CN112307307B - Insurance product recommendation method and apparatus - Google Patents
Insurance product recommendation method and apparatus Download PDFInfo
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- CN112307307B CN112307307B CN201910671035.0A CN201910671035A CN112307307B CN 112307307 B CN112307307 B CN 112307307B CN 201910671035 A CN201910671035 A CN 201910671035A CN 112307307 B CN112307307 B CN 112307307B
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- 238000000034 method Methods 0.000 title claims abstract description 43
- 201000010099 disease Diseases 0.000 claims abstract description 403
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 403
- 238000013441 quality evaluation Methods 0.000 claims abstract description 69
- 238000011156 evaluation Methods 0.000 claims description 60
- 238000004590 computer program Methods 0.000 claims description 18
- 238000002372 labelling Methods 0.000 claims description 10
- 208000024891 symptom Diseases 0.000 claims description 5
- 208000028399 Critical Illness Diseases 0.000 claims 3
- 238000003058 natural language processing Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 4
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
The invention provides an insurance product recommending method and device, wherein the method comprises the following steps: acquiring the serious disease grouping information of each insurance product in the K insurance products; the method comprises the steps of respectively carrying out heavy disease grouping quality evaluation according to heavy disease grouping information of each of the K insurance products to obtain heavy disease grouping quality evaluation values of each of the K insurance products; calculating multiple odds and pay multiplying power of each of the K insurance products according to the serious disease grouping information of each of the K insurance products; determining a recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products; and recommending the insurance products according to the recommended value of each insurance product in the K insurance products. The insurance product recommendation method provided by the invention can improve the accuracy of the insurance product recommendation of major disease guarantee type.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to an insurance product recommendation method and device.
Background
With the rapid development of insurance industry, the variety of insurance products is also becoming more and more. At present, serious disease guarantee type insurance products (namely serious disease insurance products) are selected by more and more people, however, the dimensions of serious disease guarantee types, waiting periods, light disease types, light disease avoidance, physical guarantee, guarantee continuation, multiple reimbursement and the like of different serious disease insurance products are different, and aiming at the various serious disease insurance products, people usually select the serious disease insurance products according to the recommendation of professional agents, and the insurance recommendation mode is high in subjectivity and is easily influenced by factors such as the quantity of insurance products agency by the professional agents, sales division proportion of different insurance products and the like, so that the accuracy of the recommended insurance products is low.
Therefore, the problem of low accuracy of heavy insurance product recommendation exists in the prior art.
Disclosure of Invention
The embodiment of the invention provides an insurance product recommending method and device, which are used for solving the problem of low accuracy of serious insurance product recommending in the prior art.
In order to solve the technical problems, the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides an insurance product recommendation method. The method comprises the following steps:
Obtaining the serious disease grouping information of each insurance product in K insurance products, wherein K is a positive integer;
the method comprises the steps of respectively carrying out heavy disease grouping quality evaluation according to heavy disease grouping information of each of the K insurance products to obtain heavy disease grouping quality evaluation values of each of the K insurance products;
calculating multiple odds and pay multiplying power of each of the K insurance products according to the serious disease grouping information of each of the K insurance products;
determining a recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products;
and recommending the insurance products according to the recommended value of each insurance product in the K insurance products.
In a second aspect, the embodiment of the invention also provides an insurance product recommendation device. The insurance product recommendation device includes:
the first acquisition module is used for acquiring the serious disease grouping information of each insurance product in K insurance products, wherein K is a positive integer;
the first evaluation module is used for evaluating the heavy disease grouping quality according to the heavy disease grouping information of each of the K insurance products respectively to obtain the heavy disease grouping quality evaluation value of each of the K insurance products;
The calculating module is used for calculating the multiple odds and advantages of each insurance product in the K insurance products according to the serious disease grouping information of each insurance product in the K insurance products respectively;
the determining module is used for determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds multiplying power of each of the K insurance products;
and the recommending module is used for recommending the insurance products according to the recommending value of each insurance product in the K insurance products.
In a third aspect, an embodiment of the present invention further provides an insurance product recommendation device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, where the computer program when executed by the processor implements the steps of the insurance product recommendation method described above.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of the insurance product recommendation method described above.
In the embodiment of the invention, the recommendation value of each insurance product is calculated by calculating the serious disease grouping quality evaluation value and the multiple odds ratio of each insurance product and calculating the recommendation value of each insurance product according to the serious disease grouping quality evaluation value and the multiple odds ratio of each insurance product, so that the recommendation of the insurance product is performed, and the guarantee quality of the serious disease guarantee type insurance product can be objectively reflected due to the serious disease grouping quality and the multiple odds ratio, so that the accuracy of the recommendation of the serious disease guarantee type insurance product can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a flow chart of an insurance product recommendation method provided by an embodiment of the present invention;
FIG. 2 is a block diagram of an insurance product recommendation device provided by an embodiment of the present invention;
fig. 3 is a block diagram of an insurance product recommending apparatus according to still another embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides an insurance product recommending method. Referring to fig. 1, fig. 1 is a flowchart of an insurance product recommendation method provided by an embodiment of the present invention, as shown in fig. 1, including the following steps:
Step 101, obtaining the serious disease grouping information of each insurance product in K insurance products, wherein K is a positive integer.
