CN110706054A - Scheme pushing method and device, computer device and readable storage medium - Google Patents
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Abstract
The invention provides a scheme pushing method, a scheme pushing device, a computer device and a computer readable storage medium. The method comprises the following steps: obtaining multi-dimensional information of a policy applicant; preprocessing multi-dimensional information and sorting the multi-dimensional information into a first information set and a second information set; acquiring the continuous guarantee contribution score of each continuous guarantee scoring factor in the first information set, and sequencing each continuous guarantee scoring factor according to the continuous guarantee contribution score; selecting a plurality of renewal scoring factors with the top scores from the first information set according to the requirements of the renewal scoring factors of a preset number, and inputting the renewal scoring factors into a preset renewal tracking model to obtain a renewal scoring prediction result of the policy applicant; inputting the renewal score prediction result and the second information set into a preset renewal tracking model to obtain a renewal recommendation scheme of the policy applicant; and pushing the renewal recommendation scheme to the policy applicant. The invention relates to the technical field of data analysis, and can realize omnibearing understanding of the continuous guarantee willingness of a client and improve the continuous guarantee success rate.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a method and an apparatus for pushing a solution, a computer apparatus, and a computer-readable storage medium.
Background
With the popularity of insurance businesses, more and more individuals or companies choose to purchase insurance products to provide more assurance of future uncertainty. For an insurance company, after the insurance product purchased by the insurance user is expired, the insurance company wants to continue the insurance with the user, and even if the insurance company does not wish to continue the insurance with the user, the insurance company wants to continue the insurance with the user by continuing the insurance communication. However, until now, there is no effective method to fully understand the client's continuous insurance will, and thus different continuous insurance policies cannot be made to achieve continuous insurance.
Disclosure of Invention
In view of the above, the present invention provides a scheme pushing method, device, computer device and computer readable storage medium, which can achieve renewal evaluation modeling by obtaining multi-dimensional information of a policy applicant, thereby improving the success rate of renewal.
An embodiment of the present application provides a scheme pushing method, including:
obtaining multi-dimensional information of a policy applicant;
preprocessing the multi-dimensional information and arranging the multi-dimensional information into a first information set and a second information set;
acquiring the continuous guarantee contribution score of each continuous guarantee scoring factor in the first information set, and sequencing each continuous guarantee scoring factor according to the continuous guarantee contribution score;
selecting a plurality of renewal scoring factors with top scores from the first information set according to the requirements of a preset number of renewal scoring factors, and inputting the renewal scoring factors into a preset renewal tracking model to obtain a renewal scoring prediction result of the policy applicant;
inputting the renewal score prediction result and the second information set into the preset renewal tracking model to obtain a renewal recommendation scheme of the policy applicant; and
and pushing the continuation recommendation scheme to the policy applicant.
Preferably, the multidimensional information comprises: the information of the applicant is the basic information, the history information of the application, the information of the application target, the information of the claim settlement record and the information of the application behavior.
Preferably, the preset keep-alive tracking model is obtained by training through the following steps:
establishing a neural network model, wherein the neural network model comprises an input layer, a plurality of hidden layers and an output layer; and
and training the neural network model by using the sample data of a plurality of preset insurance policy applicants to obtain the preset continuous insurance tracking model, wherein the plurality of preset insurance policy applicants comprise a plurality of continuous insurance applicants and a plurality of non-continuous insurance applicants.
Preferably, the step of training the neural network model by using sample data of a plurality of preset policy applicants to obtain the preset renewal tracking model comprises:
dividing the sample data of a plurality of preset policy applicants into a training set and a verification set;
training the neural network model by using the training set;
verifying the trained neural network model by using the verification set, and counting according to each verification result to obtain a model prediction accuracy;
judging whether the model prediction accuracy is smaller than a preset threshold value or not;
and if the model prediction accuracy is not less than the preset threshold value, taking the trained neural network model as the preset continuous tracking model.
Preferably, the step of judging whether the model prediction accuracy is smaller than a preset threshold further includes:
if the model prediction accuracy is smaller than the preset threshold, adjusting parameters of the neural network model, and retraining the adjusted neural network model by using the training set;
verifying the retrained neural network model by using the verification set, carrying out statistics again according to each verification result to obtain a model prediction accuracy, and judging whether the model prediction accuracy obtained by statistics again is smaller than a preset threshold value or not;
if the model prediction accuracy obtained by the re-statistics is not smaller than the preset threshold value, taking the neural network model obtained by the retraining as the preset continuous tracking model; and
if the model prediction accuracy obtained by the re-statistics is smaller than the preset threshold, repeating the steps until the model prediction accuracy obtained by the verification of the verification set is not smaller than the preset threshold;
the parameters of the neural network model comprise the total number of layers and the number of neurons in each layer.
