Reinsurance pricing calculation method based on multi-factor model
The multi-factor model-based method for calculating reinsurance consideration solves the problem of insufficient accuracy in existing technologies, achieving accurate calculation of reinsurance consideration and market adaptability, optimizing reinsurance arrangements, reducing costs, and improving market competitiveness.
Patent Information
- Application Number
- CN202610182297.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-09
- Publication Date
- 2026-05-19
AI Technical Summary
Existing reinsurance valuation methods lack precision, struggle to comprehensively consider various key factors, are not easily adaptable to different market environments, and are costly.
A multi-factor model is adopted, which selects key factors affecting reinsurance consideration and establishes linear regression, generalized linear and time series models. Combined with cross-validation and backtesting, data analysis and prediction are carried out. The reinsurance consideration calculation process includes target determination, data collection, model building, validation, calculation and report writing, and continuous tracking and adjustment.
It improves the accuracy of reinsurance consideration calculation, enhances the adaptability and flexibility of the model, can reflect market changes in real time, provide reliable decision-making basis, optimize reinsurance arrangements, reduce costs, and improve market competitiveness.
Smart Images
Figure CN122066529A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of multi-factor model technology, specifically relating to a method for calculating reinsurance consideration based on a multi-factor model. Background Technology
[0002] Reinsurance consideration calculation refers to the process of evaluating the price and terms of a reinsurance contract in a reinsurance transaction. Reinsurance companies diversify risk by transferring a portion of insured risk to another company, and consideration calculation helps determine reasonable reinsurance costs. This process typically includes assessing the insured risk to be transferred, calculating expected losses and costs using pricing models, analyzing contract terms and conditions, comparing the calculation results with other products in the market, and ultimately determining the contract consideration. Reinsurance consideration calculation requires not only precise technical calculations but also a deep understanding of market conditions to ensure the fairness and sustainability of the contract.
[0003] Existing reinsurance consideration calculation methods lack precision, are not suitable for comprehensively considering various key factors, are not adaptable to different market environments, and are difficult to reduce costs. Therefore, we propose a reinsurance consideration calculation method based on a multi-factor model to solve the above problems. Summary of the Invention
[0004] The purpose of this invention is to provide a reinsurance consideration calculation method based on a multi-factor model, which can improve the accuracy of reinsurance consideration calculation by comprehensively considering various key factors. The model utilizes historical and actual data, adapts to different market environments, and ensures its accuracy through cross-validation and backtesting. Detailed reports and analyses support decision-making, helping to optimize reinsurance arrangements, reduce costs, and improve market competitiveness.
[0005] The specific technical solution adopted by this invention is as follows:
[0006] A method for calculating reinsurance consideration based on a multi-factor model, the method comprising the following steps:
[0007] S1. Define the objective: Clarify the purpose of the reinsurance consideration calculation, assess the reasonableness of the reinsurance contract, or compare the advantages and disadvantages of different reinsurance schemes;
[0008] S2. Selection Factors: Select key factors that affect the reinsurance price. These factors include the risk characteristics underwritten by the insurance company, historical claims data, market environment, and the supply and demand situation in the reinsurance market.
[0009] S3. Data Collection: Collect data related to the selected factors, including historical claims data of insurance companies, loss distribution, industry data, and economic indicators;
[0010] S4. Establish a multi-factor model: Set up a multi-factor model based on the selected factors. The model includes linear regression model, generalized linear model, and time series model. Estimate the parameters in the model through statistical methods and perform regression analysis or other statistical tests on the collected data.
[0011] S5. Model Validation: Use historical data for backtesting or use cross-validation to validate the established model, verifying the model's predictive ability and stability under different conditions;
[0012] S6. Calculate the consideration: Use models to predict the reinsurance consideration, including calculating the theoretical price of the reinsurance contract and adjusting it according to the actual market conditions;
[0013] S7. Results Analysis: Analyze the results calculated by the model, assess whether the reinsurance consideration is reasonable, and explore possible areas for improvement;
[0014] S8. Report Writing: Compile the calculation process and results into a report to support subsequent decision-making;
[0015] S9. Continuous tracking and adjustment: Based on changes in the actual situation, continuously track the model's performance and make necessary adjustments and updates.
