Loan decision system

Abstract

The first challenge is to determine whether or not to provide a loan based on the cost of implementing measures to reduce greenhouse gas emissions. The second challenge is to provide a loan determination system that identifies the loan amount and / or the interest rate on the loan based on the cost of implementing measures to reduce greenhouse gas emissions. [Solution] In a loan decision system comprising one or more server devices and one or more terminal devices, the server device includes a loan eligibility determination means that determines whether or not to provide a loan to a business based on the business's financial information and the costs incurred by the business to implement measures to reduce greenhouse gas emissions, and performs a process to identify the amount that can be loaned to the business and / or the interest rate of the loan based on the business's financial information and the costs incurred by the business to implement measures to reduce greenhouse gas emissions.

Description

TECHNICAL FIELD

[0001] The present invention relates to a loan determination system.

BACKGROUND ART

[0002] In recent years, efforts to reduce greenhouse gas emissions have been promoted worldwide. In particular, business operators are required to reduce greenhouse gas emissions discharged through their business activities, and are considering implementing emission reduction measures such as equipment renewal, introduction of energy-saving technologies, and utilization of renewable energy. However, measures to reduce greenhouse gas emissions often require high costs, and business operators are required to raise funds through loans or the like in order to implement these measures.

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0003] As the problems of the present invention, for example, any of the following problems can be exemplified, but the present invention is not limited thereto. A first problem is to determine whether a loan is approved based on the cost for implementing measures to reduce greenhouse gas emissions. A second problem is to specify the loanable amount and / or the loan interest rate based on the cost for implementing measures to reduce greenhouse gas emissions. A third problem is to determine whether a loan is approved based on the financial information of a business operator. A fourth problem is to specify the loanable amount and / or the loan interest rate based on the financial information of a business operator.

MEANS FOR SOLVING THE PROBLEMS

[0004] The problems of the present invention are solved by: [1] A system comprising at least one computer device, the system comprising: loan approval determination means for determining whether a loan to a business operator is approved based on the financial information of the business operator and the cost for the business operator to implement measures to reduce greenhouse gas emissions; [2] The system described in [1] above, wherein the loan eligibility determination means further determines whether to provide a loan to the business operator based on the revenue derived from carbon credits obtained by the business operator implementing measures to reduce greenhouse gas emissions; [3] The system described in [1] or [2] above, wherein the loan eligibility determination means takes the financial information of another business as input data, and uses a machine learning-developed predictive model to determine whether or not to provide a loan to the other business, based on the financial information of the other business, as output data; [4] A system comprising at least one computer device, comprising a means for identifying the amount of a loan that can be provided to a business operator and / or the interest rate on the loan, based on the business operator's financial information and the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions; [5] The loan eligibility determination means of determining the loan amount and / or lending interest rate to the business operator based on the revenue derived from carbon credits obtained by the business operator as a result of implementing measures to reduce greenhouse gas emissions, as described in [4] above; [6] The system described in [4] or [5] above, wherein the means for determining the amount of loans available to another business operator takes the financial information of another business operator as input data, and uses a machine learning-trained predictive model to determine the amount of loans available to the other business operator and / or the interest rate on loans, which are determined based on the financial information of the other business operator, as output data; [7] A system according to any one of [1] to [6] above, comprising an input means for receiving input of greenhouse gas emission reduction measures, and a cost identification means for identifying the cost of implementing greenhouse gas emission reduction measures based on the received emission reduction measures; [8] A system according to any one of [1] to [7] above, comprising an input means for receiving input of greenhouse gas emission reduction measures, and a revenue identification means for identifying revenue derived from carbon credits obtained by implementing greenhouse gas emission reduction measures based on the received input; [9] Financial information is information relating to the business operator's balance sheet and / or income statement, as described in any of the systems described in [1] to [8] above;

[10] A method performed in a system comprising at least one computer device, the method comprising a loan eligibility determination step of determining whether to provide a loan to a business operator based on the business operator's financial information and the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions;

[11] A method performed in a system comprising at least one computer device, the method having a loan availability identification step that identifies the amount of a loan available to the business operator and / or the interest rate on the loan, based on the business operator's financial information and the costs incurred by the business operator to implement measures to reduce greenhouse gas emissions;

[12] A program that causes a computer device to function as a means of determining whether or not to provide a loan to a business operator based on the business operator's financial information and the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions;

[13] A program that causes a computer device to function as a means of determining the amount of a loan that can be made to a business operator and / or the interest rate on such loan, based on the business operator's financial information and the costs incurred by the business operator to implement measures to reduce greenhouse gas emissions;

