Method and system for measuring unexpired liabilities of a property insurance contract

Abstract

The application provides a kind of property insurance contract unexpired liability measurement method and system, it is related to data processing technical field, the application obtains multidimensional business and expense data and verifies, based on risk distribution characteristic mark matching corresponding risk distribution mode, or based on stop and resume information correction unexpired amortization proportion, adopt weighted unexpired amortization proportion split premium and investment component, calculate unexpired liability non-loss value according to policy, risk type, batch order three level granularity;Predict future performance cash flow and discount, determine loss contract group and amount;Based on multidimensional parameter calculation policy allocation ratio, allocate contract group liability to policy, according to preset dimension summary generation measurement report.The application solves the problems of single model, stop and resume processing loss, insufficient allocation granularity and other problems in traditional method, significantly reduces systematic bias, improves measurement accuracy and efficiency, meets IFRS17 standard compliance and fine management needs.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for measuring unexpired liabilities under property insurance contracts. Background Technology

[0002] Currently, the internal management of the property insurance industry is undergoing a profound transformation towards a value creation-oriented approach, placing unprecedented demands on the measurement of unearned premium liabilities under insurance contracts, requiring greater precision and transparency. As a core component of property insurance companies' financial statements, the measurement results of unearned premium liabilities directly impact the accuracy and compliance of financial data, as well as the scientific basis of management's decision-making. Furthermore, short-term direct insurance business, a core business segment for property insurance companies, presents a significant challenge for the industry: how to accurately measure unearned premium liabilities based on the premium allocation method.

[0003] Currently, the industry generally adopts the simplified method of baseline linear amortization for measuring unearned liabilities in short-term direct insurance business. This method calculates the earned premium and unearned liabilities by evenly amortizing the policy premium over the number of days in the insurance period. The operation process is simple and the calculation logic is clear, which can meet the basic accounting needs and is widely used in the industry.

[0004] However, this uniform amortization method has significant technical flaws. It cannot adapt to the differentiated risk distribution characteristics of different policies. For policies with non-uniform risk characteristics such as agricultural insurance with seasonal fluctuations, engineering insurance with parabolic distribution, and auto insurance policies with stop-and-go driving situations, the measurement results deviate systematically from the actual risk exposure. At the same time, it cannot be dynamically adjusted for special scenarios. In addition, current property insurance measurement technology is limited by computational complexity and system architecture, and generally adopts contract group or policy-level measurement, which makes it difficult to distinguish the true value of different types of insurance under the same policy. It is also difficult to meet the requirements for refined measurement and compliant disclosure of unearned liabilities, thus restricting the risk control and value management capabilities of property insurance companies. Summary of the Invention

[0005] This invention provides a method and system for measuring unmatured liabilities under property insurance contracts. It overcomes the limitations of traditional single risk distribution models and solves the problems of missing measurement and insufficient allocation granularity in scenarios of suspension and resumption of operation. At the same time, it ensures comprehensive data coverage and complete calculation logic, enabling refined measurement of unmatured liabilities and providing core data support for IFRS 17 compliance and refined management.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: Firstly, a method for measuring unearned liabilities under property insurance contracts is provided. The method includes: acquiring insurance business and expense-related data, which includes basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, exchange rate information, cumulative data from the previous period, and measurement indicators from the previous period. The cumulative data from the previous period includes the cumulative present value of the decomposed investment components, cumulative premiums collected, cumulative insurance contract revenue, cumulative acquisition costs paid and amortized, and cumulative unearned interest. The measurement indicators from the previous period include the non-loss amount of unearned liabilities from the previous period and the insurance contract revenue recognized in the current month of the previous period. The method involves: performing field length validation, necessary field non-empty validation, and business rule validation on the insurance business and expense-related data to determine if the validation passes; if the validation passes, based on the insurance business and expense-related data, selecting the corresponding risk distribution mode according to the risk distribution characteristic identifier, dynamically correcting the unearned amortization ratio in conjunction with the suspension and resumption information, separating earned premiums from unearned premiums, separating insurance service components from investment components, and calculating the investment. The present value of components is calculated at three levels: policy, type of insurance, and endorsement, to determine the non-loss value of unearned premium liabilities. Based on this non-loss value and actuarial parameters, future cash flows are predicted, and the present value is calculated by matching discount factors at the cash flow occurrence points. The present value is then compared with the non-loss value of unearned premium liabilities to determine loss-making contract groups and loss amounts, generating loss contract profit and loss data. Based on the cross-dimensional three rates, non-financial risk adjustment factor, and unearned premiums in the insurance business and expense-related data, each policy is calculated based on a preset allocation basis. The unearned premium loss at the contract group level is allocated to the policy level according to the allocation ratio, resulting in a policy-level liability measurement result. The preset allocation basis includes unearned premium, acquisition costs, and premium difference. The policy-level liability measurement results are summarized according to preset dimensions to generate a measurement report. The measurement report includes the unearned premium non-loss value, loss amount, earned premium, unearned premium, investment component, amortized acquisition costs, cash flow - premium received, cash flow - acquisition costs paid, and loss on loss contracts.

[0007] The method provided by this invention acquires comprehensive multi-dimensional data, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information. After verification, it matches the corresponding risk distribution pattern according to the risk distribution characteristics, adjusts the amortization ratio by combining the suspension and resumption information, separates the premium and investment components, and calculates the non-loss value at three levels of granularity: policy, type of insurance, and endorsement. Then, it generates a measurement report through cash flow forecasting, loss determination, multi-dimensional allocation, and result aggregation. This method not only breaks through the limitations of traditional single risk distribution models but also solves the problems of missing measurement and insufficient allocation granularity in suspension and resumption scenarios. At the same time, it ensures the comprehensiveness of data coverage and the integrity of calculation logic, realizing the refined measurement of unearned liabilities and providing core data support for IFRS 17 compliance and refined management.

[0008] In one possible implementation of the first aspect, the risk distribution pattern includes uniform distribution, non-uniform distribution in the first segment, non-uniform distribution in the second segment, special treatment of short-term policies, parabolic distribution, and specific rule distribution. Different risk distribution patterns correspond to different mathematical calculation models.

[0009] The method provided by this invention clearly defines risk distribution patterns, including uniform distribution, uneven distribution in the early stage, uneven distribution in the later stage, special treatment for short-term policies, parabolic distribution, and distribution according to specific rules. Each pattern corresponds to a specific mathematical calculation model. Compared with the traditional uniform 1 / 365 linear amortization method, it can accurately adapt to the risk characteristics of different policies, making the calculation of the amortization ratio before maturity highly consistent with the actual risk exposure, significantly reducing the systematic bias of the measurement results, and improving the accuracy and reliability of liability measurement.

[0010] In one possible implementation of the first aspect, the step of selecting a corresponding risk distribution pattern based on the risk distribution feature identifiers of the insurance business and expense-related data, dynamically correcting the unearned premium amortization ratio in conjunction with the suspension and resumption information, splitting earned premiums and unearned premiums, splitting insurance service components and investment components and calculating the present value of investment components, and calculating the non-loss value of unearned liability at three levels of granularity: policy, type of insurance, and endorsement, includes: extracting risk distribution feature identifiers from the insurance business and expense-related data and matching them with corresponding risk distribution patterns; calculating the initial unearned premium amortization ratio based on the mathematical calculation model corresponding to the risk distribution pattern; extracting suspension and resumption information from the insurance business and expense-related data, associating suspension and resumption details, and adjusting the ratio based on the monthly progress period. Expanding on a monthly basis, the initial unexpired amortization ratio is adjusted based on the number of days of downtime in the current month to obtain the adjusted unexpired amortization ratio, and the downtime and resumption correction cash flow data is generated. Based on the adjusted unexpired amortization ratio, earned premiums and unearned premiums are separated. The insurance service component and investment component in the insurance business and expense-related data are separated using a dynamic ratio method, and the present value of the investment component is calculated based on the time value principle combined with interest rate information. At the three-level granularity of policy, type of insurance, and endorsement, the non-loss value of unexpired liability is calculated based on the earned premiums, unearned premiums, present value of investment components, and actuarial parameters such as theoretical loss ratio, theoretical maintenance expense ratio, and non-financial risk adjustment rate, combined with the current and previous period liability changes, unexpired interest, and exchange rate gains and losses.

