Method for evaluating risk of sales behavior in insurance industry

Abstract

The invention provides a method for evaluating the risk of sales behaviors in the insurance industry. The method comprises the following steps: S1, collecting data indexes; s2, performing feature selection processing on the collected data indexes; s3, constructing a risk assessment model; s4, evaluating a sales behavior risk; according to the method for evaluating the risk of the sales behavior in the insurance industry, data indexes over the years are collected, the average change rate of the data indexes is calculated, the risk evaluation model is constructed based on the average change rate, and a user inputs the years into the risk evaluation model to evaluate the risk of the sales behavior in the insurance industry. The risk assessment model performs risk judgment on the insurance premium amount and the claim settlement amount of the user; according to the method, the risk assessment model can be dynamically updated according to years, the problem that the risk assessment result is inaccurate due to different economic cycles and regional characteristics is solved, and the risk assessment result has higher timeliness and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for evaluating sales behavior risks in the insurance industry. Background Art

[0002] At present, domestic insurance companies have generally established a sales behavior evaluation system, which includes an algorithm model. By inputting the collected relevant indicator data of sales behavior into the algorithm model, an evaluation result is given on whether the sales personnel or sales behavior is compliant; among them, claims, insurance premiums, and customer income are important indicator data and have a greater impact on the final evaluation results.

[0003] On the one hand, with the rapid development of the national economy and the increase in people's income, claims and insured income have shown an increasing trend year by year over time, which has a significant impact on model results. This can cause significant deviations in the results given by the algorithm model, making the entire evaluation system unusable in the long term. Continuous model training is required, which is very costly.

[0004] For example, in 2000, the per capita disposable income of Chinese households was 9,279 yuan. If the insurance premium was 10,000 yuan, which was higher than the per capita income at the time, the evaluation system's algorithm would indicate that the premium was high-risk. If the system were used continuously until 2023, when per capita income increased to 39,218 yuan, the evaluation system's algorithm would continue to indicate that the premium was high-risk, which is no longer realistic.

[0005] On the other hand, there is a large income gap between regions. In 2023, the per capita income in the southeastern coastal areas was 76,805 yuan, and the per capita income in the central and western regions was 39,218 yuan. According to statistics, the income in the southeastern coastal areas is higher than that in the central and western regions. Based on the national per capita income level, the algorithm model of the evaluation system will give a high-risk result for sales behavior in the economically developed southeastern coastal areas. For sales behaviors with actual higher risks in the central and western regions, the algorithm model of the evaluation system will give a normal result that the sales behavior is compliant, making the evaluation system unable to be applied to the national unified version. Summary of the Invention

[0006] The purpose of the present invention is to address the shortcomings of the prior art and to propose a method for assessing the risks of sales behavior in the insurance industry.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for assessing sales behavior risks in the insurance industry, comprising the following steps:

[0009] S1: collect data indicators;

[0010] Based on public information from institutions such as the National Bureau of Statistics, the National Health Commission, and the China Insurance Association, relevant data indicators such as GDP, consumer price index, inflation rate, and rate of change in medical costs for each year were collected;

[0011] Specifically, the GDP of the developed southeastern coastal areas in each year is recorded as HGDP, and the GDP of the central and western regions is recorded as LGDP;

[0012] The consumer price index of the southeast coastal developed areas in each year is recorded as HCPI, and the consumer price index of the central and western regions is recorded as LCPI;

[0013] The inflation rate of the developed southeast coastal areas in each year is recorded as HINF, and the inflation rate of the central and western regions is recorded as LINF;

[0014] The change rate of medical costs in the developed southeast coastal areas in each year is recorded as

[0015] The rate of change in medical costs in the HMCC and Midwest regions is recorded as LMCC.

