Information processing system and information processing method

Abstract

This enables the acquisition and identification of urban environmental data that takes into account the impact of building complexes, which is necessary for risk assessment. [Solution] The information processing system 1, which designs financial derivatives based on information about the target business, uses a wind condition analysis unit 3 to analyze wind conditions in a predetermined spatial area based on topographic data and building data to obtain wind condition analysis data, a risk calculation unit 4 to calculate the risk of the target business in the spatial area using a predetermined wind condition index based on information about the target business and the wind condition analysis data to obtain risk data, a generation unit 5 to assign the wind condition index and risk data to a 3D map space to generate 3D spatial information, and an option calculation unit 6 to determine the value of the predetermined wind condition index based on the risk data as the option generation condition for the financial derivative.

Description

Technical Field

[0001] The present invention relates to an information processing system and an information processing method used for designing financial derivative products and insurance products that hedge business risks concerned due to, for example, adverse weather conditions.

Background Art

[0002] Currently, on a global scale, there are concerns about the impact of climate change and ecosystem on the expansion of social activities. Efforts to suppress the impact of social activities on the environment and make society sustainable, such as achieving carbon neutrality and sustainability, are being carried out not only by countries and regions but also by each business operator as an essential requirement.

[0003] Furthermore, as the direct impact of global climate change on the business environment of each business operator increases, the interest in weather risk-related financial products that hedge business risks concerned due to adverse weather conditions is growing.

[0004] Weather risk-related financial products include derivatives and parametric insurance. For example, a weather risk-related financial product called a weather derivative is such that an operator presumed to suffer some damage due to weather conditions pays a certain option fee as a contract amount in advance to a provider of financial derivative products, such as a property insurance company, a reinsurance company, or a bank, and when the contract conditions are satisfied, such as when the weather conditions determined at the time of contract continue for a certain period, a compensation payment is promptly made.

[0005] Transactions using weather derivatives are considered an effective means to improve the resilience of business continuity against weather risks because the determination and receipt of compensation amounts are rapid.

[0006] Furthermore, there is a proposal to predict the value of a weather-based structured financial product that calculates a predicted value based on predicted weather data for a predetermined period and region, calculates reference weather data from past weather data for a predetermined period and region, and then calculates a quality index indicating the predictive quality associated with the predicted weather data based on the predicted weather data and the reference weather data (Patent Document 1). [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2012-198933 [Overview of the project] [Problems that the invention aims to solve]

[0008] Providers of financial derivatives, such as non-life insurance companies, reinsurance companies, and banks, offer weather risk-related financial products to businesses and conduct risk assessments for adverse weather conditions.

[0009] Generally, as shown in Patent Document 1, the risk is evaluated based on past global weather data at a specific target location for which weather risk is to be calculated, and this is used to calculate option premiums. However, in urban areas, there are many businesses that are easily affected by weather (wind conditions) but also need to ensure continuity as infrastructure, such as transportation, shipping, ports, airports, construction, and tourism. Although weather risk-related financial products such as the aforementioned derivatives and parametric insurance are effective as risk hedging means, risk assessment requires non-steady wind condition distribution information caused by the building clusters, which are the dominant flow field in urban areas. For example, risk assessment based on global weather data that does not consider the building clusters, as shown in Patent Document 1, is insufficient.

[0010] Therefore, the object of the present invention is to provide an information processing system and method for performing risk assessment of weather risk-related financial products such as derivatives and parametric insurance, by acquiring non-steady wind condition distributions caused by building groups, which are dominant flow fields in urban areas, based on three-dimensional spatial information such as simulation results and feature group information obtained by machine learning, setting risk thresholds based on wind condition indicators (velocity fluctuations, etc.), extracting regions as three-dimensional information, and performing risk assessment of weather risk-related financial products such as derivatives and parametric insurance.

[0011] In this way, the present invention contributes to stabilizing the business of companies that are susceptible to the effects of extreme weather and adverse weather conditions by providing weather risk-related financial products. [Means for solving the problem]

[0012] To solve the above-mentioned problems and achieve the above objectives, one embodiment of the present invention is an information processing system for designing financial derivatives based on information about a target business, comprising: a wind condition analysis unit that analyzes wind conditions in a predetermined spatial area based on topographic data and building data and acquires wind condition analysis data; a risk calculation unit that calculates the risk of the target business in the spatial area using a predetermined wind condition index based on information about the target business and the analyzed wind condition analysis data and acquires risk data; a generation unit that assigns the wind condition index and the calculated risk data to a three-dimensional map space and generates three-dimensional spatial information; and an option calculation unit that determines the value of the predetermined wind condition index as an option generation condition for the financial derivative based on the calculated risk data.

