CN115099986A - Vehicle insurance renewal processing method and device and related equipment - Google Patents
Vehicle insurance renewal processing method and device and related equipment Download PDFInfo
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Abstract
The application belongs to the technical field of data processing, and provides a processing method, a device, computer equipment and a computer readable storage medium for vehicle insurance renewal, aiming at solving the problem of low processing efficiency of the vehicle insurance renewal rate, the vehicle insurance clients are classified into passenger groups according to non-vehicle-insurance vehicle service data of the vehicle insurance clients, a vehicle insurance renewal rate statistical model corresponding to each vehicle insurance passenger group is constructed, the vehicle insurance service data and the non-vehicle-insurance vehicle service data of the vehicle insurance clients are obtained, the vehicle insurance clients are subjected to image drawing according to the non-vehicle-insurance vehicle service data to obtain a target vehicle insurance passenger group type to which the vehicle insurance clients belong, then the vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type is searched, the vehicle insurance service data is input into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance clients, and then whether the vehicle insurance clients are vehicle insurance renewal clients or not is judged, the calculation efficiency and the calculation pertinence of the vehicle insurance continuation-keeping rate statistical model can be improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing a vehicle insurance renewal, a computer device, and a computer-readable storage medium.
Background
In recent years, the insurance industry in China is steadily developing, and car insurance is one of the most important risk types of insurance companies and always is a place which is bound by the soldiers of the insurance companies. With the improvement of the platform of relevant policies and the increase of the automobile transaction volume, the insurance rate of the automobile insurance is higher and higher, but because the automobile insurance products are basically homogeneous, particularly after the expense is changed, the strong price competition and the diversified marketing means make the insurance company difficult to maintain the existing market share, and the profits generated by most new customers are not as good as those of old customers, the improvement of the renewal rate of the automobile insurance customers is especially important. According to statistics, the cost for winning a new client is 5 to 6 times of the cost for reserving an old client, so that the reservation of the old client and the promotion of the customer loyalty are core problems which should be concerned by an insurance company, and have important strategic significance for the long-term development of the insurance company. At present, for the statistics of the continuous guarantee rate, continuous guarantee data related to insurance is generally adopted, analysis is carried out based on a traditional machine learning algorithm, the machine learning model is complex, the time complexity of the model is high, the memory utilization rate is low, and the operation efficiency of the continuous guarantee data is reduced.
Disclosure of Invention
The application provides a processing method and device for vehicle insurance renewal, computer equipment and a computer readable storage medium, which can solve the technical problem of low operation efficiency of renewal data in the prior art.
In a first aspect, the present application provides a method for processing vehicle insurance renewal, including: the method comprises the steps of obtaining vehicle insurance service data and non-vehicle insurance vehicle service data of vehicle insurance clients, and drawing images of the vehicle insurance clients according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance client group type to which the vehicle insurance clients belong, wherein the non-vehicle insurance vehicle service data are service data which are not directly related to vehicles and insurance of the vehicle insurance clients; searching a vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, and inputting the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain a vehicle insurance renewal rate corresponding to the vehicle insurance customer; judging whether the vehicle insurance renewal rate is greater than or equal to a preset vehicle insurance renewal rate threshold value or not; and if the vehicle insurance renewal rate is greater than or equal to the preset vehicle insurance renewal rate threshold value, determining that the vehicle insurance client is a vehicle insurance renewal client.
In a second aspect, the present application further provides a processing apparatus for vehicle insurance renewal, including: the image unit is used for acquiring vehicle insurance service data and non-vehicle insurance vehicle service data of vehicle insurance clients, and performing image drawing on the vehicle insurance clients according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance client group type to which the vehicle insurance clients belong, wherein the non-vehicle insurance vehicle service data is service data which is not directly related to vehicles and insurance of the vehicle insurance clients; the searching unit is used for searching the vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, and inputting the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance customer; the first judgment unit is used for judging whether the vehicle insurance renewal rate is greater than or equal to a preset vehicle insurance renewal rate threshold value or not; and the judging unit is used for judging that the vehicle insurance client is the vehicle insurance client if the vehicle insurance renewal rate is greater than or equal to the preset vehicle insurance renewal rate threshold.
In a third aspect, the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the processing method for vehicle insurance renewal when executing the computer program.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method for processing vehicle insurance renewal.