[0016] S2: Perform feature selection processing on the collected data indicators;

[0017] The following sub-steps are included:

[0018] S21: Calculate data indicators based on different regional values;

[0019] Let the region value be V, where the region value V of the developed southeast coastal area is 0, and the region value V of the central and western regions is 1;

[0020] The gross domestic product (GDP), consumer price index (CPI), inflation rate (INF), and medical cost change rate (MCC) for each year are calculated based on the regional values. The specific calculation method is as follows:

[0021] Gross domestic product (GDP) = HGDP × (1-V) + LGDP × (V);

[0022] Consumer Price Index (CPI) = HCPI × (1-V) + LCPI × (V);

[0023] Inflation rate INF = HINF × (1-V) + LINF × (V);

[0024] Medical cost change rate MCC = HMCC × (1-V) + LMCC × (V);

[0025] S22: Calculate the change rate of data indicators in each year in turn;

[0026] The rate of change of GDP in each year is recorded as RGDP i , the rate of change of the Consumer Price Index in each year is recorded as RCPI i The rate of change of inflation in each year is recorded as RINF i The rate of change of medical costs in each year is recorded as RMCC i ; where i represents the i-th year; the specific calculation method is as follows:

[0027] RGDP i =(GDP i -GDP i-1 ) / GDP i-1 ;

[0028] RCPI i =(CPI i -CPI i-1 ) / GDP i-1 ;

[0029] RINF i =(INF i -INF i-1 ) / INF i-1 ;

[0030] RMCC i =(MCC i -MCC i-1 ) / MCC i-1 ;

[0031] S23: Calculate the average rate of change of data indicators;

[0032] Based on the change rates of the data indicators in each year obtained in step S22, the average change rate of the data indicators is calculated according to the following formula:

[0033] The average rate of change of gross domestic product (GDP) is AGDP = ∑(1 + RGDP i ) / n-1;

[0034] The average rate of change of the consumer price index ACPI = ∑(1 + RCPI i ) / n-1;

[0035] Average rate of change of inflation rate AINF=∑(1+RINF i ) / n-1;

[0036] Average rate of change of medical cost AMCC=∑(1+RMCC i ) / n-1.

[0037] S3: Build a risk assessment model;

[0038] The risk assessment model includes a premium model and a claim model;

[0039] The following sub-steps are included:

[0040] S31: Constructing premium model;

[0041] Based on the correlation between premiums and GDP, consumer price index, and inflation rate, the premium model is constructed using the following formula;

[0042] FV1=PV1×(1+AGDP) n ×(1+ACPI) n ×(1+AINF) n ;

[0043] Wherein, FV1 is the premium amount assessed in the current year; PV1 is the premium amount in the initial year, which refers to the first year in the data indicators collected in step S1; n is the difference between the year corresponding to PV1 and the year corresponding to FV1;

[0044] S32: Build a claims model;

[0045] Based on the correlation between claim amounts and inflation rates and medical cost change rates, a claim model is constructed using the following formula;

[0046] FV2=PV2×(1+AINF) n ×(1+AMCC) n ;

[0047] Among them, FV2 is the claim amount assessed in the current year; PV2 is the claim amount in the initial year, and the initial year refers to the first year in the data indicators collected in step S1; n is the difference between the year corresponding to PV2 and the year corresponding to FV2.

[0048] S4: Assess sales behavior risks;

[0049] The risk assessment model evaluates the user's sales behavior. If the user's premium amount and corresponding year are known, the process proceeds to step S41; if the year and claim amount are known, the process proceeds to step S42.

[0050] S41: Assess whether the premium amount is at risk;

[0051] The corresponding year is input into the premium model, and the premium model outputs the value of FV1, which is the premium amount assessed for the current year;

[0052] Compare the obtained FV1 value with the known user premium amount. If the FV1 value is greater than or equal to the known user premium amount, the premium model will give a result that the sales behavior is compliant; if the FV1 value is less than the known user premium amount, the premium model will give a result that the sales behavior is high-risk.

[0053] S42: Assess whether there is risk in the claim amount;

[0054] The current year is input into the claims model, and the claims model outputs the value of FV2, that is, the claims amount assessed in the current year. If the FV2 value is greater than or equal to the known user claims amount, the premium model gives a result that the sales behavior is compliant; if the FV1 value is less than the known user claims amount, the premium model gives a result that the sales behavior is high-risk.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention proposes a method for assessing sales behavior risks in the insurance industry. This method collects historical data indicators, calculates the average rate of change of these indicators, and constructs a risk assessment model based on the average rate of change. Users input the year into the risk assessment model, which then assesses the risk of the user's premium and claim amounts. The risk assessment model constructed by the present invention incorporates relevant data indicators such as the gross domestic product, consumer price index, inflation rate, and rate of change of medical costs for each year, ensuring that risk assessment is not solely dependent on a single factor and providing a more comprehensive risk assessment result.

