Reinsurance pricing calculation method based on multi-factor model

Abstract

The invention belongs to the technical field of multi-factor models, and particularly relates to a multi-factor model-based reinsurance pricing calculation method, which comprises the following steps of: S1, determining a target: determining the purpose of reinsurance pricing calculation, and evaluating the rationality of a reinsurance contract or comparing the advantages and disadvantages of different reinsurance schemes; s2, factors are selected, wherein key factors influencing reinsurance price comparison are selected, and the factors comprise insurance company underwriting risk characteristics, historical claim settlement data, market environment and reinsurance market supply and demand conditions; according to the method, the accuracy of reinsurance pricing calculation can be improved by comprehensively considering various key factors, the model utilizes historical and actual data and adapts to different market environments, the accuracy is ensured through cross validation and back test, detailed reports and analysis support decisions are provided, and reinsurance arrangement optimization, cost reduction and market competitiveness improvement are facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-factor models, and in particular relates to a method for calculating reinsurance consideration based on a multi-factor model. Background Art

[0002] Reinsurance consideration measurement refers to the process of evaluating the price and terms of reinsurance contracts in reinsurance transactions. Reinsurance companies spread risks by transferring part of the insurance risk to another company, and consideration measurement helps determine reasonable reinsurance costs. This process usually includes evaluating the insurance risk to be transferred, calculating expected losses and expenses using pricing models, analyzing contract terms and conditions, comparing the measurement results with other products on the market, and finally determining the consideration of the contract. Reinsurance consideration measurement requires not only precise technical calculations, but also in-depth understanding of market conditions to ensure the fairness and sustainability of the contract.

[0003] The existing methods for calculating reinsurance consideration have limited accuracy, are not convenient for comprehensively considering various key factors, are not convenient for adapting to different market environments, and are difficult to reduce costs. Therefore, we propose a method for calculating reinsurance consideration based on a multi-factor model to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a method for calculating reinsurance consideration based on a multi-factor model, which can improve the accuracy of reinsurance consideration calculation by comprehensively considering various key factors. The model uses historical and actual data, adapts to different market environments, and ensures its accuracy through cross-validation and backtesting. Detailed reports and analysis support decision-making, which helps to optimize reinsurance arrangements, reduce costs and improve market competitiveness.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A reinsurance consideration calculation method based on a multi-factor model, the consideration calculation method comprising the following steps:

[0007] S1. Determine the objectives: clarify the purpose of reinsurance consideration calculation, evaluate the rationality of the reinsurance contract or compare the advantages and disadvantages of different reinsurance plans;

[0008] S2. Select factors: Select key factors that affect the reinsurance price, including the risk characteristics of the insurance company's underwriting, historical claims data, market environment, and supply and demand in the reinsurance market;

[0009] S3. Data collection: Collect data related to the selected factors, including insurance companies’ historical claims data, 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 a linear regression model, a generalized linear model, and a 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 to verify the model's predictive ability and stability under different conditions;

[0012] S6. Calculate consideration: Use the model to predict the reinsurance consideration, including calculating the theoretical price of the reinsurance contract and adjusting it based on actual market conditions;

[0013] S7. Result analysis: Analyze the results of the model calculation, evaluate whether the reinsurance consideration is reasonable, and explore possible room for improvement;

[0014] S8. Report writing: Organize the measurement process and results into a report to support subsequent decision-making;

[0015] S9. Continuous tracking and adjustment: Continuously track the performance of the model according to changes in actual conditions and make necessary adjustments and updates.

[0016] In a preferred embodiment, the determination of objectives also includes understanding the characteristics of the company's insurance business, risk status and current reinsurance arrangements, identifying major issues that need to be addressed, setting specific objectives based on business needs, clarifying evaluation indicators, including the rationality of reinsurance consideration, risk transfer effectiveness, and cost-effectiveness, determining the types of risks covered by the measurement and related insurance products or business lines, risk types including natural disasters, accident risks, and market risks, determining the specific uses of the measurement results, including for internal decision-making, reporting to shareholders, or negotiating with reinsurance companies, setting standards for successful measurement, developing a detailed timetable, planning the progress of work at each stage, and identifying potential problems or challenges that may affect the achievement of measurement objectives, including data quality issues and unreasonable model assumptions.