The application provides a processing method, a device, computer equipment and a computer readable storage medium for vehicle insurance renewal, wherein the processing method classifies vehicle insurance customers according to non-vehicle insurance vehicle service data of the vehicle insurance customers, then constructs a vehicle insurance renewal rate statistical model corresponding to each vehicle insurance customer group type aiming at each vehicle insurance customer group type, obtains the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance customers, figures the vehicle insurance customers according to the non-vehicle insurance vehicle service data to obtain target vehicle insurance customer group types to which the vehicle insurance customers belong, then searches the vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance customer group types according to the target vehicle insurance customer group types, inputs the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rates corresponding to the vehicle insurance customers, then judging whether the vehicle insurance renewal rate is greater than or equal to a preset vehicle insurance renewal rate threshold value, if the vehicle insurance renewal rate is greater than or equal to the preset vehicle insurance renewal rate threshold value, judging that the vehicle insurance client is a vehicle insurance renewal client, therefore, by analyzing the non-vehicle insurance vehicle service data, the incidence relation between the non-vehicle insurance vehicle service data and the vehicle insurance renewal of the vehicle insurance customers is mined and analyzed, and based on the vehicle insurance renewal rate statistical model corresponding to each vehicle insurance passenger group, the renewal possibility of the vehicle insurance customers is counted and predicted, compared with the situation that the same model is adopted for all the customers based on the vehicle insurance fee data, the vehicle insurance user renewal possibility is predicted, the calculation efficiency and calculation pertinence of the vehicle insurance renewal rate statistical model can be improved, the vehicle insurance marketing pertinence is further improved, and the utilization efficiency of vehicle insurance marketing resources is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for vehicle insurance renewal according to an embodiment of the present application;
FIG. 2 is a schematic view of a first sub-flow of a method for vehicle insurance renewal according to an embodiment of the present application;
FIG. 3 is a second sub-flowchart of a method for vehicle insurance renewal according to an embodiment of the present application;
FIG. 4 is a third sub-flowchart illustrating a vehicle insurance renewal process according to an embodiment of the present application;
FIG. 5 is a schematic block diagram of a processing device for vehicle insurance renewal provided by an embodiment of the present application;
fig. 6 is a schematic block diagram of a computer device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The embodiment of the application provides a vehicle insurance renewal processing method, and the statistical method can be applied to computer equipment such as a server and can be applied to a car insurance renewal business scene in the insurance industry. Referring to fig. 1, fig. 1 is a schematic flow chart of a method for processing a vehicle insurance renewal according to an embodiment of the present application. As shown in FIG. 1, the method includes the following steps S11-S15:
s11, acquiring vehicle insurance service data and non-vehicle insurance vehicle service data of a vehicle insurance customer, and drawing the vehicle insurance customer according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance customer group type to which the vehicle insurance customer belongs, wherein the non-vehicle insurance vehicle service data is service data which is not directly related to the vehicle and the vehicle insurance of the vehicle insurance customer.
Specifically, data directly related to the vehicle insurance of the vehicle insurance customer is called vehicle insurance service data, the vehicle insurance service data comprises service data directly related to the vehicle insurance, such as insurance policy data and claim settlement data, the vehicle insurance customer can have vehicle service data of non-vehicle insurance besides the vehicle insurance service data, and can be called non-vehicle insurance service data, the non-vehicle insurance service data is service data indirectly related to the vehicle and the vehicle insurance of the vehicle insurance customer, the non-vehicle insurance service data comprises vehicle refueling data, vehicle maintenance data, vehicle annual inspection data and other vehicle related service data of the vehicle insurance customer, and the non-vehicle insurance service data can be obtained through a data interface of a third party after the third party agrees. The refuelling data of the vehicle particularly refers to the more loyal vehicle insurance customers to the refuelling service, the higher the renewal rate of the refuelling service, the refuelling data of the vehicle comprises a refuelling observation window, the refuelling observation window of the refuelling data can be determined in advance for the refuelling data of the vehicle, the refuelling observation window is used for describing the time period for observing the refuelling condition of the vehicle insurance customers, the refuelling data of the vehicle comprises the refuelling condition in the time period, the refuelling data of the vehicle comprises refuelling contents such as the refuelling times, the refuelling frequency and the refuelling amount, and the refuelling data of the vehicle can adopt the following six indexes to construct an RFMLCS model of the vehicle insurance customers: 1) r is the number of days from the date of the latest refueling to the ending time of the refueling observation window; 2) f is the total refueling times in the refueling observation window; 3) the average monthly refuelling amount in the refuelling observation window; 4) l is the number of days of the difference between the ending time of the refueling observation window and the first refueling time, so that the time length of the vehicle insurance client entering the refueling service is referred; 5) c is the average refueling time interval in the refueling observation window; 6) when the value of the number of refuelling orders/total number of orders within the oil price fluctuation range exceeds the threshold value, the vehicle insurance client is considered as the oil price sensitive client, otherwise, the vehicle insurance client is considered as the non-oil price fluctuation sensitive client. Compared with the problem that the traditional RFM model subdivides more passenger groups, and some indexes cannot reflect the value of customers, so that the accurate marketing cost is too high, the RFMLCS model is used for classifying the vehicle insurance customers, each type of vehicle insurance passenger groups have the characteristics obviously different from other passenger groups, and further a vehicle insurance continuation rate statistical model is constructed based on the homogeneous passenger groups, so that the complexity of the vehicle insurance continuation rate statistical model and the memory utilization rate of the model are reduced, the performance advantage of the vehicle insurance continuation rate statistical model is improved, the relationship between the independent variable and the dependent variable of the vehicle insurance service data of the vehicle insurance customers is rapidly excavated based on the vehicle insurance continuation rate statistical model, the calculation efficiency of the vehicle insurance continuation rate statistical model is improved, and the accuracy of vehicle insurance continuation rate processing can be improved.