[0057] At the same time, the risk assessment model can be dynamically updated according to the year, overcoming the problem of inaccurate risk assessment results caused by different economic cycles and regional characteristics, making the risk assessment results more timely and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flowchart of the steps of a method for assessing sales behavior risks in the insurance industry according to the present invention. DETAILED DESCRIPTION

[0059] In order to provide a further understanding of the purpose, structure, features, and functions of the present invention, the present invention is described in detail below with reference to the embodiments.

[0060] like Figure 1 As shown, a method for assessing sales behavior risks in the insurance industry includes the following steps:

[0061] S1: collect data indicators;

[0062] Based on public information from institutions such as the National Bureau of Statistics, the National Health Commission, and the China Insurance Association, relevant data indicators such as GDP, consumer price index, inflation rate, and rate of change in medical costs for each year were collected;

[0063] Specifically, the GDP of the developed southeastern coastal areas in each year is recorded as HGDP, and the GDP of the central and western regions is recorded as LGDP;

[0064] The consumer price index of the southeast coastal developed areas in each year is recorded as HCPI, and the consumer price index of the central and western regions is recorded as LCPI;

[0065] The inflation rate of the developed southeast coastal areas in each year is recorded as HINF, and the inflation rate of the central and western regions is recorded as LINF;

[0066] The change rate of medical costs in the developed southeast coastal areas in each year is recorded as

[0067] The rate of change in medical costs in the HMCC and Midwest regions is recorded as LMCC.

[0068] S2: Perform feature selection processing on the collected data indicators;

[0069] The following sub-steps are included:

[0070] S21: Calculate data indicators based on different regional values;

[0071] Let the region value be V, where the region value V of the developed southeast coastal area is 0, and the region value V of the central and western regions is 1;

[0072] The gross domestic product (GDP), consumer price index (CPI), inflation rate (INF), and medical cost change rate (MCC) for each year are calculated based on the regional values. The specific calculation method is as follows:

[0073] Gross domestic product (GDP) = HGDP × (1-V) + LGDP × (V);

[0074] Consumer Price Index (CPI) = HCPI × (1-V) + LCPI × (V);

[0075] Inflation rate INF = HINF × (1-V) + LINF × (V);

[0076] Medical cost change rate MCC = HMCC × (1-V) + LMCC × (V);

[0077] S22: Calculate the change rate of data indicators in each year in turn;

[0078] The rate of change of GDP in each year is recorded as RGDP i , the rate of change of the Consumer Price Index in each year is recorded as RCPI i The rate of change of inflation in each year is recorded as RINF i The rate of change of medical costs in each year is recorded as RMCC i ; where i represents the i-th year; the specific calculation method is as follows:

[0079] RGDP i =(GDP i -GDP i-1 ) / GDP i-1 ;

[0080] RCPI i =(CPI i -CPI i-1 ) / GDP i-1 ;

[0081] RINF i =(INF i -INF i-1 ) / INF i-1 ;

[0082] RMCC i =(MCC i -MCC i-1 ) / MCC i-1 ;

[0083] S23: Calculate the average rate of change of data indicators;

[0084] Based on the change rates of the data indicators in each year obtained in step S22, the average change rate of the data indicators is calculated according to the following formula:

[0085] The average rate of change of gross domestic product (GDP) is AGDP = ∑(1 + RGDP i ) / n-1;

[0086] The average rate of change of the consumer price index ACPI = ∑(1 + RCPI i ) / n-1;

[0087] Average rate of change of inflation rate AINF=∑(1+RINF i ) / n-1;

[0088] Average rate of change of medical cost AMCC=∑(1+RMCC i ) / n-1.