In this embodiment, the above-mentioned heavy-disease grouping information of each insurance product may include the heavy-disease grouping included in the heavy-disease grouping information and the heavy-disease grouping included in each heavy-disease grouping, for example, the heavy-disease grouping information of the insurance product a may include a heavy-disease grouping a and a heavy-disease grouping B, where the heavy-disease grouping a includes heavy-disease A1 to heavy-disease a20, and the heavy-disease grouping B includes heavy-disease B1 to heavy-disease B25.
For example, the term information of the major illness guarantee type insurance products sold in the market can be collected, the serious illness term information of each insurance product can be extracted from the collected term information of the insurance products, and then natural language processing (Natural Language Processing, NLP) can be respectively carried out on the serious illness term information of each insurance product, so that the serious illness grouping information of each insurance product can be obtained.
It should be noted that, in the embodiment of the present invention, the serious disease grouping information of each insurance product may be manually marked in advance, which is not limited in this embodiment.
And 102, evaluating the quality of the repeated-disease grouping according to the repeated-disease grouping information of each of the K insurance products, and obtaining the repeated-disease grouping quality evaluation value of each of the K insurance products.
In practice, the quality of the critical packets has a large impact on the multiple payouts. In general, the higher incidence of the severe disease is distributed to different severe disease groups, and the guard can be improved as compared with the case where the higher incidence of the severe disease is distributed to the same severe disease group. Therefore, the embodiment can evaluate the heavy-disease grouping quality according to the heavy-disease grouping information of each insurance product, so as to evaluate the heavy-disease grouping quality of each insurance product, and further calculate the recommended value of each insurance product according to the heavy-disease grouping quality of each insurance product.
Alternatively, the total disease occurrence rate of each serious disease group of a certain insurance product can be counted according to the serious disease group information of the certain insurance product, and then the serious disease group quality evaluation value of the insurance product can be evaluated according to the total disease occurrence rate of each serious disease group of the insurance product. For example, the standard deviation of the total disease occurrence rate of all the serious disease groups of the insurance product may be calculated, and the serious disease group quality evaluation value of the insurance product may be determined based on the value corresponding to the preset standard deviation range in which the standard deviation is located.
It should be noted that the total disease occurrence rate of each severe disease packet may be the sum of occurrence rates of severe diseases included in each severe disease packet. For example, the total disease occurrence rate of the heavy disease packet a is the sum of the occurrence rates of heavy diseases included in the heavy disease packet a, and the total disease occurrence rate of the heavy disease packet B is the sum of the occurrence rates of heavy diseases included in the heavy disease packet B.
And 103, calculating the multiple odds and advantages of each insurance product in the K insurance products according to the serious disease grouping information of each insurance product in the K insurance products.
For example, multiple odds for each insurance product may be calculated based on the number of severe disease groupings for each insurance product and the total disease incidence for each severe disease grouping, respectively.
It should be noted that, the above steps 102 and 103 may be performed in parallel, or may be performed in series, that is, step 103 may be performed first, then step 102 may be performed, or step 102 may be performed first, then step 103 may be performed, which is not limited in this embodiment.
And 104, determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products.
For example, a correspondence relationship between the different severe disease group quality evaluation values, the multiple odds and the recommended values may be established in advance, so that the recommended value of each insurance product may be quickly searched from the correspondence relationship according to the severe disease group quality evaluation values and the multiple odds of each insurance product.
Optionally, the step may further determine the recommended value of the insurance product in combination with other dimensional information of the insurance product, for example, the recommended value of the insurance product may be determined in combination with an evaluation value of at least one of a number of disclaimers dimension, a waiting period claim-free dimension, a light symptom-exemption dimension, an statutory guarantee dimension, and a guarantee renewal dimension.
And 105, recommending the insurance products according to the recommended value of each insurance product in the K insurance products.
For example, insurance products may be recommended to the user in order of the recommendation value from the large to the small, or may be recommended to the user in combination with the user information and the recommendation value of the insurance product.
In the embodiment of the invention, the recommendation of the insurance products is performed by calculating the serious disease grouping quality evaluation value and the multiple odds ratio of each insurance product and calculating the recommendation value of each insurance product according to the serious disease grouping quality evaluation value and the multiple odds ratio of each insurance product.
For convenience of description, in this embodiment, taking the first insurance product as an example, how to perform the severe disease grouping quality evaluation according to the severe disease grouping information of the insurance product, and obtain a description of the severe disease grouping quality evaluation value of the insurance product, where the first insurance product may be any insurance product of the K insurance products.