[0016] In a preferred embodiment, determining the objectives further includes understanding the company's insurance business characteristics, risk profile, and current reinsurance arrangements; identifying key issues that need to be addressed; setting specific objectives based on business needs; defining evaluation indicators, including the reasonableness of reinsurance consideration, risk transfer effectiveness, and cost-effectiveness; determining the types of risks covered by the calculation and related insurance products or business lines, including natural disasters, accident risks, and market risks; determining the specific uses of the calculation results, including for internal decision-making, reporting to shareholders, or negotiating with reinsurance companies; setting criteria for successful calculation; developing a detailed timetable; planning the work progress at each stage; and identifying potential problems or challenges that may affect the achievement of the calculation objectives, including data quality issues and unreasonable model assumptions.
[0017] In a preferred embodiment, the selection factors further include clarifying the specific problem or objective of reinsurance consideration calculation. After clarifying the objective, the types of factors to be analyzed are determined, including policy coverage, coverage period, deductible, natural disasters, accidents, health risks, payout amount, payout frequency, and payout distribution. The correlation between each factor and reinsurance consideration is analyzed, and factors with significant impact on the objective are screened out. The significance of the factors is tested using correlation coefficient analysis and regression analysis. The degree of influence of each factor on reinsurance consideration is assessed, and its importance in the model is determined. The stability and reliability of the factors under different conditions are evaluated. Based on the screening and evaluation results, key factors are selected as input variables of the model. The selected factors are appropriately transformed and standardized to be included in the model.
[0018] In a preferred embodiment, the data collection further includes determining the types of data to be collected based on selected factors, including historical claims data, loss distribution, and market economic indicators; clarifying the data collection objectives, including policy records, claims records, and loss reports; extracting relevant data from the company's information system, including annual reports and market research reports of the insurance industry; developing a data collection plan, including the time range, frequency, and format of the data; using tools such as SQL to extract the required data from the company's database; exporting data files from the system; verifying the consistency of the data; and verifying the accuracy of the data by comparing it with historical data or using other data sources.
[0019] In a preferred embodiment, establishing the multi-factor model further includes using a linear regression model, the linear regression model being in the form: Y = β0 + β1X1 + β2X2 + … + βnXn + ϵ
[0020] Impute or remove missing values, identify and handle outliers in the data, divide the data into training and test sets to verify the model's generalization ability, use k-fold cross-validation to further evaluate the model's performance, select a model based on data characteristics and research objectives, construct a mathematical or statistical model according to the selected model type, estimate model parameters using least squares or maximum likelihood estimation, evaluate the model's goodness of fit, check the distribution of residuals, evaluate the model's predictive performance using training and test set data, and use cross-validation to further evaluate the model's robustness and generalization ability.
[0021] In a preferred embodiment, the model validation further includes defining the main validation objectives, including validating the model's predictive accuracy, stability, or generalization ability; collecting historical data for model validation; dividing the data into training, validation, and test sets; performing different types of validation; backtesting the model using historical data; evaluating the model's performance on historical data; making necessary adjustments and optimizations to the model based on the backtesting results; dividing the data into k subsets; training the model using k-1 subsets in turn; validating the model using the remaining subset; performing validation once for each data point; splitting the data according to the selected cross-validation method; training the model in each compromise and evaluating its performance on the validation set; summarizing the validation results of all compromises; calculating the average performance index; analyzing the model's sensitivity to different parameter settings; checking the model's stability under parameter variations; evaluating the impact of factor changes on the model's prediction results; checking the distribution of model residuals; detecting the presence of heteroscedasticity in the residuals; summarizing the performance indicators obtained from backtesting and cross-validation, including accuracy and error; summarizing the model's stability and adaptability to different conditions; interpreting the model's performance and results; and analyzing the model's advantages and disadvantages.