Based on the sample data of the car insurance customers, a preset car insurance customer classification model can be trained through the sample data of the car insurance customers, all the car insurance customers are classified in advance, so that all the car insurance customers are classified into different car insurance customer group types, the car insurance customer group types are used for describing the group classification categories of the car insurance customers, the car insurance customer group types can comprise group types of important value car insurance customers, potential car insurance customers, important excavation car insurance customers, new car insurance customers, lost car insurance customers and the like, and each car insurance customer group type can be divided based on the score of the users to obtain different passenger group types. After the non-vehicle insurance vehicle service data of the vehicle insurance customer are obtained, the vehicle insurance customer is portrayed according to the non-vehicle insurance vehicle service data to classify the vehicle insurance customer, and the target vehicle insurance customer group type to which the vehicle insurance customer belongs is obtained, so that the vehicle insurance customer is classified by using the non-vehicle insurance vehicle service data, and the vehicle insurance rate of the vehicle insurance customer is counted by fully using the incidence relation between the non-vehicle insurance vehicle service data and the vehicle insurance continuation. Particularly, the car insurance clients are portrayed based on the car refueling data of the car insurance clients to obtain the car insurance client group types of the car insurance clients, the more loyal the car insurance clients are to the refueling service, the higher the renewal rate is, the refueling data of the car insurance clients are analyzed according to the six indexes of the RFMLCS model, and the probability of car insurance renewal of the clients is analyzed according to the refueling data of the clients, so that the incidence relation between the car using conditions of the car insurance clients and the car insurance renewal is fully utilized, the accuracy of the car insurance renewal rate can be improved, and the utilization rate and the utilization effect of car insurance marketing resources are improved.
S12, searching the vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, and inputting the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance customer.
Specifically, for each type of vehicle insurance customer group, a vehicle insurance rate statistical model corresponding to each type of vehicle insurance customer group is pre-constructed, the vehicle insurance rate statistical model may be a classification model in machine learning, particularly, the vehicle insurance rate statistical model may be a model obtained by training and integrating with a base classifier (decision tree), for example, the vehicle insurance rate statistical model may be an XGBoost model or a LightGBM model, and if the vehicle insurance rate statistical model is a LightGBM model, the vehicle insurance rate statistical model may have better calculation efficiency and higher expandability, for example, for the important value vehicle insurance customers, a corresponding important value vehicle insurance customer statistical model is established, and for the potential vehicle insurance customers, a potential vehicle insurance customer statistical model is established, and the like.
After the type of the target vehicle insurance passenger group to which the vehicle insurance customer belongs is determined, the vehicle insurance rate statistical model corresponding to the type of the target vehicle insurance passenger group is found out from the vehicle insurance rate statistical models corresponding to the types of the plurality of vehicle insurance passenger groups, and the vehicle insurance service data is input into the vehicle insurance rate statistical model to obtain the vehicle insurance rate corresponding to the vehicle insurance customer. Because the corresponding vehicle insurance rate statistical model is constructed for each type of vehicle insurance passenger group, when the vehicle insurance rate statistical model is carried out on a vehicle insurance client, only the vehicle insurance rate statistical model corresponding to the type of vehicle insurance passenger group is needed to be called, and compared with the method for carrying out the vehicle insurance rate statistical on all the vehicle insurance clients by adopting one vehicle insurance rate statistical model, the complexity of the vehicle insurance rate statistical model can be reduced, and the calculation efficiency and the statistical accuracy of the vehicle insurance rate statistical model are improved. For the training of the vehicle insurance renewal rate statistical model, independent variables and target variables 'whether to renew insurance' can be adopted for model training, wherein the characteristic dimensions of the independent variables comprise customer characteristics, insurance acceptance characteristics and insurance product characteristics, the customer characteristics comprise customer gender, customer grade and the like, the insurance acceptance characteristics comprise vehicle age, insurance category, use characteristics, NCD (national center of distribution), vehicle insurance sales channels and the like, and the insurance product characteristics comprise insurance policy characteristics, whether to guarantee vehicle personnel, ticket insurance premium, settlement amount and the like, so that the accuracy of the vehicle insurance renewal rate statistical model for vehicle insurance customer statistics is improved by adopting multi-dimension and multi-content.