Optionally, step 102, that is, the evaluating the quality of the heavy disease group according to the heavy disease group information of each of the K insurance products, may include:
calculating the standard deviation of the total disease incidence of the L heavy disease groups according to the total disease incidence of each heavy disease group in the L heavy disease groups of a first insurance product, wherein the first insurance product is any insurance product in the K insurance products, the heavy disease group information of the first insurance product comprises the L heavy disease groups and heavy diseases contained in each heavy disease group in the L heavy disease groups, and L is an integer greater than 1;
dividing all the heavy diseases included in the L heavy disease groups into M groups according to the occurrence rate of each heavy disease included in the L heavy disease groups to obtain M heavy disease groups, wherein M is equal to L, and the heavy disease with the occurrence rate of M in the L heavy disease groups is located in different heavy disease groups in the M heavy disease groups;
calculating the standard deviation of the total disease occurrence rate of the M severe disease groups according to the total disease occurrence rate of each of the M severe disease groups;
And calculating the quality evaluation value of the heavy disease group of the first insurance product according to the standard deviation of the total disease occurrence rate of the L heavy disease groups and the standard deviation of the total disease occurrence rate of the M heavy disease groups.
For example, the present embodiment may sort the heavy diseases included in the L heavy-disease packets in order of occurrence rate from high to low, and disperse the heavy diseases whose occurrence rate is M before in different heavy-disease packets in the M heavy-disease packets.
Alternatively, in this embodiment, each of the multiple serious diseases included in the L serious disease packets may be sequentially re-divided into M serious disease packets according to the above-mentioned sorting. For example, if L and M are 3 and L heavy disease packets include 20 heavy diseases, the heavy disease with the highest occurrence rate of 20 heavy diseases may be divided into heavy disease packet A1, the heavy disease with the next highest occurrence rate of 20 heavy diseases may be divided into heavy disease packet A2, the heavy disease with the highest occurrence rate of 20 heavy diseases may be divided into heavy disease packet A3, the heavy disease with the third highest occurrence rate of 20 heavy diseases may be divided into heavy disease packet A3, the heavy disease with the fourth highest occurrence rate of 20 heavy diseases may be divided into heavy disease packet A1, the heavy disease with the fifth highest occurrence rate of 20 heavy diseases may be divided into heavy disease packet A2, and so on until the packet of 20 heavy diseases is completed.
The above-mentioned calculating the quality evaluation value of the first protection product according to the standard deviation of the total disease occurrence rate of the L number of heavy disease groups and the standard deviation of the total disease occurrence rate of the M number of heavy disease groups, for example, may calculate the difference between the standard deviation of the total disease occurrence rate of the L number of heavy disease groups and the standard deviation of the total disease occurrence rate of the M number of heavy disease groups, and determine the quality evaluation value of the heavy disease group of the first protection product based on the obtained difference, for example, determine the value corresponding to the preset difference interval where the obtained difference is located as the quality evaluation value of the heavy disease group of the first protection product.
According to the embodiment, the heavy disease grouping quality evaluation value of the first insurance product is calculated based on the standard deviation of the total disease occurrence rate of the original heavy disease grouping of the first insurance product and the standard deviation of the total disease occurrence rate of the optimized heavy disease grouping, so that the heavy disease grouping quality of the first insurance product can be accurately and objectively reflected.
Optionally, the calculating the heavy disease group quality evaluation value of the first insurance product according to the standard deviation of the total disease occurrence rate of the L heavy disease groups and the standard deviation of the total disease occurrence rate of the M heavy disease groups may include:
Using the formula q=100×σ 1 /σ 0 Calculating the serious disease grouping quality evaluation value of the first insurance product;
wherein Q represents the critical group quality evaluation value, sigma, of the first insurance product 0 Standard deviation, sigma, representing the total disease incidence of the L severe disease groupings 1 And represents the standard deviation of the total disease incidence of the M severe disease groupings.
In this embodiment, the heavy disease grouping quality evaluation value of the insurance product is determined by calculating the ratio of the standard deviation of the total disease occurrence rate of the M heavy disease groupings and the standard deviation of the total disease occurrence rate of the L heavy disease groupings, which is simple to implement and can reflect the heavy disease grouping quality of the insurance product objectively.
For convenience of description, in this embodiment, how to calculate multiple odds and pay ratio descriptions of the insurance product according to the serious disease grouping information of the insurance product is taken as an example of a second insurance product, where the second insurance product may be any insurance product of the K insurance products, and the second insurance product may be the same as or different from the first insurance product.
Optionally, step 103, that is, calculating the multiple odds ratio of each of the K insurance products according to the serious disease grouping information of each of the K insurance products, may include:
Calculating multiple odds and odds of the second insurance product according to the following formula;
wherein M is N Representing multiple odds of the second insurance product, N representing the number of serious diseases grouped by the second insurance product, P n Representing the total probability of n times of illness, p i Indicating the total disease incidence, p, of the ith severe disease group j And indicating the total disease incidence of the j-th heavy disease grouping, wherein the second insurance product is any insurance product in the K insurance products, and the heavy disease grouping information of the second insurance product comprises N heavy disease groupings and heavy diseases included in each heavy disease grouping in the N heavy disease groupings.