[0013] Another embodiment of the present invention is an information processing method for a device that designs financial derivatives based on information about a target business, comprising: an analysis step of analyzing wind conditions in a predetermined spatial area based on topographic data and building data to obtain wind condition analysis data; a risk calculation step of calculating the risk of the target business in the spatial area using a predetermined wind condition index based on information about the target business and the analyzed wind condition analysis data to obtain risk data; a generation step of assigning the wind condition index and the calculated risk data to a three-dimensional map space to generate three-dimensional spatial information; and an option calculation step of determining the value of the predetermined wind condition index as an option generation condition for the financial derivative based on the calculated risk data. It is characterized by including. [Effects of the Invention]

[0014] According to the present invention, it becomes possible to acquire and identify urban environmental data that takes into account the impact of building groups necessary for risk assessment. [Brief explanation of the drawing]

[0015] [Figure 1] This is a block diagram showing an information processing system in a subset of data. [Figure 2A] This is an explanatory diagram showing wind condition analysis data according to one embodiment of the present invention. [Figure 2B] This is an explanatory diagram showing wind condition indicators based on one embodiment of the present invention. [Figure 3] This is a configuration diagram showing an example of the hardware configuration of an information processing system according to one embodiment of the present invention. [Figure 4] This is a flowchart illustrating the operation of one embodiment of the present invention. [Figure 5] This is an explanatory diagram illustrating three-dimensional spatial information using one embodiment of the present invention. [Modes for carrying out the invention]

[0016] The present invention will now be described in detail based on the embodiments shown in the attached drawings.

Embodiment

[0017] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the following description and drawings are merely examples for explaining the present invention, and for the sake of clarity of explanation, appropriate omissions and simplifications have been made. In addition, the present invention can be implemented in various other forms. Also, unless otherwise particularly limited, each component may be singular or plural.

[0018] In the following description, the same or similar configurations may be denoted by the same reference numerals, and redundant descriptions may be omitted. Also, in the following description, various types of information may be described using expressions such as "information" and "table", but the various types of information may be represented by other data structures. Also, as expressions for identification information, there are expressions such as "identification information", "identifier", "name", "ID", and "number", and these can be replaced with each other. Also, in the following description, "database" is denoted as "DB" and "table" is denoted as "TBL", respectively.

[0019] Hereinafter, embodiments of the present invention will be described using FIGS. 1 to 5. FIG. 1 is a block diagram showing an information processing system in the present embodiment, FIG. 2A is an explanatory diagram showing wind condition analysis data according to the present embodiment, FIG. 2B is an explanatory diagram showing wind condition indicators according to the present embodiment, FIG. 3 is a configuration diagram showing an example of the hardware configuration of the information processing system according to the present embodiment, FIG. 4 is a flowchart for explaining the operation according to the present embodiment, and FIG. 5 is an explanatory diagram for explaining three-dimensional space information according to the present embodiment.

[0020] In the present embodiment, for example, when calculating risks from weather such as rain and wind at a location where a business targeting transportation, traffic, shipping, ports, airports, construction, tourism, etc. is carried out, and determining payment conditions, it becomes information processing.

[0021] In particular, it takes into account the influence of buildings and topography, which cannot be fully captured by the Japan Meteorological Agency's observation data, and incorporates the effects of changes in wind conditions. This contributes to risk assessment that cannot be achieved with the accuracy of spatially averaged statistical data. For example, capturing even localized changes in wind flow is a crucial key to risk assessment.

[0022] As shown in Figure 1, for example, the information processing system 1 of this embodiment includes a city DB2 that stores terrain data 202, building data 203, boundary conditions 201 which are input conditions for wind condition analysis, a wind condition analysis unit 3 that calculates wind condition analysis data 301, a risk calculation unit 4 that extracts wind condition indicators 401, a generation unit 5 that generates 3D spatial information by mapping 3D spatial information 501 onto a 3D map, an option calculation unit 6 that calculates option occurrence conditions 601, and an input / output unit 7 that transmits and receives data with external devices.

[0023] Here, boundary condition 201 refers to statistical data provided by the Japan Meteorological Agency and other organizations, which is used in the design of insurance products. This boundary condition 201 includes parameters such as wind speed and wind direction for a given region.

[0024] Examples of the external devices mentioned above include an external device 10 that provides information on the target business to the information processing system 2 and obtains the conditions for option generation, and an urban information provision system 20 that provides various types of data (e.g., CAD data) to be stored in the urban DB 2.

