Insurance customer risk assessment system and assessment method

Abstract

The application discloses an insurance customer risk assessment system, which comprises a list module, a collection module and an operation module.The list module is used for displaying a plurality of relevant suitable insurance products in the form of a table to an insurance customer; the collection module is used for collecting information data corresponding to each insurance product, wherein the information data comprises an insurance product validity time limit parameter, an insurance product risk level parameter and an insurance product applicable field parameter, and the collection module is used for statistically collecting all information data sets as R; the operation module is used for operating element data in the set R through a regression function f(x) to obtain a plurality of regression parameters f(x1), f(x2),..., f(x n ); and the evaluation module is used for judging a real risk condition of each insurance product according to specific values of the regression parameters f(x1), f(x2),..., f(x n ).

Description

TECHNICAL FIELD

[0001] The present application relates to an insurance customer risk assessment system and an assessment method. BACKGROUND

[0002] Before purchasing an insurance product, an insurance customer needs to assess the product risk corresponding to the insurance product to determine whether the insurance product is suitable for the actual situation of the insurance customer; generally, the risk status of the insurance product is informed by an insurance practitioner, and the assessment is performed manually, lacking corresponding data for quantitative analysis, and the obtained criterion is insufficient, so that a precise risk assessment effect cannot be obtained; meanwhile, the timeliness of communication and interaction between the insurance customer and the insurance practitioner is poor, and useful information data cannot be transmitted to the client device of the insurance user in time. SUMMARY

[0003] The embodiments of the present application provide an insurance customer risk assessment system and an assessment method, the structure and method design are reasonable, based on the mutual cooperation of multiple function modules, the multiple different types of insurance products can be respectively detected and assessed before the insurance customer purchases the insurance product, the real risk status of each insurance product can be accurately obtained through quantitative processing, so that the insurance customer can select the insurance product with the minimum risk; meanwhile, the insurance practitioner can timely and quickly communicate and interact with the insurance customer, and timely transmit useful information data to the client device of the insurance user, so as to facilitate the insurance customer to select the most suitable insurance product, and solve the problems existing in the prior art.

[0004] The technical scheme adopted by the present application to solve the above technical problems is:

[0005] The insurance customer risk assessment system comprises:

[0006] A list module is configured to display multiple relevant suitable insurance products to the insurance customer in the form of a table;

[0007] A collection module is configured to collect information data corresponding to each insurance product, wherein the information data comprises an insurance product validity time limit parameter, an insurance product risk level parameter and an insurance product application field parameter, and the collection of all information data is counted as R;

[0008] An operation module is configured to operate the element data in the set R through a regression function f(x) to obtain multiple regression parameters f(x1), f(x2), …, f(xn). n

[0009] The regression function f(x) is:

[0010] ​

[0011] wherein, μ is a regression parameter, and the specific value is in the interval (π, 10), σ is a calibration parameter, and the specific value is in the interval (e, e 2 );

[0012] The evaluation module is configured to determine the real risk status of each insurance product according to the specific value of the regression parameter f(x1), f(x2), …, f(x n );

[0013] The clock module is configured to set time nodes for the interaction between the insurance customers and the insurance practitioners at equal time intervals, so as to facilitate the timely and effective communication between the insurance customers and the insurance practitioners before the purchase of the insurance products.

[0014] The feedback module is configured to feed back the real risk status of each insurance product to the insurance customers according to the time nodes, so that the insurance customers can select the insurance product with the minimum risk status.

[0015] The collection module comprises:

[0016] The marking module is configured to set a marking check code for each of the insurance product validity time parameter, the insurance product risk level parameter and the insurance product applicable field parameter.

[0017] The classification module is configured to separately collect three subsets R1, R2 and R3 about the insurance product validity time parameter, the insurance product risk level parameter and the insurance product applicable field parameter according to the different marking check codes.

[0018] The summary module is configured to sequentially summarize the three subsets R1, R2 and R3 according to the data, so as to obtain a set R about the insurance product information data.