[0011] The method provided by this invention extracts risk distribution feature identifiers to match corresponding patterns and calculates the initial and weighted amortization ratios for unexpired premiums. It then correlates the details of suspension and resumption of operations by monthly progress periods and corrects the ratios, simultaneously generating revised cash flow data for suspension and resumption of operations. Next, it separates premiums and investment components, and finally integrates earned premiums, unearned premiums, present value of investment components, cumulative indicators, and actuarial parameters such as theoretical loss ratio, theoretical maintenance expense ratio, and non-financial risk adjustment rate. It calculates the non-loss value of unexpired liabilities at a three-level granularity, using the formula: Non-loss portion of unexpired liabilities = Non-loss of unexpired liabilities from the previous period + Current month's actual premiums + Unexpired interest + Acquisition costs of amortization - Acquisition costs paid in the current period - Insurance contract revenue - Decomposed investment components. This method not only solves the problem of missing processing for suspension and resumption scenarios in existing technologies but also improves the calculation logic by incorporating the non-financial risk adjustment rate. Simultaneously, it generates revised cash flow data to ensure the accuracy of subsequent measurements, achieving temporal consistency and traceability of measurement results in suspension and resumption scenarios.

[0012] In one possible implementation of the first aspect, the step of predicting future performance cash flows based on the non-loss value of the unearned premium liability and actuarial parameters, calculating the present value by matching discount factors according to the cash flow occurrence time, and determining the loss contract group and loss amount by comparing the present value with the non-loss value of the unearned premium liability, and generating loss contract profit and loss data, includes: extracting actuarial parameters from the insurance business and expense-related data, the actuarial parameters including expected loss ratio, interest rate curve, discount factor, liquidity premium parameter and unearned premium payment mode parameter, and linking them to the previous period's loss measurement results; predicting future performance cash flows within the remaining policy term based on the actuarial parameters and basic policy information, the future performance cash flows including expected premium cash flows, expected acquisition cost cash flows, expected maintenance cost cash flows and expected claims cash flows; and matching the present value with the occurrence time characteristics of different types of cash flows. Using a discount factor adjusted for liquidity premium, the present value of different types of cash flows is calculated and aggregated to obtain the total present value of future performance cash flows. The total present value of future performance cash flows for each contract is compared with the non-loss value of unearned liabilities. If the total present value of future performance cash flows is greater than the non-loss value of unearned liabilities, the contract is classified as a loss contract group, and the difference between the total present value of future performance cash flows and the non-loss value of unearned liabilities is the loss amount. The change in current period's loss is calculated based on the previous period's loss balance. Based on the exchange rate and the proportion of each currency in the unearned non-loss RMB amount within the contract group, the RMB equivalent and original currency amounts are measured separately for policies with foreign currency, yielding the calculation results. Based on these calculation results, loss contract profit and loss data is generated, including the loss contract group identifier, loss amount, change in current period's loss amount, and multi-currency measurement results.

[0013] The method provided by this invention extracts actuarial parameters including liquidity premium parameters and correlates them with previous period loss results to predict multiple types of future performance cash flows during the remaining policy term, including expected premium cash flows, expected acquisition cost cash flows, expected maintenance cost cash flows, and expected claims cash flows. The present value of each type of cash flow is calculated separately by matching a discount factor adjusted for liquidity premium according to the timing characteristics of the cash flows, and then the results are combined to obtain the total present value of future performance cash flows. The calculation formula is: Total present value of future performance cash flows = - Present value of expected premium cash flows + Present value of expected acquisition cost cash flows + Present value of expected maintenance cost cash flows + Present value of expected claims cash flows. By comparing the total present value of future performance cash flows at the contract group level with the non-loss value of unexpired liabilities, loss-making contract groups and amounts are determined. Combining the exchange rate and the proportion of unexpired non-loss RMB amounts in each currency within the contract group, the original currency and RMB equivalent amounts are separately measured for foreign currency policies. This method improves the accuracy of cash flow present value calculation by adjusting the discount factor with liquidity premium, clarifies the allocation rules for multi-currency measurement, solves the ambiguity problem of traditional multi-currency processing, ensures the completeness and accuracy of loss contract identification and measurement, and meets the needs of international business scenarios.

[0014] In one possible implementation of the first aspect, based on the cross-dimensional three ratios, non-financial risk adjustment factor, and unexpired premiums in the insurance business and expense-related data, the allocation ratio for each policy is calculated based on a preset allocation basis. The unexpired liability loss value at the contract group level is allocated to the policy level according to the allocation ratio to obtain the policy-level liability measurement result. This includes: extracting business dimension information, cross-dimensional three ratios, non-financial risk adjustment factor, and unexpired premiums from the insurance business and expense-related data; constructing a complete allocation data basis by associating it with the previous period's allocation results; the business dimension information includes cost center segment, channel segment, insurance type segment, and accounting unit; the cross-dimensional three ratios include loss ratio, maintenance expense ratio, and surrender rate; constructing an allocation carrier based on the granularity of policy, insurance type, and endorsement; and integrating the business dimension information, expected future cash flow data, and insurance cash flow data to form... A complete allocation carrier data structure is established; based on the preset allocation basis, combined with the three cross-dimensional rates and non-financial risk adjustment factors, the allocation weight of each policy is calculated, wherein the allocation weight calculation formula is: max[0, acquisition expense ratio + surrender rate + loss ratio * (1 - surrender rate) * (1 + non-financial risk adjustment factor) + maintenance expense ratio * (1 + non-financial risk adjustment factor) - 1] * unearned premium cash flow; the allocation weight of all policies in the contract group is summarized to obtain the total weight, and the allocation ratio of each policy is determined by the ratio of the allocation weight of each policy to the total weight; the non-loss value and loss amount of the unearned liability at the contract group level are allocated to each policy according to the allocation ratio; the insurance contract income, the amount of the decomposed investment component, and the amortization data of insurance acquisition cash flow at the policy level are calculated, and combined with the current and previous period liability changes, a policy-level liability measurement result including complete indicators is generated.

[0015] The method provided by this invention extracts business dimension information, cross-dimensional three rates, non-financial risk adjustment factors, and unexpired premiums, and constructs an allocation data foundation by linking it with previous period results. It builds a carrier at a three-level granularity and integrates multiple types of data. Based on a preset allocation foundation, it calculates allocation weights and ratios, and allocates non-loss values ​​and loss amounts at the contract group level to the policy. Finally, it generates policy-level results containing indicators such as insurance contract income and decomposed investment components. This method achieves refined allocation from the contract group to the policy level, and ensures the continuity of allocation by linking with previous period data. It accurately reflects the true value contribution of different insurance types under the same policy, providing high-precision data support for product portfolio optimization and channel benefit analysis.

[0016] In one possible implementation of the first aspect, the preset dimensions include a basic identification dimension, a business management dimension, and a financial accounting dimension; wherein, the basic identification dimension includes a unique policy identifier, a contract group number, and a contract combination attribution identifier; the business management dimension includes the type of insurance, business channel, cost center segment, underwriting year, and risk level; and the financial accounting dimension includes currency, assessment time point, and progress period.

[0017] The method provided by this invention ensures that the measurement results can be accurately located to the specific measurement object through multi-dimensional aggregation logic, while also meeting the dual needs of business operation analysis and financial accounting disclosure. This enables the measurement report to support internal and external audit traceability and provide management with multi-perspective decision-making basis, thereby improving the flexibility and comprehensiveness of data application.

[0018] In one possible implementation of the first aspect, the measurement report is used for downstream business processing, which includes: outstanding IBNR allocation, outstanding measurement and allocation using the direct insurance premium allocation method, and transmitting current valuation period cash flow data to the accounting engine to generate journal entries and financial documents.

[0019] The method provided by this invention applies measurement reports to outstanding IBNR allocations and outstanding measurement and allocations under the direct insurance premium allocation method, and transmits cash flow data to the accounting engine to generate journal entries and financial vouchers. This constructs a complete data link from liability measurement to downstream business applications, ensuring the consistency of outstanding allocation and liability measurement data, automating the generation of financial vouchers, reducing the error rate caused by manual intervention, improving the efficiency and compliance of financial processing, and providing complete data chain support for audit traceability.