[0089] S3: Build a risk assessment model;

[0090] The risk assessment model includes a premium model and a claim model;

[0091] The following sub-steps are included:

[0092] S31: Constructing premium model;

[0093] Based on the correlation between premiums and GDP, consumer price index, and inflation rate, the premium model is constructed using the following formula;

[0094] FV1=PV1×(1+AGDP) n ×(1+ACPI) n ×(1+AINF) n ;

[0095] Wherein, FV1 is the premium amount assessed in the current year; PV1 is the premium amount in the initial year, which refers to the first year in the data indicators collected in step S1; n is the difference between the year corresponding to PV1 and the year corresponding to FV1;

[0096] For example, if the premium amount in 2000 is RMB 10,000, the average GDP change rate from 2000 to 2023 is 3%, the average consumer price index change rate is 2%, and the average inflation change rate is 4%. After 23 years, the corresponding premium evaluation value in 2023 is RMB 70,841. The specific data is substituted into the formula for calculation as follows:

[0097] FV=10000×(1+0.03) 23 ×(1+0.02) 23 ×(1+0.04) 23 ;

[0098] FV≈70841;

[0099] S32: Build a claims model;

[0100] Based on the correlation between claim amounts and inflation rates and medical cost change rates, a claim model is constructed using the following formula;

[0101] FV2=PV2×(1+AINF) n ×(1+AMCC) n ;

[0102] Wherein, FV2 is the claim amount assessed in the current year; PV2 is the claim amount in the initial year, which refers to the first year in the data indicators collected in step S1; n is the difference between the year corresponding to PV2 and the year corresponding to FV2;

[0103] For example, if the claim amount in 2000 is RMB 10,000, the annual inflation rate from 2000 to 2023 is 4%, and the medical cost change rate is 2%. After 23 years, the corresponding evaluation value of the claim in 2023 is RMB 40,385. The specific data is substituted into the formula to calculate as follows:

[0104] FV=10000×(1+0.04) 23 ×(1+0.02) 23 ;

[0105] FV≈40385.

[0106] S4: Assess sales behavior risks;

[0107] The risk assessment model evaluates the user's sales behavior. If the user's premium amount and corresponding year are known, the process proceeds to step S41; if the year and claim amount are known, the process proceeds to step S42.

[0108] S41: Assess whether the premium amount is at risk;

[0109] The corresponding year is input into the premium model, and the premium model outputs the value of FV1, which is the premium amount assessed for the current year;

[0110] Compare the obtained FV1 value with the known user premium amount. If the FV1 value is greater than or equal to the known user premium amount, the premium model will give a result that the sales behavior is compliant; if the FV1 value is less than the known user premium amount, the premium model will give a result that the sales behavior is high-risk.

[0111] S42: Assess whether there is risk in the claim amount;

[0112] The current year is input into the claims model, and the claims model outputs the value of FV2, that is, the claims amount assessed in the current year. If the FV2 value is greater than or equal to the known user claims amount, the premium model gives a result that the sales behavior is compliant; if the FV1 value is less than the known user claims amount, the premium model gives a result that the sales behavior is high-risk.

[0113] The present invention has been described with reference to the above embodiments. However, the above embodiments are merely exemplary embodiments of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the present invention. On the contrary, modifications and improvements that do not depart from the spirit and scope of the present invention are intended to be protected by the present invention.

Claims

1. A method for assessing the risk of sales behavior in the insurance industry, characterized by: The following steps are involved: S1: Collect data indicators; Based on the public information of the National Bureau of Statistics, the National Health Commission and the China Insurance Association, data indicators for each year are collected; The data indicators include gross domestic product, consumer price index, inflation rate, and rate of change of medical costs; S2: Perform feature selection processing on the collected data indicators; The following sub-steps are included: S21: Calculate data indicators based on different regional values; S22: Calculate the change rate of data indicators in each year in turn; S23: Calculate the average rate of change of data indicators; S3: Build a risk assessment model; The risk assessment model includes a premium model and a claim model; Constructing a premium model and a claim settlement model according to the average change rate calculated in step S2; S4: Assess sales behavior risks; The risk assessment model evaluates the user's sales behavior. If the user's premium amount and the corresponding year are known, the process proceeds to step S41; if the year and claim amount are known, the process proceeds to step S42; S41: Assess whether the premium amount is at risk; S42: Assess whether there is a risk in the claim amount.