[0017] In a preferred embodiment, the selection of factors also includes clarifying the specific problems or goals of reinsurance consideration measurement. After clarifying the goals, determining the types of factors that need to be analyzed, including policy coverage, protection period, deductible, natural disasters, accidents, health risks, compensation amount, compensation frequency, and compensation distribution, analyzing the correlation between each factor and reinsurance consideration, screening out factors that have a significant impact on the goals, using correlation coefficient analysis and regression analysis to test the significance of factors, evaluating the impact of each factor on reinsurance consideration, determining its importance in the model, evaluating the stability and reliability of factors under different conditions, selecting key factors as input variables of the model based on the screening and evaluation results, and appropriately transforming and standardizing the selected factors so that they can be included in the model.

[0018] In a preferred embodiment, the data collection also includes determining the type of data to be collected based on selected factors, including historical claims data, loss distribution, and market economic indicators, clarifying the goals of data collection, including policy records, claims records, and loss reports, extracting relevant data from the company's information system, including annual reports and market research reports on the insurance industry, formulating a data collection plan, including the time range, frequency, and format of the data, using tools such as SQL to extract required data from the company's database, exporting data files from the system, verifying data consistency, and verifying data accuracy by comparing historical data or using other data sources.

[0019] In a preferred embodiment, the establishment of the multi-factor model further includes selecting a linear regression model, and the linear regression model is in the form of:

[0020] Y=β0+β1X1+β2X2+…+βnXn+∈

[0021] Fill in or delete missing values, identify and handle outliers in the data, divide the data into training and test sets to verify the generalization ability of the model, use the k-fold cross-validation method to further evaluate the performance of the model, select the model based on the data characteristics and research purpose, build a mathematical or statistical model according to the selected model type, estimate the model parameters by least squares or maximum likelihood estimation, evaluate the goodness of fit of the model, check the distribution of residuals, use training and test set data to evaluate the predictive performance of the model, and use cross-validation methods to further evaluate the robustness and generalization ability of the model.

[0022] In a preferred embodiment, the model validation also includes clarifying the main objectives of the validation, including the prediction accuracy, stability or generalization ability of the validation model, collecting historical data for model validation, dividing the data into training sets, validation sets and test sets, performing different types of validation, backtesting the model using historical data, evaluating the performance of the model on historical data, making necessary adjustments and optimizations to the model based on the backtest results, dividing the data into k subsets, using k-1 subsets for training in turn, using the remaining subset for validation, validating each data point once, 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 sensitivity of the model to different parameter settings, checking the stability of the model under parameter changes, evaluating the impact of factor changes on the model prediction results, checking the distribution of model residuals, detecting whether the residuals have heteroscedasticity problems, summarizing the performance indicators obtained in backtesting and cross-validation, including accuracy and error, summarizing the stability of the model and its adaptability to different conditions, explaining the performance and results of the model, and analyzing the advantages and disadvantages of the model.

[0023] In a preferred embodiment, the measuring of consideration also includes determining the purpose of measuring consideration, including for pricing, risk assessment or negotiation, confirming the type of reinsurance contract to be measured, including proportional reinsurance and excess loss reinsurance, obtaining detailed terms of the reinsurance contract, including the insured amount, deductible, and compensation limit, collecting historical claims data 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 an established multi-factor model, performing 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 compensation levels based on the loss distribution predicted by the model, evaluating risk indicators under different compensation situations, comparing the actual consideration of similar reinsurance contracts in the same industry or market, performing market comparison, and adjusting the risk premium based on market conditions and specific terms of the contract.

[0024] In a preferred embodiment, the result analysis also includes obtaining the theoretical consideration from the model, organizing the measurement results into a clear data format, including comparison tables and charts, comparing the theoretical consideration predicted by the model with the actual consideration in the market, identifying the differences, analyzing the differences between the theoretical consideration and the actual consideration, and evaluating the accuracy of the model prediction results.

