Car insurance market demand prediction method based on big data

Abstract

The invention relates to the technical field of vehicle insurance, and discloses a big data-based vehicle insurance market demand prediction method, which comprises the following steps: firstly, obtaining data, specifically historical vehicle insurance sales data, macroeconomic data, a vehicle population ratio and market competition data, processing and calculating the obtained data to obtain a vehicle insurance market demand prediction result; the method comprises the following steps: preprocessing vehicle insurance sales data to obtain final historical vehicle insurance sales data, final macroeconomic data, final vehicle population ratio and final market competition data, fusing the preprocessed data, extracting vehicle insurance prediction features, and generating a vehicle insurance prediction forecast according to the vehicle insurance prediction features. And comprehensive analysis and prediction are carried out on car insurance market demands by comprehensively combining multiple aspects, so that market trends can be more accurately grasped, potential growth points or risk areas can be identified, companies can be helped to accurately grasp market trends and optimize resource allocation, business innovation can be promoted, operation risks can be reduced, and sustainable development can be finally realized.

Description

Technical Field

[0001] The present invention relates to the technical field of auto insurance, and in particular to a method for predicting auto insurance market demand based on big data. Background Art

[0002] Auto insurance market demand forecast refers to the estimation and analysis of the demand of the auto insurance market within a specific time period based on certain directions and data. This forecast is intended to help enterprises or government agencies understand the potential scale and changing trends of the future market, so as to make more informed decisions. For insurance companies, auto insurance market demand forecast is an important basis for formulating business strategies and marketing plans. By predicting market demand, companies can reasonably allocate resources, optimize product design, and improve market share and profitability. Automakers can understand the changes in consumer demand after purchasing a car through auto insurance market demand forecast, so as to adjust production plans and sales strategies. In addition, manufacturers can also cooperate with insurance companies to provide customized insurance products to enhance customer stickiness. Government agencies can use auto insurance market demand forecast to evaluate industry development trends and formulate relevant policies to promote the healthy development of the market. For example, the government can adjust tax policies, regulatory measures, etc. based on the forecast results to guide the market to develop in a more standardized and efficient direction. When considering entering the auto insurance market or investing in related companies, investors need to refer to market demand forecasts to evaluate investment risks and return potential. By predicting the changing trend of market demand, investors can more accurately judge the market prospects and make more informed investment decisions.

[0003] It is currently impossible to accurately predict the demand in the auto insurance market. There are certain defects in predicting the demand in the auto insurance market, which may cause insurance companies to be unable to accurately grasp market dynamics and growth trends. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the existing technology, the present invention provides a method for predicting the auto insurance market demand based on big data, which has the advantages of comprehensively analyzing and predicting the auto insurance market demand from multiple aspects, more accurately grasping market trends, identifying potential growth points or risk areas, helping companies to accurately grasp market dynamics, optimize resource allocation, promote business innovation, reduce operational risks, and ultimately achieve sustainable development.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a method for predicting auto insurance market demand based on big data, comprising the following steps:

[0008] Step 1: Obtain data, specifically historical auto insurance sales data, macroeconomic data, vehicle population ratio, and market competition data;

[0009] Step 2: Process and calculate the acquired data to obtain the final historical auto insurance sales data, final macroeconomic data, final vehicle population ratio, and final market competition data;

[0010] Step 3: Fuse the preprocessed data to extract the auto insurance prediction features;

[0011] Step 4: Generate a car insurance prediction forecast based on the car insurance prediction characteristics.

[0012] Preferably, in step one, the historical auto insurance sales data includes the number of policies, premium income, and claim data, and every 10 pieces of historical auto insurance sales data form a group.

[0013] Preferably, in step one, the macroeconomic data include GDP growth, inflation rate, unemployment rate, average resident income level, policy changes and resident insurance awareness decline index, with every 10 macroeconomic data forming a group.

[0014] Preferably, in step one, the vehicle-to-population ratio includes the vehicle-to-population ratio values ​​of the previous 12 months.