[0019] The operation module comprises:

[0020] The modeling module is configured to establish a regression function f(x).

[0021] The sampling module is configured to determine a specific sampling amount n in the set R according to the actual demand of the insurance customers.

[0022] The execution module is configured to calculate n regression parameters according to the regression function f(x) and the specific sampling amount n.

[0023] The evaluation module comprises:

[0024] The median module is configured to calculate the regression parameters f(x1), f(x2), …, f(x nThe average value θ;

[0025] The determination module is used to determine whether the average value θ is within the normal parameter range (lnπ, 2lnπ). When the average value θ is within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively small. When the average value θ is not within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively large.

[0026] Insurance customer risk assessment method, the assessment method includes the following steps:

[0027] S1 displays multiple relevant and suitable insurance products to insurance customers in a table format;

[0028] S2, Collect information data corresponding to each insurance product. The information data includes the insurance product's effective period parameter, the insurance product's risk level parameter, and the insurance product's applicable field parameter, and statistically summarize all the information data into R.

[0029] S3, by performing operations on the element data in set R using the regression function f(x), multiple regression parameters f(x1), f(x2), ..., f(x3) are obtained. n );

[0030] S4, based on the regression parameters f(x1), f(x2), ..., f(x... n The specific values ​​are used to determine the true risk profile of each insurance product;

[0031] S5 sets multiple time points for interaction between insurance customers and insurance professionals at equal intervals to facilitate timely and effective communication between insurance customers and insurance professionals before purchasing insurance products.

[0032] S6 transmits the true risk status of each insurance product to the insurance customer according to the time nodes, so that the insurance customer can select the insurance product with the lowest risk.

[0033] Collect information data corresponding to each insurance product, including parameters such as the effective period of the insurance product, the risk level of the insurance product, and the applicable field of the insurance product. Then, statistically analyze all the information data into a set R, including the following steps:

[0034] S2.1, each of the parameters for the effective period of the insurance product, the risk level of the insurance product, and the applicable field of the insurance product is marked with a verification code;

[0035] S2.2, based on the different marker check codes, three subsets R1, R2 and R3 are collected separately for parameters related to the effective period of insurance products, risk level of insurance products and applicable fields of insurance products;

[0036] S2.3, summarize the three subsets R1, R2 and R3 according to the data in sequence to obtain the set R of insurance product information data.

[0037] By performing a regression function f(x) on the element data in set R, multiple regression parameters f(x1), f(x2), ..., f(x3) are obtained. n This includes the following steps:

[0038] S3.1, Establish the regression function f(x);

[0039] S3.2, Determine the specific sampling quantity n in set R based on the actual needs of insurance customers;

[0040] S3.3, n regression parameters are calculated based on the regression function f(x) and the specific sample size n.

[0041] Based on the regression parameters f(x1), f(x2), ..., f(x) n To determine the true risk profile of each insurance product using specific numerical values, the following steps are involved:

[0042] S4.1, Calculate the regression parameters f(x1), f(x2), ..., f(x) n The average value θ;

[0043] S4.2 Determine whether the average value θ is within the normal parameter range (lnπ, 2lnπ). When the average value θ is within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively small. When the average value θ is not within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively large.

[0044] This invention employs the aforementioned structure, using a list module to display multiple relevant and suitable insurance products to insurance customers; a collection module to gather information data corresponding to each insurance product and compile it into a set R; a calculation module to perform calculations on the elements in set R to obtain multiple regression parameters; an evaluation module to determine the true risk status of each insurance product; a clock module to set multiple time points for interaction between insurance customers and insurance professionals; and a feedback module to transmit the true risk status of each insurance product back to insurance customers according to the time points. This invention boasts advantages of accuracy, efficiency, simplicity, and practicality. Attached image description:

[0045] Figure 1 This is a schematic diagram of the structure of the present invention.

