A method for simulating catastrophe insurance losses based on aggregated risk data

Abstract

The present invention relates to a method for simulating catastrophe insurance losses based on aggregated risk data, which splits the aggregated risk data to obtain its spatial position and value, and by assigning aggregated attributes to the split targets, calculates the aggregated vulnerability curve and residential, commercial and industrial values ​​corresponding to the targets, and then combines the existing earthquake disaster intensity to obtain the total loss of the targets at each split point, and then accumulates the total losses of all targets to obtain the final total loss of the aggregated risk data. The present invention can quickly generate target data of spatial position and value information distributed in a certain area based on the given aggregated risk data.

Description

Technical Field

[0001] The present invention relates to the field of reinsurance, and in particular to a method for simulating catastrophe insurance losses based on aggregated risk data. Background Art

[0002] In recent years, many countries and regions around the world have been frequently affected by serious disasters, resulting in a large number of casualties and serious economic losses, becoming one of the major obstacles to the sustainable development of regional economic and social development. From a global perspective, catastrophe insurance is one of the important means to disperse the risks of the above-mentioned catastrophe accidents. In 2016, with the introduction of a series of policies such as the "Implementation Plan for the Establishment of an Urban and Rural Residential Earthquake Catastrophe Insurance System", the "13th Five-Year Plan for the Development of China's Insurance Industry", and the "Earthquake Catastrophe Insurance Regulations", catastrophe insurance has once again attracted attention and entered a development stage.

[0003] In actual catastrophe insurance business, the quality and accuracy of detailed policy data often cannot meet the calculation requirements; especially in the business of reinsurance companies, considering the risk asymmetry, the business data usually obtained by reinsurance companies are the aggregated risk accumulation data after the insurance companies have processed the detailed policies. Therefore, the reinsurance business often obtains the total insured amount information of the regional scope, such as giving a total insured amount according to each province or city (less often to the district and county level), that is, the initial insurance data is mostly regional aggregated risk data. Since there is no specific target information in the policy of aggregated risk data, it is difficult to directly calculate earthquake insurance losses. Summary of the invention

[0004] In order to solve the above problems, the present invention discloses a method for constructing a catastrophe insurance loss simulation system based on aggregated risk data, which splits the aggregated risk data to obtain its spatial position and value, and by assigning the aggregated attributes to the split targets, calculates the aggregated vulnerability curve and residential, commercial and industrial values ​​corresponding to the targets, and then combines the existing earthquake disaster intensity to obtain the total loss of the targets at each split point, and then accumulates the total losses of all targets to obtain the final total loss of the aggregated risk data. It includes the following steps:

[0005] Get the total insured value TIV and the number of risk exposures N in a certain area;

[0006] Randomly select N grid points in the area in the industry risk exposure database, each of the grid points including location information and location weight;

[0007] The extracted N grid points are taken as targets, and the new weight of each target is calculated according to Formula I:

[0008]

[0009] Among them, w k_ new is the new weight of the kth target, w k is the position weight of the kth extracted grid point, w i is the position weight of the i-th extracted grid point;

[0010] According to Formula II, the total insured amount is split into N target insured amounts according to the new weight of each target:

[0011] TIV k =TIV×W k_new II

[0012] Among them, TIV k is the insured amount of the kth target, w k_new is the new weight of the kth target, TIV is the total insured amount;

[0013] Assigning an aggregate attribute to each of the targets according to the location information, and obtaining a comprehensive proportion coefficient of each of the aggregate attributes, wherein the aggregate attributes include a number of vulnerable attributes;

[0014] Matching each of the aggregated attributes with the vulnerability data of the corresponding property, and aggregating the vulnerability data according to the comprehensive proportion coefficient to obtain the aggregated vulnerability data of each of the targets;

[0015] According to the earthquake intensity distribution in the region, the location information of each target, the aggregated vulnerability data, and the insured amount of the target are combined to obtain the loss of each target, and the total loss of the region is obtained by summing up the losses of all targets.