[0015] Preferably, in step 1, the market competition data includes competitors' market share, new product launches, and average market price reduction, with every 10 pieces of market competition data forming a group.

[0016] Preferably, in step 2, the calculation formula for the final historical auto insurance sales data is:

[0017]

[0018] In the above formula, Zcx represents the final historical auto insurance sales data, CXxs i represents the i-th historical auto insurance sales data, n represents a total of n historical auto insurance sales data, is the mean of each group of historical auto insurance sales data, is the mean of all historical auto insurance sales data, and is the final historical auto insurance sales data Zcx.

[0019] Preferably, in step 2, the calculation formula for the final macroeconomic data is:

[0020]

[0021] In the above formula, Zhj represents the final macroeconomic data, HGjj i represents the i-th macroeconomic data, n represents a total of n macroeconomic data, is the mean of each set of macroeconomic data, is the mean of all macroeconomic data, and is the final macroeconomic data Zhj.

[0022] Preferably, in step 2, the final vehicle population ratio is calculated as follows:

[0023]

[0024] In the above formula, RKzb i is the population ratio data of the ith vehicle.

[0025] Preferably, in step 2, the calculation formula for the final market competition data is:

[0026]

[0027] In the above formula, Zsj is the final market competition data, is the mean of each group of market competition data, is the mean of all market competition data, and is the final market competition data Zsj.

[0028] Preferably, in step 3, the calculation formula for the vehicle insurance prediction feature is:

[0029]

[0030] In the above formula, CXtz represents the prediction characteristics of auto insurance, and α, β, γ, and δ are the corresponding weight factors of the final historical auto insurance sales data, the final macroeconomic data, the final vehicle-to-population ratio, and the final market competition data, respectively.

[0031] Compared with the prior art, the present invention provides a method for predicting auto insurance market demand based on big data, which has the following beneficial effects:

[0032] 1. The present invention provides valuable information about past market trends through historical data, which helps insurance companies analyze consumer behavior, purchasing habits and demand changes. Macroeconomic data is formed through GDP growth, inflation rate, unemployment rate, average resident income level, policy changes and resident insurance awareness decline index. By analyzing the changing trends and interrelationships of these data, insurance companies can more accurately grasp market dynamics and changes in consumer demand, thereby formulating more scientific and reasonable market strategies and product design plans. By analyzing the proportion of vehicles to the population, insurance companies can understand the distribution of major vehicle models on the market, thereby designing auto insurance products that better meet market demand. By analyzing the market share of competitors, insurance companies can understand the main competitors in the market and their strength distribution, which helps companies evaluate their own position in the market and formulate corresponding competitive strategies.

[0033] 2. The present invention processes the acquired data to form final data, which can significantly improve the accuracy and completeness of the data, promote data integration and format optimization, and lay a solid foundation for subsequent analysis. The pre-processed data is integrated to comprehensively analyze and predict the demand for auto insurance market from multiple aspects, which can more accurately grasp market trends, identify potential growth points or risk areas, help companies accurately grasp market dynamics, optimize resource allocation, promote business innovation, reduce operational risks, and ultimately achieve sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a step diagram of the method of the present invention; DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1 , a method for predicting auto insurance market demand based on big data, comprising the following steps:

[0037] Step 1: Obtain data, specifically historical auto insurance sales data, macroeconomic data, vehicle population ratio, and market competition data;

[0038] The historical auto insurance sales data includes the number of policies, premium income, and claims data, with 10 historical auto insurance sales data as a group;

[0039] Historical data provides valuable information about past market trends, helping insurance companies analyze consumer behavior, purchasing habits, and changes in demand. By digging deeper into these data, companies can more accurately predict future market developments and develop more effective marketing strategies. They can also help insurance companies identify high-risk areas or groups and take preventive measures to reduce accidents. By analyzing historical sales data, insurance companies can not only consolidate existing market share but also discover new growth opportunities.