S13, judging whether the vehicle insurance renewal rate is larger than or equal to a preset vehicle insurance renewal rate threshold value;
s14, if the vehicle insurance renewal rate is greater than or equal to the preset vehicle insurance renewal rate threshold value, determining that the vehicle insurance client is the vehicle insurance renewal client;
s15, if the vehicle insurance renewal rate is smaller than the preset vehicle insurance renewal rate threshold value, determining that the vehicle insurance client is not a vehicle insurance renewal client.
Specifically, after the vehicle insurance renewal rate corresponding to the vehicle insurance customer is counted, whether the vehicle insurance customer is possibly a vehicle insurance renewal customer is predicted according to a defined preset vehicle insurance renewal rate threshold, wherein the preset vehicle insurance renewal rate threshold can be set to be a probability value such as 60%, and the preset vehicle insurance renewal rate threshold can be obtained through counting according to historical data of the vehicle insurance customer serving as a training sample. Judging whether the vehicle insurance continuous guarantee rate is larger than or equal to a preset vehicle insurance continuous guarantee rate threshold value or not, if the vehicle insurance continuous guarantee rate is larger than or equal to the preset vehicle insurance continuous guarantee rate threshold value, judging that the vehicle insurance customer is a vehicle insurance continuous guarantee customer, further carrying out corresponding important business marketing on the vehicle insurance continuous guarantee customer, such as carrying out welfare or great-benefit marketing, and the like, if the vehicle insurance continuous guarantee rate is smaller than the preset vehicle insurance continuous guarantee rate threshold value, judging that the vehicle insurance customer is not a vehicle insurance continuous guarantee customer, further carrying out not carrying out large marketing resources on the vehicle insurance continuous guarantee customer, and improving the pertinence and the utilization efficiency of the marketing resource use.
According to the embodiment of the application, the vehicle insurance clients are classified into the passenger groups according to the non-vehicle insurance vehicle service data of the vehicle insurance clients, then the vehicle insurance continuation rate statistical model corresponding to each type of vehicle insurance passenger groups is built according to each type of vehicle insurance passenger groups, and the vehicle insurance continuation rate statistical model is built according to each type of homogeneous passenger groups, so that the vehicle insurance continuation rate statistical model is favorably counted and analyzed for the homogeneous passenger groups. After acquiring the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance customer, firstly, drawing a figure of the vehicle insurance customer according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance customer group type to which the vehicle insurance customer belongs, then searching a vehicle insurance continuation rate statistical model corresponding to the target vehicle insurance customer group type, inputting the vehicle insurance service data into the vehicle insurance continuation rate statistical model to obtain a vehicle insurance continuation rate corresponding to the vehicle insurance customer, then judging whether the vehicle insurance continuation rate is greater than or equal to a preset vehicle insurance continuation rate threshold value, if the vehicle insurance rate is greater than or equal to the preset vehicle insurance continuation rate threshold value, judging that the vehicle insurance customer is the vehicle insurance continuation customer, and mining and analyzing the incidence relation between the non-vehicle insurance vehicle service data and the vehicle insurance customer, compared with the method that the guarantee continuation possibility of the vehicle insurance users is predicted by adopting the same model for all the clients based on the vehicle insurance fee data, the calculation efficiency and the calculation pertinence of the vehicle insurance guarantee continuation rate statistical model can be improved, the pertinence of vehicle insurance marketing is further improved, and the utilization efficiency of vehicle insurance marketing resources is improved.
In an embodiment, the representing the vehicle insurance customer according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance customer group type to which the vehicle insurance customer belongs includes:
inputting the non-vehicle insurance vehicle service data into a preset vehicle insurance passenger group type model based on a K-Means clustering algorithm, and carrying out portrait drawing on the vehicle insurance customers to obtain the target vehicle insurance passenger group types to which the vehicle insurance customers belong.
Specifically, the vehicle insurance customer is portrayed according to the non-vehicle insurance vehicle service data, especially when indexes contained in the non-vehicle insurance vehicle service data are large, a preset vehicle insurance customer group type model based on a K-Means clustering algorithm can be pre-established, then the non-vehicle insurance vehicle service data are input into the preset vehicle insurance customer group type model to perform feature clustering analysis on the non-vehicle insurance vehicle service data, so that the vehicle insurance customer is portrayed, and a target vehicle insurance customer group type to which the vehicle insurance customer belongs is obtained. When the car insurance clients are portrayed, a clustering algorithm is adopted to analyze indexes contained in the non-car insurance vehicle service data, so that accurate portrayal of the car insurance clients can be realized, and the accuracy of subsequent car insurance continuation processing of the car insurance clients is improved.