[0025] In a preferred embodiment, the report preparation also includes clarifying the information and objectives to be conveyed by the report, including providing decision makers with an assessment of the reinsurance consideration, or providing pricing advice to clients, collecting all measurement 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.

[0026] In a preferred embodiment, the continuous tracking and adjustment also includes determining key indicators for tracking model performance, including prediction error, model accuracy, risk indicators, clarifying the type of data that needs to be collected, including model prediction results, actual business data, and market change data, determining the frequency of tracking, setting up anomaly detection mechanisms based on business needs and data updates, regularly evaluating model performance, and analyzing the deviation between model prediction results and actual results.

[0027] The technical effects achieved by the present invention are:

[0028] The multi-factor model can more accurately reflect the actual risk situation. By comprehensively considering multiple key factors, the accuracy of consideration calculation can be improved. The multi-factor model can comprehensively evaluate various influencing factors, including policy coverage, protection period, deductible, etc., so that the risk transfer effect of the reinsurance contract can be more comprehensively analyzed. Since the model covers multiple factors, it can adapt to different market environments and the supply and demand of the reinsurance market, enhancing the flexibility and adaptability of the model.

[0029] The use of historical data and actual business data to establish and verify the model makes the calculation results more reliable and can reflect the impact of market changes on reinsurance consideration in real time. The model can be verified by cross-validation and backtesting methods to evaluate the prediction accuracy and stability of the model, thereby ensuring the effectiveness of the model in practical applications. Detailed reports and result analysis can help decision makers evaluate the rationality of reinsurance consideration, provide valuable decision-making basis, and provide data support when negotiating with reinsurance companies;

[0030] Through continuous tracking and adjustment, the model can be continuously optimized according to changes in actual conditions to ensure the continued accuracy and effectiveness of the measurement results. Through more accurate consideration measurement, it can help insurance companies optimize reinsurance arrangements, reduce unnecessary costs, and improve resource utilization efficiency. Accurate consideration measurement can help insurance companies formulate more competitive reinsurance strategies in the market, thereby increasing market share and competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of a reinsurance consideration calculation method based on a multi-factor model of the present invention. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0033] See also Figure 1 As shown, the present invention provides a method for calculating reinsurance consideration based on a multi-factor model, and the method for calculating reinsurance consideration includes the following steps:

[0034] S1. Determine the objectives: clarify the purpose of reinsurance consideration calculation, evaluate the rationality of the reinsurance contract or compare the advantages and disadvantages of different reinsurance plans;

[0035] S2. Select factors: Select key factors that affect the reinsurance price, including the risk characteristics of the insurance company's underwriting, historical claims data, market environment, and supply and demand in the reinsurance market;

[0036] S3. Data collection: Collect data related to the selected factors, including insurance companies’ historical claims data, loss distribution, industry data, and economic indicators;

[0037] S4. Establish a multi-factor model: Set up a multi-factor model based on the selected factors. The model includes a linear regression model, a generalized linear model, and a time series model. Estimate the parameters in the model through statistical methods, and perform regression analysis or other statistical tests on the collected data;

[0038] S5. Model validation: Use historical data for backtesting or use cross-validation to validate the established model to verify the model's predictive ability and stability under different conditions;

[0039] S6. Calculate consideration: Use the model to predict the reinsurance consideration, including calculating the theoretical price of the reinsurance contract and adjusting it based on actual market conditions;

[0040] S7. Result analysis: Analyze the results of the model calculation, evaluate whether the reinsurance consideration is reasonable, and explore possible room for improvement;

[0041] S8. Report writing: Organize the measurement process and results into a report to support subsequent decision-making;

[0042] S9. Continuous tracking and adjustment: Continuously track the performance of the model according to changes in actual conditions and make necessary adjustments and updates.

