 28.4 |
| 1–2 years | 46.3 | |
| 2–3 years | 15.2 | |
| 3–4 years | 4.2 | |
| ≥5 years | 5.9 | |
| Vehicle value | ≤¥100k | 14.2 |
| ¥100–200k | 26.9 | |
| ¥200–300k | 35.1 | |
| ¥300–400k | 17.7 | |
| ¥400–500k | 4.8 | |
| ≥¥500k | 1.3 | |
| Driver age | 18–25 | 11.1 |
| 26–35 | 38.2 | |
| 36–45 | 32.6 | |
| 46–55 | 13.6 | |
| 56–65 | 4.4 | |
| Driving experience | <10 years | 54.7 |
| 11–20 years | 35.3 | |
| >20 years | 10.0 | |
| Driver gender | Female | 37.0 |
| Male | 63.0 | |
| Individual/group | Individual | 68.7 |
| Group | 31.3 | |
| New/renew/transfer | New | 72.6 |
| Renewal | 19.2 | |
| Transfer | 8.3 | |
| Channel | Agency | 77.5 |
| Broker | 5.8 | |
| Direct | 10.4 | |
| Other | 6.3 |
The high share of new electric vehicles, 72.6%, is worth noting. Many new buyers purchase an electric vehicle after experiencing the convenience of charging at home or after receiving generous fiscal incentives. However, new ownership is correlated with lower familiarity with high-voltage systems and with the unique silent operation of the electric vehicle. Group-owned electric vehicles account for 31.3% of the sample, and many of them are operated by transportation companies or car-sharing platforms. Because these vehicles are frequently driven by multiple users, the resulting claim count is much higher than that of a personally owned electric vehicle.
3.3 Correlation analysis among risk factors
I used the Pearson correlation coefficient to examine the linear association between each factor and the claim frequency of the electric vehicle. The coefficient is defined as
$$ r_{xy} = \frac{\sum_{i=1}^{N}(x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum_{i=1}^{N}(x_i-\bar{x})^2}\sqrt{\sum_{i=1}^{N}(y_i-\bar{y})^2}}, $$
where \(N\) is the number of samples, \(x_i\) is a risk factor, and \(y_i\) is the claim frequency of the electric vehicle. Values of \(r_{xy}\) close to 1 indicate a strong positive relationship, values close to -1 indicate a strong negative relationship, and values near 0 indicate almost no linear relationship. After computing the full correlation matrix, I observed that the claim frequency of the electric vehicle is most closely associated with the individual/group attribute, the vehicle’s usage nature, the age or service life of the electric vehicle, and the driver’s experience. These variables form a coherent group of risk indicators that can be used at the early stage of underwriting.
3.4 Construction of the XGBoost risk measurement model
To predict the risk level of each electric vehicle insurance policy, I selected the XGBoost model. The reasons for this choice are straightforward. First, the electric vehicle insurance data consist of mixed categorical and numerical variables, and tree-based gradient boosting handles non-linear interactions well. Second, XGBoost uses the sum of multiple decision trees to correct errors in previous trees. Let each decision tree be a function \(f_k(\mathbf{x})\). For a policy with feature vector \(\mathbf{x}_i\), the predicted claim frequency of the electric vehicle is
$$ \hat{y}_i = \sum_{k=1}^{K} f_k(\mathbf{x}_i), \quad f_k \in \mathcal{F}, $$