[14] A system comprising at least one computer device, comprising a loan eligibility determination means for determining whether or not to provide a loan to a business operator based on the financial information of the business operator, wherein the loan eligibility determination means uses a machine learning-developed predictive model to determine whether or not to provide a loan to the business operator, using the financial information of another business operator as input data and information regarding the eligibility of providing a loan to the other business operator determined based on the financial information of the other business operator as output data;

[15] The system described in

[14] above, comprising a means for identifying the amount of a loan that can be provided to a business operator and / or the interest rate of the loan based on the financial information of the business operator, wherein the loan eligibility determination means and the loan eligibility determination means use the financial information of other business operators as input data, and use a machine learning-developed predictive model to determine whether a loan can be provided to the business operator and, if it is determined that a loan can be provided to the business operator, to identify the amount of a loan that can be provided to the business operator and / or the interest rate of the loan based on the financial information of the other business operator as output data, and if it is determined that a loan can be provided to the business operator, to identify the amount of a loan that can be provided to the business operator and / or the interest rate of the loan;

[16] A system comprising at least one computer device, comprising a means for identifying the amount of loans that can be made to a business operator and / or the interest rate of such loans based on the financial information of the business operator, wherein the means for identifying the amount of loans that can be made to a business operator takes the financial information of another business operator as input data, and uses a machine learning-trained predictive model, with the amount of loans that can be made to the other business operator and / or the interest rate of such loans identified based on the financial information of the other business operator as output data, to identify the amount of loans that can be made to the business operator and / or the interest rate of such loans;

[17] A method to be performed in a system comprising at least one computer device, comprising a loan eligibility determination step of determining whether or not to provide a loan to a business operator based on the financial information of the business operator, wherein in the loan eligibility determination step, the financial information of another business operator is used as input data, and information regarding the eligibility of providing a loan to the other business operator determined based on the financial information of the other business operator is used as output data, and a machine learning-developed predictive model is used to determine whether or not to provide a loan to the business operator;

[18] A method to be performed in a system comprising at least one computer device, comprising a loan availability identification step of identifying the loan amount available to a business operator and / or the interest rate on loans based on the financial information of the business operator, wherein in the loan availability identification step, the financial information of another business operator is used as input data, and the loan amount available to the other business operator and / or the interest rate on loans identified based on the financial information of the other business operator is used as output data to identify the loan amount available to the business operator and / or the interest rate on loans;

[19] A computer device functions as a loan eligibility determination means for determining whether or not to provide a loan to a business operator based on the business operator's financial information, and the loan eligibility determination means uses the financial information of other business operators as input data, and uses a machine learning-developed predictive model as output data to determine whether or not to provide a loan to the business operator in question;

[20] A computer device functions as a means of identifying the amount of loans that can be made to a business operator and / or the interest rate on loans based on the financial information of the business operator, and the means of identifying the amount of loans that can be made to a business operator takes the financial information of another business operator as input data, and uses a machine learning-developed predictive model to identify the amount of loans that can be made to the other business operator and / or the interest rate on loans based on the financial information of the other business operator as output data; This can be resolved. [Effects of the Invention]

[0005] The effects of the present invention include, but are not limited to, the following: First, the feasibility of providing a loan can be determined based on the cost of implementing greenhouse gas emission reduction measures. Second, the loan amount and / or interest rate can be determined based on the cost of implementing greenhouse gas emission reduction measures. Third, the feasibility of providing a loan can be determined based on the financial information of the business operator. Fourth, the loan amount and / or interest rate can be determined based on the financial information of the business operator. [Brief explanation of the drawing]

[0006] [Figure 1] This is a block diagram showing the configuration of the system according to the embodiment. [Figure 2] This is a block diagram showing the hardware configuration of a user terminal according to an embodiment. [Figure 3] This is a block diagram showing the hardware configuration of a server device according to an embodiment. [Figure 4] This figure shows a flowchart of the loan decision process according to the embodiment. [Modes for carrying out the invention]

[0007] The embodiments of the present invention will be described below, but the present invention is not limited to the following embodiments unless it contradicts the spirit of the invention. The order of each process constituting the flowchart described below is not limited to any order that does not cause contradictions or inconsistencies in the processing content, and it is also possible to omit some of the processes constituting the flowchart or to add new processes to each process constituting the flowchart, as long as it does not contradict the processing content. Furthermore, the device that is the main entity that executes each process constituting the flowchart can be changed to another device unless it contradicts the spirit of the invention. In that case, it is possible to change the processing content so as not to cause contradictions or inconsistencies in the processing content.