In this embodiment, the total disease occurrence rate may be calculated according to the occurrence rate of each of the serious diseases included in each of the serious disease groups, for example, the sum of the occurrence rates of the serious diseases included in the serious disease groups is taken as the total disease occurrence rate.
According to the embodiment, the multiple odds multiplying power of the insurance product is calculated through the formula, and the accuracy of the multiple odds multiplying power calculation can be improved.
Optionally, before the step 104, that is, before the determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products, the method may further include:
Obtaining an evaluation value of a target dimension of each of the K insurance products, wherein the target dimension comprises at least one of the following: the number of disclaimers dimension, the waiting period claim-free dimension, the light symptom exemption dimension, the statue guarantee dimension and the renewal dimension are guaranteed;
the determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products includes:
calculating the product evaluation value of each of the K insurance products according to the serious disease grouping quality evaluation value of each of the K insurance products and the evaluation value of the target dimension;
and calculating the recommended value of each of the K insurance products according to the product evaluation value and the multiple odds ratio of each of the K insurance products.
In this embodiment, if the target dimension includes multiple dimensions, the evaluation value of each of the multiple dimensions may be obtained. For example, if the target dimension includes a number of exemptions dimension, a waiting period exemption dimension, a light illness exemption dimension, an statutory guarantee dimension, and a guarantee renewal dimension, the evaluation value of each of the number of exemptions dimension, the waiting period exemption dimension, the light illness exemption dimension, the statutory guarantee dimension, and the guarantee renewal dimension may be acquired, respectively.
Optionally, the embodiment may pre-establish a correspondence between the value of the target dimension and the evaluation value, so that the evaluation value of the target dimension may be quickly determined based on the correspondence. For example, for the number of disclaimers dimension, a correspondence between the number of disclaimers and the evaluation value may be established in advance; for the waiting period dimension, a corresponding relation between the waiting period duration and the evaluation value can be pre-established; for the waiting period claim-free dimension, a correspondence relationship between the value of the waiting period claim and the evaluation value may be established in advance, for example, the diagnosis corresponds to the evaluation value a and the illness corresponds to the evaluation value b. Alternatively, the embodiment may calculate a percentage grade of the target dimension, and determine the evaluation value of the target dimension based on the percentage grade.
Calculating the product evaluation value of each of the K insurance products according to the serious disease grouping quality evaluation value of each of the K insurance products and the evaluation value of the target dimension, for example, an evaluation value vector can be generated according to the serious disease grouping quality evaluation value of the insurance products and the evaluation value of the target dimensionAnd can obtain the weight of each dimension to obtain the weight vector +.>Then +. >And->The inner product of (2) is used as the product evaluation value of the insurance product, wherein the serious disease grouping also belongs to one dimension.
For calculating the recommended value of each of the K insurance products according to the product evaluation value and the multiple odds ratio of each of the K insurance products, for example, the product of the product evaluation value and the multiple odds ratio of the insurance product may be used as the recommended value of the insurance product.
According to the embodiment, the recommended value of the insurance product is calculated by integrating the evaluation values of the insurance product in multiple dimensions, so that the accuracy of the recommendation of the insurance product can be further improved.
Optionally, the calculating the recommended value of each of the K insurance products according to the product evaluation value and the multiple odds ratio of each of the K insurance products may include:
respectively acquiring the insurance amount and the insurance fee of each insurance product in the K insurance products;
and calculating the recommended value of each of the K insurance products according to the insurance amount, the premium, the product evaluation value and the multiple odds ratio of each of the K insurance products.
In this embodiment, the recommended value of the insurance product may be calculated together with the insurance amount, premium, product evaluation value, and multiple odds. For example, the recommended value for the insurance product may be calculated based on the following formula:
Wherein, IA R Indicating the amount of the deposit, IF indicating the amount of the deposit, P indicating the product evaluation value, M N Representing multiple odds.
According to the embodiment, the recommended value of the insurance product is calculated through the sum of the insurance product, the premium, the product evaluation value and the multiple odds and the multiplying power, so that the accuracy of the recommendation of the insurance product can be further improved.
Optionally, the obtaining the evaluation value of the target dimension of each of the K insurance products may include:
acquiring a labeling grade value of a target dimension of each insurance product in the K insurance products;
according to the labeling grade value of the target dimension of each of the K insurance products, calculating the percentage grade of the target dimension of each of the K insurance products respectively;
and calculating the evaluation value of the target dimension of each of the K insurance products according to the percentage grade of the target dimension of each of the K insurance products.
In this embodiment, the level value of each dimension of each insurance product may be manually marked, or the level value of each dimension of each insurance product may be marked by NLP. In the case where the grade value of each dimension of each insurance product is marked by the NLP, the grade value of each dimension of each insurance product marked by the NLP may be manually checked and modified.