Preferably, the step of adjusting parameters of the neural network model comprises:
adjusting the total number of layers and/or the number of neurons in each layer of the neural network model.
Preferably, the preset renewal tracking model comprises a first renewal tracking model and a second renewal tracking model, the first renewal tracking model is used for obtaining a renewal score prediction result of the policy applicant according to the input renewal score factor, and the second renewal tracking model is used for obtaining a renewal recommendation scheme of the policy applicant according to the input renewal score prediction result and the second information set.
An embodiment of the present application provides a scheme pushing apparatus, the apparatus includes:
the acquisition module is used for acquiring multi-dimensional information of the policy applicant;
the sorting module is used for preprocessing the multi-dimensional information and sorting the multi-dimensional information into a first information set and a second information set;
the sorting module is used for acquiring the continuous guarantee contribution score of each continuous guarantee scoring factor in the first information set and sorting each continuous guarantee scoring factor according to the continuous guarantee contribution score;
the first prediction module is used for selecting a plurality of renewal scoring factors with the top scores from the first information set according to the demands of a preset number of renewal scoring factors and inputting the plurality of renewal scoring factors into a preset renewal tracking model to obtain a renewal scoring prediction result of the policy applicant;
the second prediction module is used for inputting the renewal score prediction result and the second information set into the preset renewal tracking model to obtain a renewal recommendation scheme of the policy applicant; and
and the pushing module is used for pushing the renewal recommendation scheme to the insurance policy applicant.
An embodiment of the present application provides a computer device, where the computer device includes a processor and a memory, where the memory stores a plurality of computer programs, and the processor is configured to implement the steps of the scheme pushing method as described above when executing the computer programs stored in the memory.
An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the scheme pushing method as described above.
According to the scheme pushing method, device, computer device and computer readable storage medium, the multi-dimensional information of the policy applicant is obtained to realize renewal evaluation modeling, so that the renewal score of the policy applicant can be predicted, and the strength of the renewal intention of the client is known according to the level of the renewal score, so that different renewal schemes are made, the renewal intention of the client can be comprehensively known, and the success rate of renewal is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating steps of a scheme pushing method according to an embodiment of the present invention.
FIG. 2 is a functional block diagram of a solution pushing apparatus according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a computer device according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings. In addition, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Preferably, the scheme push method of the invention is applied to one or more computer devices. The computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook computer, a tablet computer, a server, a mobile phone and other computing equipment. The computer device can be in man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The first embodiment is as follows:
FIG. 1 is a flow chart of the steps of a preferred embodiment of the push method of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
Referring to fig. 1, the scheme pushing method specifically includes the following steps.
And step S11, multi-dimensional information of the policy applicant is obtained.
In one embodiment, the policy applicant is preferably a policy applicant to be renewed. The multi-dimensional information may include the following dimensional information: the information of the applicant is the basic information, the history information of the application, the information of the application target, the information of the claim record, the information of the application behavior, etc. The policy insurance applicant to be continued can be an enterprise client or an individual client, and the related insurance types can be personal and/or enterprise-oriented property insurance, personal health insurance or personal safety insurance and the like. The following description will take the insurance policy to be renewed as the insurance policy for the car insurance.
The applicant's basic information may include basic identity information (gender, age, etc.), life habit information (lifestyle habits, hobbies, etc.), consumption ability information, website/APP registration information, etc.; the insurance application history information records the insurance risk purchase record and the insurance application amount of the insurance applicant; the information of the insurance application mark can be the basic information characteristics of the vehicle, such as a manufacturer, the age of the vehicle, the purchase price of a new vehicle, the seat number and the like; the claim settlement record information can be the insurance record information, the estimated renewal NCD, the claim return visit score, the claim settlement case type, the historical claim money record, the claim settlement time length and the like; the insurance application behavior information can be information such as advance price inquiry time, consistency of insured persons, historical insurance application channels, advance insurance application days, and continuous insurance years.