[0020] Secondly, this invention provides a measurement system for unexpired liabilities under property insurance contracts. The system includes: a data acquisition module for acquiring insurance business and expense-related data, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information; a data verification module for performing field length verification, necessary field non-empty verification, and business rule verification on the insurance business and expense-related data, and determining whether the verification passes; a first calculation module, which, if the verification passes, based on the insurance business and expense-related data, selects the corresponding risk distribution mode according to the risk distribution characteristic identifier, dynamically corrects the unexpired amortization ratio based on the suspension and resumption information, separates earned premiums and unearned premiums, separates insurance service components and investment components and calculates the present value of the investment components, and calculates the non-loss value of unexpired liabilities at three levels: policy, type of insurance, and endorsement; and a second calculation module, which calculates the non-loss value of unexpired liabilities based on the non-loss value of the unexpired liabilities. The system uses loss value and actuarial parameters to predict future performance cash flows, calculates the present value by matching discount factors at the time of cash flow occurrence, and determines the loss contract group and loss amount by comparing the present value with the non-loss value of unearned premium liabilities, generating loss contract profit and loss data. The liability allocation module calculates the allocation ratio for each policy based on the cross-dimensional three rates, non-financial risk adjustment factor, and unearned premium in the insurance business and expense-related data, using a preset allocation basis. It allocates the unearned premium liability loss value at the contract group level to the policy level according to the allocation ratio, obtaining policy-level liability measurement results. The preset allocation basis includes unearned premium, acquisition costs, and premium difference. The result aggregation module aggregates the policy-level liability measurement results according to preset dimensions and generates a measurement report. The measurement report includes the non-loss value of unearned premium liabilities, loss amount, earned premium, unearned premium, investment component, amortized acquisition costs, cash flow - premium received, cash flow - acquisition costs paid, and loss contract profit and loss.

[0021] Thirdly, an electronic device is provided, the electronic device including a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the method as described in any implementation of the first aspect.

[0022] Fourthly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform a method as described in any implementation of the first aspect.

[0023] Fifthly, a computer program product is provided that, when run on a computer, causes the computer to perform the method in any implementation of the first aspect.

[0024] Understandably, the beneficial effects achieved by the system of the second aspect, the electronic device of the third aspect, the computer-readable storage medium of the fourth aspect, and the computer program product of the fifth aspect provided above can be referred to with reference to the beneficial effects of the first aspect and any of its possible design embodiments, which will not be repeated here. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a method for measuring unexpired liabilities under a property insurance contract, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of a metering system provided in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. The "or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A or B can represent: A alone, A and B simultaneously, and B alone. A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.

[0027] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0028] In this embodiment of the invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this embodiment of the invention should not be construed as superior or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0029] Currently, the internal management of the property insurance industry is undergoing a profound transformation towards a value creation-oriented approach, placing unprecedented demands on the measurement of unearned premium liabilities under insurance contracts, requiring greater precision and transparency. As a core component of property insurance companies' financial statements, the measurement results of unearned premium liabilities directly impact the accuracy and compliance of financial data, as well as the scientific basis of management's decision-making. Furthermore, short-term direct insurance business, a core business segment for property insurance companies, presents a significant challenge for the industry: how to accurately measure unearned premium liabilities based on the premium allocation method.

[0030] Currently, the industry generally adopts the simplified method of baseline linear amortization for measuring unearned liabilities in short-term direct insurance business. This method calculates the earned premium and unearned liabilities by evenly amortizing the policy premium over the number of days in the insurance period. The operation process is simple and the calculation logic is clear, which can meet the basic accounting needs and is widely used in the industry.

[0031] However, this uniform amortization method has significant technical flaws. It cannot adapt to the differentiated risk distribution characteristics of different policies. For policies with non-uniform risk characteristics such as agricultural insurance with seasonal fluctuations, engineering insurance with parabolic distribution, and auto insurance policies with stop-and-go driving situations, the measurement results deviate systematically from the actual risk exposure. At the same time, it cannot be dynamically adjusted for special scenarios. In addition, current property insurance measurement technology is limited by computational complexity and system architecture, and generally adopts contract group or policy-level measurement, which makes it difficult to distinguish the true value of different types of insurance under the same policy. It is also difficult to meet the requirements for refined measurement and compliant disclosure of unearned liabilities, thus restricting the risk control and value management capabilities of property insurance companies.

[0032] In view of this, embodiments of the present invention provide a method and system for measuring unearned liabilities under property insurance contracts. The method includes: acquiring insurance business and expense-related data, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information; performing field length validation, necessary field non-empty validation, and business rule validation on the insurance business and expense-related data, and determining whether the validation passes; if the validation passes, based on the insurance business and expense-related data, selecting the corresponding risk distribution mode according to the risk distribution characteristic identifier, dynamically correcting the unearned amortization ratio in combination with the suspension and resumption information, splitting earned premiums and unearned premiums, splitting insurance service components and investment components and calculating the present value of investment components, and calculating indicators such as the non-loss value of unearned liabilities and insurance contract revenue at three levels of granularity: policy, type of insurance, and endorsement; and based on the non-loss value of unearned liabilities... The system uses actuarial parameters to predict future performance cash flows, calculates the present value by matching discount factors at the time of cash flow occurrence, and determines the loss contract group and loss amount by comparing the present value with the non-loss value of unearned premium liabilities, generating loss contract profit and loss data. Based on the cross-dimensional three rates, non-financial risk adjustment factor, and unearned premium in the insurance business and expense related data, the system calculates the allocation ratio for each policy based on a preset allocation basis, and allocates the unearned premium liability loss value at the contract group level to the policy level according to the allocation ratio, obtaining the policy-level liability measurement result. The preset allocation basis includes unearned premium, acquisition costs, and premium difference. The system summarizes the policy-level liability measurement results according to preset dimensions and generates a measurement report. The measurement report includes the non-loss value of unearned premium liabilities, loss amount, earned premium, unearned premium, investment component, amortized acquisition costs, cash flow - premium received, cash flow - acquisition costs paid, and loss contract profit and loss.

[0033] The method provided by this invention acquires comprehensive multi-dimensional data, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information. After verification, it matches the corresponding risk distribution pattern according to the risk distribution characteristics, adjusts the amortization ratio by combining the suspension and resumption information, separates the premium and investment components, and calculates the non-loss value at three levels of granularity: policy, type of insurance, and endorsement. Then, it generates a measurement report through cash flow forecasting, loss determination, multi-dimensional allocation, and result aggregation. This method not only breaks through the limitations of traditional single risk distribution models but also solves the problems of missing measurement and insufficient allocation granularity in suspension and resumption scenarios. At the same time, it ensures the comprehensiveness of data coverage and the integrity of calculation logic, realizing the refined measurement of unearned liabilities and providing core data support for IFRS 17 compliance and refined management.

[0034] In some embodiments, the method for measuring unexpired liabilities under a property insurance contract provided by the present invention can be executed by a measurement system 100 for unexpired liabilities under a property insurance contract (hereinafter referred to as measurement system 100).

[0035] As an example, the metering system 100 can be any electronic device 200 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer. The specific implementation of the metering system 100 is not limited here.

[0036] Figure 1 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown. The electronic device 200 includes a processor 210, a memory 220, and a communication interface 230.

[0037] Processor 210 may include one or more processing cores. Processor 210 connects to various parts within electronic device 200 using various interfaces and lines, and performs various functions and processes data of electronic device 200 by running or executing instructions, programs, code sets, or instruction sets stored in memory 220, and by calling data stored in memory 220. Optionally, processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0038] The memory 220 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 220 may include a non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a program storage area. This program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc.

[0039] Communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing devices or Ethernet, wireless access network (RAN), wireless local area network (WLAN), etc.

[0040] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, and communication interface 230) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be located within the same device, i.e., as different devices within the same device, similar to the deployment of devices or components in a distributed system.

[0041] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 200. In other embodiments of the present invention, the electronic device 200 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0042] The following description, in conjunction with the accompanying drawings, illustrates a method for measuring unexpired liabilities under a property insurance contract, as provided by an embodiment of the present invention.

[0043] Figure 2 This is a flowchart illustrating a method for measuring unexpired liabilities under a property insurance contract, provided as an embodiment of the present invention. Optionally, this method can be... Figure 1 The illustrated electronic device 200 performs this method, which includes the following steps: S1. Obtain insurance business and expense related data, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information.

[0044] In one example, basic policy data includes a unique identifier, contract group number, contract combination number, policy number, endorsement number, insurance type code, policy start and end dates, policy days, underwriting year, business channel, cost center segment, and accounting unit.

[0045] Risk characteristic data includes risk distribution characteristic identifier, non-uniform distribution parameter, ultimate payout ratio, suspension and resumption identifier, suspension and resumption date period, number of suspension days in the current month, suspension and resumption details, and risk level.