[0046] Figure 2 This is a schematic diagram of the collection module of the present invention.

[0047] Figure 3 This is a schematic diagram of the structure of the computing module of the present invention.

[0048] Figure 4 This is a schematic diagram of the evaluation module of the present invention.

[0049] Figure 5 This is a schematic diagram of the process of the present invention. Detailed implementation method:

[0050] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0051] like Figures 1-5 As shown, the insurance customer risk assessment system includes:

[0052] A list module, which is used to display multiple relevant and suitable insurance products to insurance customers in a tabular format;

[0053] The collection module is used to collect information data corresponding to each insurance product. The information data includes the insurance product's effective period parameter, the insurance product's risk level parameter, and the insurance product's applicable field parameter. The collection of all information data is statistically summarized as R.

[0054] The computation module is used to perform calculations on the element data in set R using a regression function f(x) to obtain multiple regression parameters f(x1), f(x2), ..., f(x3). n ),

[0055] The regression function f(x) is:

[0056]

[0057] Where μ is the regression parameter, with a specific value in the interval (π, 10), and σ is the calibration parameter, with a specific value in the interval (e, e). 2 );

[0058] The evaluation module is used to evaluate the regression parameters f(x1), f(x2), ..., f(x) based on the regression parameters f(x1), f(x2), ..., f(x). n The specific values ​​are used to determine the true risk profile of each insurance product;

[0059] The clock module is used to set multiple time points for interaction between insurance customers and insurance practitioners at equal time intervals, so as to facilitate timely and effective communication between insurance customers and insurance practitioners before purchasing insurance products;

[0060] The feedback module is used to transmit the actual risk status of each insurance product to the insurance customer according to the time nodes, so that the insurance customer can select the insurance product with the lowest risk.

[0061] The collection module includes:

[0062] The marking module is used to set marking verification codes for the insurance product's validity period parameter, insurance product risk level parameter, and insurance product applicable field parameter, respectively.

[0063] The classification module is used to collect three subsets R1, R2 and R3 of the insurance product's effective period parameter, insurance product risk level parameter and insurance product applicable field parameter separately according to the different marker check codes;

[0064] The aggregation module is used to aggregate the three subsets R1, R2 and R3 in sequence according to the data to obtain a set R of insurance product information data.

[0065] The computing module includes:

[0066] A modeling module, which is used to establish a regression function f(x);

[0067] The sampling module is used to determine the specific sampling quantity n in the set R according to the actual needs of the insurance customers;

[0068] The execution module is used to calculate n regression parameters based on the regression function f(x) and the specific sample size n.

[0069] The evaluation module includes:

[0070] The median module is used to calculate the regression parameters f(x1), f(x2), ..., f(x). n The average value θ;

[0071] The determination module is used to determine whether the average value θ is within the normal parameter range (lnπ, 2lnπ). When the average value θ is within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively small. When the average value θ is not within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively large.

[0072] Insurance customer risk assessment method, the assessment method includes the following steps:

[0073] S1 displays multiple relevant and suitable insurance products to insurance customers in a table format;

[0074] S2, Collect information data corresponding to each insurance product. The information data includes the insurance product's effective period parameter, the insurance product's risk level parameter, and the insurance product's applicable field parameter, and statistically summarize all the information data into R.

[0075] S3, by performing operations on the element data in set R using the regression function f(x), multiple regression parameters f(x1), f(x2), ..., f(x3) are obtained. n );

[0076] S4, based on the regression parameters f(x1), f(x2), ..., f(x... n The specific values ​​are used to determine the true risk profile of each insurance product;

[0077] S5 sets multiple time points for interaction between insurance customers and insurance professionals at equal intervals to facilitate timely and effective communication between insurance customers and insurance professionals before purchasing insurance products.

[0078] S6 transmits the true risk status of each insurance product to the insurance customer according to the time nodes, so that the insurance customer can select the insurance product with the lowest risk.