[0016] On this basis, the present invention further discloses a computer program product, including a computer program / instruction, which implements all the steps of the present invention when executed by a processor.

[0017] On this basis, the present invention also discloses a catastrophe insurance loss simulation system based on aggregated risk data, which is characterized in that all steps of the present invention are implemented when executed by a processor.

[0018] The present invention can quickly generate target data of spatial location and value information distributed in a certain area based on the given aggregated risk data, and by assigning aggregation attributes to the split targets, calculate the aggregated vulnerability curve and residential, commercial and industrial values ​​corresponding to the targets, and then combine the existing earthquake disaster intensity to obtain the total loss of the targets at each split point, and then accumulate the total losses of all targets to obtain the final total loss of the aggregated risk data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 , one Schematic diagram of the flow chart of the technical solutions involved in some embodiments;

[0020] Figure 2 , one Schematic diagram of earthquake intensity calculation involved in some implementation methods. DETAILED DESCRIPTION

[0021] Some embodiments involve a method for simulating catastrophe insurance losses based on aggregated risk data, comprising the following steps:

[0022] Get the total insured value TIV and the number of risk exposures N in a certain area;

[0023] Randomly select N grid points in the area in the industry risk exposure database, each of the grid points including location information and location weight;

[0024] The extracted N grid points are taken as targets, and the new weight of each target is calculated according to Formula I:

[0025]

[0026] Among them, w k_new is the new weight of the kth target, w k is the position weight of the kth extracted grid point, w i is the position weight of the i-th extracted grid point;

[0027] According to Formula II, the total insured amount is split into N target insured amounts according to the new weight of each target:

[0028] TIV k =TIV×W k_new II

[0029] Among them, TIV k is the insured amount of the kth target, w k_new is the new weight of the kth target, TIV is the total insured amount;

[0030] Assigning an aggregate attribute to each of the targets according to the location information, and obtaining a comprehensive proportion coefficient of each of the aggregate attributes, wherein the aggregate attributes include a number of vulnerable attributes;

[0031] Matching each of the aggregated attributes with the vulnerability data of the corresponding property, and aggregating the vulnerability data according to the comprehensive proportion coefficient to obtain the aggregated vulnerability data of each of the targets;

[0032] According to the earthquake intensity distribution in the region, the location information of each target, the aggregated vulnerability data, and the insured amount of the target are combined to obtain the loss of each target, and the total loss of the region is obtained by summing up the losses of all targets.

[0033] The term "industry risk exposure database" includes a database constructed based on the integration of multi-source basic data and spatial distribution data; the data is stored in the form of tables (including but not limited to txt, xlsx, xls, Jason and other formats), and each grid point usually uses data with a spatial accuracy of 1km×1km. The data of each grid point includes but is not limited to the id or location identifier, longitude, latitude, location weight and other information of each grid. The location weight includes (but is not limited to) the population spatial distribution data, lighting index, GDP distribution data, housing construction value distribution data, and data obtained from the risk exposure database in the region.

[0034] Some other implementation methods further include the following steps:

[0035] The proportion coefficients of all the vulnerable attributes of the aggregated attribute are multiplied to obtain the comprehensive proportion coefficient of the aggregated attribute.

[0036] Some other embodiments further include the following steps: the aggregate vulnerability data of each target is obtained according to formula III:

[0037]

[0038] Wherein, mixMDR represents the aggregate vulnerability data, MDR i represents the vulnerability data of the i-th aggregated attribute property, R i represents the comprehensive proportion coefficient of the i-th aggregation attribute, and m represents the number of the aggregation attributes.

[0039] Some implementation methods further include the step of stratified sampling of the N grids in the region by a Latin hypercube sampling method, specifically including the following steps:

[0040] Sort all grids in the industry risk exposure database according to their location weights;

[0041] Adding the position weights of all grids in the industry risk exposure database to obtain W;

[0042] The step size of each sampling is set to W / N, and the N grids are obtained by sampling the N times according to the step size in the sorted industry risk exposure database.

