Recently, auto insurance revenue data for the first half of the year came out. The good news is that the growth rate of the auto insurance business has also maintained an upward trend, reaching 5.4%. Compared with the pessimistic expectations of negative growth in January of this year, it has exceeded market expectations. The bad news is that this growth rate is irrelevant to most small and medium-sized insurance companies.
Small and medium-sized insurance companies fall into a loss cycle
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Judging from the combined ratio of auto insurance ranked 11th to 20th in 2023, 7 insurance companies exceed 100%. It is obvious that most of them are losing money. Although Yingda Property & Casualty Insurance is 97%, it can only be said that they are not making much.
Why do you make more and more losses? ——Big companies eat meat, small companies may drink poison.
In addition to high operating costs, poor risk selection, malicious claims and untimely rate adjustments, immature business models account for most of the reasons. In the market, most small and medium-sized property insurance companies have not yet established their own business models and passively adopt a "follower" strategy, that is, small companies copy the work of large companies. However, there is a very cruel fact. Faced with the same business environment, high-quality insurance companies The meat of the business has been eaten by the big companies, and it is the small companies' turn to eat the niche business. The content of non-performing business in the niche business is often high, and if it is done too much, it is likely to have a backlash and increase the compensation of small and medium-sized insurance companies. Rate. Therefore, the Matthew effect appears in the insurance industry, where the performance of large companies is getting better and better, while the performance of small companies is getting worse and worse.
The spoilers are here too, and those who kill colleagues are often those from across the industry.
At present, small and medium-sized insurance companies are struggling to survive under the shadow of large companies. Car companies have also come in with new energy car insurance to disrupt the situation, and they are doing so in a menacing manner. Nowadays, the ones who kill their peers are often cross-industry, just like instant noodles. The biggest competitor turns out to be the food delivery platform.
So where should small and medium-sized insurance companies go from here? It is generally believed in the industry that small and medium-sized companies should hone their internal skills, find accurate positioning, carry out differentiated operations, give full play to their comparative advantages, and be small and beautiful rather than small and comprehensive; in addition, they should focus on risk protection and strive to solve the problems of segmented customer groups. The main pain point is to develop products that are simple, have a substantial impact on customers, and can achieve scale.
Taking the wrong approach, truck insurance becomes a new business growth point
Choice is more important than effort. For truck insurance business, the industry generally believes that it is a bad risk, and leading insurance companies are not willing to take the initiative. Otherwise, there will be no coordinated insurance. For small and medium-sized insurance companies, this is not the case. It's an opportunity. The first thing to admit is that the safety factor of trucks itself is improving year by year. In recent years, with the improvement of policies and the upgrading of technology, domestic trucks have been continuously upgraded in safety configurations. Some common active and passive safety configurations in passenger vehicles have They are also found on trucks. If you take off the truck, it will be a "car accident". Secondly, as more data directly related to trucks is shared, truck risk assessment models are becoming more and more sophisticated, and the results are becoming more and more accurate, enabling truck risk quantification.
Databao truck risk assessment model allows wild lilies to have spring
Currently, truck insurance has problems such as homogeneous business products, unreasonable business structure, and unsaturated business scale. As a result, insurance companies face difficulties such as declining premiums, increasing costs, and difficulty in risk control. Databao integrates state-owned vehicle highway big data from multiple dimensions such as vehicle static information and vehicle dynamic driving data, and uses advanced technologies such as machine learning to accurately assess the risks of operating and non-operating trucks and generate truck risk assessment models to help insurance companies Quickly identify and distinguish trucks with different risk levels, thereby optimizing the auto insurance business structure, reducing costs, and improving claims efficiency.
Invisible risks, visible risk scores
Databao truck risk assessment model uses a quantifiable method to score truck risks from "1 to 10". The higher the score, the higher the risk. Insurance companies can make business selection decisions based on the scores. With the implementation of the second comprehensive reform, the premium coefficient has been adjusted up and down to [0.5-1.5]. Combined with the scores of the truck risk assessment model, insurance companies can fully realize "one vehicle, one price".
In addition to displaying risk scores, the interface design of Databao's truck assessment model also includes information such as map routes, top 5 frequently run routes, source of goods, and permanent cities. On the interface, high-weighted factors and medium-weighted factors will be highlighted, such as the total number of overloads, the total number of trips, the proportion of fatigue driving for 8 hours, the proportion of night driving, etc. Through the display of this information, the risk status of the vehicle can be intuitively understood, and risk scoring and screening can be performed.
Databao truck insurance risk assessment model has many value-added functions and the ability to differentiate pricing, which can solve the problem of low allocation and high compensation, optimize the auto insurance business structure, reduce costs and improve efficiency. For high-risk vehicles, we recommend eliminating the business, while for low-risk vehicles, we can seize them at low prices to achieve accurate pricing. Specifically manifested in the following aspects:
**Provide accurate risk assessment:**The truck insurance risk assessment model can accurately assess the risk of trucks through multi-dimensional data analysis and modeling. The model takes into account multiple factors such as the driving environment, vehicle behavior, and activity range. It can comprehensively understand the risk status of trucks and help insurance companies accurately determine premium pricing and underwriting strategies.
Assisted risk control and claims management : The truck insurance risk assessment model can help insurance companies identify high-risk vehicles and recommend eliminating the business or adjusting premiums, thereby reducing the insurance company's risk exposure. At the same time, the model can also monitor the driving behavior of trucks, promptly detect violations such as speeding and overloading, and help insurance companies carry out risk control and compensation management.
Optimize business structure and reduce costs : The truck insurance risk assessment model can help insurance companies identify vehicles with different levels of risk, adjust premiums according to risk levels to seize high-quality business, and eliminate bad business, thereby optimizing the business structure and improving the profitability of insurance companies. At the same time, the model can also help insurance companies reduce compensation costs, avoid underwriting high-risk businesses and reduce compensation risks through accurate risk assessment and screening.
**A good after-sales service. **Databao truck risk assessment model products also provide full-link after-sales services, including product introduction, understanding customer needs, parameter research, product testing, etc., to ensure that customers can get a satisfactory service experience.
Behind accurate scoring lies unique data capabilities
**Multiple real state-owned big data. ** Databao truck risk assessment model is based on authoritative authorization and fully covers vehicle highway big data and real compensation data. Through the innovative application of multi-dimensional big data integration, it can feedback the driving behavior of the vehicle in the whole trajectory, and can provide truck insurance models and business screening. More accurate positioning provides effective business guidance for truck risk assessment services.
**Highly matched truck highway big data. **Databao truck risk assessment model covers 235 million+ road vehicles and more than 39.29 million trucks nationwide, with comprehensive data support. At the same time, we have also added multiple unique factors such as meteorological factors, environmental factors, and fatigue factors to make the model more accurate.
**Industry-leading T+1 update. ** Asked the canal where it could be so clear, because it had a source of living water. The data of Databao truck risk assessment model are authoritatively authorized by state-owned data sources, and the data sources are updated in a timely manner. Among them, vehicle highway data is an industry-leading T+1 update, which collects data in real time and performs model training to ensure the timeliness and accuracy of the data. We also adopted the real compensation data model of the entire industry, covering the data from June 1, 2017 to the present, and the training effect is more in line with actual needs.
**120+ models by province and region across the country. ** Databao is divided into provincial modeling, and the model is subdivided based on the different policies, environment, dangerous road sections, landform complexity and other factors of each province and city. Each province is divided into static and dynamic, business and non-business, including 4 Models, all 120+ provincial models across the country have been completed.