Referring to fig. 2, fig. 2 is a schematic view of a first sub-flow of a method for processing an insurance renewal provided in the embodiment of the present application, and as shown in fig. 2, in the embodiment, the inputting the insurance service data into the insurance renewal rate statistical model to obtain the insurance renewal rate corresponding to the insurance client includes:
s21, inputting the car insurance service data into a preset logistic regression model for multiple collinearity preprocessing to obtain target car insurance service data;
s22, inputting the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance client.
Specifically, in order to avoid the problem that the vehicle insurance service data is inaccurate in statistics of the vehicle insurance continuous rate statistical model when independent variables included in the vehicle insurance service data are related to each other, before the vehicle insurance service data are input into the vehicle insurance continuous rate statistical model, the vehicle insurance service data can be input into a preset logistic regression model to perform multiple collinearity preprocessing, so that multiple collinearity features existing in the logistic regression model are eliminated based on a multiple collinearity principle in the logistic regression model, and therefore, the correlation among the features is reduced, the independent variables have better explanatory power for the dependent variables, the operation data volume of the vehicle insurance continuous rate statistical model can be reduced, and the accuracy of the operation of the vehicle insurance continuous rate statistical model can be improved. The correlation of the independent variables of the regression model is called multiple collinearity, that is, the collinearity independent variables provide repeated information, when two or more independent variables in the regression model (linear regression, logistic regression) are correlated with each other, it is called that multiple collinearity exists in the regression model, the multiple collinearity provides repeated information for the model, the multiple collinearity may cause instability of the model, for the multiple collinearity, the multiple collinearity may be detected and processed by calculating correlation coefficients between the respective variables and performing significance test.
Referring to fig. 3, fig. 3 is a schematic view of a second sub-flow of a processing method for vehicle insurance renewal provided in the embodiment of the present application, and as shown in fig. 3, in this embodiment, the inputting the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance customer includes:
s31, acquiring data characteristic dimensions of service data contained in the target car insurance service data, and acquiring importance factors corresponding to the data characteristic dimensions according to the data characteristic dimensions;
and S32, inputting the importance factor and the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance client.
Specifically, the target car insurance service data may include different service data such as insurance policy data, claim settlement data, and car insurance service life, each service data corresponds to one data characteristic dimension, the different service data describes contents of the car insurance service data from the different data characteristic dimensions, and influence factors of each service data in influencing car insurance continuation are different, so that a corresponding importance degree may be set for the data characteristic dimension of each service data, and therefore, the importance degree factor is adopted to describe an influence degree of the corresponding service data in influencing car insurance continuation, and the importance degree factor may be obtained by counting historical car insurance service data of different car insurance users. When the vehicle insurance renewal rate of a certain vehicle insurance user is counted, the data characteristic dimension of service data contained in the target vehicle insurance service data is obtained, the importance factor corresponding to the data characteristic dimension is obtained according to the data characteristic dimension, and then the importance factor and the target vehicle insurance service data are input into the vehicle insurance renewal rate statistical model, so that the vehicle insurance renewal rate statistical model calculates the target vehicle insurance service data based on the importance factor to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance client.
Referring to fig. 4, fig. 4 is a schematic sub-flowchart of a third processing method for vehicle insurance renewal according to an embodiment of the present application, as shown in fig. 4, before acquiring vehicle insurance service data and non-vehicle insurance service data of a vehicle insurance customer, the method further includes:
s41, acquiring a client identifier of the vehicle insurance client, and judging whether the vehicle insurance client is a personal vehicle insurance client or not according to the vehicle insurance client identifier;
s42, if the vehicle insurance client is a personal vehicle insurance client, executing the step of acquiring the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance client;
and S43, if the vehicle insurance client is not a personal vehicle insurance client, the vehicle insurance service data and the non-vehicle insurance service data of the vehicle insurance client are not acquired.
Specifically, different types of customer identifications are set in advance for the personal car insurance customer and the company car insurance customer, for example, the personal car insurance customer is described by "0", and the company car insurance customer is described by "1", so that the customer type to which the car insurance customer belongs can be distinguished through the customer identification. After a customer identifier of an automobile insurance customer is obtained, whether the automobile insurance customer is a personal automobile insurance customer is judged according to the automobile insurance customer identifier, if the automobile insurance customer is the personal automobile insurance customer, the automobile insurance service data and the non-automobile insurance vehicle service data of the automobile insurance customer are obtained, the automobile insurance renewal rate of the automobile insurance customer is counted by adopting the method described in the embodiment, if the automobile insurance customer is not the personal automobile insurance customer, for example, the automobile insurance customer is a company automobile insurance customer, the automobile insurance service data and the non-automobile insurance vehicle service data of the automobile insurance customer are not obtained, the automobile insurance renewal rate of the automobile insurance customer is not counted by adopting the method described in the embodiment, and the processing flow of the company automobile insurance customer is adopted for corresponding processing. Due to the fact that the individual car insurance clients and the company car insurance clients have different car insurance attributes, especially for the individual car insurance clients, the individual car insurance clients are required to be grouped to accurately identify the client value of the car insurance clients and realize accurate marketing for the individual car insurance clients, so that the types of the car insurance clients are identified first, and the statistics of the car insurance renewal rate of all the car insurance clients by the method described in the embodiment is avoided.