[0022] In a preferred embodiment, the calculation of consideration further includes determining the purpose of the consideration calculation, including pricing, risk assessment, or negotiation; confirming the type of reinsurance contract to be calculated, including proportional reinsurance and excess loss reinsurance; obtaining the detailed terms of the reinsurance contract, including the sum insured, deductible, and payout limit; collecting historical claims and loss data related to the reinsurance contract; organizing and formatting the data related to the reinsurance contract; checking the accuracy and completeness of the data; inputting the organized data into a pre-established multi-factor model for prediction; obtaining the model's prediction results for the reinsurance consideration, including theoretical price and risk assessment; calculating the theoretical consideration using the model's prediction results; calculating the reinsurance consideration under different payout levels based on the loss distribution predicted by the model; assessing risk indicators under different payout conditions; comparing the actual consideration of similar reinsurance contracts in the same industry or market; conducting market comparison; and adjusting the risk premium according to market conditions and the specific terms of the contract.
[0023] In a preferred embodiment, the results analysis further includes obtaining the theoretical price from the model, organizing the calculation results into a clear data format, including comparison tables and charts, comparing the theoretical price predicted by the model with the actual price in the market, identifying differences, analyzing the differences between the theoretical price and the actual price, and evaluating the accuracy of the model prediction results.
[0024] In a preferred embodiment, the report writing also includes clarifying the information and objectives to be conveyed in the report, including providing decision-makers with an assessment of reinsurance consideration or providing pricing advice to clients, collecting all calculation data, results and analysis processes, planning the structure of the report and the outline of each section, and describing in detail the model construction, testing methods and results.
[0025] In a preferred embodiment, the continuous tracking and adjustment further includes identifying key metrics for tracking model performance, including prediction error, model accuracy, and risk indicators; defining the types of data to be collected, including model prediction results, actual business data, and market change data; determining the tracking frequency; setting up anomaly detection mechanisms based on business needs and data update status; periodically evaluating model performance; and analyzing the deviation between model prediction results and actual results.
[0026] The technical effects achieved by this invention are as follows:
[0027] Multi-factor models can more accurately reflect the actual risk situation. By comprehensively considering multiple key factors, they can improve the accuracy of consideration calculation. Multi-factor models can comprehensively evaluate various influencing factors, including policy coverage, coverage period, deductible, etc. This allows for a more comprehensive analysis of the risk transfer effect of reinsurance contracts. Since the model covers multiple factors, it can adapt to different market environments and the supply and demand situation of the reinsurance market, enhancing the flexibility and adaptability of the model.
[0028] By using historical and actual business data to build and validate models, the calculation results become more reliable and can reflect the impact of market changes on reinsurance consideration in real time. By validating the model through methods such as cross-validation and backtesting, the predictive accuracy and stability of the model can be evaluated, thereby ensuring the effectiveness of the model in practical applications. Detailed reports and results analysis can help decision-makers assess the rationality of reinsurance consideration, provide valuable decision-making basis, and provide data support when negotiating with reinsurance companies.
[0029] By continuously tracking and adjusting, the model can be optimized based on changes in actual conditions, ensuring the continuous accuracy and effectiveness of the calculation results. More accurate consideration calculation can help insurance companies optimize reinsurance arrangements, reduce unnecessary costs, and improve resource utilization efficiency. Accurate consideration calculation can also help insurance companies develop more competitive reinsurance strategies in the market, thereby increasing market share and competitiveness. Attached Figure Description
[0030] Figure 1 This is a schematic diagram of a reinsurance consideration calculation method based on a multi-factor model according to the present invention. Detailed Implementation
[0031] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0032] Please see Figure 1 As shown, this invention provides a method for calculating reinsurance consideration based on a multi-factor model. The method includes the following steps:
[0033] S1. Define the objective: Clarify the purpose of the reinsurance consideration calculation, assess the reasonableness of the reinsurance contract, or compare the advantages and disadvantages of different reinsurance schemes;
[0034] S2. Selection Factors: Select key factors that affect the reinsurance price. These factors include the risk characteristics underwritten by the insurance company, historical claims data, market environment, and the supply and demand situation in the reinsurance market.
