Information processing system and information processing method

Abstract

An information processing system (1) for designing financial derivative products on the basis of information related to a target business has: a wind condition analysis unit (3) that analyzes wind conditions in a prescribed spatial region on the basis of topography data and structure data to obtain wind condition analysis data; a risk calculation unit (4) that, on the basis of information related a target business and the wind condition analysis data, calculates risk for the target business in a spatial region on the basis of a prescribed wind condition index to obtain risk data; a generation unit (5) that assigns the wind condition index and risk data to a three-dimensional map space to generate three-dimensional spatial information; and an option calculation unit (6) that determines the value of the prescribed wind condition index as an option generation condition of the financial derivative product on the basis of the risk data.

Description

Information Processing System and Information Processing Method

[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 feared due to adverse weather conditions, for example.

[0002] Currently, on a global scale, there are concerns about the impact of climate change and ecosystem due to 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 on the business environment of each business operator due to global climate change increases, the interest in weather risk-related financial products that hedge business risks feared due to adverse weather conditions is growing.

[0004] Weather risk-related financial products include derivatives and parametric insurance, etc. For example, a weather risk-related financial product called a weather derivative is one in which a business operator estimated 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 reinsurer, or a bank, and compensation is promptly paid when contract conditions are met, such as when the weather conditions determined at the time of contract continue for a certain period.

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

[0006] In addition, there is a proposal to calculate a predicted value based on predicted weather data for a predetermined period and a predetermined region, calculate reference weather data from past weather data for the predetermined period and the predetermined region, and calculate a quality index indicating the predicted quality associated with the predicted weather data based on the predicted weather data and the reference weather data to predict the value of a weather-based structured financial product (Patent Document 1).

[0007] Japanese Patent Application Laid-Open No. 2012-198933

[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.

[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.

[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.

[0015] This is a block diagram illustrating an information processing system according to one embodiment of the present invention. This is an explanatory diagram showing wind condition analysis data according to one embodiment of the present invention. This is an explanatory diagram showing wind condition indicators according to one embodiment of the present invention. 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. This is a flowchart illustrating the operation according to one embodiment of the present invention. This is an explanatory diagram illustrating three-dimensional spatial information according to one embodiment of the present invention.

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

[0017] Embodiments of the present invention will be described below with reference to the drawings. Note that the following description and drawings are merely illustrative examples for explaining the present invention, and have been omitted or simplified as appropriate for clarity of explanation. Furthermore, the present invention can be implemented in various other forms. Also, unless otherwise specified, each component may be singular or plural.

[0018] In the following explanation, identical or similar structures may be denoted by the same code, and redundant explanations may be omitted. Also, in the following explanation, various types of information may be described using expressions such as "information" and "table," but these types of information may be represented by data structures other than these. Furthermore, while expressions such as "identification information," "identifier," "name," "ID," and "number" may be used to represent identification information, these can be substituted for each other. In the following explanation, "database" will be abbreviated as "DB" and "table" as "TBL."

[0019] The embodiments of the present invention will be described below with reference to Figures 1 to 5. Figure 1 is a block diagram showing the information processing system in this embodiment, Figure 2A is an explanatory diagram showing wind condition analysis data according to this embodiment, Figure 2B is an explanatory diagram showing wind condition indicators according to this embodiment, Figure 3 is a configuration diagram showing an example of the hardware configuration of the information processing system according to this embodiment, Figure 4 is a flowchart explaining the operation according to this embodiment, and Figure 5 is an explanatory diagram explaining three-dimensional spatial information according to this embodiment.

[0020] In this embodiment, for example, the information processing involves calculating risks from weather conditions such as rain and wind at locations where businesses such as transportation, shipping, ports, airports, construction, and tourism are carried out, and determining payment terms.

[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 DB 2 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 three-dimensional spatial information by mapping three-dimensional spatial information 501 onto a three-dimensional 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 conditions 201 refer to statistical data provided by the Japan Meteorological Agency and other organizations, which are used in the design of insurance products. These boundary conditions 201 include 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 acquires the conditions for the occurrence of options, and an urban information provision system 20 that provides various types of data (for example, 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, a velocity gradient 402, a turbulence intensity 403 indicating the strength of the flow turbulence, wind speed fluctuations 404, wind direction fluctuations 405, a gust factor 406 indicating the maximum value relative to the average wind speed, and a peak factor 407 indicating the peak of each fluctuation, as shown in Figure 2B.

[0027] The hardware configuration of the information processing system 1, as shown in Figure 3 for example, consists of a CPU 101 that controls the entire device, a memory 103 that stores programs 102 such as processes to be executed by the CPU 101 (see Figure 4) and various data being executed, an operating device 104 equipped with a keyboard and a display device, an external storage device 105 that registers and stores various data such as city DB 2 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, the operation will be explained 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 performed 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 is 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 three-dimensional 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 three-dimensional spatial information 501 is composed of a wind condition index 401, a three-dimensional spatial region 501A, and a three-dimensional grid 501B.

