阅读说明：本技术 巨灾保险损失再分配模块及实现损失再分配的方法 (Huge disaster insurance loss redistribution module and method for realizing loss redistribution ) 是由 贾凯 杨浩 鲁涵 符杰翔 于 2021-08-06 设计创作，主要内容包括：本发明主要涉及一种巨灾保险损失再分配模块及实现损失再分配的方法,首先,创建若干参与合并的风险源节点和若干待关联参与合并的风险源节点的第一合同节点；创建至少一个拆分组件并将其与至少一个第一合同节点关联,拆分组件被配置为：定义参与合并的风险源节点以及拆分后的风险目的地节点；将每个拆分后的风险目的地节点均关联与某个风险源节点或某些风险源节点相关联的第二合同节点；在此基础上,还提出一种基于巨灾保险损失再分配模块实现再保险业务中的巨灾保险损失再分配的方法。本发明既可实现将多个风险的损失结果进行合并功能,又可以实现对巨灾保险混合损失的再分配功能。(The invention mainly relates to a module for redistributing the loss of a disaster prevention and a method for realizing the redistribution of the loss, which comprises the following steps of firstly, establishing a plurality of risk source nodes participating in combination and a first contract node of a plurality of risk source nodes to be associated and participating in combination; creating and associating at least one split component with at least one first contract node, the split component configured to: defining risk source nodes participating in combination and split risk destination nodes; associating each split risk destination node with a second contract node associated with a certain risk source node or certain risk source nodes; on the basis, a method for realizing the redistribution of the disaster insurance loss in the reinsurance business based on the disaster insurance loss redistribution module is also provided. The invention can realize the function of combining the loss results of a plurality of risks and the function of redistributing the mixed loss of the disaster relief.)

1. A method for creating a redistribution module for disaster insurance loss is characterized by comprising the following steps:

creating a plurality of risk source nodes participating in combination and a plurality of first contract nodes to be associated with the risk source nodes;

creating and associating at least one split component with at least one first contract node, the split component configured to:

defining risk source nodes participating in combination and split risk destination nodes;

associating each of the risk destination nodes with a second contract node associated with a certain risk source node or certain risk source nodes.

2. A method for realizing the redistribution of the huge disaster insurance loss in the reinsurance business based on the huge disaster insurance loss redistribution module is characterized by comprising the following steps:

introducing a corresponding distribution module of the disaster insurance loss according to the insurance acceptance relation between the risk and the contract related in the current reinsurance business;

configuring a YLT data table for each risk source node participating in merging, wherein the YLT data table comprises an occurrence time list TR, an event identification list EID, a risk category list RID and a Loss value list Loss;

merging the YLT data tables of all risk source nodes participating in merging to obtain a mixed YLT data table; the mixed YLT data table is processed by a first contract node to obtain a calculated mixed YLT data table; the calculation mixed YLT data table is processed by a plurality of risk destination nodes to obtain a split YLT data table of each risk destination node;

and the splitting YLT data table passes through a second contract node to obtain a calculation splitting YLT data table.

3. The method of claim 2, wherein the obtaining of the hybrid YLT data table specifically comprises the steps of:

creating a first Index1 and a second Index 2;

sorting a YLT data table YLT1 of a first risk source node and a YLT data table YLT2 of a second risk source node which participate in merging according to the sequence of the occurrence time from small to large to obtain a sorted YLT1 and a sorted YLT 2;

sequentially comparing the occurrence time of the sorted YLT1 with the occurrence time of the sorted YLT2, recording YLT data corresponding to a smaller value of the occurrence times as current mixed YLT data, and traversing the sorted YLT1 and the sorted YLT 2;

traverse the Index1 and record the current Index value as Index1_ i and its order i, while: recording the ith value in the occurrence time list TR1 of the sorted yl 1 as the Index1_ i occurrence time value of the mixed yl data table, the ith value in the event identification list EID1 of the sorted yl 1 as the Index1_ i event identification value of the mixed yl data table, and the ith value in the Loss value list Loss1 of the sorted yl 1 as the Index1_ i Loss value of the mixed yl data table;

traverse the Index2 and record the current Index value as Index2_ j and its order j, while: recording the jth value in the occurrence time list TR2 of the sorted yl 2 as the Index2_ j occurrence time value of the mixed yl data table, recording the jth value in the event identification list EID2 of the sorted yl 2 as the Index2_ j event identification value of the mixed yl data table, and recording the jth value in the Loss value list Loss2 of the sorted yl 2 as the Index2_ j Loss value of the mixed yl data table.