And step S12, preprocessing the multi-dimensional information and sorting the multi-dimensional information into a first information set and a second information set.
In one embodiment, after the multi-dimensional information for the policy applicant is obtained, the multi-dimensional information may be pre-processed. For example, for the insurance application history information, the total number of insurance products purchased by the applicant can be added according to the names of all insurance products purchased by the applicant to obtain the total number of insurance products held by the applicant, the amount of the insurance premium required by the applicant for purchasing the insurance products, the total amount of the insurance premium required by the applicant, and the like for statistics; the claim record information can be added according to the times and the amount of the historical claim money records of the applicant to obtain the total historical claim amount and the total historical claim times.
In one embodiment, the preprocessing of the multidimensional information is further used to enable the preprocessed multidimensional information to be used as an input to a renewal tracking model described below to perform renewal score estimation and customize a renewal service plan.
In an embodiment, after the multi-dimensional information is preprocessed, the preprocessed multi-dimensional information is further sorted into a first information set and a second information set, and each of the first information set and the second information set may include a plurality of information factors. The first information set is for input to a first renewal tracking model and the information of the second information set is for input to the second renewal tracking model. The first set of information includes a plurality of renewal scoring factors for the first set of information and the second set of information includes a plurality of renewal scheme factors for the second set of information. The first information set and the second information set may include the same information factors, such as the first information set and the second information set each include information factors of age, occupation, etc. of the policy applicant.
Step S13, obtaining the renewal contribution score of each renewal scoring factor in the first information set, and sorting each renewal scoring factor according to the renewal contribution score.
In one embodiment, the different renewal scoring factors may contribute differently to the renewal prediction, e.g., the policy applicant's gender, age, etc. may contribute less to the renewal prediction, while the policy applicant's job nature, payroll status, claim return visit information, etc. may contribute more to the renewal prediction. The continuous guarantee contribution degrees corresponding to different continuous guarantee scoring factors can be obtained by presetting different information scores. For example, the system developer sets a score between 0 and 100 for each renewal scoring factor in advance, wherein a higher score indicates a higher degree of renewal contribution of the renewal scoring factor, and a lower score indicates a lower degree of renewal contribution of the renewal scoring factor. And further obtaining the continuous guarantee contribution score of each continuous guarantee scoring factor in the first information set, and sorting each continuous guarantee scoring factor according to the continuous guarantee contribution score.
And step S14, selecting a plurality of renewal scoring factors with the top scores from the first information set according to the requirements of the renewal scoring factors with the preset number, and inputting the plurality of renewal scoring factors into a preset renewal tracking model to obtain a renewal scoring prediction result of the policy applicant.
In one embodiment, when predicting the renewal score of the policy applicant, the predetermined renewal tracking model may choose not to input all of the renewal scoring factors in the first information set, and the number of renewal scoring factors may be selected from among the renewal scoring factors set according to the renewal prediction. For example, if the set number of the renewal scoring factors of the policy applicant is 10, the top 10 renewal scoring factors can be selected according to the descending order, and the 10 renewal scoring factors are input into the preset renewal tracking model, so as to obtain the renewal scoring prediction result of the policy applicant.
In one embodiment, the predetermined renewal tracking model may be trained based on a neural network model and sample data from a plurality of policy applicants. The policy applicant includes a plurality of successful renewal applicants and a plurality of unsuccessful renewal applicants. The neural network model includes an input layer, a plurality of hidden layers, and an output layer. The input layer is for receiving sample data of a plurality of policy applicants, each hidden layer comprising a respective plurality of nodes (neurons), each node in each hidden layer being configured to perform a linear or non-linear transformation on an output from at least one node of an adjacent lower layer in the model. Wherein the input of a node of an upper hidden layer may be based on the output of one or several nodes in an adjacent lower layer. Each hidden layer has a corresponding weight value, wherein the weight value is obtained based on training sample data. When the model is trained, the model can be pre-trained by utilizing a supervised learning process to obtain the initial weight of each hidden layer. The fine adjustment of the weight of each hidden layer can be performed by using a Back Propagation (BP) algorithm, and the output layer is used for receiving an output signal from the last hidden layer.