[0008] (system) Figure 1 is a block diagram showing the configuration of a system according to an embodiment. System 10 includes at least one computer device. System 10 may also include a user terminal 1 and a server device 2.

[0009] User terminal 1 is a terminal operated by a user. The user is not particularly limited and may be, for example, a business that receives a loan from a financial institution (including an employee belonging to the business), a financial institution that provides a loan to a business (including an employee belonging to the financial institution), or a third party different from these businesses or financial institutions.

[0010] User terminal 1 and server device 2 are connected to each other via communication network 3, enabling communication between them. The number of user terminals 1 is not particularly limited and may be one or more.

[0011] The system 10 may consist of, for example, one computer device (standalone type), one or more server devices and one or more terminal devices (client-server type), or one host device and one or more guest devices (peer-to-peer type). Furthermore, server device 2 may function distributed across multiple computer devices. For example, instead of server device 2, distributed ledger technology such as blockchain may be used.

[0012] <User terminal> Figure 2 is a block diagram showing the hardware configuration of a user terminal according to an embodiment. User terminal 1 comprises a control unit 11, RAM 12, storage unit 13, input unit 14, display unit 15, and communication interface 16, all of which are connected by a bus.

[0013] The control unit 11 is composed of a CPU and a ROM. The control unit 11 executes programs stored in the storage unit 13 to control the user terminal 1. The RAM 12 is a work area for the control unit 11. The storage unit 13 is a storage medium for storing programs and data. The control unit 11 performs arithmetic processing based on the programs and data read from the RAM 12, as well as data input via the input unit 14. The programs may be stored in a recording medium such as a CD-ROM or a USB memory.

[0014] The display unit 15 has a display screen. The control unit 11 outputs a video signal for displaying an image on the display screen in accordance with the result of the arithmetic processing.

[0015] The communication interface 16 can be connected to the communication network 3 by wireless or wired means, and can transmit and receive data to and from other computer devices via the communication network 3. Data received via the communication interface 16 is loaded into the RAM 12 and subjected to arithmetic processing by the control unit 11.

[0016] <Server Device> Figure 3 is a block diagram illustrating the hardware configuration of the server device according to the embodiment. The server device 2 at least includes a control unit 21, a RAM 22, a storage unit 23, and a communication interface 24, which are each connected via an internal bus.

[0017] The control unit 21 is composed of a CPU and a ROM, executes programs stored in the storage unit 23, and controls the server device 2. The control unit 21 also includes an internal timer that counts time. The RAM 22 is a work area for the control unit 21. The storage unit 23 is a storage area for storing programs and data. That is, the storage unit 23 functions as a recording medium storing the programs. The control unit 21 reads programs and data from the RAM 22, and executes program execution processing based on information received from the user terminal 1 and the like.

[0018] The system of this embodiment can determine whether or not to provide a loan to a business based on the business's financial information. The system of this embodiment can also identify the amount of the loan that can be provided to the business and / or the interest rate on the loan.

[0019] A business operator is an entity that conducts business activities, and the concept includes corporations (including for-profit and non-profit corporations) and sole proprietors. The business field of a business operator is not particularly limited. "Financial information" is information that shows the economic soundness and performance of a corporation or sole proprietor. There is no particular limit to what constitutes "financial information," but examples include the balance sheet, income statement, and cash flow statement themselves, or the information contained in the balance sheet, income statement, and cash flow statement. More specifically, "financial information" may include fixed assets, current assets, fixed liabilities, current liabilities, net assets, sales, cost of goods sold, selling expenses, general and administrative expenses, personnel expenses, gross profit, operating profit, ordinary profit, net profit for the period, cash flow from operating activities, cash flow from investing activities, and cash flow from financing activities.

[0020] "Loan" refers to a financial institution or other entity providing funds lending money to a business under certain conditions. "Loan amount" refers to the maximum amount that the lending entity deems possible to lend to the business. The loan amount is generally determined based on the business's financial information and other credit information. "Interest rate" refers to the interest rate that the borrowing business must pay in connection with the loan. There are no particular limitations on the "interest rate," but examples include fixed interest rates and variable interest rates.