[0035] S3. Data Collection: Collect data related to the selected factors, including historical claims data of insurance companies, loss distribution, industry data, and economic indicators;
[0036] S4. Establish a multi-factor model: Set up a multi-factor model based on the selected factors. The model includes linear regression model, generalized linear model, and time series model. Estimate the parameters in the model through statistical methods and perform regression analysis or other statistical tests on the collected data.
[0037] S5. Model Validation: Use historical data for backtesting or use cross-validation to validate the established model, verifying the model's predictive ability and stability under different conditions;
[0038] S6. Calculate the consideration: Use models to predict the reinsurance consideration, including calculating the theoretical price of the reinsurance contract and adjusting it according to the actual market conditions;
[0039] S7. Results Analysis: Analyze the results calculated by the model, assess whether the reinsurance consideration is reasonable, and explore possible areas for improvement;
[0040] S8. Report Writing: Compile the calculation process and results into a report to support subsequent decision-making;
[0041] S9. Continuous tracking and adjustment: Based on changes in the actual situation, continuously track the model's performance and make necessary adjustments and updates.
[0042] Defining objectives also includes understanding the company's insurance business characteristics, risk profile, and current reinsurance arrangements; identifying key issues that need to be addressed; setting specific objectives based on business needs; defining evaluation indicators, including the reasonableness of reinsurance consideration, risk transfer effectiveness, and cost-effectiveness; determining the types of risks covered by the assessment and related insurance products or business lines, including natural disasters, accident risks, and market risks; determining the specific uses of the assessment results, including for internal decision-making, reporting to shareholders, or negotiating with reinsurance companies; setting criteria for successful assessment; developing a detailed timeline; planning the work progress at each stage; and identifying potential problems or challenges that may affect the achievement of the assessment objectives, including data quality issues and unreasonable model assumptions.
[0043] Factor selection also includes clarifying the specific problems or objectives of reinsurance consideration calculation. After clarifying the objectives, the types of factors to be analyzed are determined, including policy coverage, coverage period, deductible, natural disasters, accidents, health risks, payout amount, payout frequency, and payout distribution. The correlation between each factor and reinsurance consideration is analyzed, and factors with significant impact on the objectives are screened out. Correlation coefficient analysis and regression analysis are used to test the significance of the factors, assess the degree of influence of each factor on reinsurance consideration, determine its importance in the model, evaluate the stability and reliability of factors under different conditions, and select key factors as input variables for the model based on the screening and evaluation results. The selected factors are then appropriately transformed and standardized for inclusion in the model.
[0044] Data collection also includes determining the types of data to be collected based on selected factors, including historical claims data, loss distribution, and market economic indicators; clarifying the data collection objectives, including policy records, claims records, and loss reports; extracting relevant data from the company's information systems, including annual reports and market research reports from the insurance industry; developing a data collection plan, including the time range, frequency, and format of the data; using tools such as SQL to extract the required data from the company's database; exporting data files from the system; verifying the consistency of the data; and verifying the accuracy of the data by comparing it with historical data or using other data sources.
[0045] Establishing a multi-factor model also includes using a linear regression model, which has the following form: Y = β0 + β1X1 + β2X2 + … + βnXn + ϵ
[0046] Impute or remove missing values, identify and handle outliers in the data, divide the data into training and test sets to verify the model's generalization ability, use k-fold cross-validation to further evaluate the model's performance, select a model based on data characteristics and research objectives, construct a mathematical or statistical model according to the selected model type, estimate model parameters using least squares or maximum likelihood estimation, evaluate the model's goodness of fit, check the distribution of residuals, evaluate the model's predictive performance using training and test set data, and use cross-validation to further evaluate the model's robustness and generalization ability.