In one embodiment, the keep alive tracking model may be trained by:
d1, dividing the sample data of the policy policemen into a training set and a verification set, establishing a neural network model and training the neural network model by using the training set;
d2, verifying the trained neural network model by using the verification set to obtain a model prediction accuracy;
d3, judging whether the model prediction accuracy is smaller than a preset threshold value, and if the model prediction accuracy is not smaller than the preset threshold value, taking the trained neural network model as the preset continuous tracking model;
and D4, if the model prediction accuracy is smaller than the preset threshold, adjusting the parameters of the neural network model, and retraining the adjusted neural network model by using the training set until the model prediction accuracy obtained by verification of the verification set is not smaller than the preset threshold.
In an embodiment, the parameters of the neural network model include a total number of layers, a number of neurons per layer, and the adjusting the parameters of the neural network model may be adjusting the total number of layers and/or the number of neurons per layer of the neural network model.
In an embodiment, the training set is used for training the neural network model, and the validation set is used for validating the trained neural network model. Specifically, the training set is used for training a neural network model to obtain an intermediate model, sample data of the policy applicant in the verification set is input into the intermediate model for continuation scoring and accuracy verification of a continuation service scheme, model prediction accuracy can be obtained through statistics according to each verification result, and whether the model prediction accuracy is smaller than a preset threshold value or not is judged. When the model prediction accuracy is not less than the preset threshold, the prediction effect of the intermediate model is better, the use requirement is met, and the intermediate model can be used as the preset continuous tracking model. When the model prediction accuracy is smaller than the preset threshold, the prediction effect of the intermediate model is poor, improvement is needed, at the moment, the parameters of the neural network model are adjusted, the adjusted neural network model is trained again by using the training set to obtain a new intermediate model, and then the newly obtained intermediate model is verified by using the verification set again to obtain a new model prediction accuracy. The preset threshold may be set according to actual use requirements, for example, the preset threshold is 95%.
In an embodiment, if the new model prediction accuracy is still smaller than the preset threshold, the above steps need to be repeated again until the model prediction accuracy obtained through the verification set is not smaller than the preset threshold.
And step S15, inputting the renewal score prediction result and the second information set into the preset renewal tracking model to obtain a renewal recommendation scheme of the insurance policy applicant.
In one embodiment, inputting the renewal score prediction and the second set of information into the predetermined renewal tracking model may result in a renewal recommendation for the policy applicant. Different renewal scores may result in different renewal recommendations being output. For example, for a high renewal score, the renewal tracking service may be simpler to take because the applicant has a higher probability of renewal, and for a low renewal score, the renewal tracking service may be more complex/diverse because the applicant has a lower probability of renewal, and various ways of competing for the success of renewal of the customer are required.
In one embodiment, the renewal recommendation scheme may include recommended renewal insurance types, amounts, preferential information, renewal modes, renewal service modes and the like, so as to provide more reasonable dangerous combination services for renewal applicants, provide different renewal services for new and old owners, make novices more clear and more convenient and faster for old and old owners, provide APP one-key renewal services for applicants who have registered APP of good owners, provide more reasonable discounts according to renewal NCD, provide different services according to different ages, for example, young people have strong acceptance, can provide renewal services of web pages/mobile terminals, and people of the past years can provide previous renewal services, can provide VIP renewal service schemes according to the number of applicants' premiums, amounts and annual salaries, and provide more ways of renewing and giving benefits for earlier than setting.
In one embodiment, the predetermined renewal tracking model may include a first renewal tracking model for making renewal score predictions and a second renewal tracking model for making renewal service plan recommendations. The first renewal tracking model is used for obtaining a renewal score prediction result of the policy applicant according to the input renewal score factor, and the second renewal tracking model is used for obtaining a renewal recommendation scheme of the policy applicant according to the input renewal score prediction result and the second information set.
And step S16, pushing the continuation and guarantee recommendation scheme to the policy applicant.
In one embodiment, when a renewal recommendation for a policy applicant is obtained, the renewal recommendation can be pushed to the policy applicant for renewal reference.
According to the scheme pushing method, the multi-dimensional information of the policy insurance applicant is obtained to realize renewal evaluation modeling, so that the renewal score of the policy insurance applicant can be predicted, and the strength of the renewal desire of the client is known according to the level of the renewal score, so that different renewal schemes are made, the renewal desire of the client can be comprehensively known, and the success rate of renewal is improved.
Example two:
fig. 2 is a functional block diagram of a push device according to a preferred embodiment of the present invention.