4. The method of claim 3, further comprising the steps of:

and if the occurrence time of the sorted YLT1 is equal to the occurrence time of the sorted YLT2, comparing the risk type of the sorted YLT1 with the risk type of the sorted YLT2, and recording YLT data corresponding to a value with a smaller risk type as current mixed YLT data.

5. The method of claim 4, further comprising the steps of:

and if the risk type of the sorted YLT1 is the same as the risk type of the sorted YLT2, comparing the event identifier of the sorted YLT1 with the event identifier of the sorted YLT2, and recording YLT data corresponding to the value with smaller risk identifier as current mixed YLT data.

6. The method of claim 5, further comprising the steps of:

if the event identifier of the sorted YLT1 is the same as the event identifier of the sorted YLT2, the YLT data corresponding to the sorted YLT1 or/and the sorted YLT2 is recorded as the current hybrid YLT data.

7. The method of claim 6, wherein splitting the YLT data table is obtained by:

traversing the compute hybrid YLT data table, the YLT data table for the first risk destination node, and the hybrid YLT data table, starting with the first of the compute hybrid YLT data table, the YLT data table for the first risk destination node, and the first of the hybrid YLT data table, to obtain a loss value for the split YLT data table according to:

ResultLoss[m]＝AllocSource Loss[m]×Loss1[n]/SourceAggLoss[m]

wherein AllocSourceLoss [ m ] is the mth Loss value in the YLT data table for computing the hybrid YLT, Loss1[ n ] is the nth Loss value in the YLT data table for the first risk destination node, SourceAggLoss [ m ] is the mth Loss value in the hybrid YLT data table, and ResultLoss [ m ] is the mth Loss value in the YLT data table for the first risk destination node.

8. The method of claim 7, further comprising the steps of:

traversing said computation hybrid YLT data table and the YLT data table of the first risk destination node starting from the first data of said computation hybrid YLT data table and the YLT data table of the first risk destination node, comparing in sequence the m-th occurrence time of said computation hybrid YLT data table and the n-th occurrence time in the YLT data table of the first risk destination node;

considering the m +1 th occurrence time of the calculated hybrid YLT data table if the m-th occurrence time of the calculated hybrid YLT data table is less than the n-th occurrence time in the YLT data table of the first risk destination node;

considering the (n + 1) th occurrence time in the YLT data table of the first risk destination node if the mth occurrence time of the computing hybrid YLT data table is greater than the nth occurrence time in the YLT data table of the first risk destination node;

comparing the mth risk category of the calculated hybrid YLT data table with the nth risk category in the YLT data table of the first risk destination node if the mth occurrence time of the calculated hybrid YLT data table is equal to the nth occurrence time in the YLT data table of the first risk destination node;

considering the m +1 th risk category of the computational hybrid YLT data table if the m risk category of the computational hybrid YLT data table is less than the n risk category in the YLT data table of the first risk destination node;

considering the (n + 1) th risk category in the YLT data table of the first risk destination node if the m-th risk category of the computational hybrid YLT data table is greater than the n-th risk category in the YLT data table of the first risk destination node;

comparing the mth event identification of the computing hybrid YLT data table with the nth event identification in the YLT data table of the first risk destination node if the mth risk category of the computing hybrid YLT data table is equal to the nth risk category in the YLT data table of the first risk destination node;

considering the m +1 th event id of the computed hybrid YLT data table if the m event id of the computed hybrid YLT data table is less than the n event id in the YLT data table of the first risk destination node;

considering the (n + 1) th event id in the YLT data table of the first risk destination node if the mth event id of the computational hybrid YLT data table is greater than the nth event id in the YLT data table of the first risk destination node;

adding the nth occurrence time, the nth risk category and the nth event identification of the calculation hybrid YLT data table to the split YLT data table if the mth event identification of the calculation hybrid YLT data table is equal to the nth event identification of the YLT data table of the first risk destination node.

9. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method of claims 1-8.

10. A computing system for implementing redistribution of catastrophic insurance losses in a reinsurance business, the computing system implementing the method of claims 1-8 when executed.

Technical Field

The invention relates to the field of reinsurance business, in particular to a huge disaster insurance loss redistribution module and a method for realizing loss redistribution.