[0079] Collect information data corresponding to each insurance product, including parameters such as the effective period of the insurance product, the risk level of the insurance product, and the applicable field of the insurance product. Then, statistically analyze all the information data into a set R, including the following steps:

[0080] S2.1, each of the parameters for the effective period of the insurance product, the risk level of the insurance product, and the applicable field of the insurance product is marked with a verification code;

[0081] S2.2, based on the different marker check codes, three subsets R1, R2 and R3 are collected separately for parameters related to the effective period of insurance products, risk level of insurance products and applicable fields of insurance products;

[0082] S2.3, summarize the three subsets R1, R2 and R3 according to the data in sequence to obtain the set R of insurance product information data.

[0083] By performing a regression function f(x) on the element data in set R, multiple regression parameters f(x1), f(x2), ..., f(x3) are obtained. n This includes the following steps:

[0084] S3.1, Establish the regression function f(x);

[0085] S3.2, Determine the specific sampling quantity n in set R based on the actual needs of insurance customers;

[0086] S3.3, n regression parameters are calculated based on the regression function f(x) and the specific sample size n.

[0087] Based on the regression parameters f(x1), f(x2), ..., f(x) n To determine the true risk profile of each insurance product using specific numerical values, the following steps are involved:

[0088] S4.1, Calculate the regression parameters f(x1), f(x2), ..., f(x) n The average value θ;

[0089] S4.2 Determine whether the average value θ is within the normal parameter range (lnπ, 2lnπ). When the average value θ is within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively small. When the average value θ is not within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively large.

[0090] The working principle of the insurance customer risk assessment system and method in this embodiment of the invention is as follows: based on the cooperation of multiple functional modules, it can detect and assess multiple different types of insurance products before the insurance customer purchases insurance products, quantify and process them, and accurately obtain the true risk status of each insurance product, so that the insurance customer can select the insurance product with the lowest risk; at the same time, insurance practitioners can communicate and interact with insurance customers in a timely manner, and transmit useful information data to the insurance user's client device in a timely manner, so as to facilitate the insurance customer to select the most suitable insurance product.

[0091] The overall solution mainly includes a list module, which displays multiple relevant and suitable insurance products to insurance customers in tabular form; a collection module, which collects information data corresponding to each insurance product, including parameters such as the product's validity period, risk level, and applicable field, and summarizes all the information data into a set R; and a calculation module, which performs calculations on the elements in the set R using a regression function f(x) to obtain multiple regression parameters f(x1), f(x2), ..., f(x...). n The evaluation module is used to evaluate the regression parameters f(x1), f(x2), ..., f(x) based on the regression parameters. n The specific values ​​are used to determine the true risk status of each insurance product; the clock module is used to set multiple time points for interaction between insurance customers and insurance practitioners at equal time intervals, so as to facilitate timely and effective communication between insurance customers and insurance practitioners before purchasing insurance products.

[0092] The system obtains the true risk profile of each insurance product and transmits it to insurance customers according to pre-defined timeframes, enabling them to select the insurance product with the lowest risk profile.

[0093] Generally, insurance professionals present 4 to 8 insurance products to insurance clients. Too many or too few insurance products to choose from will not provide the most suitable insurance products for the clients.

[0094] Preferably, the collection module includes: a marking module, used to set marking verification codes for the insurance product's validity period parameter, insurance product risk level parameter, and insurance product applicable field parameter respectively; a classification module, used to collect three subsets R1, R2, and R3 related to the insurance product's validity period parameter, insurance product risk level parameter, and insurance product applicable field parameter separately according to the different marking verification codes; and a summarizing module, used to summarize the three subsets R1, R2, and R3 according to the data in sequence to obtain a set R of information data about the insurance product, thereby obtaining a complete and comprehensive set of data, which can prevent the omission of information data while subdividing the categories.