[0025] The wind condition analysis data 301 is composed of a combination of position information 302 and velocity vectors 303, as shown in Figure 2A, for example, and is data arranged in a time series with an arbitrary time width. Specifically, the wind condition analysis data 301 is a single data set composed of velocity vectors u, v, and w corresponding to three-dimensional position information x, y, and z.

[0026] The wind condition index 401 is composed of, for example, the velocity gradient 402, the turbulence intensity 403 which indicates the strength of the flow turbulence, the wind speed fluctuation 404, the wind direction fluctuation 405, the gust factor 406 which indicates the maximum value relative to the average wind speed, and the peak factor 407 which indicates the peak of each fluctuation, as shown in Figure 2B.

[0027] The hardware configuration of the information processing system 1 consists of, for example, a CPU 101 that controls the entire device, a memory 103 that stores programs 102 such as processing to be executed by the CPU 101 (see Figure 4) and various data being executed, an operating device 104 equipped with a keyboard and display device, an external storage device 105 that registers and stores various data such as city DB2 in formats such as DB and TBL, a communication IF 106 (corresponding to the input / output unit 7) connected to the network 30 and responsible for communication with various external devices (external device 10, city information provision system 20, etc.), and a bus 107 connected to each unit within the device and responsible for communication of data, signals, etc. within the device.

[0028] Next, we will explain the operation using Figures 4 and 5. Providers of financial derivatives, such as non-life insurance companies, reinsurance companies, and banks, use the information processing system 1 to perform processing. The following processing is executed by the CPU 101 using program 102.

[0029] It is assumed that the city DB2 already stores boundary conditions 201, terrain data 202, and building data 203, having received data from the city information provision system 20. Then, for the aforementioned businesses such as non-life insurance companies, reinsurance companies, and banks, i.e., the target businesses, the following information processing for risk assessment will be performed.

[0030] First, the city DB2 stored in the external storage device 105 is referenced, and boundary conditions 201, terrain data 202, and building data 203 are read out (step S101). Specifically, terrain and buildings in 3D space are extracted and used in the subsequent wind condition analysis unit 3.

[0031] The wind condition analysis unit 3 first creates a wind condition analysis model by a three-dimensional CFD (Computational Fluid Dynamics) simulation (step S102), and then performs the wind condition analysis (step S103). Specifically, it uses topographic data 202 and building data 203 of the urban area where weather risk-related financial products such as derivatives and parametric insurance are expected to be applied, and calculates wind condition analysis data 301 for the assumed wind conditions (wind direction, wind speed) in the target urban area according to the boundary conditions 201, which are the input conditions for the wind condition analysis (step S104).

[0032] To elaborate further, the wind condition analysis unit 3 performs transient analysis and time-series analysis to capture fluctuations in wind flow caused by buildings based on statistical boundary conditions 201. The wind condition analysis data 301 is a wind speed and wind direction distribution in three-dimensional space (x, y, z) with position information 302 and velocity vectors 303, and is time-history data over a certain period (for example, several hundred seconds in length).

[0033] Next, the risk calculation unit 4 converts the wind condition analysis data 301 into a wind condition index 401 (step S105). The wind condition index 401 is a group of transient three-dimensional data based on wind speed and wind direction obtained from the wind condition analysis data 301, and is data converted into velocity gradient 402, turbulence intensity 403, wind speed fluctuation 404, wind direction fluctuation 405, gust factor 406, peak factor 407, etc.

[0034] As shown in Figure 5, the 3D spatial information 501 is composed of a wind condition index 401, a 3D spatial region 501A, and a 3D grid 501B.

[0035] Then, the generation unit 5 stores the wind condition index 401 in the 3D grid 501B based on the position information 302 (step S106) and generates 3D spatial information 501 (step S107). In this way, the process of calculating the risk and then mapping it as 3D spatial information 501 makes the risk at a certain point apparent, and using this for option calculation is important. Here, the 3D spatial information 501 is the result of simulations or feature group information obtained from machine learning.

[0036] In the three-dimensional spatial domain 501A, the wind condition index 401 is stored in a three-dimensional grid 501B, which is formed by dividing urban areas where weather risk-related financial products such as derivatives and parametric insurance are expected to be applied, at arbitrary intervals specified by the user. Note that the three-dimensional grid 501B may be a cube or any other polyhedron shape.

[0037] Next, the option calculation unit 6 calculates the option generation conditions 601 based on wind condition indices 401 (3D velocity gradient 402, turbulence intensity 403, wind speed fluctuation 404, wind direction fluctuation 405, gust factor 406, peak factor 407) stored as 3D spatial information 501 (step S108).