[0047] Model validation also includes defining the main validation objectives, such as validating the model's predictive accuracy, stability, or generalization ability; collecting historical data for model validation; dividing the data into training, validation, and test sets; performing different types of validation; backtesting the model using historical data; evaluating the model's performance on historical data; making necessary adjustments and optimizations to the model based on the backtesting results; dividing the data into k subsets; training the model using k-1 subsets in turn; validating the model using the remaining subset; performing validation once for each data point; splitting the data according to the selected cross-validation method; training the model in each compromise and evaluating its performance on the validation set; summarizing the validation results of all compromises; calculating the average performance index; analyzing the model's sensitivity to different parameter settings; checking the model's stability under parameter variations; evaluating the impact of factor changes on the model's prediction results; checking the distribution of model residuals; detecting the presence of heteroscedasticity in the residuals; summarizing the performance metrics obtained from backtesting and cross-validation, including accuracy and error; summarizing the model's stability and adaptability to different conditions; interpreting the model's performance and results; and analyzing the model's advantages and disadvantages.
[0048] Calculating the reinsurance consideration also includes determining the purpose of the consideration calculation, such as for pricing, risk assessment, or negotiation; identifying the type of reinsurance contract to be calculated, including proportional reinsurance and excess loss reinsurance; obtaining the detailed terms of the reinsurance contract, including the sum insured, deductible, and payout limits; collecting historical claims and loss data related to the reinsurance contract; organizing and formatting the data related to the reinsurance contract; checking the accuracy and completeness of the data; inputting the organized data into the established multi-factor model for prediction; obtaining the model's prediction results for the reinsurance consideration, including theoretical prices and risk assessments; calculating the theoretical consideration using the model's prediction results; calculating the reinsurance consideration under different payout levels based on the loss distribution predicted by the model; assessing risk indicators under different payout conditions; comparing the actual consideration of similar reinsurance contracts in the same industry or market; conducting market comparisons; and adjusting the risk premium according to market conditions and the specific terms of the contract.
[0049] The results analysis also includes the theoretical price obtained from the model, organizing the calculation results into a clear data format, including comparison tables and charts, comparing the theoretical price predicted by the model with the actual price in the market, identifying differences, analyzing the differences between the theoretical price and the actual price, and evaluating the accuracy of the model's prediction results.
[0050] Report writing also includes clarifying the information and objectives that the report will convey, including providing decision-makers with an assessment of reinsurance consideration or providing pricing advice to clients, collecting all calculation data, results and analysis processes, planning the structure of the report and the outline of each section, and describing in detail the model construction, testing methods and results.
[0051] Continuous tracking and adjustment also includes identifying key metrics for tracking model performance, including prediction error, model accuracy, and risk indicators; defining the types of data to be collected, including model prediction results, actual business data, and market change data; determining the tracking frequency; setting up anomaly detection mechanisms based on business needs and data updates; periodically evaluating model performance; and analyzing the deviation between model prediction results and actual results.
[0052] In this invention, the multi-factor model can more accurately reflect the actual risk situation. By comprehensively considering multiple key factors, it improves the accuracy of consideration calculation. The multi-factor model can comprehensively evaluate various influencing factors, including policy coverage, coverage period, deductible, etc., which allows for a more comprehensive analysis of the risk transfer effect of reinsurance contracts. Since the model covers multiple factors, it can adapt to different market environments and the supply and demand situation of the reinsurance market, enhancing the flexibility and adaptability of the model.