where \(\mathcal{F}\) is the function space of decision trees. The overall objective \(\mathcal{L}\) combines a training loss and a regularization term that measures the complexity of the ensemble:
$$ \mathcal{L}(\theta)=\sum_{i=1}^{N} l(y_i,\hat{y}_i) + \sum_{k=1}^{K} \Omega(f_k). $$
The regularization term is defined as
$$ \Omega(f)=\gamma T + \frac{1}{2}\lambda \sum_{j=1}^{T}\omega_j^2, $$
where \(T\) is the number of leaf nodes, \(\omega_j\) is the leaf weight, and \(\gamma\), \(\lambda\) are penalty parameters. To minimize the objective, I use an additive forward algorithm. At step \(t\), the prediction for an electric vehicle policy is updated as
$$ \hat{y}_i^{(t)}=\hat{y}_i^{(t-1)} + f_t(\mathbf{x}_i). $$
Taking the second-order Taylor expansion of the loss around the current prediction gives
$$ \mathcal{L}^{(t)} \simeq \sum_{i=1}^{N}\left[l\left(y_i,\hat{y}_i^{(t-1)}\right) + g_i f_t(\mathbf{x}_i) + \frac{1}{2}h_i f_t^2(\mathbf{x}_i)\right] + \Omega(f_t), $$
where the first derivative and second derivative are, respectively,
$$ g_i = \partial_{\hat{y}_i^{(t-1)}} l\left(y_i,\hat{y}_i^{(t-1)}\right), \qquad h_i = \partial^2_{\hat{y}_i^{(t-1)}} l\left(y_i,\hat{y}_i^{(t-1)}\right). $$
Because the constant loss term does not affect optimization, the objective for each leaf can be simplified. Define \(I_j\) as the set of samples in leaf \(j\), and let \(G_j=\sum_{i\in I_j}g_i\), \(H_j=\sum_{i\in I_j}h_i\). The reduced objective is
$$ \tilde{\mathcal{L}}^{(t)} = \sum_{j=1}^{T}\left[G_j\omega_j + \frac{1}{2}\left(H_j+\lambda\right)\omega_j^2\right] + \gamma T. $$
For a fixed tree structure, the optimal leaf weight is
$$ \omega_j^{*}=-\frac{G_j}{H_j+\lambda}, $$
and the minimal objective becomes
$$ \tilde{\mathcal{L}}^{*}=-\frac{1}{2}\sum_{j=1}^{T}\frac{G_j^2}{H_j+\lambda}+\gamma T. $$
The smaller the objective value, the better the tree structure is. In this empirical study, I treat claim risk as a classification problem: whether an electric vehicle will have high claim frequency. The hyper-parameters are tuned by five-fold cross-validation to reduce the chance of overfitting.
3.5 Validation and feature importance
Five-fold cross-validation divides the 521 electric vehicle records into five roughly equal subsets. Four subsets are used for training and the remaining one for validation. This process is repeated five times, and the average of evaluation metrics is used as the final performance indicator. The number of samples is enough to allow stable estimates.
| Metric | Formula | Meaning |
|---|---|---|
| Accuracy | \(\frac{TP+TN}{TP+TN+FP+FN}\) | Proportion of correctly classified electric vehicle policies |
| Precision | \(\frac{TP}{TP+FP}\) | Share of predicted high-risk electric vehicles that are truly high-risk |
| Recall | \(\frac{TP}{TP+FN}\) | Share of true high-risk electric vehicles that are detected |
| F1 score | \(\frac{2\cdot Precision\cdot Recall}{Precision+Recall}\) | Harmonic mean of precision and recall |
The training accuracy of the XGBoost model was close to 1, while the validation accuracy was lower, indicating that the model captures complex interactions but should be carefully regularized. From the feature importance table generated by the model, I could identify which variables have the strongest influence on electric vehicle insurance claims.