In practical application, the grade of the insurance product can be determined and marked according to the value of each dimension of the insurance product, so as to obtain the marked grade value of each dimension. It should be noted that, if the target dimension includes multiple dimensions, the labeling grade value of each dimension in the multiple dimensions may be obtained, and for K insurance products, K labeling grade values may be obtained for each dimension.
For ease of description, the calculation of the percentile hierarchy of the target dimension is described below by taking a first dimension as an example, where the first dimension may be any dimension included in the target dimension.
Specifically, after obtaining the K marking grade values of the first dimension, the K marking grade values of the first dimension may be ranked according to quality from low to high, and each marking grade value is countedNumber N of (2) R The K insurance products can be grouped according to the marking grade, and the group grading of the insurance products is carried out according to the specific responsibility content aiming at the insurance products in the same group to obtain the group grade R of the insurance products G R can be counted according to the quantity of insurance products in the group G Percentile P G And may calculate a percentile rank P based on the following formula R ：
Wherein K is the number of insurance products, cf L Is less than P Gl Sum of the number of insurance products of each group, P Gl Is P G Lower percentile limit, i is the percentile intra-group distance.
Optionally, in obtaining the percentage grade P R Then, the corresponding standard Z score Z can be searched through a standard normal distribution table pr And may be according to a standard Z-score Z pr Calculating a T score T1, wherein t1=50+10×z pr 。
The T score T1 is also the evaluation value of the first dimension. For any other dimension included in the target dimension, the evaluation value of each dimension may be calculated by the manner of calculating the evaluation value of the first dimension described above.
Embodiments of the present invention are described below with reference to examples:
for example, if insurance product a divides the guaranteed serious diseases into three groups, the total disease occurrence rates of the three groups of serious diseases are respectively 0.1, 0.2 and 0.3, and the standard deviation of the total disease occurrence rates is 8.165, the prevalence rates are respectively as follows:
primary prevalence P 1 ＝0.1+0.2+0.3-2*(0.1*0.2+0.2*0.3+0.1*0.3)＝0.38；
Twice prevalence rate P 2 ＝0.1*0.2+0.2*0.3+0.1*0.3-3*0.1*0.2*0.3＝0.092；
Three prevalence rate P 3 ＝0.1*0.2*0.3＝0.006；
Multiple odds multiplying factor M N ＝(0.38+2*0.092+3*0.006)/(0.38+0.092+0.006)＝1.2176
If the total disease occurrence rate of the three groups of the heavy disease groups obtained after the heavy disease optimization grouping ensured by the insurance product A is 0.17, 0.18 and 0.25 respectively, the standard deviation of the optimization grouping is 3.559.
Specifically, insurance product a is a product evaluation score (may also be referred to as an evaluation value) as shown in table 1:
TABLE 1
Referring to fig. 2, fig. 2 is a block diagram of an insurance product recommendation device provided by an embodiment of the present invention. As shown in fig. 2, the insurance product recommendation device 200 includes:
a first obtaining module 201, configured to obtain heavy disease grouping information of each of K insurance products, where K is a positive integer;
the first evaluation module 202 is configured to perform a severe disease grouping quality evaluation according to the severe disease grouping information of each of the K insurance products, so as to obtain a severe disease grouping quality evaluation value of each of the K insurance products;
the calculating module 203 is configured to calculate multiple odds and odds of each of the K insurance products according to the serious disease grouping information of each of the K insurance products;
a determining module 204, configured to determine a recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products;
and the recommendation module 205 is configured to recommend an insurance product according to the recommendation value of each insurance product in the K insurance products.
Optionally, the first evaluation module includes:
A first calculating unit, configured to calculate, according to a total disease occurrence rate of each of L number of heavy disease groups of a first insurance product, a standard deviation of the total disease occurrence rate of the L number of heavy disease groups, where the first insurance product is any insurance product of the K number of insurance products, heavy disease group information of the first insurance product includes the L number of heavy disease groups and heavy disease included in each of the L number of heavy disease groups, and L is an integer greater than 1;
a grouping unit, configured to divide all the multiple-diseases included in the L multiple-disease groups into M groups according to the occurrence rate of each multiple-disease included in the L multiple-disease groups, to obtain M multiple-disease groups, where M is equal to L, where a multiple-disease whose occurrence rate is M in the L multiple-disease groups is located in a different multiple-disease group of the M multiple-disease groups;
a second calculating unit, configured to calculate a standard deviation of the total disease occurrence rate of the M severe disease groups according to the total disease occurrence rate of each of the M severe disease groups;
a third calculation unit, configured to calculate a severe disease group quality evaluation value of the first insurance product according to a standard deviation of the total disease occurrence rate of the L severe disease groups and a standard deviation of the total disease occurrence rate of the M severe disease groups.