[0038] This option trigger condition 601 is characterized by being obtained based on three-dimensional spatial information 501, resulting in high spatial resolution. The option trigger condition is extracted from the wind condition index 401, and the threshold is set to the point corresponding to risk based on the degree of fluctuation at the site where the project is implemented.

[0039] This option trigger condition 601 is stored in the external storage device 105 and output via the communication IF 106 in response to a request from the external device 10 (step S109).

[0040] For example, the option triggering condition 601 is evaluated based on businesses such as transportation, shipping, ports, airports, construction, and tourism. These businesses are business types of companies that are susceptible to adverse weather conditions and constitute information about the target businesses in this embodiment.

[0041] For example, in the case of transportation, the evaluation is performed based on the wind condition index 401 of a 3D grid in the planned urban space, using the vehicle or aircraft information of railways, ships, and aircraft as thresholds.

[0042] The specifications of the aircraft or vehicle bodies of railways, ships, aircraft, etc., are compared with a wind condition index 401 mapped in three-dimensional space. If there are conditions that would hinder the operation of the aircraft or vehicle bodies of railways, ships, aircraft, etc., according to the wind condition index 401, these are judged as risks and determined as option occurrence conditions 601. The option occurrence conditions 601 may be, for example, data indicating the magnitude of the risk according to the location (point) of the project (e.g., 10 levels), or data indicating the nature of the risk, and can be in any form that can be used for risk determination and risk assessment in each project.

[0043] For example, in the transportation industry, the wind resistance of a monorail running in an urban area may be assessed against 3D spatial information to determine the conditions for triggering an option. Similarly, in the construction industry, the wind resistance of a crane used in an urban area may be assessed against 3D spatial information to determine the conditions for triggering an option.

[0044] The above assessment results are mapped onto a 3D grid and stored in each external device as 3D risk assessment information for urban spaces. Furthermore, the above process will be performed similarly based on simulation results for other weather conditions such as rain, snow, and fog, in addition to wind conditions, to perform risk assessment and calculation of optional occurrence conditions.

[0045] Of course, it goes without saying that, regarding evaluation using external devices as described above, it is also possible for Information Processing System 1 to handle the processing up to obtaining 3D risk assessment information.

[0046] Furthermore, it goes without saying that the information processing system 1 in Figure 1 may be configured as a distributed system by connecting the database and various components to a network. In this case, the system configuration would involve connecting the device corresponding to the city database 2, the device corresponding to the wind condition analysis unit 3, the device corresponding to the risk calculation unit 4, the device corresponding to the generation unit 5, and the device corresponding to the option calculation unit 6 to the information processing device that controls the entire system via the network. Of course, it is also acceptable to have a system configuration in which one or more of the city database 2, wind condition analysis unit 3, risk calculation unit 4, generation unit 5, and option calculation unit 6 are distributed, rather than all of them.

[0047] As described above, according to this embodiment, based on three-dimensional spatial information such as simulation results and feature group information obtained by machine learning, an transient wind condition distribution caused by building groups (structures), which are the dominant flow fields in urban areas, is obtained. Risk thresholds are set based on wind condition indicators (velocity fluctuations, etc.), regions are extracted as three-dimensional information, and risk assessments are performed on weather risk-related financial products such as derivatives and parametric insurance. This makes it possible to obtain and identify urban environmental data that takes into account the influence of building groups necessary for risk assessment, which cannot be obtained from statistical data.

[0048] In this way, it becomes possible to achieve a highly granular risk assessment based on wind condition indicators, rather than relying solely on statistical data.

[0049] Furthermore, as in the embodiment described above, by introducing a mechanism to identify risks in advance and compensate for losses, businesses will be able to selectively choose to delay or suspend their operations.

[0050] Furthermore, although the above-described embodiment includes a city DB2 within the information processing system 1, the present invention is not limited thereto, and the city DB2 may be an external device connected to a network.

[0051] Furthermore, regarding the wind condition indicators mentioned above, it is sufficient to select the necessary indicators according to the site where the project is being carried out and perform a risk assessment. For example, in the case of a construction site, wind speed fluctuations alone may suffice, and it is possible to decide which wind condition indicators to use as appropriate.

[0052] Furthermore, while the embodiments described above use examples of businesses such as non-life insurance companies, reinsurance companies, and banks that handle financial derivatives, the present invention is not limited to these and can be applied to all commercial transactions affected by weather from a risk assessment perspective.