Background

When a reinsurance company takes care of the risk of the reinsurance company, the reinsurance company generally gives specific contract terms for the risk separated by the reinsurance company. Thus, each disaster recovery business in the reinsurance business is generally composed of a disaster risk and a recovery contract. The risks accepted by the service have different sources and forms, the risks (Risk) in the service can be given in the form of an event model (ELT), an event occurrence table (YLT) can be further generated on the basis of the event model, all event information is recorded in the form of arrays, data of corresponding positions of each array form a YLT data, and loss values caused by the occurrence of a disaster event at a certain time are recorded in detail in the data.

In some business situations, the total losses of several risks need to be known before several risks are calculated according to underwriting contracts, and each risk calculated according to the relevant clauses of the contracts produces the loss result after the contract is calculated, so that the total loss before entering the contract and the calculated total loss after entering the contract have one-to-one correspondence YLT data. In some business cases, the loss result after the contract is divided back into different risks and the loss calculated according to the contract is needed. Calculating the loss according to the contract at the different risks may also serve as a separate business of other reinsurance companies to sign a separate insurance contract.

The present reinsurance field needs a system that can implement redistribution of loss according to different business situations, especially for computing system of loss of disaster services by means of reasonably designed tools and algorithms. To date, there has been no computing system or computer program product in the industry that can fully fulfill this need.

Disclosure of Invention

In view of the technical problems in the art, one aspect of the present invention relates to a method for creating a redistribution module for catastrophic insurance loss, comprising the following steps:

creating a plurality of risk source nodes participating in combination and a plurality of first contract nodes to be associated with the risk source nodes;

creating and associating at least one split component with at least one first contract node, the split component configured to:

defining risk source nodes participating in combination and split risk destination nodes;

associating each of the risk destination nodes with a second contract node associated with a certain risk source node or certain risk source nodes.

The invention also relates to a method for realizing the redistribution of the disaster insurance loss in the reinsurance business based on the disaster insurance loss redistribution module, which comprises the following steps:

introducing a corresponding distribution module of the disaster insurance loss according to the insurance acceptance relation between the risk and the contract related in the current reinsurance business;

configuring a YLT data table for each risk source node participating in merging, wherein the YLT data table comprises an occurrence time list TR, an event identification list EID, a risk category list RID and a Loss value list Loss;

merging the YLT data tables of all risk source nodes participating in merging to obtain a mixed YLT data table;

the mixed YLT data table is processed by a first contract node to obtain a calculated mixed YLT data table;

the calculation mixed YLT data table is processed by a plurality of risk destination nodes to obtain a split YLT data table of each risk destination node;

and the splitting YLT data table passes through a second contract node to obtain a calculation splitting YLT data table.

The invention also relates to a computer program product comprising computer programs/instructions which, when executed by a processor, carry out the steps of the method proposed by the invention.

The invention also relates to a computing system for implementing the redistribution of catastrophic insurance losses in reinsurance services, which computing system implements the steps of the method proposed by the invention when it is run.

The invention provides a complete technical scheme aiming at different business conditions in the field of reinsurance, which comprises a huge disaster business loss calculation system, and related tools and algorithms, and can realize the function of combining loss results of a plurality of risks and the function of redistributing mixed loss of huge disaster insurance.

Drawings

FIG. 1 is a schematic diagram of the structure of some of the disaster insurance loss redistribution modules;

FIG. 2 is a flow diagram of some methods for implementing the redistribution of the catastrophic insurance losses in the reinsurance business based on a catastrophic insurance loss redistribution module;

FIG. 3, a diagram of a reinsurance service architecture in some embodiments;

fig. 4 is a service structure diagram after using an approximate service construction method.

English nouns in the drawings correspond to Chinese nouns: risk Risk, contract, Loss, Source splitter reassignment function, Source node participation in merge node, Destinationnode split final node.

Detailed Description

Some embodiments relate to a disaster insurance loss redistribution module as in fig. 1, the creation of such a module comprising the steps of:

creating a plurality of risk source nodes participating in combination and a plurality of first contract nodes of risk source nodes to be associated and participating in combination;

creating and associating at least one split component with at least one first contract node, the split component configured to:

defining risk source nodes participating in combination and split risk destination nodes;

and associating each split risk destination node with a second contract node associated with a certain risk source node or certain risk source nodes.