[0021] In the system of this embodiment, a method for determining whether or not to provide a loan to a business operator based on the business operator's financial information, and for identifying the amount of the loan that can be provided and / or the interest rate of the loan, is to estimate future financial information (e.g., information on the income statement and balance sheet) based on current or past financial information, and then determine whether or not to provide a loan to the business operator and identify the amount of the loan that can be provided and / or the interest rate of the loan based on this estimated future financial information. Furthermore, future financial information can be estimated based on the rate of change in management indicators such as sales from the past to the present, such as the sales growth rate from the past to the present.

[0022] Furthermore, the system of this embodiment can determine whether or not to provide a loan to a business operator based on the business operator's financial information and the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions. The system of this embodiment can also determine the amount of a loan that can be provided to the business operator and / or the interest rate on the loan based on the business operator's financial information and the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions.

[0023] Greenhouse gases are gases that, when emitted into the atmosphere, make it more difficult for the Earth to escape the heat it receives from the sun, thereby increasing the Earth's temperature. While not limited to specific examples, greenhouse gases include carbon dioxide, methane, nitrous oxide, and chlorofluorocarbons (CFCs). Emission reduction measures refer to all measures implemented to reduce the emission of environmentally harmful substances, including greenhouse gases.

[0024] While there are no particular limitations on emission reduction measures, examples include introducing renewable energy, upgrading to high-efficiency equipment, and implementing energy-saving measures. Examples of renewable energy introduction include installing solar power generation systems, introducing wind power generation equipment, introducing biomass boilers, utilizing geothermal energy, and introducing small-scale hydropower generation. Examples of upgrading to high-efficiency equipment include switching to high-efficiency air conditioning and boiler equipment, replacing lighting with LEDs, and introducing cogeneration systems. Examples of energy-saving measures include utilizing energy management systems and strengthening insulation and heat shielding.

[0025] In the system of this embodiment, a method for determining whether to provide a loan to a business operator and identifying the loan amount and / or interest rate based on the business operator's financial information and the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions is, for example, to estimate future financial information (e.g., information on the income statement and balance sheet) that takes into account the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions based on current or past financial information, and then to determine whether to provide a loan to the business operator and identify the loan amount and / or interest rate based on this estimated future financial information.

[0026] One method for estimating future financial information, based on current or past financial information, that takes into account the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions, is to estimate future income statements (or information included in income statements, such as sales revenue, cost of goods sold, selling expenses, general and administrative expenses, personnel expenses, gross profit, operating profit, ordinary profit, and net profit) by taking into account increased costs due to the introduction of emission reduction measures (e.g., increased depreciation and maintenance costs due to capital investment) and cost reduction effects (e.g., reduction in fuel costs) based on past income statements.

[0027] Furthermore, one method for estimating future financial information, based on current or past financial information and taking into account the costs incurred by the business operator in implementing greenhouse gas emission reduction measures, is to estimate future balance sheets (or information such as fixed assets, current assets, fixed liabilities, current liabilities, and equity included in the balance sheet) based on past balance sheets, taking into account increases in assets due to the introduction of emission reduction measures (for example, increases in assets due to the introduction of equipment), increases in liabilities due to loans for the introduction of emission reduction measures, and changes in operating profit and net profit due to the introduction of emission reduction measures.

[0028] Furthermore, the system of this embodiment can determine whether or not to provide a loan to a business operator based on the business operator's financial information, the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions, and the revenue derived from carbon credits obtained by the business operator in implementing those measures. In addition, the system of this embodiment can also identify the amount of a loan that can be provided to the business operator and / or the interest rate on the loan based on the business operator's financial information, the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions, and the revenue derived from carbon credits obtained by the business operator in implementing those measures.

[0029] Carbon credits are a quantifiable representation of the amount of greenhouse gas emissions reduced or absorbed, made tradable. Not only reductions or absorptions of carbon dioxide emissions, but also reductions or absorptions of other greenhouse gases such as methane, nitrous oxide, and chlorofluorocarbons (CFCs) are eligible for carbon credits. In the system of this embodiment, the carbon credits that can be used are not particularly limited, but for example, credits issued under domestic emissions trading schemes and credits traded on overseas emissions exchanges can be used. Revenue derived from carbon credits refers to profits obtained by selling carbon credits acquired through greenhouse gas emission reductions to third parties.

[0030] In the system of this embodiment, a method for determining whether to provide a loan to a business operator and identifying the loan amount and / or interest rate based on the business operator's financial information, the costs incurred by the business operator for implementing greenhouse gas emission reduction measures, and the revenue derived from carbon credits obtained by the business operator as a result of implementing greenhouse gas emission reduction measures is, for example, to estimate future financial information based on current or past financial information, taking into account the costs incurred by the business operator for implementing greenhouse gas emission reduction measures and the revenue derived from carbon credits obtained by the business operator as a result of implementing greenhouse gas emission reduction measures, and then to determine whether to provide a loan to the business operator and identify the loan amount and / or interest rate based on this estimated future financial information (e.g., information on the income statement and balance sheet).