[0040] Macroeconomic data include GDP growth, inflation rate, unemployment rate, average resident income level, policy changes, and the index of declining resident insurance awareness, with 10 macroeconomic data in a group;

[0041] GDP is the core indicator for measuring the size and growth rate of a country's economy. Inflation refers to the general rise in prices. The unemployment rate reflects the labor market and social employment. The average resident income level is directly related to consumers' purchasing power. The impact of policy changes on the auto insurance market cannot be ignored. The improvement of residents' insurance awareness helps to enhance consumers' risk prevention awareness and encourage more people to buy auto insurance to deal with potential risks. Macroeconomic data is formed by GDP growth, inflation rate, unemployment rate, average resident income level, policy changes and the index of declining residents' insurance awareness. By analyzing the changing trends and interrelationships of these data, insurance companies can more accurately grasp market dynamics and changes in consumer demand, thereby formulating more scientific and reasonable market strategies and product design plans.

[0042] The vehicle-to-population ratio includes the vehicle-to-population ratio values ​​for the previous 12 months;

[0043] A higher vehicle-to-population ratio usually means that the region's automobile market is more mature and the potential of the auto insurance market is relatively large. Insurance companies can evaluate the market capacity of different regions based on this ratio and formulate corresponding market entry strategies. Different vehicle types and usage scenarios have different demands for auto insurance. By analyzing the vehicle-to-population ratio, insurance companies can understand the distribution of major vehicle models on the market and design auto insurance products that better meet market demand. Changes in the vehicle-to-population ratio directly affect the competition landscape, premium levels, and risk levels of the auto insurance market, and have important reference value for comparing auto insurance market demand forecasts.

[0044] Market competition data include competitors’ market share, new product launches, and average market price reduction, with 10 market competition data as a group;

[0045] By analyzing competitors' market share, insurance companies can understand the main competitors in the market and their strength distribution, which helps companies evaluate their own position in the market and formulate corresponding competitive strategies. The launch of new products reflects the market's innovation dynamics and technological development trends. Insurance companies can track the launch of competitors' new products and understand the latest technology and product development direction of the industry, so as to adjust their own product and service strategies in a timely manner and maintain market competitiveness. The data on the decrease in the average market price can help insurance companies understand the changing trend of market prices and more accurately predict the changes in demand in the future auto insurance market. Understanding competitors' new product launches and market feedback can provide valuable references for insurance companies' product development and innovation. Market competition data can also help insurance companies better identify and manage risks.

[0046] Step 2: Process and calculate the acquired data to obtain the final historical auto insurance sales data, final macroeconomic data, final vehicle population ratio, and final market competition data;

[0047] The final calculation formula for historical auto insurance sales data is:

[0048]

[0049] In the above formula, Zcx represents the final historical auto insurance sales data, CXxs i represents the i-th historical auto insurance sales data, n represents a total of n historical auto insurance sales data, is the mean of each group of historical auto insurance sales data, is the mean of all historical auto insurance sales data, and is the final historical auto insurance sales data Zcx;

[0050] The calculation formula for the final macroeconomic data is:

[0051]

[0052] In the above formula, Zhj represents the final macroeconomic data, HGjj i represents the i-th macroeconomic data, n represents a total of n macroeconomic data, is the mean of each set of macroeconomic data, is the mean of all macroeconomic data, is the final macroeconomic data Zhj;

[0053] The final formula for calculating the vehicle population ratio is:

[0054]

[0055] In the above formula, RKzb i is the population ratio of the ith vehicle;

[0056] The calculation formula for the final market competition data is:

[0057]

[0058] In the above formula, Zsj is the final market competition data, is the mean of each group of market competition data, is the mean of all market competition data, and is the final market competition data Zsj;

[0059] Step 3: Fuse the preprocessed data to extract the auto insurance prediction features;

[0060] The calculation formula of the auto insurance prediction feature is:

[0061]