| Rank | Risk factor | Managerial meaning |
|---|---|---|
| 1 | Individual/group attribute | Fleet or corporate usage of the electric vehicle creates a high exposure environment |
| 2 | Vehicle service age | Battery and component degradation increase over time, raising the claim risk of an electric vehicle |
| 3 | Usage nature | Commercial operation of an electric vehicle leads to more accumulated kilometres |
| 4 | Battery technology type | Different chemical systems show different fire and damage probabilities |
| 5 | Insurance sales channel | Channel characteristics influence policyholder risk screening |
| 6 | Curb weight | Heavier electric vehicle structure amplifies collision severity |
| 7 | Driver experience | More experienced drivers reduce the chance of accidents in an electric vehicle |
| 8 | Driver age | Age-related reactions and driving style correlate with claim counts |
| 9 | Vehicle value | Expensive electric vehicle models normally carry higher repair costs |
| 10 | Driver gender | Behavioral differences between male and female electric vehicle drivers |
| 11 | Renewal/transfer marker | Insurance history reflects past losses of the electric vehicle |
| 12 | Powertrain type | Pure electric and hybrid electric vehicle show different risk profiles |
The first-ranked factor, individual/group attribute, clearly distinguishes the private electric vehicle from the fleet-operated electric vehicle. In China’s green transportation transition, many public institutions and enterprises replace their official fleet with electric vehicles. Public buses, taxis, and ride-hailing electric vehicles often run more than 14 hours per day and cover more than 80,000 kilometers per year. My data show that the annual claim frequency of a taxi electric vehicle can be about 2.3 times, whereas a private electric vehicle may have fewer than 0.8 claim events per year. This concentrated risk is a major cause of the industry-wide underwriting losses in electric vehicle insurance.
4. Optimization Strategies for Electric Vehicle Insurance
4.1 Building a scenario-based insurance product portfolio
The first recommendation is to develop differentiated products for different usage scenes of the electric vehicle. For a private electric vehicle, the insurer can provide a usage-based product that charges according to mileage, battery state-of-health, and driving time. For a fleet electric vehicle used in car sharing, the insurer should offer a fleet policy with a strong fleet-safety-management component. For an electric vehicle equipped with advanced autonomous driving functions, liability sharing should be redesigned according to the driver’s mode and the system’s mode. When the autonomous driving system is active, the vehicle manufacturer should bear liability for algorithm defects; when a human driver controls the electric vehicle, the driver’s liability insurance should apply. Such a distinction can prevent endless litigation after accidents involving the electric vehicle.
4.2 Improving fine-grained underwriting and claims management
One major source of loss in electric vehicle insurance is the blurring of private and commercial usage. Many electric vehicles are bought as private cars but are subsequently registered on ride-hailing platforms. This hidden commercial use exposes the insurer to a risk level that was never priced into the private electric vehicle policy. I recommend that authorities require clear registration or declaration of the intended operating use of every electric vehicle at the first-time insurance application. If a policyholder changes an electric vehicle from personal to commercial use, the insurer should be notified and the premium should be recalculated.
At the same time, insurance companies must strengthen the technical ability of loss adjusters. For the electric vehicle, damage assessment is not limited to body panels and mechanical parts; it also involves high-voltage safety checks, battery capacity diagnosis, and repair versus replacement decisions. I have seen claim files where a minor underbody scratch on an electric vehicle was followed by a full battery replacement claim of tens of thousands of RMB. Standardized assessment guidelines and battery testing equipment are needed. Insurers should partner with battery manufacturers to establish a certification system for electric vehicle repair shops. Only certified mechanics should be allowed to open the high-voltage circuit of an electric vehicle after a collision.
4.3 Introducing exclusive risk factors and refining pricing models
Traditional rating variables based on engine displacement do not correctly measure the risk of an electric vehicle. I suggest that insurers should introduce exclusive factors such as battery type, battery energy density, mileage, charging habits, and the number of fast-charge cycles. For example, battery fire risk is related to over-charging, mechanical abuse, thermal runaway, and battery management system failure. A pure electric vehicle with a cobalt-rich ternary lithium battery may face a different thermal-runaway profile than a pure electric vehicle with a lithium iron phosphate battery. When an accident damages the battery tray, the loss could be disproportionate to the visible body damage. Therefore, the pricing model of the electric vehicle should link battery information from the manufacturer with claim data.
In addition, I recommend the use of telematics data for the electric vehicle. Modern electric vehicles collect rich data through the battery management system and the onboard diagnostics port. The data include battery voltage, temperature distribution, state of charge, charging frequency, abrupt acceleration, heavy braking, and location. Telematics policies can price each electric vehicle according to its actual driving exposure. For instance, a young driver who drives a high-performance electric vehicle late at night in an urban center should pay more than a middle-aged driver who uses the same electric vehicle only for short commuting during daylight hours. This kind of dynamic pricing can reduce cross-subsidization among heterogeneous electric vehicle users.