[0046] Financial data includes actual premiums received, insurance cash flow, maintenance expenses, expected premium cash flow, expected cash flow from insurance acquisition expenses, expected cash flow from maintenance expenses, expected cash flow from claims, investment component amount, and investment component amortization amount.

[0047] Actuarial parameters include theoretical loss ratio, expected loss ratio, theoretical maintenance expense ratio, theoretical cash flow, adjusted theoretical loss ratio assumptions, non-financial risk adjustment rate, discount factor, liquidity premium parameter, interest rate curve data, out-of-maturity payout model parameter, investment component ratio, timing of various types of cash flows, and seasonality coefficient.

[0048] The contract group information includes the contract group affiliation identifier, the policy association within the contract group, the previous period's loss balance, and the percentage of outstanding non-loss RMB amounts in each currency within the contract group.

[0049] Interest rate information includes interest rate curve data; exchange rate information includes multi-currency exchange rate conversion data and a snapshot of the exchange rate used for measuring foreign currency insurance policies.

[0050] It should be understood that the measurement system 100 simultaneously retrieves cumulative data from the database for the previous period, including cumulative present value of investment components, cumulative premiums collected, cumulative insurance contract income, cumulative acquisition costs paid and amortized, and cumulative accrued interest on unearned premiums, etc., for calculating current period cumulative indicators; at the same time, it also retrieves various previous period measurement indicators such as the non-loss amount of unearned premium liabilities and the insurance contract income recognized in the previous month, for use in rolling over or accruing interest on current period indicators.

[0051] It should be understood that the above examples are for illustrative purposes only. The basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information may include more or fewer types of data or parameters than those mentioned above. This embodiment of the invention does not impose any particular limitation on this.

[0052] S2. Perform field length validation, necessary field non-empty validation, and business rule validation on the insurance business and expense-related data, and determine whether the validation passes.

[0053] In some embodiments, the method provided by the present invention further includes: Standardize and pre-aggregate insurance business and expense-related data.

[0054] The standardization process includes format conversion of raw data, unified field mapping, and preliminary calculation of the proportion of non-expired days based on the number of days of suspension and resumption of driving. The pre-aggregation process includes integrating premium and commission cash flows by contract group and progress period, preliminarily integrating actuarial assumption configuration data and policy basic data, and constructing a standardized and structured data structure to adapt to the subsequent measurement process.

[0055] S3. If the verification is successful, based on the insurance business and expense-related data, select the corresponding risk distribution mode according to the risk distribution characteristic identifier, dynamically correct the unexpired amortization ratio in combination with the suspension and resumption information, split the earned premium and unearned premium, split the insurance service component and investment component and calculate the present value of the investment component, and calculate the non-loss value of unexpired liability at the three-level granularity of policy, type of insurance and endorsement. In one possible implementation, the risk distribution pattern includes uniform distribution, non-uniform distribution in the first half, non-uniform distribution in the second half, special treatment for short-term policies, parabolic distribution, and specific rule distribution. Different risk distribution patterns correspond to different mathematical calculation models.

[0056] Specifically, the risk distribution feature identifier is a classification mark embedded in insurance business and expense-related data, used to directly associate specific risk distribution patterns. Each risk distribution pattern is equipped with a dedicated mathematical calculation model, and the core parameters and calculation logic of the model are customized according to the corresponding risk characteristics.

[0057] Among them, the uniform distribution model uses the 365-day method as its core logic, calculating the unexpired amortization ratio by the ratio of the actual number of days the policy lasts to the total number of days in the insurance period. This model is suitable for policies such as ordinary property insurance where risks are released steadily throughout the insurance period. The uneven distribution model at the beginning introduces an early-stage risk coefficient, which is preset based on the early-stage risk exposure of the insurance type. Through weighted calculation, it makes the unexpired amortization ratio in the early stage of the policy higher than that in the later stage, making it suitable for businesses such as agricultural insurance where risks are concentrated in the early stages of underwriting. The uneven distribution model at the end focuses on setting the later-stage risk coefficient, allowing the unexpired amortization ratio in the later stages of the policy to be higher. The amortization ratio is higher, which can match policy types where risks are concentrated at the end of the insurance period; the mathematical calculation model for the special handling mode of short-term policies simplifies redundant parameters and improves processing efficiency by shortening the calculation link, specifically adapting to policies with extremely short insurance periods; the mathematical calculation model for the parabolic distribution mode uses a quadratic function formula to fit the risk change curve, and accurately matches the distribution characteristics of risks such as engineering insurance that rise first and then fall by setting vertex parameters, slope coefficients, etc.; the mathematical calculation model for the specific rule distribution mode supports different calculation methods for the start date, such as using the square ratio calculation method for policies with the start date before a specific date to achieve accurate measurement. The calculation formula is: Unexpired amortization ratio = 1 - (Number of days elapsed / Total number of days in the insurance period)².

[0058] This invention overcomes the limitations of the traditional 1 / 365 linear amortization method by configuring differentiated risk distribution patterns and corresponding mathematical calculation models for policies with different risk characteristics. This allows the calculation of the unexpired amortization ratio to closely reflect the actual risk exposure of various policies, fundamentally reducing the systematic bias caused by a uniform calculation method. This makes the measurement results of unexpired liabilities of property insurance contracts more reliable and provides more accurate data support for subsequent financial accounting and business decisions.

[0059] Furthermore, S3 specifically includes: extracting risk distribution feature identifiers from insurance business and expense-related data, and matching them with corresponding risk distribution patterns; calculating the initial unexpired amortization ratio based on the mathematical calculation model corresponding to the risk distribution pattern; extracting suspension and resumption information from the insurance business and expense-related data, associating suspension and resumption details and expanding based on the monthly progress period, and correcting the initial unexpired amortization ratio based on the number of suspension days in the current month during monthly cumulative calculation to obtain the corrected unexpired amortization ratio, and generating suspension and resumption corrected cash flow data; and based on the correction... The positive amortization ratio for unearned premiums is used to separate earned premiums and unearned premiums. A dynamic ratio method is employed to separate the insurance service component and investment component from the insurance business and expense-related data. The present value of the investment component is calculated based on the time value principle and interest rate information. At the policy, insurance type, and endorsement levels, the non-loss value of unearned premium liabilities is calculated based on the earned premiums, unearned premiums, present value of the investment component, and actuarial parameters such as the theoretical loss ratio, theoretical maintenance expense ratio, and non-financial risk adjustment ratio. This is combined with changes in liabilities between the current and previous periods, unearned interest accrual, and exchange rate gains and losses.

[0060] The method provided by this invention extracts risk distribution feature identifiers to match corresponding patterns and calculates the initial and weighted amortization ratios for unexpired liabilities. It then correlates the details of suspension and resumption of operations by monthly progress periods and corrects the ratios, simultaneously generating revised cash flow data for suspension and resumption. Based on the proportion of accrual premiums and investment components, it calculates the present value of investment components and the investment components decomposed from claims and expenses. Finally, it integrates earned premiums, unearned premiums, the present value of investment components, cumulative indicators, and actuarial parameters such as theoretical loss ratio, theoretical maintenance expense ratio, and non-financial risk adjustment rate. The method calculates the non-loss value of unexpired liabilities at a three-level granularity. The calculation formula is: Unexpired Liabilities - Non-Loss Portion = Previous Period Unexpired Liabilities - Non-Loss + Current Month's Paid Premiums + Unexpired Interest + Acquisition Costs of Amortization - Acquisition Costs Paid in the Current Period - Insurance Contract Revenue - Decomposed Investment Components. This method not only solves the problem of missing processing for suspension and resumption scenarios in existing technologies but also improves the calculation logic by incorporating the non-financial risk adjustment rate. Simultaneously, it generates revised cash flow data to ensure the accuracy of subsequent measurements, achieving temporal consistency and traceability of measurement results in suspension and resumption scenarios.

[0061] S4. Based on the non-loss value of the unexpired liability and actuarial parameters, predict future performance cash flows, calculate the present value by matching the discount factor at the time of cash flow occurrence, determine the loss contract group and loss amount by comparing the present value with the non-loss value of the unexpired liability, and generate loss contract profit and loss data.