[0062] In the above formula, CXtz represents the prediction characteristics of auto insurance, and α, β, γ, and δ are the corresponding weight factors of the final historical auto insurance sales data, the final macroeconomic data, the final vehicle population ratio, and the final market competition data;

[0063] By processing the acquired data to form the final data, the accuracy and completeness of the data can be significantly improved, which promotes data integration and format optimization, and lays a solid foundation for subsequent analysis. By integrating the pre-processed data and comprehensively analyzing and predicting the demand for auto insurance market from multiple aspects, it is possible to more accurately grasp market trends, identify potential growth points or risk areas, help companies accurately grasp market dynamics, optimize resource allocation, promote business innovation, reduce operational risks, and ultimately achieve sustainable development.

[0064] Step 4: Generate a car insurance prediction forecast based on the car insurance prediction characteristics.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting auto insurance market demand based on big data, characterized in that: The following steps are involved: Step 1: Obtain data, specifically historical auto insurance sales data, macroeconomic data, vehicle population ratio, and market competition data; Step 2: Process and calculate the acquired data to obtain the final historical auto insurance sales data, final macroeconomic data, final vehicle population ratio, and final market competition data; Step 3: Fuse the preprocessed data to extract the auto insurance prediction features; Step 4: Generate a car insurance prediction forecast based on the car insurance prediction characteristics.

2. The method for predicting auto insurance market demand based on big data according to claim 1 is characterized by: In step 1, the historical auto insurance sales data includes the number of policies, premium income, and claim data, and every 10 historical auto insurance sales data form a group.

3. The method for predicting auto insurance market demand based on big data according to claim 1 is characterized by: In the step 1, the macroeconomic data include GDP growth, inflation rate, unemployment rate, average resident income level, policy changes and resident insurance awareness decline index, with every 10 macroeconomic data forming a group.

4. The method for predicting auto insurance market demand based on big data according to claim 1 is characterized by: In the step 1, the vehicle-to-population ratio includes the vehicle-to-population ratio values ​​of the previous 12 months.

5. The method for predicting auto insurance market demand based on big data according to claim 1 is characterized by: In the step 1, the market competition data includes competitors' market share, new product launches, and average market price reduction, with 10 market competition data forming a group.

6. The method for predicting auto insurance market demand based on big data according to claim 2, characterized in that: In step 2, the final calculation formula for historical auto insurance sales data is: In the above formula, Zcx represents the final historical auto insurance sales data, CXxs i represents the i-th historical auto insurance sales data, n represents a total of n historical auto insurance sales data, j is the mean of each group of historical auto insurance sales data, is the mean of all historical auto insurance sales data, and is the final historical auto insurance sales data Zcx.

7. The method for predicting auto insurance market demand based on big data according to claim 3 is characterized by: In step 2, the calculation formula for the final macroeconomic data is: In the above formula, Zhj represents the final macroeconomic data, HGjj i represents the i-th macroeconomic data, n represents a total of n macroeconomic data, j is the mean of each group of macroeconomic data, is the mean of all macroeconomic data, and is the final macroeconomic data Zhj.

8. The method for predicting auto insurance market demand based on big data according to claim 4 is characterized by: In step 2, the final calculation formula for the vehicle population ratio is: In the above formula, RKzb i is the population ratio data of the ith vehicle.

9. The method for predicting auto insurance market demand based on big data according to claim 5 is characterized by: In step 2, the calculation formula for the final market competition data is: In the above formula, Zsj is the final market competition data, j is the mean of each group of market competition data, is the mean of all market competition data, and is the final market competition data Zsj.

10. The method for predicting auto insurance market demand based on big data according to claim 9, characterized in that: In step 3, the calculation formula of the vehicle insurance prediction feature is: In the above formula, CXtz represents the prediction characteristics of auto insurance, and α, β, γ, and δ are the corresponding weight factors of the final historical auto insurance sales data, the final macroeconomic data, the final vehicle-to-population ratio, and the final market competition data, respectively.

Images