[0053] By using historical and actual business data to build and validate models, the calculation results become more reliable and can reflect the impact of market changes on reinsurance consideration in real time. By validating the model through methods such as cross-validation and backtesting, the predictive accuracy and stability of the model can be evaluated, thereby ensuring the effectiveness of the model in practical applications. Detailed reports and results analysis can help decision-makers assess the rationality of reinsurance consideration, provide valuable decision-making basis, and provide data support when negotiating with reinsurance companies.
[0054] By continuously tracking and adjusting, the model can be optimized based on changes in actual conditions, ensuring the continuous accuracy and effectiveness of the calculation results. More accurate consideration calculation can help insurance companies optimize reinsurance arrangements, reduce unnecessary costs, and improve resource utilization efficiency. Accurate consideration calculation can also help insurance companies develop more competitive reinsurance strategies in the market, thereby increasing market share and competitiveness.
[0055] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.
Claims
1. A method for calculating reinsurance consideration based on a multi-factor model, characterized in that: The method for calculating the consideration includes the following steps: S1. Define the objective: Clarify the purpose of the reinsurance consideration calculation, assess the reasonableness of the reinsurance contract, or compare the advantages and disadvantages of different reinsurance schemes; S2. Selection Factors: Select key factors that affect the reinsurance price. These factors include the risk characteristics underwritten by the insurance company, historical claims data, market environment, and the supply and demand situation in the reinsurance market. S3. Data Collection: Collect data related to the selected factors, including historical claims data of insurance companies, loss distribution, industry data, and economic indicators; S4. Establish a multi-factor model: Set up a multi-factor model based on the selected factors. The model includes linear regression model, generalized linear model, and time series model. Estimate the parameters in the model through statistical methods and perform regression analysis or other statistical tests on the collected data. S5. Model Validation: Use historical data for backtesting or use cross-validation to validate the established model, verifying the model's predictive ability and stability under different conditions; S6. Calculate the consideration: Use models to predict the reinsurance consideration, including calculating the theoretical price of the reinsurance contract and adjusting it according to the actual market conditions; S7. Results Analysis: Analyze the results calculated by the model, assess whether the reinsurance consideration is reasonable, and explore possible areas for improvement; S8. Report Writing: Compile the calculation process and results into a report to support subsequent decision-making; S9. Continuous tracking and adjustment: Based on changes in the actual situation, continuously track the model's performance and make necessary adjustments and updates.
2. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The determination of objectives also includes understanding the company's insurance business characteristics, risk status, and current reinsurance arrangements; identifying the main problems that need to be addressed; setting specific objectives based on business needs; clarifying the evaluation indicators, including the reasonableness of reinsurance consideration, risk transfer effectiveness, and cost-effectiveness; determining the types of risks covered by the calculation and related insurance products or business lines, including natural disasters, accident risks, and market risks; determining the specific uses of the calculation results, including for internal decision-making, reporting to shareholders, or negotiating with reinsurance companies; setting the criteria for successful calculation; developing a detailed timetable; planning the work progress of each stage; and identifying potential problems or challenges that may affect the achievement of the calculation objectives, including data quality issues and unreasonable model assumptions.
3. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The selection of factors also includes clarifying the specific problems or objectives of reinsurance consideration calculation. After clarifying the objectives, the types of factors to be analyzed are determined, including policy coverage, coverage period, deductible, natural disasters, accidents, health risks, payout amount, payout frequency, and payout distribution. The correlation between each factor and reinsurance consideration is analyzed, and factors with significant impact on the objectives are screened out. Correlation coefficient analysis and regression analysis are used to test the significance of the factors, assess the degree of influence of each factor on reinsurance consideration, determine its importance in the model, evaluate the stability and reliability of the factors under different conditions, and select key factors as input variables for the model based on the screening and evaluation results. The selected factors are then appropriately transformed and standardized for inclusion in the model.
4. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The data collection also includes determining the types of data to be collected based on selected factors, including historical claims data, loss distribution, and market economic indicators; clarifying the data collection objectives, including policy records, claims records, and loss reports; extracting relevant data from the company's information system, including annual reports and market research reports of the insurance industry; developing a data collection plan, including the time range, frequency, and format of the data; using tools such as SQL to extract the required data from the company's database; exporting data files from the system; verifying the consistency of the data; and verifying the accuracy of the data by comparing it with historical data or using other data sources.
5. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The establishment of the multi-factor model also includes selecting a linear regression model, which has the form: Y=β0+β1X1+β2X2+…+βnXn+ϵ Impute or remove missing values, identify and handle outliers in the data, divide the data into training and test sets to verify the model's generalization ability, use k-fold cross-validation to further evaluate the model's performance, select a model based on data characteristics and research objectives, construct a mathematical or statistical model according to the selected model type, estimate model parameters using least squares or maximum likelihood estimation, evaluate the model's goodness of fit, check the distribution of residuals, evaluate the model's predictive performance using training and test set data, and use cross-validation to further evaluate the model's robustness and generalization ability.
6. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The model validation process also includes defining the main validation objectives, such as validating the model's predictive accuracy, stability, or generalization ability; collecting historical data for model validation; dividing the data into training, validation, and test sets; performing different types of validation; backtesting the model using historical data; evaluating the model's performance on historical data; making necessary adjustments and optimizations to the model based on the backtesting results; dividing the data into k subsets; training the model using k-1 subsets in turn; validating the model using the remaining subset; performing validation once for each data point; splitting the data according to the selected cross-validation method; training the model in each compromise and evaluating its performance on the validation set; summarizing the validation results of all compromises; calculating the average performance index; analyzing the model's sensitivity to different parameter settings; checking the model's stability under parameter variations; evaluating the impact of factor changes on the model's prediction results; checking the distribution of model residuals; detecting the presence of heteroscedasticity in the residuals; summarizing the performance indicators obtained from backtesting and cross-validation, including accuracy and error; summarizing the model's stability and adaptability to different conditions; interpreting the model's performance and results; and analyzing the model's advantages and disadvantages.
7. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The calculation of consideration also includes determining the purpose of the consideration calculation, including pricing, risk assessment, or negotiation; confirming the type of reinsurance contract to be calculated, including proportional reinsurance and excess loss reinsurance; obtaining the detailed terms of the reinsurance contract, including the sum insured, deductible, and payout limit; collecting historical claims and loss data related to the reinsurance contract; organizing and formatting the data related to the reinsurance contract; checking the accuracy and completeness of the data; inputting the organized data into the established multi-factor model for prediction; obtaining the model's prediction results for reinsurance consideration, including theoretical price and risk assessment; calculating the theoretical consideration using the model's prediction results; calculating the reinsurance consideration under different payout levels based on the loss distribution predicted by the model; assessing risk indicators under different payout conditions; comparing the actual consideration of similar reinsurance contracts in the same industry or market; conducting market comparison; and adjusting the risk premium according to market conditions and the specific terms of the contract.
8. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The results analysis also includes obtaining the theoretical price from the model, organizing the calculation results into a clear data format, including comparison tables and charts, comparing the theoretical price predicted by the model with the actual price in the market, identifying differences, analyzing the differences between the theoretical price and the actual price, and evaluating the accuracy of the model's prediction results.
9. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The report writing also includes clarifying the information and objectives to be conveyed in the report, including providing decision-makers with an assessment of reinsurance consideration or providing pricing advice to clients, collecting all calculation data, results and analysis processes, planning the structure of the report and the outline of each section, and describing in detail the model construction, testing methods and results.
10. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The continuous tracking and adjustment also includes identifying key metrics for tracking model performance, including prediction error, model accuracy, and risk indicators; defining the types of data to be collected, including model prediction results, actual business data, and market change data; determining the tracking frequency; setting up anomaly detection mechanisms based on business needs and data update status; periodically evaluating model performance; and analyzing the deviation between model prediction results and actual results.