[0062] In some embodiments, S4 above includes: extracting actuarial parameters from the insurance business and expense-related data, the actuarial parameters including expected loss ratio, interest rate curve, discount factor, liquidity premium parameter, and unearned premium payout mode parameter, and associating them with the previous period's loss measurement results; based on the actuarial parameters and basic policy information, predicting future performance cash flows over the remaining policy term, the future performance cash flows including expected premium cash flows, expected acquisition cost cash flows, expected maintenance cost cash flows, and expected payout cash flows; matching the discount factor adjusted by the liquidity premium according to the timing characteristics of different types of cash flows, calculating the present value of different types of cash flows separately, and summing them up to obtain the total present value of future performance cash flows; compared For each contract, the present value of the total future cash flows and the non-loss value of the unearned liabilities are compared. If the present value of the total future cash flows is greater than the non-loss value of the unearned liabilities, the contract is classified as a loss contract group, and the difference between the present value of the total future cash flows and the non-loss value of the unearned liabilities is the loss amount. The change in current period's loss is calculated based on the previous period's loss balance. Based on the exchange rate and the proportion of each currency in the unearned non-loss RMB amount within the contract group, the RMB equivalent and the original currency amount are measured separately for policies with foreign currency, yielding the calculation results. Based on these calculation results, loss contract profit and loss data is generated, including the loss contract group identifier, loss amount, change in current period's loss amount, and multi-currency measurement results.

[0063] The method provided by this invention extracts actuarial parameters including liquidity premium parameters and correlates them with previous period loss results to predict multiple types of future performance cash flows during the remaining policy term, including expected premium cash flows, expected acquisition cost cash flows, expected maintenance cost cash flows, and expected claims cash flows. The present value of each type of cash flow is calculated separately by matching a discount factor adjusted for liquidity premium according to the timing characteristics of the cash flows, and then the results are combined to obtain the total present value of future performance cash flows. The calculation formula is: Total present value of future performance cash flows = - Present value of expected premium cash flows + Present value of expected acquisition cost cash flows + Present value of expected maintenance cost cash flows + Present value of expected claims cash flows. By comparing the total present value of future performance cash flows at the contract group level with the non-loss value of unexpired liabilities, loss-making contract groups and amounts are determined. Combining the exchange rate and the proportion of unexpired non-loss RMB amounts in each currency within the contract group, the original currency and RMB equivalent amounts are separately measured for foreign currency policies. This method improves the accuracy of cash flow present value calculation by adjusting the discount factor with liquidity premium, clarifies the allocation rules for multi-currency measurement, solves the ambiguity problem of traditional multi-currency processing, ensures the completeness and accuracy of loss contract identification and measurement, and meets the needs of international business scenarios.

[0064] S5. Based on the cross-dimensional three rates, non-financial risk adjustment factor and unexpired premium in the insurance business and expense related data, calculate the allocation ratio of each policy based on the preset allocation basis, and allocate the unexpired liability loss value at the contract group level to the policy level according to the allocation ratio to obtain the policy-level liability measurement result.

[0065] The preset apportionment basis includes the unexpired premium, acquisition costs, and the premium difference.

[0066] In one possible implementation of the first aspect, S5 includes: extracting business dimension information, cross-dimensional ratios, non-financial risk adjustment factors, and unexpired premiums from the insurance business and expense-related data; constructing a complete allocation data foundation by associating it with the previous period's allocation results; the business dimension information includes cost center segment, channel segment, insurance type segment, and accounting unit; the cross-dimensional ratios include loss ratio, maintenance expense ratio, and surrender rate; constructing an allocation carrier based on policy, insurance type, and endorsement granularity; and integrating the business dimension information, expected future cash flow data, and insurance cash flow data to form a complete allocation. The data structure is as follows: Based on the preset allocation basis, combined with the three cross-dimensional rates and non-financial risk adjustment factors, the allocation weight of each policy is calculated; the total weight is obtained by summing the allocation weights of all policies within the contract group, and the allocation ratio of each policy is determined by the ratio of the allocation weight of each policy to the total weight; the non-loss value and loss amount of the unearned liability at the contract group level are allocated to each policy according to the allocation ratio; the insurance contract income, investment component amortization amount and insurance cash flow amortization data at the policy level are calculated, and combined with the current and previous period liability changes, a policy-level liability measurement result including complete indicators is generated.

[0067] The formula for calculating the apportionment weight is: max[0, acquisition expense ratio + surrender rate + loss ratio * (1 - surrender rate) * (1 + non-financial risk adjustment factor) + maintenance expense ratio * (1 + non-financial risk adjustment factor) - 1] * unearned premium cash flow.

[0068] The method provided by this invention extracts business dimension information, cross-dimensional three rates, non-financial risk adjustment factors, and unexpired premiums, and constructs an allocation data foundation by linking it with previous period results. It builds a carrier at a three-level granularity and integrates multiple types of data. Based on a preset allocation foundation, it calculates allocation weights and ratios, and allocates non-loss values ​​and loss amounts at the contract group level to the policy. Finally, it generates policy-level results containing indicators such as insurance contract income and investment component amortization. This method achieves refined allocation from the contract group to the policy level, and ensures the continuity of allocation by linking with previous period data. It accurately reflects the true value contribution of different insurance types under the same policy, providing high-precision data support for product portfolio optimization and channel benefit analysis.

[0069] S6. Summarize the policy-level liability measurement results according to preset dimensions and generate a measurement report. The measurement report includes the non-loss value of unearned premium liabilities, loss amount, earned premiums, unearned premiums, investment components, amortized acquisition costs, cash flow - premiums received, cash flow - acquisition costs paid, and loss on loss contracts.

[0070] In one possible implementation, the preset dimensions include a basic identification dimension, a business management dimension, and a financial accounting dimension; wherein, the basic identification dimension includes a unique policy identifier, a contract group number, and a contract combination attribution identifier; the business management dimension includes the type of insurance, business channel, cost center segment, underwriting year, and risk level; and the financial accounting dimension includes currency, assessment time point, and progress period.

[0071] It should be noted that the preset dimensions are the core basis for classifying and summarizing the policy-level liability measurement results. The three dimensions complement each other and cover the measurement needs of the entire scenario.

[0072] The unique policy identifier in the basic identification dimension is the core identifier for locating the measurement results of a single policy. The contract group number and contract combination attribution identifier are used to associate related policies under the same group, ensuring that data at the contract group and contract combination level can be accurately collected during aggregation.

[0073] In the business management dimension, insurance type, business channel, and cost center segment are used to classify according to business operation attributes. The underwriting year can be summarized by time period, and the risk level supports data filtering by risk attribute. These dimensions can meet the needs of management to analyze the performance and risk distribution of different business segments and channels.

[0074] In the financial accounting dimension, currency adaptation is used for multi-currency measurement scenarios, the assessment time point clarifies the time benchmark of the measurement results, and the progress period corresponds to the advancement stage of the policy during the insurance period. The three together ensure the standardization of financial statement preparation, accounting and disclosure.

[0075] The method provided by this invention constructs a three-level preset dimension system, which enables the aggregation of measurement results to take into account specific object positioning, business operation analysis and financial accounting disclosure. It can meet the traceability requirements of internal and external audits without additional data processing, and at the same time provides management with multi-perspective decision-making data, so that the same set of measurement results can be adapted to the application needs of different scenarios, which greatly improves the utilization rate and application flexibility of data.

[0076] Optionally, the measurement report is used for downstream business processing, which includes: outstanding IBNR allocation, outstanding measurement and allocation of direct insurance premium allocation method, and transmitting current valuation period cash flow data to the accounting engine to generate journal entries and financial documents.

[0077] It should be understood that the core data included in the measurement report, such as the non-loss value of unearned premium liabilities, the amount of loss, and earned premiums, are the core data support for downstream business processing. IBNR outstanding allocation needs to be estimated based on the risk exposure data and claims-related parameters in the measurement report, while the outstanding measurement and allocation of the direct insurance premium allocation method relies on the premium breakdown and liability allocation results in the report to ensure consistency in calculation logic. Both are based on the data in the measurement report to avoid data discrepancies.

[0078] Furthermore, the current valuation period cash flow data transmitted to the accounting engine comes directly from the investment component amortization data, loss contract profit and loss details, etc. in the measurement report. The accounting engine can automatically generate journal entries and financial vouchers without additional data processing, and the entire process does not require manual entry or adjustment.

[0079] The method provided by this invention enables measurement results to be directly linked to downstream core business processes. This not only ensures a high degree of consistency between outstanding allocation data and liability measurement, reducing deviations during data transmission, but also reduces errors that may arise from manual operations by automatically generating financial vouchers, significantly improving the efficiency of financial processing. At the same time, the complete data transmission chain allows for clear traceability of the source and calculation process of each piece of data, providing strong support for internal and external audits and further strengthening the compliance of business processing.