Referring to fig. 2, the scheme pushing device 10 may include an obtaining module 101, a sorting module 102, a sorting module 103, a first prediction module 104, a second prediction module 105, and a pushing module 106.
The acquisition module 101 is configured to acquire multi-dimensional information of a policy applicant.
In one embodiment, the policy applicant is preferably a policy applicant to be renewed. The multi-dimensional information may include the following dimensional information: the information of the applicant is the basic information, the history information of the application, the information of the application target, the information of the claim record, the information of the application behavior, etc. The policy insurance applicant to be continued can be an enterprise client or an individual client, and the related insurance types can be personal and/or enterprise-oriented property insurance, personal health insurance or personal safety insurance and the like. The following description will take the insurance policy to be renewed as the insurance policy for the car insurance. The obtaining module 101 may obtain multi-dimensional information of the policy applicant by accessing a preset car insurance service database.
The applicant's basic information may include basic identity information (gender, age, etc.), life habit information (lifestyle habits, hobbies, etc.), consumption ability information, website/APP registration information, etc.; the insurance application history information records the insurance risk purchase record and the insurance application amount of the insurance applicant; the information of the insurance application mark can be the basic information characteristics of the vehicle, such as a manufacturer, the age of the vehicle, the purchase price of a new vehicle, the seat number and the like; the claim settlement record information can be the insurance record information, the estimated renewal NCD, the claim return visit score, the claim settlement case type, the historical claim money record, the claim settlement time length and the like; the insurance application behavior information can be information such as advance price inquiry time, consistency of insured persons, historical insurance application channels, advance insurance application days, and continuous insurance years.
The sorting module 102 is configured to pre-process the multi-dimensional information and sort the multi-dimensional information into a first information set and a second information set.
In one embodiment, after obtaining the multi-dimensional information of the policy applicant, the collation module 102 may pre-process the multi-dimensional information. For example, for the insurance application history information, the total number of insurance products purchased by the applicant can be added according to the names of all insurance products purchased by the applicant to obtain the total number of insurance products held by the applicant, the amount of the insurance premium required by the applicant for purchasing the insurance products, the total amount of the insurance premium required by the applicant, and the like for statistics; the claim record information can be added according to the times and the amount of the historical claim money records of the applicant to obtain the total historical claim amount and the total historical claim times.
In one embodiment, the preprocessing module 102 is further configured to enable the preprocessed multidimensional information to be used as an input of a renewal tracking model described below, so as to perform renewal score estimation and customize a renewal service scheme.
In an embodiment, after the multi-dimensional information is preprocessed, the sorting module 102 further sorts the preprocessed multi-dimensional information into a first information set and a second information set, where each of the first information set and the second information set may include a plurality of information factors. The first information set is for input to a first renewal tracking model and the information of the second information set is for input to the second renewal tracking model. The first set of information includes a plurality of renewal scoring factors for the first set of information and the second set of information includes a plurality of renewal scheme factors for the second set of information. The first information set and the second information set may include the same information factors, such as the first information set and the second information set each include information factors of age, occupation, etc. of the policy applicant.
The sorting module 103 is configured to obtain a renewal contribution score of each renewal scoring factor in the first information set, and sort each renewal scoring factor according to the renewal contribution score.
In one embodiment, the different renewal scoring factors may contribute differently to the renewal prediction, e.g., the policy applicant's gender, age, etc. may contribute less to the renewal prediction, while the policy applicant's job nature, payroll status, claim return visit information, etc. may contribute more to the renewal prediction. The continuous guarantee contribution degrees corresponding to different continuous guarantee scoring factors can be obtained by presetting different information scores. For example, the system developer sets a score between 0 and 100 for each renewal scoring factor in advance, wherein a higher score indicates a higher degree of renewal contribution of the renewal scoring factor, and a lower score indicates a lower degree of renewal contribution of the renewal scoring factor. Further, the sorting module 103 may obtain the renewal contribution score of each renewal scoring factor in the first information set, and sort each renewal scoring factor according to the renewal contribution score.
The first prediction module 104 is configured to select a plurality of renewal scoring factors with top scores from the first information set according to a preset number of renewal scoring factor requirements, and input the plurality of renewal scoring factors into a preset renewal tracking model, so as to obtain a renewal scoring prediction result of the policy applicant.