The term module, as used herein, generally includes any collection of executable code and data capable of being loaded into and executed by a memory, and may be a component of a larger program of instructions, and may include components and/or nodes.

Some embodiments relate to a method for implementing redistribution of disaster insurance losses in a reinsurance business based on a disaster insurance loss redistribution module as shown in the flowchart of fig. 2, comprising the steps of:

introducing a corresponding distribution module of the disaster insurance loss according to the insurance acceptance relation between the risk and the contract related in the current reinsurance business;

configuring a YLT data table for each risk source node participating in merging, wherein the YLT data table comprises an occurrence time list TR, an event identification list EID, a risk category list RID and a Loss value list Loss;

merging the YLT data tables of all risk source nodes participating in merging to obtain a mixed YLT data table;

processing the mixed YLT data table by a first contract node to obtain a calculated mixed YLT data table;

calculating a mixed YLT data table to obtain a split YLT data table of each risk destination node through a plurality of risk destination nodes;

and the split YLT data table passes through the second contract node to obtain a calculation split YLT data table.

The YLT is an actual occurrence table of events generated by performing event simulation according to ELT, and assuming that the event occurrence within 100000 years is simulated, the actual occurrence of the events after random simulation is shown in Table 1:

TABLE 1 actual occurrence YLT

Some embodiments relate to the acquisition of a hybrid YLT data table, specifically including the steps of:

creating a first Index1 and a second Index 2;

sorting a YLT data table YLT1 of a first risk source node and a YLT data table YLT2 of a second risk source node which participate in merging according to the sequence of the occurrence time from small to large to obtain a sorted YLT1 and a sorted YLT 2;

sequentially comparing the occurrence time of the sorted YLT1 with the occurrence time of the sorted YLT2, recording YLT data corresponding to a smaller value of the occurrence time as current mixed YLT data, and traversing the sorted YLT1 and the sorted YLT 2;

traverse Index1 and record the current Index value as Index1_ i and its order i, while: recording the ith value in an occurrence time list TR1 of the sorted YLT1 as the Index1_ i occurrence time value of the mixed YLT data table, recording the ith value in an event identification list EID1 of the sorted YLT1 as the Index1_ i event identification value of the mixed YLT data table, and recording the ith value in a Loss value list Loss1 of the sorted YLT1 as the Index1_ i Loss value of the mixed YLT data table;

traverse Index2 and record the current Index value as Index2_ j and its order j, while: the jth value in the occurrence time list TR2 of the sorted yl 2 is recorded as the Index2_ j occurrence time value of the hybrid YLT data table, the jth value in the event identification list EID2 of the sorted yl 2 is recorded as the Index2_ j event identification value of the hybrid YLT data table, and the jth value in the Loss value list Loss2 of the sorted yl 2 is recorded as the Index2_ j Loss value of the hybrid YLT data table.

Preferably, if the occurrence time of the sorted YLT1 is equal to the occurrence time of the sorted YLT2, comparing the risk type of the sorted YLT1 with the risk type of the sorted YLT2, and recording YLT data corresponding to a smaller value of the risk types as current mixed YLT data;

preferably, if the risk type of the sorted YLT1 is the same as the risk type of the sorted YLT2, comparing the event identifier of the sorted YLT1 with the event identifier of the sorted YLT2, and recording the YLT data corresponding to the smaller value of the risk identifiers as the current mixed YLT data;

preferably, if the event identifier of the sorted yl 1 is the same as the event identifier of the sorted yl 2, the yl data corresponding to the sorted yl 1 or/and the sorted yl 2 is recorded as the current hybrid yl data.

Some embodiments further relate to the acquisition of the split YLT data table, specifically including the steps of:

traversing the calculation of the hybrid YLT data table, the YLT data table for the first risk destination node, and the hybrid YLT data table, starting with the calculation of the first of the hybrid YLT data table, the YLT data table for the first risk destination node, and the hybrid YLT data table, to obtain a loss value for the split YLT data table, according to the following equation:

ResultLoss[m]＝AllocSource Loss[m]×Loss1[n]/SourceAggLoss[m]

wherein AllocSourceLoss [ m ] is the mth Loss value in the YLT data table for computing the hybrid YLT, Loss1[ n ] is the nth Loss value in the YLT data table for the first risk destination node, SourceAggLoss [ m ] is the mth Loss value in the hybrid YLT data table, and ResultLoss [ m ] is the mth Loss value in the YLT data table for the first risk destination node.