[0031] One method for estimating future financial information, based on current or past financial information, that takes into account the costs incurred by the business operator in implementing greenhouse gas emission reduction measures and the revenue derived from carbon credits obtained by the business operator as a result of implementing greenhouse gas emission reduction measures, is to estimate future income statements (or information such as sales, cost of goods sold, selling expenses, general and administrative expenses, personnel expenses, gross profit, operating profit, ordinary profit, and net profit included in the income statement) based on past income statements, taking into account increased costs due to the introduction of emission reduction measures (e.g., depreciation expenses and maintenance expenses due to capital investment), cost reduction effects (e.g., reduction in fuel costs), and revenue derived from carbon credits.

[0032] Furthermore, a method for estimating future financial information, based on current or past financial information, that takes into account the costs incurred by the business operator in implementing greenhouse gas emission reduction measures and the revenue derived from carbon credits obtained by the business operator in implementing greenhouse gas emission reduction measures, is to estimate future balance sheets (or information such as fixed assets, current assets, fixed liabilities, current liabilities, and equity included in the balance sheet) based on past balance sheets, taking into account the increase in assets due to the introduction of emission reduction measures (for example, the increase in assets due to the introduction of equipment), the increase in liabilities due to loans for the introduction of emission reduction measures, and the changes in operating profit and net profit due to the introduction of emission reduction measures and revenue derived from carbon credits.

[0033] The future timeframes in the estimated future financial information can be designed as appropriate. For example, financial information may be estimated for one month from now, for a quarter from now, for a half-year from now, or for one fiscal period from now, i.e., one year from now.

[0034] In the system of this embodiment, the method for determining whether or not to provide a loan to the business operator based on future financial information, and for identifying the loan amount and / or interest rate, is not particularly limited. For example, one such method is to perform a scoring system using a predetermined point-addition / deduction method based on multiple management indicators such as the equity ratio, current ratio, sales growth rate, return on total assets, and return on equity. If the obtained score satisfies predetermined conditions (for example, if the score is below a threshold or above a threshold), the loan is determined to be "approved." Furthermore, even if the loan is deemed "approved," the loan amount may be adjusted according to the score. Similarly, the interest rate may be adjusted according to the score.

[0035] In addition, a method can be described as using a predictive model to determine whether or not to provide a loan to a business operator based on the operator's future financial information, and to identify the amount of the loan that can be provided to the business operator and / or the interest rate on the loan. The predictive model used is a machine learning model that takes the financial information of other businesses as input data, and outputs information regarding whether or not a loan can be provided to the other business operator based on the financial information of the other business operator, and if a loan to the other business operator is possible, the amount of the loan that can be provided to the other business operator and / or the interest rate on the loan.

[0036] Other businesses are different from the businesses being considered for loan approval. Other businesses may be in the same industry as the businesses being considered for loan approval, or they may be in a different industry. Furthermore, other businesses may be of a similar scale to the businesses being considered for loan approval, or they may be of a different scale.

[0037] The predictive model is generated based on financial information for numerous other businesses, the results of loan eligibility decisions made based on each of these financial information sources, and the identified loan amounts and interest rates.

[0038] The machine learning algorithm is not particularly limited and any known algorithm can be used, but it is preferable to use deep learning using a multilayer neural network. A multilayer neural network has an input layer, an output layer, and multiple hidden layers. Weights are assigned to the edges connecting the nodes in each layer. Each edge has a weight assigned to it corresponding to each input to the node. The weights corresponding to each input to the node are multiplied, and the value obtained by multiplying these weights is added to the bias. The resulting value is subjected to a nonlinear transformation using an activation function to calculate the activation value. The calculated activation value becomes the input value passed to the node of the next layer. The number of hidden layers can be designed as appropriate.

[0039] By inputting future financial information into the resulting predictive model, it is possible to determine whether or not to provide a loan to the business. Furthermore, if it is determined that a loan to the business is "approved," the amount of the loan that can be provided and / or the interest rate on the loan will be specified.

[0040] Next, the loan decision process will be explained. Figure 4 is a flowchart of the loan decision process according to an embodiment of the present invention. The user operates the user terminal 1 to launch the application, enters their ID and password, and logs in to the system 10 (step S1). The application may be a native application or a web application.