Optionally, the third computing unit is specifically configured to:
using the formula q=100×σ 1 /σ 0 Calculating the serious disease grouping quality evaluation value of the first insurance product;
wherein Q represents the critical group quality evaluation value, sigma, of the first insurance product 0 Standard deviation, sigma, representing the total disease incidence of the L severe disease groupings 1 And represents the standard deviation of the total disease incidence of the M severe disease groupings.
Optionally, the computing module is specifically configured to:
calculating multiple odds and odds of the second insurance product according to the following formula;
wherein M is N Representing multiple odds of the second insurance product, N representing the number of serious diseases grouped by the second insurance product, P n Representing the total probability of n times of illness, p i Indicating the total disease incidence, p, of the ith severe disease group j And representing the total disease incidence of the j-th heavy disease grouping, wherein the second insurance product is any insurance product in the K insurance products, and the heavy disease grouping information of the second insurance product comprises the N heavy disease groupings and heavy diseases included in each heavy disease grouping in the N heavy disease groupings.
Optionally, the apparatus further includes:
the first obtaining module is configured to obtain an evaluation value of a target dimension of each of the K insurance products before determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products, where the target dimension includes at least one of: the number of disclaimers dimension, the waiting period claim-free dimension, the light symptom exemption dimension, the statue guarantee dimension and the renewal dimension are guaranteed;
The determining module includes:
a fourth calculation unit, configured to calculate a product evaluation value of each of the K insurance products according to the serious disease grouping quality evaluation value of each of the K insurance products and the evaluation value of the target dimension, respectively;
and a fifth calculating unit, configured to calculate a recommended value of each of the K insurance products according to the product evaluation value and the multiple odds ratio of each of the K insurance products.
Optionally, the fifth calculating unit is specifically configured to:
respectively acquiring the insurance amount and the insurance fee of each insurance product in the K insurance products;
and calculating the recommended value of each of the K insurance products according to the insurance amount, the premium, the product evaluation value and the multiple odds ratio of each of the K insurance products.
Optionally, the first obtaining module is specifically configured to:
acquiring a labeling grade value of a target dimension of each insurance product in the K insurance products;
according to the labeling grade value of the target dimension of each of the K insurance products, calculating the percentage grade of the target dimension of each of the K insurance products respectively;
And calculating the evaluation value of the target dimension of each of the K insurance products according to the percentage grade of the target dimension of each of the K insurance products.
The insurance product recommending apparatus 200 provided in the embodiment of the present invention can implement each process in the above method embodiment, and in order to avoid repetition, a description thereof is omitted.
According to the insurance product recommending device 200 of the embodiment of the invention, a first obtaining module 201 is used for obtaining the serious disease grouping information of each insurance product in K insurance products, wherein K is a positive integer; the first evaluation module 202 is configured to perform a severe disease grouping quality evaluation according to the severe disease grouping information of each of the K insurance products, so as to obtain a severe disease grouping quality evaluation value of each of the K insurance products; the calculating module 203 is configured to calculate multiple odds and odds of each of the K insurance products according to the serious disease grouping information of each of the K insurance products; a determining module 204, configured to determine a recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products; the recommendation module 205 is configured to recommend an insurance product according to the recommendation value of each insurance product in the K insurance products, so that accuracy of recommendation of an insurance product with a major disease guarantee type can be improved.
Referring to fig. 3, fig. 3 is a block diagram of an insurance product recommendation device according to still another embodiment of the present invention, and as shown in fig. 3, an insurance product recommendation device 300 includes: a processor 301, a memory 302, and a computer program stored on the memory 302 and executable on the processor, the components of the insurance product recommendation device 300 being coupled together by a bus interface 303, the computer program when executed by the processor 301 performing the steps of:
obtaining the serious disease grouping information of each insurance product in K insurance products, wherein K is a positive integer;
the method comprises the steps of respectively carrying out heavy disease grouping quality evaluation according to heavy disease grouping information of each of the K insurance products to obtain heavy disease grouping quality evaluation values of each of the K insurance products;
calculating multiple odds and pay multiplying power of each of the K insurance products according to the serious disease grouping information of each of the K insurance products;
determining a recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products;
and recommending the insurance products according to the recommended value of each insurance product in the K insurance products.
Optionally, the computer program when executed by the processor 301 is further configured to:
calculating the standard deviation of the total disease incidence of the L heavy disease groups according to the total disease incidence of each heavy disease group in the L heavy disease groups of a first insurance product, wherein the first insurance product is any insurance product in the K insurance products, the heavy disease group information of the first insurance product comprises the L heavy disease groups and heavy diseases contained in each heavy disease group in the L heavy disease groups, and L is an integer greater than 1;
dividing all the heavy diseases included in the L heavy disease groups into M groups according to the occurrence rate of each heavy disease included in the L heavy disease groups to obtain M heavy disease groups, wherein M is equal to L, and the heavy disease with the occurrence rate of M in the L heavy disease groups is located in different heavy disease groups in the M heavy disease groups;
calculating the standard deviation of the total disease occurrence rate of the M severe disease groups according to the total disease occurrence rate of each of the M severe disease groups;
and calculating the quality evaluation value of the heavy disease group of the first insurance product according to the standard deviation of the total disease occurrence rate of the L heavy disease groups and the standard deviation of the total disease occurrence rate of the M heavy disease groups.