[0043] Some embodiments further include extracting the N grids within the region in the industry risk exposure database by using a Monte Carlo random sampling method.

[0044] Some embodiments further include the following steps:

[0045] Assigning a number of value attributes to each of the targets according to the location information, each of the value attribute targets having an aggregate attribute;

[0046] The value of each value attribute is obtained according to the value proportion of each value attribute;

[0047] According to the earthquake intensity distribution in the area, combined with the location information of each target, the aggregated vulnerability data, and the value of each value attribute, the loss of each target is obtained, and the total loss of the area is obtained by summing up the losses of all the targets.

[0048] In some implementations, the risk exposure number N is obtained by the following method: half of the number of kilometer grids in the industry risk exposure database within the target area is input as the risk exposure number N.

[0049] In some implementations, the earthquake intensity distribution in the target area is obtained by calculating the earthquake peak acceleration PGA influence field simulation through the attenuation relationship.

[0050] Some more specific implementation methods, such as Figure 1 As shown in Figure 1, the aggregated insurance data of a region is first split based on the total insured value (TIV) and the number of risk exposures N of the aggregated catastrophe insurance risk data of a certain region:

[0051] Spatialize the aggregated data into target data with detailed location and value. The method of splitting it can be random sampling using the Monte Carlo method, or Latin hypercube sampling method based on the value and spatial distribution of industry risk exposure data. The specific splitting steps are as follows:

[0052] 1) Determine spatial position weight

[0053] Spatial weights can be obtained through data such as population spatial distribution, lighting index, GDP distribution, and housing construction value distribution; or through methods such as fusion of multi-source data and spatial overlay analysis. For example, based on the fusion of multi-source basic data and spatial distribution data, an industry risk exposure database (national urban and rural residential construction cost distribution, national industrial and commercial construction cost distribution) is constructed, and the total value of buildings on the national kilometer grid is obtained as the spatial weight, providing a spatial weight basis for the spatial decomposition of aggregated data.

[0054] 2) Determine the number of targets after splitting N

[0055] The number of risk exposures N is usually input as known policy data. When N cannot be confirmed, half of the number of kilometers of grids divided in the risk exposure industry standard database in the area is used as the input value of N.

[0056] 3) Confirm the positions of N markers

[0057] Using certain splitting methods to split the aggregated policy data into specific location distribution and value distribution can be regarded as random sampling based on industry risk exposure data. For example, the Latin hypercube sampling method uses the value weight w of each unit grid in the industry risk exposure database. k And the number of risk exposures N that need to be sampled, select N points according to stratified random sampling and confirm their location distribution.

[0058] 4) Confirm the value of N targets

[0059] The new weights of the N points extracted using the sampling method can be expressed as:

[0060]

[0061] According to the above new weights, combined with the total insured TIV given by the user, calculate the insured TIV of each point after splitting k :

[0062] TIV k = =TIV×w k_new

[0063] 5) Give the split target aggregation attributes

[0064] The average loss rate of a building under earthquake motion is called vulnerability, which is usually represented by a vulnerability curve with the horizontal axis being the earthquake intensity (such as peak acceleration, response spectrum value, etc.) and the vertical axis being the mean damage rate (MDR). Buildings with different functions, structural types, construction years, number of floors and fortification intensity will suffer different damages under earthquakes, and their repair value (or replacement value) will vary greatly. Therefore, in addition to splitting the aggregated risk data into specific locations and values, it is also necessary to give it sufficient attribute information to support subsequent loss calculations.

[0065] For the split target data, aggregate attribute information (comprehensive attributes of five dimensions, including functional use, structural type, number of floors, construction age, and fortification intensity) is assigned to facilitate one-step matching of the corresponding vulnerability curve in the database (the vulnerability curves of different attribute dimensions in the aggregate attribute are different). According to the position of the extracted N unit grid points, the area range represented by each single point k is assigned (usually 1km 2 The aggregated attribute information (functional use, structural type, number of floors, construction age, and fortification intensity) of the building is summarized as follows: including the weight ratio of residential, commercial, and industrial buildings (as shown in Table 1), as well as the structural type, number of floors, construction age, and fortification intensity corresponding to the residential, commercial, and industrial buildings (as shown in Table 2).