[0043] Determining goals also includes understanding the company's insurance business characteristics, risk status and current reinsurance arrangements, identifying major issues that need to be addressed, setting specific goals based on business needs, and clarifying evaluation indicators, including the rationality of reinsurance consideration, risk transfer effectiveness, and cost-effectiveness. Determine the types of risks covered by the measurement and related insurance products or business lines. Risk types include natural disasters, accident risks, and market risks. Determine the specific uses of the measurement results, including for internal decision-making, reporting to shareholders, or negotiations with reinsurance companies. Set standards for successful measurement, develop a detailed timetable, plan the progress of work at each stage, and identify potential problems or challenges that may affect the achievement of measurement goals, including data quality issues and unreasonable model assumptions.

[0044] Selecting factors also includes clarifying the specific problems or goals of calculating the reinsurance consideration. After clarifying the goals, determine the types of factors that need to be analyzed, including policy coverage, protection period, deductible, natural disasters, accidents, health risks, compensation amount, compensation frequency, and compensation distribution. Analyze the correlation between each factor and the reinsurance consideration, screen out factors that have a significant impact on the goals, use correlation coefficient analysis and regression analysis to test the significance of the factors, evaluate the impact of each factor on the 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 of the model based on the screening and evaluation results. Appropriate transformation and standardization of the selected factors are performed for inclusion in the model.

[0045] Data collection also includes determining the type of data that needs to be collected based on the selected factors, including historical claims data, loss distribution, and market economic indicators; clarifying the goals of data collection, including policy records, claims records, and loss reports; extracting relevant data from the company's information system, including annual reports and market research reports for 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 historical data or using other data sources.

[0046] Establishing a multi-factor model also includes selecting a linear regression model, which is in the form of:

[0047] Y=β0+β1X1+β2X2+…+βnXn+∈

[0048] Fill in or delete missing values, identify and handle outliers in the data, divide the data into training and test sets to verify the generalization ability of the model, use the k-fold cross-validation method to further evaluate the performance of the model, select the model based on the data characteristics and research purpose, build a mathematical or statistical model according to the selected model type, estimate the model parameters by least squares or maximum likelihood estimation, evaluate the goodness of fit of the model, check the distribution of residuals, use training and test set data to evaluate the predictive performance of the model, and use cross-validation methods to further evaluate the robustness and generalization ability of the model.

[0049] Model validation also includes clarifying the main objectives of validation, including verifying the predictive accuracy, stability or generalization ability of the model, collecting historical data for model validation, dividing the data into training sets, validation sets and test sets, performing different types of validation, backtesting the model using historical data, evaluating the performance of the model on historical data, making necessary adjustments and optimizations to the model based on the backtest results, dividing the data into k subsets, alternating between using k-1 subsets for training and the remaining subset for validation, performing a validation on 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, aggregating the validation results of all compromises, calculating average performance indicators, analyzing the sensitivity of the model to different parameter settings, checking the stability of the model under parameter changes, evaluating the impact of factor changes on the model's prediction results, checking the distribution of model residuals, detecting whether the residuals have heteroscedasticity problems, summarizing the performance indicators obtained from backtesting and cross-validation, including accuracy and error, summarizing the stability of the model and its adaptability to different conditions, explaining the performance and results of the model, and analyzing the advantages and disadvantages of the model.

[0050] Measuring the consideration also includes determining the purpose of measuring the consideration, including for pricing, risk assessment or negotiation, confirming the type of reinsurance contract to be measured, including proportional reinsurance and excess of loss reinsurance, obtaining detailed terms of the reinsurance contract, including the insured amount, deductible, and compensation limit, collecting historical claims data 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, and obtaining the model's prediction results for the reinsurance consideration, including theoretical price and risk assessment, using the model's prediction results to calculate the theoretical consideration, calculating the reinsurance consideration under different compensation levels based on the loss distribution predicted by the model, evaluating risk indicators under different compensation situations, comparing the actual consideration of similar reinsurance contracts in the same industry or market, conducting market comparisons, and adjusting the risk premium based on market conditions and the specific terms of the contract.