Some embodiments further involve the steps of:

traversing the hybrid-YLT data table and the YLT data table of the first risk destination node from the first data of the hybrid-YLT data table and the YLT data table of the first risk destination node, and comparing the m-th occurrence time of the hybrid-YLT data table and the n-th occurrence time of the YLT data table of the first risk destination node in sequence;

considering calculating the m +1 th occurrence time of the hybrid YLT data table if the m-th occurrence time of the hybrid YLT data table is less than the n-th occurrence time in the YLT data table of the first risk destination node;

considering the (n + 1) th occurrence time in the YLT data table of the first risk destination node if the (m) th occurrence time of the hybrid YLT data table is calculated to be greater than the (n) th occurrence time in the YLT data table of the first risk destination node;

comparing the calculated mth risk category of the hybrid YLT data table with the nth risk category in the YLT data table of the first risk destination node if the calculated mth occurrence time of the hybrid YLT data table is equal to the nth occurrence time in the YLT data table of the first risk destination node;

considering calculating the m +1 th risk category of the hybrid YLT data table if the m-th risk category of the hybrid YLT data table is less than the n-th risk category of the YLT data table of the first risk destination node;

considering the (n + 1) th risk category in the YLT data table of the first risk destination node if the (m) th risk category of the hybrid YLT data table is calculated to be greater than the (n) th risk category in the YLT data table of the first risk destination node;

comparing the mth event identification of the computed hybrid YLT data table with the nth event identification of the YLT data table of the first risk destination node if the mth risk category of the computed hybrid YLT data table is equal to the nth risk category of the YLT data table of the first risk destination node;

if the mth event identification of the computing hybrid YLT data table is smaller than the nth event identification of the YLT data table of the first risk destination node, considering computing the (m + 1) th event identification of the hybrid YLT data table;

considering the (n + 1) th event id in the YLT data table of the first risk destination node if the mth event id of the hybrid YLT data table is calculated to be greater than the nth event id in the YLT data table of the first risk destination node;

if the mth event identification of the computing hybrid YLT data table is equal to the nth event identification in the YLT data table of the first risk destination node, the nth occurrence time, the nth risk category and the nth event identification of the computing hybrid YLT data table are added to the splitting YLT data table.

Some more specific embodiments, detailed descriptions of the following two methods, which are the technical solutions of the loss merging method and the mixed loss redistribution method, respectively:

assuming that the loss of Risk1 needs to be merged with the loss of Risk2, each Risk loss result consists of four list results, namely a Risk category list, an event occurrence time list, an event loss list and an event eventId list. Suppose that the Risk class in the Loss result of Risk1 is listed as RID1, the occurrence time is listed as TR1, the eventId of the event is listed as EID1, the Loss is listed as Loss1, the Risk class in the Loss result of Risk2 is listed as RID2, the occurrence time is listed as TR2, the eventId of the event is listed as EID2, and the Loss is listed as Loss 2. In order to calculate the merged results of the two Risk Loss results, two index lists, index1 and index2 respectively, are used to record that each of the Loss results of Risk1 corresponds to the data in the number of the merged results, and each of the Loss results of Risk2 corresponds to the data in the number of the merged results, and the merged results are also composed of four list results, which are called TR, RID, Loss and EID respectively. The specific combining steps are as follows:

1. sorting the loss results of Risk1 and Risk2 in the order from small to large according to the occurrence time list;

2. traversing loss results of Risk1 and Risk2, wherein traversal indexes are count1 and count2 respectively, and the index of the combined result is count, and at this time, count1 and count2 are all 0;

3. comparing the loss results of Risk1 with that of Risk2, the time of occurrence of the event among the loss results is first compared.

1) If TR1[ count1] < TR2[ count2], the loss result of Risk1 is added to the combined result, i.e., the combined result is

TR[count]＝TR1[count1],RID[count]＝RIS1[count1],EID[count]＝EID1[count1]，

The data position of the piece of data in Risk1 in the merged result is recorded at the same time, namely

index1[ count1] ═ count, count1 plus one, count plus one;

2) if TR1[ count1] > TR2[ count2], the loss result of Risk2 is added to the combined result, i.e., the combined result is

TR[count]＝TR2[count2],RID[count]＝RIS2[count2],EID[count]＝EID2[count2]，

Simultaneously recording the data position of the piece of data in the Risk2 in the merged result, namely index2[ count2] ═ count, count2 plus one, and count plus one;

4. if TR1[ count1] ═ TR2[ count2], the risk categories among the loss results are compared.