[0080] As can be seen from S1-S6 above, the method provided by the embodiments of the present invention obtains complete data from multiple dimensions, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information. After verification, it matches the corresponding risk distribution pattern according to the risk distribution characteristic identifier, corrects the amortization ratio by combining the suspension and resumption information, splits the premium and investment components, and calculates the non-loss value at three levels of granularity: policy, type of insurance, and endorsement. Then, through cash flow forecasting, loss determination, multi-dimensional allocation, and result summary, a measurement report is generated. This not only breaks through the limitations of the traditional single risk distribution model, but also solves the problems of missing measurement and insufficient allocation granularity in the suspension and resumption scenario. At the same time, it ensures the comprehensiveness of data coverage and the integrity of calculation logic, realizes the refined measurement of unexpired liabilities, and provides core data support for IFRS 17 compliance and refined management.

[0081] To facilitate understanding of this solution, the following detailed explanation of the method provided in this embodiment of the invention will be provided with reference to a specific example.

[0082] In one example, at the assessment date of 24:00 on June 30, 2024, Property Insurance Company A needs to measure the unearned liabilities of the engineering insurance policy. In the following examples, the beginning date refers to 24:00 on May 31, 2024, and the ending date refers to 24:00 on June 30, 2024. The method provided in this embodiment of the invention specifically includes the following steps: [Special Note: The following measurement process considers interest calculation. For demonstration purposes, the interest calculation process will be simplified; the parameters such as the investment component ratio and risk distribution characteristic identifier in this example are assumptions for demonstration purposes, used to show the complete logical flow of the IFRS 17 measurement method, and do not represent consistency with actual property insurance actuarial practice.] Obtain data related to insurance business and expenses, including: Basic policy data: Policy number A001, endorsement number A001-1, insurance type code C001, unique identifier A001-1C001 (composed of policy number / endorsement number + insurance type code); contract start and end dates: January 1, 2024 to December 31, 2024; contract group number: G001; premium excluding tax: RMB 3,600, paid in a lump sum upon policy effective date; expected commission payment: RMB 1,100, of which RMB 600 was paid on the policy effective date, RMB 100 was paid on June 1, 2024, and the remaining RMB 400 will be paid on December 31, 2024; investment component ratio: 2.78%, with investment component settlement date being the policy termination date; Financial data: Monthly premium received: 0 yuan; expected premium received at the end of the period: 0 yuan (since the policy was paid in a lump sum upon policy effectiveness, the expected future premium at the end of the period is 0); Monthly commission paid: 100 yuan; expected acquisition fee paid at the end of the period: 400 yuan. Risk characteristic data: Risk distribution characteristic identifier 4, stop and resume indicator is 0 (no stop and resume situation for engineering insurance); Actuarial assumptions: Under this risk distribution, the theoretical loss ratio of the policy is 60%, the cash flow generation ratio is 20%, and the theoretical maintenance expense ratio is 20%; the expected loss ratio of this contract group is 60%, the expected maintenance expense ratio is 20%, the expected indirect claims expense ratio is 5%, and the expected surrender rate is 5%; the non-financial risk adjustment ratio is 5%, and the liquidity premium parameter is 0.01; it is assumed that the premium collection point and the cash flow generation payment point are both at the beginning of the month, and the claim occurrence point and the maintenance expense occurrence point are both at the end of the month. Interest rate information: discount factor 0.98, yield curve data with a one-year interest rate of 2%, and USD / CNY exchange rate of 7.2.

[0083] The system also retrieves cumulative data from the database for the previous period, including: cumulative premiums collected at the beginning of the period of 3,630 yuan (interest-bearing: 3,600 * (1 + 2% / 12 * 5) = 3,630); cumulative acquisition costs paid at the beginning of the period of 605 yuan (interest-bearing: 600 * (1 + 2% / 12 * 5) = 605); present value of investment components decomposed at the beginning of the period of 27.98 yuan (the system calculates the decomposed investment components monthly according to the weighted average amortization ratio, and automatically calculates them from the settlement date of the investment components to the valuation date); present value of insurance contract income recognized at the beginning of the period of 983.73 yuan (calculated by monthly rolling accumulation and interest); present value of acquisition costs amortized at the beginning of the period of 459.8 yuan (calculated by monthly rolling accumulation and interest); and non-loss value of unearned liabilities at the beginning of the period (assumed to be 800 yuan).

[0084] After verifying and approving the above data, integrate underwriting information, financial information, actuarial assumptions, and other data to form a structured information table, and perform the following calculations according to the granularity of policy / endorsement + insurance type: The first step is to calculate the amortization ratio for the remaining period: Average amortization ratio for unexpired periods: 6 months / 12 months = 50% Non-uniform risk coefficient: Based on risk distribution identifier 4 and the insured period being before August 31, 2024, the squared ratio calculation method is adopted. The calculation is simplified here, and the actual number of days is used instead of the number of months: 1 - ((6 months) / (12 months))² = 75%; Weighted average amortization ratio for unexpired expenses: = =65%; The second step is to break down the investment components and calculate relevant indicators: Based on the premium of 3600 yuan and the investment component ratio of 2.78%, the investment component is approximately 100 yuan. This investment component needs to be discounted to the valuation date from the settlement date (after the valuation date). The discount factor is 1 / (1 + interest rate)^0.5 = 0.99, resulting in a present value of approximately 99 yuan. The final calculation for the monthly allocation of the investment component is: Month-end present value of investment component * (1 - weighted average amortization ratio) - present value of accumulated investment component from the previous period = 99 * (1 - 65%) - 27.98 = 6.67 yuan.

[0085] The third step is to calculate the acquisition costs of insurance contract revenue and amortization: First, calculate the total expected premium cash flow (including received premiums and expected future premiums) based on the above data = cumulative premiums received at the beginning of the period + premiums received in the current month + expected premiums received at the end of the period = 3630 yuan. The total expected acquisition costs (including paid premiums and expected future payments) = cumulative acquisition costs paid at the beginning of the period + acquisition costs paid in the current month + expected acquisition costs paid at the end of the period = 605 + 100 * (1 + 2% / 12) + 400 * (1 + 12%)^(-0.5) = 1083 yuan.

[0086] Next, calculate the insurance contract revenue recognized and the acquisition costs amortized for the current month: Insurance contract revenue = Total expected premium cash flow * (1 - Weighted average amortization ratio) - Present value of cumulative recognized insurance contract revenue at the end of the previous period - Investment component allocated in the current month = 3630 * (1 - 65%) - 983.73 - 6.67 = 280.1 yuan; Acquisition costs to be amortized = Total expected acquisition costs to be paid * (1 - Average amortization ratio of unexpired periods) - Present value of acquisition costs amortized in the previous period at the end of the period = 1083 * (1 - 50%) - 459.8 = 81.7 yuan; Step 4: Calculate the non-loss value of unearned liabilities. First, calculate the interest on the non-loss portion of the accumulated outstanding liabilities at the end of the period = interest on the non-loss balance of outstanding liabilities from the previous period + interest on the actual premium received in the current month - interest on the acquisition fees paid in the current month = (800*(2% / 12)+0 - 100*(2% / 12)=1.17; Recalculate the unearned premium liability - non-loss portion = unearned premium liability liability - non-loss from the previous period + actual premium received this month - decomposed investment component + interest accrued on unearned premium + amortized acquisition costs - acquisition costs paid in the current period - insurance contract income = 800 + 0 - 6.67 + 1.17 + 81.7 - 100 - 280.1 = 496.1 yuan.

[0087] Fifth step: Calculate future cash flows on a monthly basis. Based on the policy's start and end dates, starting from the assessment date (June 30, 2024), the calculation proceeds monthly to the policy termination date (December 31, 2024). For each future month (e.g., July 31, August 31, September 30, 2024), the system independently calculates the unearned percentage at the end of that month to form a future cash flow forecast. The specific calculation method is as follows: [Note: For auto insurance policies with suspension and resumption of driving, the system will first identify the suspension and resumption markers, adjust the remaining unamortized percentage for the current period based on the suspension and resumption details, and use a recursive method to calculate the earned / unearned sequence month by month starting from the suspension period. This example is for engineering insurance, which does not involve suspension and resumption of driving.] Non-uniform unearned profit ratio: Following the same calculation logic as the non-uniform risk coefficient above, taking July 31 as an example, the unearned profit ratio = 1 - (7 months / 12 months)² ≈ 66%.