[0051] The result analysis also includes the theoretical consideration obtained from the model, organizing the measurement results into a clear data format, including comparison tables and charts, comparing the theoretical consideration predicted by the model with the actual consideration in the market, identifying the differences, analyzing the differences between the theoretical consideration and the actual consideration, and evaluating the accuracy of the model's prediction results.

[0052] Report writing also includes clarifying the information and objectives of the report, including providing decision makers with an assessment of the reinsurance consideration or providing pricing advice to clients, collecting all measurement data, results and analysis processes, planning the structure of the report and the outline of each section, and describing the model construction, testing methods and results in detail.

[0053] Continuous tracking and adjustment also includes determining key indicators for tracking model performance, including prediction error, model accuracy, and risk indicators; clarifying the type of data that needs to be collected, including model prediction results, actual business data, and market change data; determining the frequency of tracking; setting up anomaly detection mechanisms based on business needs and data updates; regularly evaluating model performance; and analyzing the deviation between model prediction results and actual results.

[0054] In the present invention, the multi-factor model can more accurately reflect the actual risk situation, and improve the accuracy of consideration calculation by comprehensively considering multiple key factors. The multi-factor model can comprehensively evaluate various influencing factors, including policy coverage, protection period, deductible, etc., so that the risk transfer effect of the reinsurance contract can be more comprehensively analyzed. Since the model covers multiple factors, it can adapt to different market environments and supply and demand conditions of the reinsurance market, and enhance the flexibility and adaptability of the model;

[0055] The use of historical data and actual business data to establish and verify the model makes the calculation results more reliable and can reflect the impact of market changes on reinsurance consideration in real time. The model can be verified by cross-validation and backtesting methods to evaluate the prediction accuracy and stability of the model, thereby ensuring the effectiveness of the model in practical applications. Detailed reports and result analysis can help decision makers evaluate the rationality of reinsurance consideration, provide valuable decision-making basis, and provide data support when negotiating with reinsurance companies;

[0056] Through continuous tracking and adjustment, the model can be continuously optimized according to changes in actual conditions to ensure the continued accuracy and effectiveness of the measurement results. Through more accurate consideration measurement, it can help insurance companies optimize reinsurance arrangements, reduce unnecessary costs, and improve resource utilization efficiency. Accurate consideration measurement can help insurance companies formulate more competitive reinsurance strategies in the market, thereby increasing market share and competitiveness.

[0057] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A method for calculating reinsurance consideration based on a multi-factor model, characterized in that: The consideration calculation method comprises the following steps: S1. Determine the objectives: clarify the purpose of reinsurance consideration calculation, evaluate the rationality of the reinsurance contract or compare the advantages and disadvantages of different reinsurance plans; S2. Select factors: Select key factors that affect the reinsurance price, including the risk characteristics of the insurance company's underwriting, historical claims data, market environment, and supply and demand in the reinsurance market; S3. Data collection: Collect data related to the selected factors, including insurance companies’ historical claims data, 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 a linear regression model, a generalized linear model, and a 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 to verify the model's predictive ability and stability under different conditions; S6. Calculate consideration: Use the model to predict the reinsurance consideration, including calculating the theoretical price of the reinsurance contract and adjusting it based on actual market conditions; S7. Result analysis: Analyze the results of the model calculation, evaluate whether the reinsurance consideration is reasonable, and explore possible room for improvement; S8. Report writing: Organize the measurement process and results into a report to support subsequent decision-making; S9. Continuous tracking and adjustment: Continuously track the performance of the model according to changes in actual conditions 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 objectives also include understanding the company's insurance business characteristics, risk profile and current reinsurance arrangements, identifying key issues that need to be addressed, setting specific goals based on business needs, clarifying evaluation indicators, including the rationality of reinsurance consideration, risk transfer effectiveness, and cost-effectiveness, determining the types of risks covered by the measurement and related insurance products or business lines. Risk types include natural disasters, accident risks, and market risks. Determining the specific uses of the measurement results, including for internal decision-making, reporting to shareholders, or negotiating with reinsurance companies, setting standards for successful measurement, developing a detailed timetable, planning the progress of work at each stage, and identifying potential problems or challenges that may affect the achievement of measurement 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 goals of the reinsurance consideration measurement. After clarifying the goals, determining the types of factors that need to be analyzed, including policy coverage, protection period, deductible, natural disasters, accidents, health risks, compensation amount, compensation frequency, and compensation distribution, analyzing the correlation between each factor and the reinsurance consideration, screening out factors that have a significant impact on the goals, using correlation coefficient analysis and regression analysis to test the significance of the factors, evaluating the impact of each factor on the reinsurance consideration, determining its importance in the model, evaluating the stability and reliability of the factors under different conditions, and selecting key factors as input variables of the model based on the screening and evaluation results, and appropriately transforming and standardizing the selected factors for incorporation into 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 type of data that needs to be collected based on the selected factors, including historical claims data, loss distribution, and market economic indicators, clarifying the goals of data collection, 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, formulating 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 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, and the linear regression model is in the form of: Y=β0+β1X1+β2X2+…+βnXn+∈ Fill in or delete missing values, identify and handle outliers in the data, divide the data into training and test sets to verify the generalization ability of the model, use the k-fold cross-validation method to further evaluate the performance of the model, select the model based on the data characteristics and research purpose, build a mathematical or statistical model according to the selected model type, estimate the model parameters by least squares or maximum likelihood estimation, evaluate the goodness of fit of the model, check the distribution of residuals, use training and test set data to evaluate the predictive performance of the model, and use cross-validation methods to further evaluate the robustness and generalization ability of the model.

6. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The model validation also includes clarifying the main objectives of the validation, including the prediction accuracy, stability or generalization ability of the validation model, collecting historical data for model validation, dividing the data into training sets, validation sets and test sets, performing different types of validation, backtesting the model using historical data, evaluating the performance of the model on historical data, making necessary adjustments and optimizations to the model based on the backtest results, dividing the data into k subsets, training with k-1 subsets in turn, validating with the remaining subset, validating each data point once, splitting the data according to the selected cross-validation method, training the model in each compromise and evaluating its performance on the validation set, aggregating the validation results of all compromises, calculating average performance indicators, analyzing the sensitivity of the model to different parameter settings, checking the stability of the model under parameter changes, evaluating the impact of factor changes on the model prediction results, checking the distribution of model residuals, detecting whether the residuals have heteroscedasticity problems, summarizing the performance indicators obtained in backtesting and cross-validation, including accuracy and error, summarizing the stability of the model and its adaptability to different conditions, explaining the performance and results of the model, and analyzing the advantages and disadvantages of the model.

7. The method for calculating reinsurance consideration based on a multi-factor model according to claim 1, characterized in that: The measured consideration also includes determining the purpose of the measured consideration, including for pricing, risk assessment or negotiation, confirming the type of reinsurance contract to be measured, including proportional reinsurance and excess loss reinsurance, obtaining detailed terms of the reinsurance contract, including the insured amount, deductible, and compensation limit, collecting historical claims data and loss data related to the reinsurance contract, organizing and formatting 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 price and risk assessment, using the model's prediction results to calculate the theoretical consideration, calculating the reinsurance consideration under different compensation levels based on the loss distribution predicted by the model, evaluating risk indicators under different compensation situations, comparing the actual consideration of similar reinsurance contracts in the same industry or market, conducting market comparisons, and adjusting the risk premium based on market conditions and 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 result analysis also includes obtaining the theoretical consideration from the model, organizing the measurement results into a clear data format, including comparison tables and charts, comparing the theoretical consideration predicted by the model with the actual consideration in the market, identifying the differences, analyzing the differences between the theoretical consideration and the actual consideration, 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 by the report, including providing decision makers with an assessment of the reinsurance consideration or providing pricing advice to clients, collecting all measurement 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 determining the key indicators for tracking model performance, including prediction error, model accuracy, risk indicators, clarifying the type of data that needs 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, regularly evaluating the performance of the model, and analyzing the deviation between the model prediction results and the actual results.

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