[0066] Table 1: Proportion of residential, commercial and industrial value in different locations

[0067]

[0068] Table 2 Average industry split parameters of buildings with different functional uses

[0069]

[0070] Aggregate vulnerability calculation and earthquake insurance loss calculation of split targets.

[0071] The disaster module calculates the distribution of seismic intensity: based on the set of random earthquake events, the spatial distribution range of seismic intensity is calculated (such as calculating the earthquake peak acceleration PGA impact field through the attenuation relationship), and considering the influence of site conditions, terrain effects, etc., to obtain the seismic intensity at the location of the insured object;

[0072] The vulnerability module and risk exposure module comprehensively obtain the split point aggregate vulnerability curve:

[0073] Confirm the aggregated properties of the target after splitting: the location of the splitting point, the proportion of the insured value of residential, industrial and commercial buildings, as well as the corresponding structural type, number of floors, construction year, fortification intensity and other attribute information of residential, commercial and industrial buildings.

[0074] According to the location and aggregation attributes of the split target, calculate the comprehensive proportion coefficient R of various attribute combinations (building structure type * building height type * construction year type * fortification intensity type) under different functional uses i .

[0075] Combining Table 1 and Table 2, for the aggregated attributes of a split point k, there are 2×3×2×1=12 attribute combinations of commercial use (building structure type*building height type*construction year type*fortification intensity type). The comprehensive proportion coefficient R of the commercial use i-th combination (i=1,2,3,...,12) in the aggregated attributes is i for:

[0076]

[0077] The corresponding vulnerability curve is confirmed by the aggregated attributes of each insured object. Taking commercial buildings as an example, as shown in Table 3.

[0078] Table 3 Vulnerability curves of various combinations and their matching for commercial use

[0079]

[0080]

[0081]

[0082] The vulnerability curve diagram in Table 3 is stored in the form of a data table, and is represented in the form of a curve diagram in this embodiment for ease of understanding.

[0083] The vulnerability curves of each combination are aggregated by the comprehensive proportion coefficient to obtain the aggregate vulnerability curve. Taking Table 3 as an example, the aggregate vulnerability curve mixMDRk of the kth split point is:

[0084]

[0085] 6) Calculate the loss value

[0086] The impact field calculated based on the disaster intensity and the aggregate vulnerability curve obtained based on the aggregate attributes are as follows: Figure 2 The seismic intensity corresponding to the split point position is obtained as shown in the figure. From this, the building loss rate (MDR) of the split point k under a specific seismic intensity x can be calculated. k |IM=x)

[0087] Estimate the damage degree and earthquake loss of a single insured object under a certain earthquake intensity (such as PGA) i Taking commercial buildings as an example, the value of the kth split point under the aggregation attribute is:

[0088]

[0089]

[0090] The single point loss of the insured object is equal to the sum of the total losses of residential, commercial and industrial buildings:

[0091]

[0092] The total aggregate risk loss in this area is the sum of the single-point losses of the N insured objects after splitting.

[0093]

[0094] The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible non-transitory program carriers, for execution by a data processing device or for controlling the operation of a data processing device. A computer program (which may also be referred to or described as a program, software, software application, module, software module, script or code) may be written in any form of programming language, including a compiled language or an interpreted language or a declarative language or a procedural language, and the computer program may be deployed in any form, including as an independent program or as a module, component, subroutine or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that stores other programs or data, for example, one or more scripts stored in the following: in a markup language document; in a single file dedicated to a related program; or in multiple collaborative files, for example, files storing one or more modules, subroutines or code portions. A computer program may be deployed to execute on one or more computers, the computers being located in one place, or distributed to multiple locations and interconnected by a communication network. The embodiments of the subject matter described in this specification may be implemented in a catastrophe insurance computing system.