It should be noted that, the processing method for vehicle insurance renewal described in the above embodiments may be implemented by recombining the technical features included in different embodiments as needed to obtain a combined implementation, but all of them are within the protection scope claimed in the present application.
Referring to fig. 5, fig. 5 is a schematic block diagram of a processing device for vehicle insurance renewal according to an embodiment of the present application. Corresponding to the above vehicle insurance continuation-insurance processing method, the embodiment of the application also provides a vehicle insurance continuation-insurance processing device. As shown in fig. 5, the processing device of the vehicle insurance renewal includes a unit for executing the processing method of the vehicle insurance renewal, and the processing device of the vehicle insurance renewal can be configured in the computer device. Specifically, referring to fig. 5, the processing device 50 for continuing insurance includes an image unit 51, a search unit 52, a first determination unit 53 and a determination unit 54.
The image unit 51 is used for acquiring vehicle insurance service data and non-vehicle insurance vehicle service data of a vehicle insurance customer, and performing image drawing on the vehicle insurance customer according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance passenger group type to which the vehicle insurance customer belongs, wherein the non-vehicle insurance vehicle service data is service data which is not directly related to a vehicle and an insurance of the vehicle insurance customer;
the searching unit 52 is configured to search a vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, and input the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain a vehicle insurance renewal rate corresponding to the vehicle insurance customer;
the first judging unit 53 is configured to judge whether the vehicle insurance renewal rate is greater than or equal to a preset vehicle insurance renewal rate threshold;
and the determining unit 54 is configured to determine that the insurance customer is an insurance renewal customer if the insurance renewal is greater than or equal to the preset insurance renewal threshold.
In an embodiment, the representation unit 51 is configured to input the non-vehicle insurance vehicle service data into a preset vehicle insurance passenger group type model based on a K-Means clustering algorithm, and represent the vehicle insurance customers to obtain a target vehicle insurance passenger group type to which the vehicle insurance customers belong.
In one embodiment, the non-vehicle insurance vehicle service data includes vehicle fueling data for the vehicle insurance customer, the vehicle fueling data includes a fueling observation window describing a time period for observing fueling for the vehicle insurance customer, the vehicle fueling data includes the following indicators: the number of days from the date of the last refueling to the end time of the refueling observation window; the total refueling times in the refueling observation window; average monthly refuelling amount in the refuelling observation window; the number of days of the difference between the ending time of the refueling observation window and the first refueling time; average fueling time interval within the fueling observation window; oil price fluctuation sensitive car insurance clients/non-oil price fluctuation sensitive car insurance clients.
In one embodiment, the lookup unit 52 includes:
the first input subunit is used for inputting the vehicle insurance service data into a preset logistic regression model for multiple collinearity preprocessing to obtain target vehicle insurance service data;
and the second input subunit is used for inputting the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance customer.
In one embodiment, the second input subunit includes:
the first obtaining subunit is configured to obtain a data feature dimension of service data included in the target car insurance service data, and obtain an importance factor corresponding to the data feature dimension according to the data feature dimension;
and the second obtaining subunit is configured to input the importance factor and the target insurance service data into the insurance renewal rate statistical model, so as to obtain an insurance renewal rate corresponding to the insurance customer.
In one embodiment, the processing device 50 for vehicle insurance renewal further includes:
the second judgment unit is used for acquiring a client identifier of the car insurance client and judging whether the car insurance client is a personal car insurance client or not according to the car insurance client identifier;
and the execution unit is used for executing the obtaining of the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance client if the vehicle insurance client is a personal vehicle insurance client.
In one embodiment, the statistical model of vehicle insurance renewal rate is a LightGBM model.
It should be noted that, as can be clearly understood by those skilled in the art, the specific implementation processes of the processing apparatus and each unit of the insurance renewal may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.
Meanwhile, the division and connection manner of each unit in the processing device for vehicle insurance renewal are only used for illustration, in other embodiments, the processing device for vehicle insurance renewal can be divided into different units as required, and each unit in the processing device for vehicle insurance renewal can also adopt different connection sequences and manners to complete all or part of the functions of the processing device for vehicle insurance renewal.
The processing means of the vehicle insurance renewal described above may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 6.
Referring to fig. 6, fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a computer device such as a desktop computer or a server, or may be a component or part of another device.