[0053] Furthermore, the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Also, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0054] Furthermore, each of the above-mentioned configurations, functional units, processing units, processing means, etc., may be implemented in hardware, in whole or in part, for example, by designing them as integrated circuits. Alternatively, each of the above-mentioned configurations, functions, etc., may be implemented in software by having the processor interpret and execute programs that realize each function. Information such as programs, tables, and files that realize each function can be stored in memory, hard disks, SSDs (Solid State Drives), or other recording devices, or in recording media such as IC cards, SD cards, or DVDs.

[0055] Furthermore, the arrangement of the various functional units, processing units, and databases described above is merely an example. The arrangement of the various functional units, processing units, and databases can be changed to the optimal arrangement from the standpoint of the performance, processing efficiency, and communication efficiency of the hardware and software of these devices.

[0056] Furthermore, the configuration of the database (schema, etc.) that stores the various types of data mentioned above can be flexibly modified from the perspective of efficient resource utilization, improved processing efficiency, improved access efficiency, and improved search efficiency. [Explanation of Symbols]

[0057] 1. Information Processing System 2 City DB 3 Wind condition analysis department 4. Risk Calculation Unit 5 Generation part 6. Option Calculation Section 7 Input / output section 10 External device 20 City Information Provision System 101 CPU 102 Programs 103 memory 104 Operating Devices 105 External storage device 106 Communication IF 107 Bus 201 Boundary Conditions 301 Wind Condition Analysis Data 302 Location information 303 Velocity vector 401 Wind condition index 402 Speed ​​gradient 403 Turbulence Intensity 404 Wind speed fluctuations 405 Wind direction changes 406 Gust Factor 407 Peak Factor 501 3D spatial information 501A 3D spatial domain 501B 3D grid 601 Option Activation Conditions

Claims

1. An information processing system that designs financial derivatives based on information about the target business, A wind condition analysis unit analyzes wind conditions in a predetermined spatial area based on topographic data and building data to acquire wind condition analysis data, A risk calculation unit that calculates the risk of the target project in the spatial area using a predetermined wind condition index based on information related to the target project and the analyzed wind condition analysis data, and acquires risk data. A generation unit that assigns the wind condition index and the calculated risk data to a three-dimensional map space and generates three-dimensional spatial information, An option calculation unit that determines the value in the predetermined wind condition index as the option generation condition for the financial derivative based on the calculated risk data, An information processing system characterized by comprising the following features.

2. The information processing system according to claim 1, characterized in that the predetermined wind condition index is at least one of velocity gradient, turbulence intensity, wind speed fluctuation, wind direction fluctuation, gust factor, and peak factor.

3. The information processing system according to claim 1, characterized in that the information relating to the target business is data indicating the type of business, which includes at least one of the following: transportation, traffic, shipping, ports, airports, construction, and tourism.

4. An information processing system according to claim 1, further comprising a city database that stores the terrain data, the building data, and boundary conditions which are input conditions for wind condition analysis.

5. The information processing system according to claim 4, further characterized in that it is connected to a city information provision system via a network, and inputs and stores the topographic data, building data, and boundary conditions from the city information provision system to the city database.

6. An information processing system according to claim 5, characterized in that the wind condition analysis unit acquires wind condition analysis data based on the terrain data and the building data in accordance with the boundary conditions.

7. The information processing system according to claim 6, wherein the wind condition analysis data is a wind speed and wind direction distribution in a three-dimensional space having position information and velocity vectors, and is characterized in that it is time-series data over a certain period.

8. An information processing system according to claim 7, characterized in that the generation unit generates the dimensional spatial information by storing the predetermined wind condition index in a three-dimensional grid based on the position information.

9. The information processing system according to claim 1, characterized in that the value in the wind condition index is a level indicating the magnitude of risk.

10. An information processing system according to claim 1, further characterized in that it is connected to an external device via a network, and generates the option generation conditions by receiving information about the target business from the external device.

11. An information processing method for a device that designs financial derivatives based on information about the target business, An analysis step to obtain wind condition analysis data by analyzing wind conditions in a predetermined spatial area based on topographic data and building data, A risk calculation step of calculating the risk of the target project in the spatial area using a predetermined wind condition index based on the information regarding the target project and the analyzed wind condition analysis data, and obtaining risk data; A generation step involves assigning the wind condition index and the calculated risk data to a three-dimensional map space to generate three-dimensional spatial information. An option calculation step in which a value in the predetermined wind condition index is determined as the option trigger condition for the financial derivative based on the calculated risk data, An information processing method characterized by including

12. The information processing method according to claim 11, characterized in that the analysis step refers to terrain data and building data stored in the memory of the device.

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