[0095] Although this specification contains many specific implementation details, these should not be interpreted as limitations on the scope of any invention or the scope of what can be claimed, but rather as an illustration of features that can concretize a particular embodiment of a particular invention. The specific features described in this specification in the context of independent embodiments can also be implemented in combination with a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented independently in multiple embodiments, or in any suitable sub-combination. In addition, although the features can be described above as a combination effect and even initially required, one or more features from the required combination can be removed from the combination in some cases, and the required combination can be turned to a sub-combination or a variation of the sub-combination.

Claims

1. A method for simulating catastrophe insurance losses based on aggregated risk data, characterized in that: The following steps are involved: Get the total insured value TIV and the number of risk exposures N in a certain area; Randomly select N grid points in the area in the industry risk exposure database, each of the grid points including location information and location weight; The extracted N grid points are taken as targets, and the new weight of each target is calculated according to Formula I: Among them, w k_new is the new weight of the kth target, w k is the position weight of the kth extracted grid point, w i is the position weight of the i-th extracted grid point; According to Formula II, the total insured amount is split into N target insured amounts according to the new weight of each target: TIV k =TIV×w k_new II Among them, TIV k is the insured amount of the kth target, w k_new is the new weight of the kth target, TIV is the total insured amount; Assigning an aggregate attribute to each of the targets according to the location information, and obtaining a comprehensive proportion coefficient of each of the aggregate attributes, wherein the aggregate attributes include a number of vulnerable attributes; The aggregated attribute information specifically includes comprehensive attributes in five dimensions: functional use, structural type, number of floors, construction year, and fortification intensity; Multiplying the proportion coefficients of all the vulnerable attributes of the aggregated attribute to obtain a comprehensive proportion coefficient of the aggregated attribute; Match each of the aggregated attributes with the vulnerability data of the corresponding property, and aggregate the vulnerability data according to the comprehensive proportion coefficient to obtain the aggregated vulnerability data of each of the targets; according to the earthquake intensity distribution in the area, combined with the location information of each of the targets, the aggregated vulnerability data, and the insured amount of the targets, the loss of each target is obtained, and the losses of all targets are summed up to obtain the total loss of the area.

2. The method according to claim 1, characterized in that The following steps are also included: The aggregate vulnerability data of each target is obtained according to Formula III: Wherein, mixMDR represents the aggregate vulnerability data, MDR i represents the vulnerability data of the i-th aggregated attribute property, R i represents the comprehensive proportion coefficient of the i-th aggregation attribute, and m represents the number of the aggregation attributes.

3. The method according to claim 2, characterized in that The method also includes the step of stratified sampling of N grids in the region by a Latin hypercube sampling method, specifically including the following steps: sorting all grids in the industry risk exposure database according to their location weights; Adding the position weights of all grids in the industry risk exposure database to obtain W; The step size of each sampling is set to W / N, and N grids are obtained by sampling N times according to the step size in the sorted industry risk exposure database.

4. The method according to claim 3, characterized in that The following steps are also included: Assigning a number of value attributes to each of the targets according to the location information, each of the value attribute targets having an aggregate attribute; The value of each value attribute is obtained according to the value proportion of each value attribute; According to the earthquake intensity distribution in the area, combined with the location information of each target, the aggregated vulnerability data, and the value of each value attribute, the loss of each target is obtained, and the total loss of the area is obtained by summing up the losses of all the targets.

5. The method according to claim 4, characterized in that When the risk exposure number N cannot be confirmed, half of the number of kilometer grids in the area in the industry risk exposure database is input as N.

6. The method according to claim 5, characterized in that The earthquake intensity distribution in the region is obtained by calculating the earthquake peak acceleration PGA influence field simulation through the attenuation relationship.

7. The method according to claim 1, characterized in that The following steps are also included: N grids are extracted in the region in the industry risk exposure database by using a Monte Carlo random sampling method.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to claims 1-7 are implemented.

9. A catastrophe insurance loss simulation system based on aggregated risk data, characterized in that: When executed by a processor, the steps of the method according to claims 1 to 7 are implemented.

Images