[0088] Future monthly non-uniform unearned premium cash flow: Based on the liability premium of 3600 yuan and the non-uniform unearned premium ratio at the end of each month, the future monthly non-uniform unearned premium cash flow is calculated (e.g., on July 31: 3600 * 66% = 2376). Non-uniform earned premium cash flow at the end of each future month: calculated by subtracting the unearned premium at the end of the current month from the unearned premium at the end of the previous month. For example, if the unearned premium cash flow at the end of July is 2376 yuan and the unearned premium at the end of August is 2016 yuan, then the earned premium cash flow in August will be 360 ​​yuan.

[0089] Step 6: Summarize cash flows and identify loss-making contracts: The cash flows generated in the previous step are summarized by contract group, and the discount factors at the corresponding time points are matched according to each cash flow type to obtain four types of cash flow present values, including: present value of expected premium cash flow, present value of expected acquisition cost cash flow, present value of expected maintenance cost cash flow (total earned premiums of each contract group + progress period level * maintenance cost rate, then summarized by contract group and discounted), and present value of expected claims cash flow (calculated month by month using the claims time pattern configured by actuarial science: claims cash flow = monthly earned premiums * loss ratio * (1 - surrender rate) * (1 + ulae rate), then summarized by contract group and discounted).

[0090] The present value of future performance cash flows is calculated as follows: - Present value of expected premium cash flows + Present value of expected acquisition cost cash flows + Present value of expected maintenance cost cash flows + Present value of expected claims cash flows.

[0091] The present value of future performance cash flows and the adjustment for outstanding non-financial risks at the contract group level are compared with the outstanding non-loss portion of outstanding liabilities at the contract group level to generate the final outstanding liability loss amount = max(Total present value of performance cash flows + (Present value of expected compensation cash flows + Present value of expected maintenance cost cash flows) * Outstanding non-financial risk adjustment rate - Outstanding non-loss portion of outstanding liabilities, 0). If the loss amount <= 0, it is a non-loss contract; if the loss amount > 0, the contract group is identified as a loss contract and loss allocation is required.

[0092] Step 7: Calculate the allocation ratio and allocate the losses. Extract business dimension information (contract group number, secondary institution code, channel), cross-dimensional three ratios (such as loss ratio 60%, maintenance expense ratio 20%, surrender rate 5%), etc. to construct the allocation basis, and calculate the allocation weight of the policy by policy + insurance type + endorsement granularity = max[0, get expense ratio + surrender rate + loss ratio * (1 - surrender rate) * (1 + non-financial risk adjustment rate) + maintenance expense ratio * (1 + non-financial risk adjustment rate) - 1] * unearned premium cash flow; Next, the allocation weights of all policies at the contract group level are aggregated, and the allocation ratio is obtained by dividing each policy's weight by the total weight. If the aggregate weights are 0, the ratio is calculated using either expected unearned premiums or "expected future acquisition costs - expected future premiums" as alternative factors according to priority. Finally, the loss amount at the contract group level is allocated according to the allocation ratio of each policy, generating policy-level liability measurement results that include indicators such as insurance contract income, unearned premium liabilities (non-loss), and unearned premium liabilities (loss).

[0093] Finally, the data is aggregated across three dimensions: basic identification (policy number + endorsement number + insurance type code), business management (secondary organization code, cost center code, channel, etc.), and financial accounting (currency, contract group number, contract combination, valuation method, valuation date, etc.). This generates a measurement report containing core indicators such as insurance contract revenue, unearned premium liabilities (non-loss portion), unearned premium liabilities (loss portion), loss on loss contracts, interest on unearned premium liabilities, and exchange rate gains / losses. The measurement report is then output separately at the policy level and contract group level for IBNR outstanding allocation and direct premium allocation method outstanding measurement and allocation. Cash flow data, including unearned premium cash flow, expected claims cash flow, and expected expense cash flow, is extracted and transmitted to the accounting engine to generate journal entries and financial vouchers.

[0094] As can be seen from the above examples, the method provided by the embodiments of the present invention has the following technical advantages compared with the traditional 1 / 365 linear amortization method: First, through differentiated risk distribution patterns and dedicated mathematical calculation models, it achieves accurate measurement of policies with non-uniform risk characteristics, significantly reducing systematic bias; second, through a dynamic correction mechanism for suspension and resumption information, it solves the problem of measurement accuracy in special scenarios; third, through innovative methods such as weighted unearned premium amortization ratio calculation, dynamic splitting of investment components, and multi-dimensional cross-rate allocation, it greatly improves the accuracy and calculation efficiency of unearned premium liability measurement, providing core data support for IFRS 17 compliance and refined management.

[0095] The foregoing mainly describes the solutions of the embodiments of the present invention from a methodological perspective. It is understood that, in order to achieve the above-mentioned functions, the metering system 100 includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present invention.

[0096] In this embodiment of the invention, the metering system 100 can be divided into functional units according to the above method example. For example, the metering system 100 can be divided into functional units corresponding to various functions, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0097] For example, Figure 3This diagram illustrates a hardware structure of a metering system according to an embodiment of the present invention. The metering system 100 includes: a data acquisition module 110, used to acquire insurance business and expense-related data, including policy basic data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information; a data verification module 120, used to perform field length verification, necessary field non-empty verification, and business rule verification on the insurance business and expense-related data, and determine whether the verification passes; a first calculation module 130, used, if the verification passes, based on the insurance business and expense-related data, to select the corresponding risk distribution mode according to the risk distribution characteristic identifier, dynamically correct the unexpired amortization ratio in conjunction with the suspension and resumption information, split earned premiums and unearned premiums, split insurance service components and investment components and calculate the present value of the investment components, and calculate the non-loss value of unexpired liabilities at three levels of granularity: policy, type of insurance, and endorsement; and a second calculation module 140, used to predict the unexpired liability non-loss value based on the unexpired liability non-loss value and actuarial parameters. The system generates loss and loss data for loss contracts by comparing the present value of the cash flow with the non-loss value of unearned premiums at the time of occurrence, and then using the liability allocation module 150 to calculate the allocation ratio for each policy based on the cross-dimensional three rates, non-financial risk adjustment factor, and unearned premiums in the insurance business and expense-related data, and allocating the unearned premium loss value of the contract group level to the policy level according to the allocation ratio. The preset allocation basis includes unearned premiums, acquisition costs, and premium difference. The result summary module 160 summarizes the policy-level liability measurement results according to preset dimensions and generates a measurement report. The measurement report includes the non-loss value of unearned premiums, loss amount, earned premiums, unearned premiums, investment components, amortized acquisition costs, cash flow - premiums received, cash flow - acquisition costs paid, and loss and loss data for loss contracts.

[0098] It should be understood that specific descriptions of the above-mentioned optional methods can be found in the foregoing method embodiments, and will not be repeated here. Furthermore, explanations of any of the metering systems 100 provided above, as well as descriptions of their beneficial effects, can be found in the corresponding method embodiments described above, and will not be repeated here.

[0099] This invention also provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of the various embodiments described above. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0100] This invention also provides a chip. This chip integrates a control circuit for implementing the functions of the metering system 100 described above, and one or more ports. Optionally, the functions supported by this chip are as described above, and will not be repeated here.

[0101] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0102] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0103] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0104] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for measuring unmatured liabilities under a property insurance contract, characterized in that, The method includes: Obtain insurance business and expense related data, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information; Perform field length validation, necessary field non-empty validation, and business rule validation on the insurance business and expense-related data, and determine whether the validation passes. If the verification is successful, based on the insurance business and expense-related data, the corresponding risk distribution mode is selected according to the risk distribution characteristic identifier, and the amortization ratio of the unexpired premium is dynamically corrected in combination with the suspension and resumption information. Earned premiums and unearned premiums are separated, insurance service components and investment components are separated and the present value of investment components is calculated. The non-loss value of the unexpired liability is calculated at the three-level granularity of policy, type of insurance and endorsement. Based on the non-loss value of the outstanding liabilities and actuarial parameters, predict future performance cash flows, calculate the present value by matching the discount factor according to the cash flow occurrence time, determine the loss contract group and loss amount by comparing the present value with the non-loss value of the outstanding liabilities, and generate loss contract profit and loss data. Based on the cross-dimensional three rates, non-financial risk adjustment factor and unexpired premium in the insurance business and expense related data, the allocation ratio of each policy is calculated based on the preset allocation basis. The unexpired liability loss value at the contract group level is allocated to the policy level according to the allocation ratio to obtain the policy-level liability measurement result. The preset allocation basis includes unexpired premium, acquisition cost and premium difference. The policy-level liability measurement results are summarized according to preset dimensions to generate a measurement report. The measurement report includes the non-loss value of unearned premium liabilities, loss amount, earned premiums, unearned premiums, investment components, amortized acquisition costs, cash flow - premiums received, cash flow - acquisition costs paid, and profit or loss of loss contracts.