In one embodiment, when predicting the renewal score of the policy applicant, the first prediction module 104 may choose not to input all of the renewal scoring factors in the first information set to the predetermined renewal tracking model, and may choose among the renewal scoring factors based on the number of renewal scoring factors set by the renewal prediction. For example, if the set number of the renewal scoring factors of the policy applicant needs to be acquired is 10, the first prediction module 104 may select the top 10 renewal scoring factors according to the descending order, and input the 10 renewal scoring factors into the preset renewal tracking model, so as to obtain the renewal scoring prediction result of the policy applicant.
In one embodiment, the predetermined renewal tracking model may be trained based on a neural network model and sample data from a plurality of policy applicants. The policy applicant includes a plurality of successful renewal applicants and a plurality of unsuccessful renewal applicants. The neural network model includes an input layer, a plurality of hidden layers, and an output layer. The input layer is for receiving sample data of a plurality of policy applicants, each hidden layer comprising a respective plurality of nodes (neurons), each node in each hidden layer being configured to perform a linear or non-linear transformation on an output from at least one node of an adjacent lower layer in the model. Wherein the input of a node of an upper hidden layer may be based on the output of one or several nodes in an adjacent lower layer. Each hidden layer has a corresponding weight value, wherein the weight value is obtained based on training sample data. When the model is trained, the model can be pre-trained by utilizing a supervised learning process to obtain the initial weight of each hidden layer. The fine adjustment of the weight of each hidden layer can be performed by using a Back Propagation (BP) algorithm, and the output layer is used for receiving an output signal from the last hidden layer.
In one embodiment, the keep alive tracking model may be trained by:
d1, dividing the sample data of the policy policemen into a training set and a verification set, establishing a neural network model and training the neural network model by using the training set;
d2, verifying the trained neural network model by using the verification set to obtain a model prediction accuracy;
d3, judging whether the model prediction accuracy is smaller than a preset threshold value, and if the model prediction accuracy is not smaller than the preset threshold value, taking the trained neural network model as the preset continuous tracking model;
and D4, if the model prediction accuracy is smaller than the preset threshold, adjusting the parameters of the neural network model, and retraining the adjusted neural network model by using the training set until the model prediction accuracy obtained by verification of the verification set is not smaller than the preset threshold.
In an embodiment, the parameters of the neural network model include a total number of layers, a number of neurons per layer, and the adjusting the parameters of the neural network model may be adjusting the total number of layers and/or the number of neurons per layer of the neural network model.
In an embodiment, the training set is used for training the neural network model, and the validation set is used for validating the trained neural network model. Specifically, the training set is used for training a neural network model to obtain an intermediate model, sample data of the policy applicant in the verification set is input into the intermediate model for continuation scoring and accuracy verification of a continuation service scheme, model prediction accuracy can be obtained through statistics according to each verification result, and whether the model prediction accuracy is smaller than a preset threshold value or not is judged. When the model prediction accuracy is not less than the preset threshold, the prediction effect of the intermediate model is better, the use requirement is met, and the intermediate model can be used as the preset continuous tracking model. When the model prediction accuracy is smaller than the preset threshold, the prediction effect of the intermediate model is poor, improvement is needed, at the moment, the parameters of the neural network model are adjusted, the adjusted neural network model is trained again by using the training set to obtain a new intermediate model, and then the newly obtained intermediate model is verified by using the verification set again to obtain a new model prediction accuracy. The preset threshold may be set according to actual use requirements, for example, the preset threshold is 95%.
In an embodiment, if the new model prediction accuracy is still smaller than the preset threshold, the above steps need to be repeated again until the model prediction accuracy obtained through the verification set is not smaller than the preset threshold.
The second prediction module 105 is configured to input the renewal score prediction result and the second information set to the preset renewal tracking model to obtain a renewal recommendation scheme of the policy applicant.
In one embodiment, the second prediction module 105 inputs the renewal score prediction result and the second set of information into the predetermined renewal tracking model to obtain a renewal recommendation of the policy applicant. Different renewal scores may result in different renewal recommendations being output. For example, for a high renewal score, the renewal tracking service may be simpler to take because the applicant has a higher probability of renewal, and for a low renewal score, the renewal tracking service may be more complex/diverse because the applicant has a lower probability of renewal, and various ways of competing for the success of renewal of the customer are required.