2. The method for assessing the risk of sales behavior in the insurance industry as claimed in claim 1, characterized in that: The specific content of step S1 is as follows: The GDP of the developed southeast coastal areas in each year is recorded as HGDP, and the GDP of the central and western regions is recorded as LGDP; The consumer price index of the southeast coastal developed areas in each year is recorded as HCPI, and the consumer price index of the central and western regions is recorded as LCPI; The inflation rate of the southeast coastal developed regions in each year is recorded as HINF, and the inflation rate of the central and western regions is recorded as LINF; The change rate of medical costs in the developed southeast coastal areas in each year is recorded as The rate of change in medical costs in HMCC and the Midwest region is recorded as LMCC.

3. The method for assessing the risk of sales behavior in the insurance industry as claimed in claim 1, characterized in that: The specific contents of step S2 are as follows: S21: Calculate data indicators based on different regional values; Let the region value be V, where the region value V of the developed southeast coastal area is 0, and the region value V of the central and western regions is 1; The gross domestic product (GDP), consumer price index (CPI), inflation rate (INF), and medical cost change rate (MCC) of each year are calculated based on the regional values. The specific calculation method is as follows: Gross domestic product GDP = HGDP × (1-V) + LGDP × (V); Consumer Price Index CPI = HCPI × (1-V) + LCPI × (V); Inflation rate INF = HINF × (1-V) + LINF × (V); Medical cost change rate MCC = HMCC × (1-V) + LMCC × (V); S22: Calculate the change rate of data indicators in each year in turn; The rate of change of GDP in each year is recorded as RGDP i , the rate of change of the consumer price index in each year is recorded as RCPI i The rate of change of inflation rate in each year is recorded as RINF i , the rate of change of medical costs in each year is recorded as RMCC i ; where i represents the i-th year; the specific calculation method is as follows: RGDP i = (GDP i - GDP i-1 ) / GDP i-1 ; RCPI i = (CPI i - CPI i-1 ) / GDP i-1 ; RINF i = (INF i - INF i-1 ) / INF i-1 ; RMCC i = (MCC i - MCC i-1 ) / MCC i-1 ; S23: Calculate the average rate of change of data indicators; Based on the change rate of the data indicator in each year obtained in step S22, the average change rate of the data indicator is calculated according to the following formula; Average rate of change of gross domestic product (GDP) ; Average rate of change of the consumer price index ; Average rate of change in inflation ; Average rate of change in medical cost .

4. The method for assessing the risk of sales behavior in the insurance industry as claimed in claim 1, characterized in that: Step S3 includes the following sub-steps: S31: Constructing premium model; Based on the correlation between premiums and GDP, consumer price index, and inflation rate, the premium model is constructed using the following formula; FV1=PV1×(1+AGDP) n ×(1+ACPI) n ×(1+AIF) n ; Wherein, FV1 is the premium amount evaluated in the current year; PV1 is the premium amount in the initial year, and the initial year refers to the first year in the data indicators collected in step S1; n is the year difference between the year corresponding to PV1 and the year corresponding to FV1; S32: Build a claims model; Based on the correlation between the claim amount and the inflation rate and the rate of change of medical costs, the claim model is constructed using the following formula; <h2 style=";text-align:left;direction:ltr">FV2=PV2×(1+AINF)<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ×(1+AMCC)<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ; Among them, FV2 is the claim amount evaluated in the current year; PV2 is the claim amount in the initial year, and the initial year refers to the first year in the data indicators collected in step S1; n is the difference between the year corresponding to PV2 and the year corresponding to FV2.

5. The method for assessing the risk of sales behavior in the insurance industry as claimed in claim 1, characterized in that: The specific contents of step S4 are as follows: S41: Assess whether the premium amount is at risk; The corresponding year is input into the premium model, and the premium model outputs the value of FV1, which is the premium amount assessed for the current year; The obtained FV1 value is compared with the known user premium amount. If the FV1 value is greater than or equal to the known user premium amount, the premium model gives a result that the sales behavior is compliant; if the FV1 value is less than the known user premium amount, the premium model gives a result that the sales behavior is high-risk; S42: Assess whether there is risk in the claim amount; The current year is input into the claims model, and the claims model outputs the value of FV2, that is, the claims amount assessed for the current year. If the FV2 value is greater than or equal to the known user claims amount, the premium model gives a result that the sales behavior is compliant; if the FV1 value is less than the known user claims amount, the premium model gives a result that the sales behavior is high-risk.

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