2. The method according to claim 1, characterized in that, The risk distribution patterns include uniform distribution, uneven distribution in the first half, uneven distribution in the second half, special treatment for short-term policies, parabolic distribution, and specific rule distribution. Different risk distribution patterns correspond to different mathematical calculation models.

3. The method according to claim 2, characterized in that, Based on the aforementioned insurance business and expense-related data, the system selects the corresponding risk distribution model according to risk distribution characteristics, dynamically adjusts the unearned premium amortization ratio in conjunction with suspension and resumption information, separates earned premiums from unearned premiums, separates insurance service components from investment components and calculates the present value of the investment components, and calculates the non-loss value of unearned liabilities at three levels of granularity: policy, type of insurance, and endorsement. Extract risk distribution feature identifiers from insurance business and expense-related data, and match them with corresponding risk distribution patterns; The initial unexpired amortization ratio is calculated based on the mathematical calculation model corresponding to the risk distribution pattern. Extract the suspension and resumption information from the insurance business and expense related data, associate the suspension and resumption details and expand based on the monthly progress period. When calculating monthly, adjust the initial unexpired amortization ratio according to the number of suspension days in the current month to obtain the adjusted unexpired amortization ratio, and generate suspension and resumption corrected cash flow data. Based on the corrected amortization ratio for unexpired premiums, earned premiums and unearned premiums are obtained. The dynamic proportional method is used to separate the insurance service component and investment component in insurance business and expense-related data, and the present value of the investment component is calculated based on the time value principle in conjunction with interest rate information. Based on the three levels of granularity—policy, type of insurance, and endorsement—and taking into account the earned premiums, unearned premiums, present value of investment components, and actuarial parameters such as theoretical loss ratio, theoretical maintenance expense ratio, and non-financial risk adjustment rate, combined with changes in liabilities between the current and previous periods, accrued interest before maturity, and exchange rate gains and losses, the non-loss value of unmatured liabilities is calculated.

4. The method according to claim 1, characterized in that, The process involves forecasting future performance cash flows based on the non-loss value of the outstanding liabilities and actuarial parameters, calculating the present value by matching discount factors at the time of cash flow occurrence, determining the loss contract group and loss amount by comparing the present value with the non-loss value of the outstanding liabilities, and generating loss contract profit and loss data, including: Extract actuarial parameters from the insurance business and expense-related data. These actuarial parameters include expected loss ratio, interest rate curve, discount factor, liquidity premium parameter, and outstanding claims mode parameter, and correlate them with the previous period's loss measurement results. Based on the actuarial parameters and basic policy information, the future performance cash flow during the remaining term of the policy is predicted. The future performance cash flow includes expected premium cash flow, expected acquisition cost cash flow, expected maintenance cost cash flow, and expected claims cash flow. Based on the timing characteristics of different types of cash flows, a discount factor adjusted by the liquidity premium is matched, and the present value of different types of cash flows is calculated separately and then combined to obtain the total present value of future performance cash flows. Compare the total present value of the future performance cash flows of each contract with the non-loss value of the unearned liability. If the total present value of the future performance cash flows is greater than the non-loss value of the unearned liability, the contract is classified into a loss contract group, and the difference between the total present value of the future performance cash flows and the non-loss value of the unearned liability is the loss amount. The change in current period loss is calculated by combining the previous period's loss balance. Based on the exchange rate and the proportion of each currency in the outstanding non-loss RMB amount within the contract group, the RMB equivalent amount and the original currency amount are measured separately for policies with foreign currency, and the calculation results are obtained. Based on the calculation results, loss and profit data for loss contracts are generated, including loss contract group identifiers, loss amounts, current period loss changes, and multi-currency measurement results.

5. The method according to claim 1, characterized in that, Based on the cross-dimensional three ratios, non-financial risk adjustment factor, and unearned premiums in the aforementioned insurance business and expense-related data, the allocation ratio for each policy is calculated based on a preset allocation basis. The unearned liability loss at the contract group level is then allocated to the policy level according to the aforementioned allocation ratio, resulting in policy-level liability measurement results, including: Extract business dimension information, cross-dimensional ratios, non-financial risk adjustment factors, and unexpired premiums from the insurance business and expense-related data, and construct a complete allocation data foundation by associating them with the previous period's allocation results. The business dimension information includes cost center segment, channel segment, insurance type segment, and accounting unit. The cross-dimensional ratios include loss ratio, maintenance expense ratio, and surrender rate. Based on the granularity of insurance policies, insurance types, and endorsements, an allocation carrier is constructed, integrating the aforementioned business dimension information, expected future cash flow data, and insurance cash flow data to form a complete allocation carrier data structure. Based on the preset allocation basis, and combined with the three cross-dimensional rates and non-financial risk adjustment factors, the allocation weight of each policy is calculated. The total weight is obtained by summing the allocation weights of all policies within the contract group. The allocation ratio of each policy is determined by the ratio of the allocation weight of each policy to the total weight. The non-loss value and loss amount of the unexpired liability at the contract group level shall be allocated to each of the aforementioned policies according to the aforementioned allocation ratio; Calculate insurance contract revenue, investment component amortization amount, and insurance cash flow amortization data at the policy level, and combine them with the current and previous period liability changes to generate policy-level liability measurement results with complete indicators.

6. The method according to claim 1, characterized in that, The preset dimensions include basic identification dimensions, business management dimensions, and financial accounting dimensions; among which, the basic identification dimensions include the unique policy identifier, contract group number, and contract combination attribution identifier; the business management dimensions include insurance type, business channel, cost center segment, underwriting year, and risk level; and the financial accounting dimensions include currency, assessment time point, and progress period.

7. The method according to claim 1, characterized in that, The measurement report is used for downstream business processing, which includes: outstanding IBNR allocation, outstanding measurement and allocation of direct insurance premium allocation method, and transmitting current valuation period cash flow data to the accounting engine to generate journal entries and financial documents.

8. A measurement system for unexpired liabilities under a property insurance contract, characterized in that, The system includes: The data acquisition module is used to acquire insurance business and expense related data, including basic policy data, risk characteristic data, financial data, actuarial parameters, contract group information, interest rate information, and exchange rate information. The data verification module is used to perform field length verification, necessary field non-empty verification, and business rule verification on the insurance business and fee-related data, and to determine whether the verification passes. The first calculation module is used to select the corresponding risk distribution mode based on the risk distribution characteristic identifier, and dynamically correct the unexpired amortization ratio in combination with the suspension and resumption information, based on the insurance business and expense related data, when the verification is passed, according to the insurance business and expense related data, and to separate earned premiums and unearned premiums, separate insurance service components and investment components and calculate the present value of investment components, and calculate the non-loss value of unexpired liability liabilities at the three-level granularity of policy, type of insurance and endorsement. The second calculation module is used to predict future performance cash flows based on the non-loss value of the unearned liability and actuarial parameters, calculate the present value by matching the discount factor according to the cash flow occurrence time, determine the loss contract group and loss amount by comparing the present value with the non-loss value of the unearned liability, and generate loss contract profit and loss data. The liability allocation module is used to calculate the allocation ratio of each policy based on the cross-dimensional three rates, non-financial risk adjustment factor and unexpired premium in the insurance business and expense related data, and to allocate the unexpired liability loss value at the contract group level to the policy level according to the allocation ratio, so as to obtain the policy-level liability measurement result. The preset allocation basis includes unexpired premium, acquisition cost and premium difference. The results aggregation module is used to aggregate the policy-level liability measurement results according to preset dimensions and generate a measurement report. The measurement report includes the non-loss value of unearned premium liabilities, loss amount, earned premiums, unearned premiums, investment components, amortized acquisition costs, cash flow - premiums received, cash flow - acquisition costs paid, and loss on loss contracts.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for measuring the unexpired liabilities of a property insurance contract as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to implement the method for measuring unexpired liabilities under a property insurance contract as described in any one of claims 1-7.

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