Referring to fig. 6, the computer device 500 includes a processor 502, a memory, which may include a non-volatile storage medium 503 and an internal memory 504, which may also be a volatile storage medium, and a network interface 505 connected by a system bus 501.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform a method for vehicle insurance renewal as described above.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be enabled to execute a processing method of vehicle insurance renewal.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 6, and are not described herein again.
Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to perform the steps of: the method comprises the steps of obtaining vehicle insurance service data and non-vehicle insurance vehicle service data of vehicle insurance clients, and drawing images of the vehicle insurance clients according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance client group type to which the vehicle insurance clients belong, wherein the non-vehicle insurance vehicle service data are service data which are not directly related to vehicles and insurance of the vehicle insurance clients; searching a vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, and inputting the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain a vehicle insurance renewal rate corresponding to the vehicle insurance customer; judging whether the vehicle insurance renewal rate is greater than or equal to a preset vehicle insurance renewal rate threshold value or not; and if the vehicle insurance renewal rate is greater than or equal to the preset vehicle insurance renewal rate threshold value, determining that the vehicle insurance client is a vehicle insurance renewal client.
In an embodiment, when the processor 502 implements the representing of the car insurance customer according to the non-car insurance vehicle service data to obtain the target car insurance customer group type to which the car insurance customer belongs, the following steps are specifically implemented:
inputting the non-vehicle insurance vehicle service data into a preset vehicle insurance passenger group type model based on a K-Means clustering algorithm, and portraying the vehicle insurance customers to obtain the target vehicle insurance passenger group types to which the vehicle insurance customers belong.
In an embodiment, when the processor 502 implements the acquiring of the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance customer and the representing of the vehicle insurance customer according to the non-vehicle insurance vehicle service data, the non-vehicle insurance vehicle service data includes vehicle refueling data of the vehicle insurance customer, the vehicle refueling data includes a refueling observation window, the refueling observation window is used for describing a time period for observing a refueling condition of the vehicle insurance customer, and the vehicle refueling data includes the following indexes: the number of days from the date of the last refueling to the end time of the refueling observation window; the total refueling times in the refueling observation window; average monthly refuelling amount in the refuelling observation window; the number of days of the difference between the finish time of the refueling observation window and the first refueling time; average fueling time interval within the fueling observation window; oil price fluctuation sensitive car insurance clients/non-oil price fluctuation sensitive car insurance clients.
In an embodiment, when the processor 502 implements the step of inputting the car insurance service data into the car insurance renewal rate statistical model to obtain the car insurance renewal rate corresponding to the car insurance customer, the following steps are specifically implemented:
inputting the vehicle insurance service data into a preset logistic regression model for multiple co-linear preprocessing to obtain target vehicle insurance service data;
and inputting the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance client.
In an embodiment, when the processor 502 implements the inputting of the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance customer, the following steps are specifically implemented:
acquiring data characteristic dimensions of service data contained in the target car insurance service data, and acquiring importance factors corresponding to the data characteristic dimensions according to the data characteristic dimensions;
and inputting the importance factor and the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance client.
In one embodiment, the processor 502 further implements the following steps before implementing the acquiring the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance customer:
acquiring a client identification of a vehicle insurance client, and judging whether the vehicle insurance client is a personal vehicle insurance client or not according to the vehicle insurance client identification;
and if the vehicle insurance client is a personal vehicle insurance client, acquiring the vehicle insurance service data and non-vehicle insurance vehicle service data of the vehicle insurance client.
In an embodiment, when the processor 502 performs the search for the vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, the vehicle insurance renewal rate statistical model is a LightGBM model.
It should be understood that in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiments may be implemented by a computer program, and the computer program may be stored in a computer readable storage medium. The computer program is executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present application also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, causes the processor to perform the steps of:
a computer program product which, when run on a computer, causes the computer to perform the steps of the method of vehicle insurance renewal described in the embodiments above.
The computer readable storage medium may be an internal storage unit of the aforementioned device, such as a hard disk or a memory of the device. The computer readable storage medium may also be an external storage device of the device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the device. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the apparatus.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The storage medium is an entity and non-transitory storage medium, and may be various entity storage media capable of storing computer programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
Those of ordinary skill in the art will appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the application can be combined, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solutions of the present application may substantially or partially contribute to the prior art, or all or part of the technical solutions may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for processing a vehicle insurance renewal, the method comprising:
the method comprises the steps of obtaining vehicle insurance service data and non-vehicle insurance vehicle service data of vehicle insurance customers, and drawing images of the vehicle insurance customers according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance passenger group type to which the vehicle insurance customers belong, wherein the non-vehicle insurance vehicle service data are service data which are not directly related to vehicle insurance;
searching a vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, and inputting the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain a vehicle insurance renewal rate corresponding to the vehicle insurance customer;
judging whether the vehicle insurance renewal rate is greater than or equal to a preset vehicle insurance renewal rate threshold value or not;
and if the vehicle insurance renewal rate is greater than or equal to the preset vehicle insurance renewal rate threshold value, determining that the vehicle insurance client is the vehicle insurance renewal client.