In one embodiment, the renewal recommendation scheme may include recommended renewal insurance types, amounts, preferential information, renewal modes, renewal service modes and the like, so as to provide more reasonable dangerous combination services for renewal applicants, provide different renewal services for new and old owners, make novices more clear and more convenient and faster for old and old owners, provide APP one-key renewal services for applicants who have registered APP of good owners, provide more reasonable discounts according to renewal NCD, provide different services according to different ages, for example, young people have strong acceptance, can provide renewal services of web pages/mobile terminals, and people of the past years can provide previous renewal services, can provide VIP renewal service schemes according to the number of applicants' premiums, amounts and annual salaries, and provide more ways of renewing and giving benefits for earlier than setting.
In one embodiment, the predetermined renewal tracking model may include a first renewal tracking model for making renewal score predictions and a second renewal tracking model for making renewal service plan recommendations. The first renewal tracking model is used for obtaining a renewal score prediction result of the policy applicant according to the input renewal score factor, and the second renewal tracking model is used for obtaining a renewal recommendation scheme of the policy applicant according to the input renewal score prediction result and the second information set.
The push module 106 is configured to push the renewal recommendation to the policy applicant.
In one embodiment, when the renewal recommendation is obtained for a policy applicant, the push module 106 may push the renewal recommendation to the policy applicant for renewal reference.
According to the scheme pushing device, the multi-dimensional information of the policy insurance applicant is obtained to realize renewal evaluation modeling, so that the renewal score of the policy insurance applicant can be predicted, and the strength of the renewal desire of the client is known according to the level of the renewal score, so that different renewal schemes are made, the renewal desire of the client can be comprehensively known, and the success rate of renewal is improved.
FIG. 3 is a diagram of a computer device according to a preferred embodiment of the present invention.
The computer device 1 comprises a memory 20, a processor 30 and a computer program 40, such as a schema push program, stored in the memory 20 and executable on the processor 30. The processor 30, when executing the computer program 40, implements the steps in the embodiment of the push method of the above scheme, such as the steps S11-S16 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 40, implements the functions of the modules in the pushing device embodiment of the above-mentioned scheme, such as the modules 101 to 106 in fig. 2.
Illustratively, the computer program 40 may be partitioned into one or more modules/units that are stored in the memory 20 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, the instruction segments describing the execution process of the computer program 40 in the computer apparatus 1. For example, the computer program 40 may be partitioned into an acquisition module 101, a sorting module 102, a ranking module 103, a first prediction module 104, a second prediction module 105, and a push module 106 in fig. 2. See embodiment two for specific functions of each module.
The computer device 1 may be a desktop computer, a notebook, a palm computer, a mobile phone, a tablet computer, a cloud server, or other computing devices. It will be appreciated by a person skilled in the art that the schematic diagram is merely an example of the computer apparatus 1, and does not constitute a limitation of the computer apparatus 1, and may comprise more or less components than those shown, or some components may be combined, or different components, for example, the computer apparatus 1 may further comprise an input and output device, a network access device, a bus, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor 30 may be any conventional processor or the like, the processor 30 being the control center of the computer device 1, various interfaces and lines connecting the various parts of the overall computer device 1.
The memory 20 may be used for storing the computer program 40 and/or the module/unit, and the processor 30 implements various functions of the computer device 1 by running or executing the computer program and/or the module/unit stored in the memory 20 and calling data stored in the memory 20. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data) created according to the use of the computer apparatus 1, and the like. Further, the memory 20 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash memory Card (FlashCard), at least one magnetic disk storage device, a flash memory device, or other non-volatile solid state storage device.
The modules/units integrated with the computer device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
In the embodiments provided in the present invention, it should be understood that the disclosed computer apparatus and method can be implemented in other ways. For example, the above-described embodiments of the computer apparatus are merely illustrative, and for example, the division of the units is only one logical function division, and there may be other divisions when the actual implementation is performed.
In addition, functional units in the embodiments of the present invention may be integrated into the same processing unit, or each unit may exist alone physically, or two or more units are integrated into the same unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The units or computer means recited in the computer means claims may also be implemented by the same unit or computer means, either in software or in hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for pushing a scenario, the method comprising:
obtaining multi-dimensional information of a policy applicant;
preprocessing the multi-dimensional information and arranging the multi-dimensional information into a first information set and a second information set;
acquiring the continuous guarantee contribution score of each continuous guarantee scoring factor in the first information set, and sequencing each continuous guarantee scoring factor according to the continuous guarantee contribution score;
selecting a plurality of renewal scoring factors with top scores from the first information set according to the requirements of a preset number of renewal scoring factors, and inputting the renewal scoring factors into a preset renewal tracking model to obtain a renewal scoring prediction result of the policy applicant;
inputting the renewal score prediction result and the second information set into the preset renewal tracking model to obtain a renewal recommendation scheme of the policy applicant; and
and pushing the continuation recommendation scheme to the policy applicant.