4.4 Building a data-driven dynamic risk-management ecosystem
Data exchange among the electric vehicle manufacturer, the insurer, and the repair network is the key to future risk reduction. Battery health data collected by the electric vehicle manufacturer can help insurers assess the future probability of thermal events. If a battery management system reports an abnormal temperature rise or a capacity degradation below a certain threshold, the insurer can trigger an automated inspection service. Preventive maintenance that avoids a serious failure of the electric vehicle is much cheaper than paying a catastrophic fire claim.
Moreover, the insurance ecosystem should be aligned with charging infrastructure providers. Charging station operators can provide data on charging voltage fluctuations and frequency; such data can indicate whether an electric vehicle owner is using unsafe or incompatible chargers. An insurance company can use these signals to design incentive programs. For example, an insurer may provide a 10% premium discount to an electric vehicle owner who only uses certified charging piles, participates in regular maintenance, and permits the insurer to access battery health data. This approach is more efficient than raising premiums across the whole electric vehicle market.
4.5 Jointly promoting risk-reduction services
Insurance companies should not remain passive payers of claims. I believe the insurance industry must cooperate with automobile manufacturers and government authorities to reduce the occurrence of accidents involving the electric vehicle. Conducting offline safety lectures and online training courses can improve drivers’ understanding of the electric vehicle’s operating principles. Some drivers of the electric vehicle are not aware that the engine is silent and thus may not hear the approach of an electric vehicle when walking behind it; drivers should learn to use acoustic vehicle alert systems properly. More importantly, the government can invest in intelligent traffic infrastructure and enforce the punishment of drunk driving, speeding, and fatigued driving. For high-risk groups such as electric taxi and ride-hailing drivers, insurance companies should require the installation of advanced driver monitoring systems.
I also suggest establishing a national database of high-risk components for the electric vehicle. If a certain batch of battery cells shows an abnormal failure rate, insurers should be able to warn affected electric vehicle owners before a fire occurs. The electric vehicle industry should share incident data without disclosing personal information. Public trust and safety can be improved through such data cooperatives. In the future, a unified platform could combine insurance claim data, maintenance records, charging records, traffic violations, and weather conditions to train better machine learning models. Those models would help insurers identify dangerous electric vehicle usage patterns and prevent losses in real time rather than only pricing them after the claims have accumulated.
5. Conclusion
Through this research, I have been able to identify the key risk dimensions that distinguish the electric vehicle from traditional automobiles. The electric vehicle insurance market is expanding quickly, but it is also laboring under high claim frequency, high repair costs, and insufficient underwriting experience. My empirical analysis, based on 521 claim records, demonstrates that the most important risk factors are group/individual ownership, vehicle age, usage nature, battery technology, driving experience, and vehicle value. The XGBoost model offers a robust method for evaluating these multidimensional risks, and the feature importance ranking confirms that fleet operations pose disproportionate loss pressure for insurers.
To solve the loss problem in electric vehicle insurance, I propose a combination of actions: develop scenario-specific products, refine the premium rating models with exclusive electric-vehicle risk factors, strengthen claim damage assessment, enhance data sharing between manufacturers and insurers, and launch preventive risk-reduction services. These strategies require cooperation across the industrial chain. Automobile manufacturers must design safer battery systems and supply repair data; insurers must adapt their pricing and claims processes to the architecture of the electric vehicle; regulators must promote transparent market information and monitor unfair pricing.
In conclusion, the electric vehicle represents a fundamental transformation of mobility, not merely a technological upgrade of the internal-combustion automobile. The insurance industry has no choice but to rebuild its risk-analysis framework around the electric vehicle. By doing so, insurers can support the worldwide transition to low-carbon transport while maintaining financial stability. The future of electric vehicle insurance will be driven by data, collaboration, and a deep understanding of the unique risk characteristics of every electric vehicle. Only through such a comprehensive approach can the electric vehicle industry and the insurance industry achieve healthy and sustainable development together, and secure the long-run viability of insurance protection for all electric vehicle users.