[0095] Preferably, the calculation module includes: a modeling module for establishing a regression function f(x); a sampling module for determining the specific sample size n in the set R according to the actual needs of insurance customers; and an execution module for calculating n regression parameters based on the regression function f(x) and the specific sample size n. In actual use, the sample size should be set according to the actual application scenario, and the value of n will not exceed 5 under normal circumstances, simplifying the calculation steps and reducing operational errors.

[0096] Preferably, the evaluation module includes: a median module, used to calculate the regression parameters f(x1), f(x2), ... f(x... n The average value θ of the insurance product is used to determine whether the average value θ is within the normal parameter range (lnπ, 2lnπ). When the average value θ is within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively small. When the average value θ is not within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively large. The actual risk of each insurance product is quantitatively compared based on the degree to which the average value θ deviates from the normal parameter range.

[0097] The risk assessment method for insurance customers mainly includes the following steps: S1, displaying multiple suitable insurance products to the insurance customer in tabular form; S2, collecting information data corresponding to each insurance product, including parameters such as the effective period of the insurance product, the risk level of the insurance product, and the applicable field of the insurance product, and statistically summing all the information data into a set R; S3, performing calculations on the element data in the set R using a regression function f(x) to obtain multiple regression parameters f(x1), f(x2)...f(x... nS4, based on the regression parameters f(x1), f(x2)...f(x) n The system uses specific values ​​to determine the true risk status of each insurance product; S5 sets multiple time points for interaction between insurance customers and insurance professionals at equal intervals to facilitate timely and effective communication between them before purchasing insurance products; S6 transmits the true risk status of each insurance product to the insurance customer according to the time points so that the insurance customer can select the insurance product with the lowest risk. Based on the cooperation of multiple functional modules, the above steps are executed to select the most suitable and lowest-risk insurance product from multiple insurance products.

[0098] Defining the true risk profile of each insurance product with accurate quantitative data, rather than relying solely on the experience and assessments of insurance professionals, makes the assessment results more accurate.

[0099] It should be noted that due to the different sizes of insurance companies, there may be slight discrepancies between insurance products. In order to eliminate these slight discrepancies, multiple assessments are required to ensure the reliability of the assessment. At the same time, insurance professionals also have an obligation to remind insurance customers to avoid risks and protect their interests.

[0100] In summary, the insurance customer risk assessment system and method in this embodiment of the invention, based on the synergistic effect of multiple functional modules, can detect and assess multiple different types of insurance products before insurance customers purchase them, quantify and accurately obtain the true risk status of each insurance product, thereby enabling insurance customers to select the insurance product with the lowest risk. At the same time, insurance professionals can communicate and interact with insurance customers in a timely manner, transmitting useful information data to the insurance user's client device, facilitating the selection of the most suitable insurance product for the insurance customer.

[0101] The above specific embodiments should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, any alternative improvements or modifications made to the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0102] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. An insurance customer risk assessment system, characterized in that, The risk assessment system includes: A list module, which is used to display multiple relevant and suitable insurance products to insurance customers in a tabular format; The collection module is used to collect information data corresponding to each insurance product. The information data includes the insurance product's effective period parameter, the insurance product's risk level parameter, and the insurance product's applicable field parameter. The collection of all information data is statistically summarized as R. The computation module is used to perform calculations on the element data in set R using a regression function f(x) to obtain multiple regression parameters f(x1), f(x2), ..., f(x3). n ), The regression function f(x) is: Where μ is the regression parameter, with a specific value in the interval (π, 10), and σ is the calibration parameter, with a specific value in the interval (e, e). 2 ); The evaluation module is used to evaluate the regression parameters f(x1), f(x2), ..., f(x) based on the regression parameters f(x1), f(x2), ..., f(x). n The specific values ​​are used to determine the true risk profile of each insurance product; The clock module is used to set multiple time points for interaction between insurance customers and insurance practitioners at equal time intervals, so as to facilitate timely and effective communication between insurance customers and insurance practitioners before purchasing insurance products; The feedback module is used to transmit the actual risk status of each insurance product to the insurance customer according to the time nodes, so that the insurance customer can select the insurance product with the lowest risk. The evaluation module includes: The median module is used to calculate the regression parameters f(x1), f(x2), ..., f(x). n The average value θ; The determination module is used to determine whether the average value θ is within the normal parameter range (lnπ, 2lnπ). When the average value θ is within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively small. When the average value θ is not within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively large. The collection module includes: The marking module is used to set marking verification codes for the insurance product's validity period parameter, insurance product risk level parameter, and insurance product applicable field parameter, respectively. The classification module is used to collect three subsets R1, R2 and R3 of the insurance product's effective period parameter, insurance product risk level parameter and insurance product applicable field parameter separately according to the different marker check codes; The aggregation module is used to aggregate the three subsets R1, R2 and R3 in sequence according to the data to obtain a set R of insurance product information data.