Optionally, the computer program when executed by the processor 301 is further configured to:
using the formula q=100×σ 1 /σ 0 Calculating the serious disease grouping quality evaluation value of the first insurance product;
wherein Q represents the critical group quality evaluation value, sigma, of the first insurance product 0 Standard deviation, sigma, representing the total disease incidence of the L severe disease groupings 1 And represents the standard deviation of the total disease incidence of the M severe disease groupings.
Optionally, the computer program when executed by the processor 301 is further configured to:
calculating multiple odds and odds of the second insurance product according to the following formula;
wherein M is N Representing multiple odds of the second insurance product, N representing the number of serious diseases grouped by the second insurance product, P n Representing the total probability of n times of illness, p i Indicating the total disease incidence, p, of the ith severe disease group j Representing the total disease incidence of the jth heavy disease grouping, wherein the second insurance product is any insurance product in the K insurance products, and the heavy disease grouping information of the second insurance product comprises N heavy disease groupings and N heavy disease groupingsThe heavy diseases included in each heavy disease packet.
Optionally, the computer program when executed by the processor 301 is further configured to:
Before determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products, the evaluation value of the target dimension of each of the K insurance products is obtained, wherein the target dimension comprises at least one of the following: the number of disclaimers dimension, the waiting period claim-free dimension, the light symptom exemption dimension, the statue guarantee dimension and the renewal dimension are guaranteed;
accordingly, the computer program when executed by the processor 301 is further configured to:
calculating the product evaluation value of each of the K insurance products according to the serious disease grouping quality evaluation value of each of the K insurance products and the evaluation value of the target dimension;
and calculating the recommended value of each of the K insurance products according to the product evaluation value and the multiple odds ratio of each of the K insurance products.
Optionally, the computer program when executed by the processor 301 is further configured to:
respectively acquiring the insurance amount and the insurance fee of each insurance product in the K insurance products;
And calculating the recommended value of each of the K insurance products according to the insurance amount, the premium, the product evaluation value and the multiple odds ratio of each of the K insurance products.
Optionally, the computer program when executed by the processor 301 is further configured to:
acquiring a labeling grade value of a target dimension of each insurance product in the K insurance products;
according to the labeling grade value of the target dimension of each of the K insurance products, calculating the percentage grade of the target dimension of each of the K insurance products respectively;
and calculating the evaluation value of the target dimension of each of the K insurance products according to the percentage grade of the target dimension of each of the K insurance products.
The embodiment of the invention also provides an insurance product recommending device, which comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program realizes all the processes of the insurance product recommending method embodiment when being executed by the processor, and can achieve the same technical effects, and the repetition is avoided, so that the description is omitted.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the processes of the above embodiment of the insurance product recommendation method, and can achieve the same technical effects, and in order to avoid repetition, the description is omitted here. Wherein the computer readable storage medium is selected from Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (10)
1. A method of recommending insurance products, comprising:
obtaining the serious disease grouping information of each insurance product in K insurance products, wherein K is a positive integer;
the method comprises the steps of respectively carrying out heavy disease grouping quality evaluation according to heavy disease grouping information of each of K insurance products to obtain heavy disease grouping quality evaluation values of each of the K insurance products, wherein the heavy disease grouping quality evaluation values of the insurance products are determined based on standard deviations of total disease occurrence rates of all heavy disease groupings of the insurance products, and the total disease occurrence rates of all heavy disease groupings of the insurance products are obtained based on heavy disease grouping information statistics of the insurance products;
calculating multiple odds and advantages of each insurance product in the K insurance products according to the severe disease grouping information of each insurance product in the K insurance products, wherein the multiple odds and advantages of each insurance product are determined according to the severe disease grouping number of the insurance product and the total disease occurrence rate of each severe disease grouping;
determining a recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products;
And recommending the insurance products according to the recommended value of each insurance product in the K insurance products.
2. The method of claim 1, wherein the performing the heavy disease grouping quality evaluation according to the heavy disease grouping information of each of the K insurance products to obtain the heavy disease grouping quality evaluation value of each of the K insurance products includes:
calculating the standard deviation of the total disease incidence of the L heavy disease groups according to the total disease incidence of each heavy disease group in the L heavy disease groups of a first insurance product, wherein the first insurance product is any insurance product in the K insurance products, the heavy disease group information of the first insurance product comprises the L heavy disease groups and heavy diseases contained in each heavy disease group in the L heavy disease groups, and L is an integer greater than 1;
dividing all the heavy diseases included in the L heavy disease groups into M groups according to the occurrence rate of each heavy disease included in the L heavy disease groups to obtain M heavy disease groups, wherein M is equal to L, and the heavy disease with the occurrence rate of M in the L heavy disease groups is located in different heavy disease groups in the M heavy disease groups;
Calculating the standard deviation of the total disease occurrence rate of the M severe disease groups according to the total disease occurrence rate of each of the M severe disease groups;
and calculating the quality evaluation value of the heavy disease group of the first insurance product according to the standard deviation of the total disease occurrence rate of the L heavy disease groups and the standard deviation of the total disease occurrence rate of the M heavy disease groups.