2. The method for processing an insurance renewal according to claim 1, wherein the representing the vehicle insurance client according to the non-vehicle insurance vehicle service data to obtain the type of the target vehicle insurance client group to which the vehicle insurance client belongs comprises:
inputting the non-vehicle insurance vehicle service data into a preset vehicle insurance passenger group type model based on a K-Means clustering algorithm, and carrying out portrait drawing on the vehicle insurance customers to obtain the target vehicle insurance passenger group types to which the vehicle insurance customers belong.
3. The method as claimed in claim 1, wherein the non-vehicle insurance vehicle service data comprises vehicle refueling data of the vehicle insurance customer, the vehicle refueling data comprises a refueling observation window for describing a time period for observing refueling of the vehicle insurance customer, and the vehicle refueling data comprises the following indexes: the number of days from the date of the last refueling to the end time of the refueling observation window; the total refueling times in the refueling observation window; average monthly refuelling amount in the refuelling observation window; the number of days of the difference between the ending time of the refueling observation window and the first refueling time; average fueling time interval within the fueling observation window; oil price fluctuation sensitive car insurance clients/non-oil price fluctuation sensitive car insurance clients.
4. The method for processing vehicle insurance renewal according to claim 1, wherein the step of inputting the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance customer comprises the steps of:
inputting the car insurance service data into a preset logistic regression model for multiple co-linear preprocessing to obtain target car insurance service data;
and inputting the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance client.
5. The method of claim 4, wherein the step of inputting the target insurance service data into the insurance renewal rate statistical model to obtain the insurance renewal rate corresponding to the insurance client comprises:
acquiring data characteristic dimensions of service data contained in the target car insurance service data, and acquiring importance factors corresponding to the data characteristic dimensions according to the data characteristic dimensions;
and inputting the importance factor and the target vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance client.
6. The method for processing vehicle insurance renewal according to claim 1, wherein before acquiring the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance client, the method further comprises:
acquiring a client identifier of a vehicle insurance client, and judging whether the vehicle insurance client is a personal vehicle insurance client or not according to the vehicle insurance client identifier;
and if the vehicle insurance client is the personal vehicle insurance client, executing the step of acquiring the vehicle insurance service data and the non-vehicle insurance vehicle service data of the vehicle insurance client.
7. The method of claim 1, wherein the vehicle insurance renewal rate statistical model is a LightGBM model.
8. A vehicle insurance renewal process, comprising:
the image unit is used for acquiring vehicle insurance service data and non-vehicle insurance vehicle service data of vehicle insurance clients, and performing image drawing on the vehicle insurance clients according to the non-vehicle insurance vehicle service data to obtain a target vehicle insurance client group type to which the vehicle insurance clients belong, wherein the non-vehicle insurance vehicle service data is service data which is not directly related to vehicles and insurance of the vehicle insurance clients;
the searching unit is used for searching the vehicle insurance renewal rate statistical model corresponding to the target vehicle insurance passenger group type, and inputting the vehicle insurance service data into the vehicle insurance renewal rate statistical model to obtain the vehicle insurance renewal rate corresponding to the vehicle insurance customer;
the first judgment unit is used for judging whether the vehicle insurance renewal rate is greater than or equal to a preset vehicle insurance renewal rate threshold value or not;
and the judging unit is used for judging that the vehicle insurance client is the vehicle insurance client if the vehicle insurance renewal rate is greater than or equal to the preset vehicle insurance renewal rate threshold.
9. A computer device, comprising a memory and a processor coupled to the memory; the memory is used for storing a computer program; the processor is adapted to run the computer program to perform the steps of the method according to any of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when being executed by a processor, realizes the steps of the method according to any one of claims 1 to 7.
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| CN202210742525.7A CN115099986A (en) | 2022-06-28 | 2022-06-28 | Vehicle insurance renewal processing method and device and related equipment |
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| CN202210742525.7A CN115099986A (en) | 2022-06-28 | 2022-06-28 | Vehicle insurance renewal processing method and device and related equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362900A (en) * | 2023-04-04 | 2023-06-30 | 探保网络科技(广州)有限公司 | Method and system for processing insurance information of vehicle insurance |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362900A (en) * | 2023-04-04 | 2023-06-30 | 探保网络科技(广州)有限公司 | Method and system for processing insurance information of vehicle insurance |
| CN116362900B (en) * | 2023-04-04 | 2023-10-27 | 探保网络科技(广州)有限公司 | Method and system for processing insurance information of vehicle insurance |
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