1) If RID1[ count1] < RID2[ count2], the loss result of Risk1 is added to the combined result, i.e., the combined result is

TR[count]＝TR1[count1],RID[count]＝RIS1[count1],EID[count]＝EID1[count1]，

The data position of the piece of data in Risk1 in the merged result is recorded at the same time, namely

index1[ count1] ═ count, count1 plus one, count plus one;

2) if RID1[ count1] > RID2[ count2], the loss result of Risk2 is added to the merged result, i.e., the merged result is

TR[count]＝TR2[count2],RID[count]＝RIS2[count2],EID[count]＝EID2[count2]，

The data position of the piece of data in Risk2 in the merged result is recorded at the same time, namely

index2[ count2] ═ count, count2 plus one, count plus one;

5. if RID1[ count1] ═ RID2[ count2], the EventId of the event among the loss results is compared.

1) If EID1[ count1] < EID2[ count2], the loss result of Risk1 is added to the merged result, i.e., the merged result is

TR[count]＝TR1[count1],

RID[count]＝RIS1[count1],

EID[count]＝EID1[count1]，

The data position of the piece of data in Risk1 in the merged result is recorded at the same time, namely

index1[count1]＝count,

count1 plus one, count plus one;

2) if EID1[ count1] > EID2[ count2], the loss result of Risk2 is added to the merged result, i.e., the merged result is

TR[count]＝TR2[count2],

RID[count]＝RIS2[count2],

EID[count]＝EID2[count2]，

Simultaneously recording the data position of the piece of data in the Risk2 in the merged result, namely index2[ count2] ═ count, count2 plus one, and count plus one;

6. if EID1[ count1] ═ EID2[ count2], then the loss results of both Risk1 and Risk2 are added to the combined result, i.e., the result is added to the combined result

TR[count]＝TR1[count1],

RID[count]＝RIS1[count1],

EID[count]＝EID1[count1]，

The data position of the piece of data in Risk1 and Risk2 in the merged result is recorded at the same time, namely

index1[count1]＝count,index2[count2]＝count，

count1 plus one, count2 plus one, count plus one;

7. traversing index1, for each index i among index1 and the corresponding value index1[ i ]:

TR[index1[i]]＝TR1[i]，

EID[index1[i]]＝EID1[i],

Loss[index1[i]]＝Loss1[i]；

8. traversing index2, for each index i among index2 and the corresponding value index2[ i ]:

TR[index2[i]]＝TR2[i],

EID[index2[i]]＝EID2[i],

Loss[index2[i]]＝Loss[index2[i]]+Loss2[i]；

some specific embodiments provide a redistribution function component SourceSplitter, which is a component that is equal to Risk and contect, when building a reinsurance service, the SourceSplitter may be connected below the contect, which nodes included in the nodes participating in merging are filled in parameters in the SourceSplitter, which are called sourcenodes, and which nodes included in the split final nodes, which are called destinationnodes, for example, the service splits Risk1 and Risk2 from the merging loss results after Contract calculation from Risk1, Risk2, and Risk3, and after using the SourceSplitter component, the service structure is as shown in fig. 3.

After the system designs the SourceSplitter component, the system needs to use reasonable algorithm steps to split the combined loss, and introduces a redistribution method of the mixed loss by taking the service structure as an example, and the specific method comprises the following steps:

1. splitting the combined loss of Risk1 and Risk2 from the mixed loss, firstly splitting the individual losses of Risk1 and Risk2, then combining the loss result of Risk1 and the loss result of Risk2 according to the loss combining method to obtain the combined loss result of Risk1 and Risk2, wherein the splitting method of the loss result of Risk1 is the same as the splitting method of the individual loss of Risk2, so that the splitting method of the individual loss of Risk1 is described below;

2. assuming that the Loss result YLT of the contractual post-computation mixture Loss has been calculated in the service structure, the YLT includes four list results, i.e., the merged Loss results of Risk category list AllocSourceRID, event occurrence time list AllocSourceTR, event Loss list AllocSourceLoss and event EventId list AllocSourceEID of the event, Risk1, Risk2, and Risk3, i.e., SourceNode merged Loss, and it also includes four list results, i.e., the Loss results of Risk category list sourceaggid, event occurrence time list SourceAggTR, event Loss list sourceagloss and event EventId sourceageid of the event, the Loss results of Risk1 also include four list results, i.e., Risk category list RID1, event occurrence time list TR1, event Loss list Loss 5, and event resentintertid list of the event, and the Loss results of the event are assumed as four list after splitting, i.e, and result includes four list split results, i.e 64, and result after the event category list resultants is assumed as Loss list identifier, and result;