2. The schema pushing method of claim 1, wherein the multi-dimensional information comprises: the information of the applicant is the basic information, the history information of the application, the information of the application target, the information of the claim settlement record and the information of the application behavior.
3. The scenario push method according to claim 1 or 2, wherein the preset keep alive tracking model is trained by the following steps:
establishing a neural network model, wherein the neural network model comprises an input layer, a plurality of hidden layers and an output layer; and
and training the neural network model by using the sample data of a plurality of preset insurance policy applicants to obtain the preset continuous insurance tracking model, wherein the plurality of preset insurance policy applicants comprise a plurality of continuous insurance applicants and a plurality of non-continuous insurance applicants.
4. The scheme pushing method according to claim 3, wherein the step of training the neural network model to obtain the preset renewal tracking model by using sample data of a plurality of preset policy applicants comprises:
dividing the sample data of a plurality of preset policy applicants into a training set and a verification set;
training the neural network model by using the training set;
verifying the trained neural network model by using the verification set, and counting according to each verification result to obtain a model prediction accuracy;
judging whether the model prediction accuracy is smaller than a preset threshold value or not;
and if the model prediction accuracy is not less than the preset threshold value, taking the trained neural network model as the preset continuous tracking model.
5. The scenario pushing method according to claim 4, wherein the step of determining whether the model prediction accuracy is smaller than a preset threshold further comprises:
if the model prediction accuracy is smaller than the preset threshold, adjusting parameters of the neural network model, and retraining the adjusted neural network model by using the training set;
verifying the retrained neural network model by using the verification set, carrying out statistics again according to each verification result to obtain a model prediction accuracy, and judging whether the model prediction accuracy obtained by statistics again is smaller than a preset threshold value or not;
if the model prediction accuracy obtained by the re-statistics is not smaller than the preset threshold value, taking the neural network model obtained by the retraining as the preset continuous tracking model; and
if the model prediction accuracy obtained by the re-statistics is smaller than the preset threshold, repeating the steps until the model prediction accuracy obtained by the verification of the verification set is not smaller than the preset threshold;
the parameters of the neural network model comprise the total number of layers and the number of neurons in each layer.
6. The scenario push method according to claim 5, wherein the step of adjusting the parameters of the neural network model comprises:
adjusting the total number of layers and/or the number of neurons in each layer of the neural network model.
7. The scenario push method according to claim 1 or 2, wherein the predefined renewal tracking model comprises a first renewal tracking model and a second renewal tracking model, the first renewal tracking model is configured to obtain a renewal score prediction result of the policy applicant according to the input renewal score factor, and the second renewal tracking model is configured to obtain a renewal recommendation scenario of the policy applicant according to the input renewal score prediction result and the second information set.
8. A solution pushing apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring multi-dimensional information of the policy applicant;
the sorting module is used for preprocessing the multi-dimensional information and sorting the multi-dimensional information into a first information set and a second information set;
the sorting module is used for acquiring the continuous guarantee contribution score of each continuous guarantee scoring factor in the first information set and sorting each continuous guarantee scoring factor according to the continuous guarantee contribution score;
the first prediction module is used for selecting a plurality of renewal scoring factors with the top scores from the first information set according to the demands of a preset number of renewal scoring factors and inputting the plurality of renewal scoring factors into a preset renewal tracking model to obtain a renewal scoring prediction result of the policy applicant;
the second prediction module is used for inputting the renewal score prediction result and the second information set into the preset renewal tracking model to obtain a renewal recommendation scheme of the policy applicant; and
and the pushing module is used for pushing the renewal recommendation scheme to the insurance policy applicant.
9. A computer arrangement comprising a processor and a memory, said memory having stored thereon a number of computer programs, characterized in that said processor is adapted to carry out the steps of the project push method according to any of claims 1-7 when executing the computer programs stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the scenario push method according to any of claims 1-7.
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