[0035] Then, the generation unit 5 stores the wind condition index 401 in the three-dimensional grid 501B based on the position information 302 (step S106) and generates three-dimensional spatial information 501 (step S107). In this way, the process of calculating the risk and then mapping it as three-dimensional spatial information 501 makes the risk at a certain point apparent, and using this for option calculation is important. Here, the three-dimensional spatial information 501 is the result of a simulation 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, into 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 indicators 401 (three-dimensional velocity gradient 402, turbulence intensity 403, wind speed fluctuation 404, wind direction fluctuation 405, gust factor 406, peak factor 407) stored as three-dimensional spatial information 501 (step S108).

[0038] The optional conditions 601 are characterized by being obtained based on three-dimensional spatial information 501, thereby achieving high spatial resolution. The optional conditions are 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] The optional 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 conditions 601 are 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 information of the aircraft or vehicle, such as railways, ships, and aircraft, is used as a threshold, and the evaluation is performed based on the wind condition index 401 of a three-dimensional grid in the planned urban space.

[0042] The specifications of the fuselage or hull of a railway vehicle, ship, aircraft, etc. are compared with the wind condition index 401 mapped in a three-dimensional space. If there are conditions where the operation of the fuselage or hull of a railway vehicle, ship, aircraft, etc. is inhibited by the wind condition index 401, it is determined as a risk and decided as an option generation condition 601. The option generation condition 601 may be, for example, data of a level (e.g., 10 levels) indicating the magnitude of the risk according to the location (place) of the business, or data indicating the content of the risk, as long as it can be used for risk determination and risk evaluation in each business.

[0043] For example, there may be cases where the option generation conditions are determined by comparing the wind resistance at the location of a monorail running in an urban area in the transportation and traffic industry with the three-dimensional space information. Also, there may be cases where the option generation conditions are determined by comparing the wind resistance at the location where a crane is used in an urban area in the construction industry with the three-dimensional space information.

[0044] The above determination results are mapped onto a three-dimensional grid and stored in each external device as three-dimensional risk assessment information in the urban space. Note that the above process is not only for wind conditions but is also similarly executed based on simulation results for other meteorological conditions such as rain, snow, and fog, and risk determination and calculation of option generation conditions are performed.

[0045] Of course, it goes without saying that the information processing system 1 may be responsible for the processing up to obtaining the three-dimensional risk assessment information regarding the evaluation in the external device as described above.

[0046] Also, regarding the information processing system 1 in FIG. 1, it goes without saying that the DB and each part may be connected to a network to form a distributed structure. In this case, a system configuration is formed in which a device corresponding to the urban DB 2, a device corresponding to the wind condition analysis unit 3, a device corresponding to the risk calculation unit 4, a device corresponding to the generation unit 5, and a device corresponding to the option calculation unit 6 are connected to the information processing device that performs overall control via the network. Of course, not all of the urban DB 2, the wind condition analysis unit 3, the risk calculation unit 4, the generation unit 5, and the option calculation unit 6 need to be distributed, and a system configuration in which one or more of them are distributed may also be used.

[0047] As described above, according to the present embodiment, based on three-dimensional space information such as simulation results and feature group information obtained by machine learning, an unsteady wind condition distribution caused by a building group (building), which is a dominant flow field in an urban block, is acquired, a risk threshold based on a wind condition index (such as speed fluctuation) is set, a region is extracted as three-dimensional information, and by performing a risk assessment of weather risk-related financial products such as derivatives and parametric insurance, it becomes possible to acquire and identify urban environmental data while considering the influence of the building group necessary for risk assessment, which cannot be obtained from statistical data.

[0048] Thus, it becomes possible to realize a high-precision risk assessment based on a wind condition index without relying solely on statistical data.

[0049] Also, as in the above-described embodiment, if a mechanism for extracting risks in advance and compensating for losses is introduced, an operator can selectively choose to delay or suspend the business.

[0050] Also, in the above-described embodiment, the urban DB 2 is provided inside the information processing system 1, but the present invention is not limited to this, and the urban DB 2 may be an external device connected to the network.

[0051] Also, in the above-described wind condition index, a necessary index may be extracted according to the site where the business is conducted for risk assessment. For example, at a construction site, only wind speed fluctuation may be sufficient in some cases, and it may be appropriately determined which wind condition index to use.

[0052] Furthermore, in the above-described embodiment, businesses such as a casualty insurance company, a reinsurance company, and a bank that handle financial derivatives are exemplified, but the present invention is not limited to this, and it can be applied to all commercial transactions affected by the weather from the perspective of risk assessment.

[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 on 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.) used to store 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.

[0057] 1 Information Processing System 2 City DB 3 Wind Condition Analysis Unit 4 Risk Calculation Unit 5 Generation Unit 6 Option Calculation Unit 7 Input / Output Unit 10 External Devices 20 City Information Provision System 101 CPU 102 Program 103 Memory 104 Operation Device 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 Velocity Gradient 403 Turbulence Intensity 404 Wind Speed ​​Fluctuation 405 Wind Direction Fluctuation 406 Gust Factor 407 Peak Factor 501 Three-Dimensional Spatial Information 501A Three-Dimensional Spatial Domain 501B Three-Dimensional Grid 601 Option Generation Conditions

Claims

1. 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.

2. An 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. An 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. An 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, characterized in that 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 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. An 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 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.

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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