3. the length Risk1lossLength of the loss result data of Risk1 is obtained;

4. traversing the loss result YLT of the hybrid loss after contract calculation, setting the traversal index as i, simultaneously traversing the loss result YLT of Risk1, setting the traversal index as j, and at the moment, setting both i and j as 0;

5. first, compare AllocSourceTR [ i ] and TR1[ j ], i + + if AllocSourceTR [ i ] < TR [ j ], and j + +, if AllocSourceTR [ i ] > TR [ j ]; if AllocSourceTR [ i ] ═ TR [ j ], AllocSourceRID [ i ] and RID1[ j ] are compared, if AllocSourceRID [ i ] < RID1[ j ], i + +,

if allocvsourcerid [ i ] > RID1[ j ], then j + +,

if AllocSourceRID [ i ] ═ RID1[ j ], AllocSourceEID [ i ] and EID1[ j ] are compared, if AllocSourceEID [ i ] < EID1[ j ], then i + +,

if AllocSourceEID [ i ] is > EID1[ j ], then j + +,

if AllocSourceEID [ i ] ═ EID1[ j ], then results need to be added to ResultRID, ResultTR, ResultLoss, and ResultEID, AllocSourceTR [ i ] is added to ResultTR, AllocSourceRID [ i ] is added to ResultRID, AllocSourceEID [ i ] is added to ResultEID, and the data added to ResultLoss is calculated by the following formula:

ResultLoss[index]＝AllocSourceLoss[i]×Loss1[j]/SourceAggLoss[i]

6. when traversing the loss result YLT of the hybrid loss after the contract calculation and the loss result YLT of Risk1, the ResultRID, ResultTR, ResultLoss and ResultEID at this time are the post-contract loss result YLT of Risk1 split by the hybrid loss.

Some embodiments can more accurately handle mixed loss redistribution traffic than other existing implementations. Because other implementation methods cannot accurately represent the service type, the service type is approximate. Assuming that the service structure is as shown in fig. 4 after using the approximate service construction method, when using this structure, an operator needs to construct a plurality of identical contexts 1 to connect with different Risk, which brings inconvenience to the construction of the service structure. Meanwhile, the calculation result of the construction mode may be inaccurate. For example, Risk1 and Risk2 all have a catastrophic event in the first year, Risk1 has a relatively earlier event occurrence time, Risk2 has a relatively later event occurrence time, and in this case, the loss value is calculated after the catastrophic event at both risks enters into the contract. However, if the number of recovery in the clause of Contract1 is 0, that is, the Contract only bears the loss compensation of one disaster event in one year, the event of Risk1 enters the Contract for loss calculation, and the disaster event of Risk2 does not enter the Contract for loss calculation because of the late occurrence time, so that after the mixed loss redistribution, the event in Risk1 has corresponding loss, but the event in Risk2 has no corresponding loss, which is obviously different from the result of the approximate structure.

Some embodiments of the invention also relate to a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method proposed by the invention.

Some embodiments of the present invention also relate to a computing system for implementing the redistribution of disaster-prone insurance losses in reinsurance services, the computing system implementing the method proposed by the present invention when running.

Implementations and functional operations of the subject matter described in this specification can be implemented in: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware, including the structures disclosed in this specification and their structural equivalents, or combinations of more than one of the foregoing. Embodiments of the subject matter described in this specification can 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, or to control the operation of, data processing apparatus. A computer program (which may also be referred to or described as a program, software application, module, software module, script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a stand-alone 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 can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in: in a markup language document; in a single file dedicated to the relevant program; or in multiple coordinated files, such as files that store one or more modules, sub programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output.

Computers suitable for carrying out computer programs include, and illustratively may be based on, general purpose microprocessors, or special purpose microprocessors, or both, or any other kind of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for executing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components in the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network ("LAN") and a wide area network ("WAN"), e.g., the Internet.

A computing system implementing disaster relief redistribution in reinsurance services may include a client and a server. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features that may embody particular implementations of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in combination and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.