3. The method according to claim 2, wherein calculating the critical illness packet quality evaluation value of the first insurance product according to the standard deviation of the total disease occurrence rate of the L critical illness packets and the standard deviation of the total disease occurrence rate of the M critical illness packets includes:
using the formula q=100×σ 1 /σ 0 Calculating the serious disease grouping quality evaluation value of the first insurance product;
wherein Q represents the critical group quality evaluation value, sigma, of the first insurance product 0 Standard deviation, sigma, representing the total disease incidence of the L severe disease groupings 1 And represents the standard deviation of the total disease incidence of the M severe disease groupings.
4. The method of claim 1, wherein calculating a multiple odds ratio for each of the K insurance products based on the heavy disease grouping information for each of the K insurance products, respectively, comprises:
Calculating multiple odds and odds of the second insurance product according to the following formula;
wherein M is N Representing multiple odds of the second insurance product, N representing the number of serious diseases grouped by the second insurance product, P n Representing the total probability of n times of illness, p i Indicating the total disease incidence, p, of the ith severe disease group j And indicating the total disease incidence of the j-th heavy disease grouping, wherein the second insurance product is any insurance product in the K insurance products, and the heavy disease grouping information of the second insurance product comprises N heavy disease groupings and heavy diseases included in each heavy disease grouping in the N heavy disease groupings.
5. The method of claim 1, wherein before determining the recommended value for each of the K insurance products based on the heavy disease group quality evaluation value and the multiple odds ratio for each of the K insurance products, respectively, the method further comprises:
obtaining an evaluation value of a target dimension of each of the K insurance products, wherein the target dimension comprises at least one of the following: the number of disclaimers dimension, the waiting period claim-free dimension, the light symptom exemption dimension, the statue guarantee dimension and the renewal dimension are guaranteed;
The determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds ratio of each of the K insurance products includes:
calculating the product evaluation value of each of the K insurance products according to the serious disease grouping quality evaluation value of each of the K insurance products and the evaluation value of the target dimension;
and calculating the recommended value of each of the K insurance products according to the product evaluation value and the multiple odds ratio of each of the K insurance products.
6. The method of claim 5, wherein calculating the recommended value for each of the K insurance products based on the product evaluation value and the multiple odds ratio for each of the K insurance products, respectively, comprises:
respectively acquiring the insurance amount and the insurance fee of each insurance product in the K insurance products;
and calculating the recommended value of each of the K insurance products according to the insurance amount, the premium, the product evaluation value and the multiple odds ratio of each of the K insurance products.
7. The method of claim 5, wherein the obtaining an evaluation value of the target dimension for each of the K insurance products comprises:
acquiring a labeling grade value of a target dimension of each insurance product in the K insurance products;
according to the labeling grade value of the target dimension of each of the K insurance products, calculating the percentage grade of the target dimension of each of the K insurance products respectively;
and calculating the evaluation value of the target dimension of each of the K insurance products according to the percentage grade of the target dimension of each of the K insurance products.
8. An insurance product recommendation device, comprising:
the first acquisition module is used for acquiring the serious disease grouping information of each insurance product in K insurance products, wherein K is a positive integer;
the first evaluation module is used for evaluating the quality of the heavy-disease grouping according to the heavy-disease grouping information of each of the K insurance products respectively to obtain the quality evaluation value of the heavy-disease grouping of each of the K insurance products, wherein the quality evaluation value of the heavy-disease grouping of the insurance products is determined based on the standard deviation of the total disease occurrence rate of all the heavy-disease groupings of the insurance products, and the total disease occurrence rate of all the heavy-disease groupings of the insurance products is obtained based on the heavy-disease grouping information statistics of the insurance products;
The calculating module is used for calculating the multiple odds and advantages of each insurance product in the K insurance products according to the severe disease grouping information of each insurance product in the K insurance products, and the multiple odds and advantages of each insurance product are determined according to the severe disease grouping number of the insurance product and the total disease occurrence rate of each severe disease grouping;
the determining module is used for determining the recommended value of each of the K insurance products according to the serious disease grouping quality evaluation value and the multiple odds multiplying power of each of the K insurance products;
and the recommending module is used for recommending the insurance products according to the recommending value of each insurance product in the K insurance products.
9. An insurance product recommendation device comprising a processor, a memory and a computer program stored on said memory and executable on said processor, said computer program implementing the steps of the insurance product recommendation method according to any of claims 1 to 7 when executed by said processor.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the insurance product recommendation method according to any of claims 1 to 7.
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