2. The insurance customer risk assessment system according to claim 1, characterized in that, The computing module includes: A modeling module, which is used to establish a regression function f(x); The sampling module is used to determine the specific sampling quantity n in the set R according to the actual needs of the insurance customers; The execution module is used to calculate n regression parameters based on the regression function f(x) and the specific sample size n.

3. A risk assessment method for insurance clients, characterized in that, The insurance customer risk assessment system according to claim 1 or 2, wherein the assessment method includes the following steps: S1 displays multiple relevant and suitable insurance products to insurance customers in a table format; S2, Collect information data corresponding to each insurance product. The information data includes the insurance product's effective period parameter, the insurance product's risk level parameter, and the insurance product's applicable field parameter, and statistically summarize all the information data into R. S3, by performing operations on the element data in set R using the regression function f(x), multiple regression parameters f(x1), f(x2), ..., f(x3) are obtained. n ); S4, based on the regression parameters f(x1), f(x2), ..., f(x... n The specific values ​​are used to determine the true risk profile of each insurance product; S5 sets multiple time points for interaction between insurance customers and insurance professionals at equal intervals to facilitate timely and effective communication between insurance customers and insurance professionals before purchasing insurance products. S6 transmits the actual risk status of each insurance product to the insurance customer according to the time nodes, so that the insurance customer can select the insurance product with the lowest risk. By performing a regression function f(x) on the element data in set R, multiple regression parameters f(x1), f(x2), ..., f(x3) are obtained. n This includes the following steps: S3.1, Establish the regression function f(x); S3.2, Determine the specific sampling quantity n in set R based on the actual needs of insurance customers; S3.3, n regression parameters are calculated based on the regression function f(x) and the specific sample size n.

4. The insurance customer risk assessment method according to claim 3, characterized in that, Based on the regression parameters f(x1), f(x2), ..., f(x) n To determine the true risk profile of each insurance product using specific numerical values, the following steps are involved: S4.1, Calculate the regression parameters f(x1), f(x2), ..., f(x) n The average value θ; S4.2 Determine whether the average value θ is within the normal parameter range (lnπ, 2lnπ). When the average value θ is within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively small. When the average value θ is not within the normal parameter range (lnπ, 2lnπ), it indicates that the actual risk of the insurance product is relatively large.

5. The insurance customer risk assessment method according to claim 3, characterized in that, Collect information data corresponding to each insurance product, including parameters such as the effective period of the insurance product, the risk level of the insurance product, and the applicable field of the insurance product. Then, statistically analyze all the information data into a set R, including the following steps: S2.1, each of the parameters for the effective period of the insurance product, the risk level of the insurance product, and the applicable field of the insurance product is marked with a verification code; S2.2, based on the different marker check codes, three subsets R1, R2 and R3 are collected separately for parameters related to the effective period of insurance products, risk level of insurance products and applicable fields of insurance products; S2.3, summarize the three subsets R1, R2 and R3 according to the data in sequence to obtain the set R of insurance product information data.

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