[0041] Next, the user operates user terminal 1 to input the financial information of the business subject to loan evaluation, and further inputs the greenhouse gas emission reduction measures that the business has implemented or plans to implement (step S2). As financial information, data related to balance sheets and income statements in Word, Excel, or PDF format can be input. Multiple greenhouse gas emission reduction measures are displayed as options on the display screen of user terminal 1, and the user may input the greenhouse gas emission reduction measures that the business has implemented or plans to implement by operating user terminal 1 and selecting from these options.

[0042] The financial information and greenhouse gas emission reduction measures entered in step S2 are transmitted from the user terminal 1 to the server device 2 via the communication network 3 (step S3). The information transmitted to the server device 2 may include identification information for identifying the business operator (business operator ID, business operator name, etc.). The server device 2 receives the financial information and greenhouse gas emission reduction measures transmitted from the user terminal 1 via the communication network 3 (step S4).

[0043] Next, the control unit 21 of the server device 2 identifies the cost required to implement the greenhouse gas emission reduction measures based on the greenhouse gas emission reduction measures received in step S4 (step S5). In the server device 2, the cost or method for calculating the cost for implementing each of the multiple greenhouse gas emission reduction measures selected in step S2 is pre-registered. In step S5, based on the pre-registered cost or method for calculating the cost for each emission reduction measure, the cost corresponding to the greenhouse gas emission reduction measures received in step S4 is identified.

[0044] The user can input the information necessary for cost calculation in step S2 or step S5 by operating user terminal 2. For example, if the emission reduction measure is the installation of solar panels, the area to be installed of the solar panels is input at user terminal 1, and the cost of installing the solar panels can be determined by multiplying the input area of ​​solar panels by the installation cost of solar panels per unit area (pre-registered in server device 2).

[0045] The control unit 21 of the server device 2 identifies the amount of greenhouse gas emission reduction resulting from implementing the greenhouse gas emission reduction measures received in step S4 (step S6). In the server device 2, for each of the multiple greenhouse gas emission reduction measures selected in step S2, the amount of greenhouse gas emission reduction resulting from implementing the emission reduction measures or the method for calculating the reduction amount is pre-registered. In step S6, based on the pre-registered reduction amount or reduction amount calculation method for each emission reduction measure, the control unit 21 identifies the reduction amount corresponding to the greenhouse gas emission reduction measures received in step S4.

[0046] The user can input the information necessary to calculate the reduction amount in step S2 or step S6 by operating user terminal 2. For example, if the emission reduction measure is the installation of solar panels, the area to be installed and the location to be installed are input at user terminal 1. Based on the input installation area of ​​the solar panels, the conversion efficiency per unit area of ​​the solar panels, the amount of solar radiation corresponding to the installation location, and the amount of sunshine duration, the amount of electricity generated by the solar panels can be determined. Then, based on the determined amount of electricity generated by the solar panels, the amount of greenhouse gas emissions if the same amount of electricity were generated from fossil fuels is determined. This amount of greenhouse gas emissions becomes the reduction amount.

[0047] Next, the control unit 21 of the server device 2 identifies the revenue derived from carbon credits based on the amount of greenhouse gas reduction identified in step S6 (step S7). Since the price of carbon credits fluctuates according to the market price, the price of carbon credits may be obtained in real time. In step S7, the revenue derived from carbon credits can be identified based on the obtained price of carbon credits.

[0048] The control unit 21 of the server device 2 predicts future financial information (step S8) based on the costs identified in step S5 and the revenues identified in step S7. Specifically, the control unit 21 may estimate future financial information (information on the balance sheet and income statement) if emission reduction measures are implemented, using the financial information received from the user as a baseline.

[0049] Based on the financial information estimated in step S8, the control unit 21 of the server device 2 uses a predictive model implemented within the server device 2 to determine whether or not a loan is possible, and if a loan is possible, it specifies the loan amount and the loan interest rate (step S9). The predictive model can be the one described above.

[0050] The information regarding the eligibility of the loan, the loan amount, and the interest rate, which were identified in step S9, are transmitted from the server device 2 to the user terminal 1 via the communication network 3 (step S10). The user terminal 1 receives the information regarding the eligibility of the loan, the loan amount, and the interest rate (step S11). Next, the display unit 15 of the user terminal 1 displays the eligibility of the loan, the loan amount, and the interest rate received in step S11 (step S12). Steps S1 to S12 complete the loan determination process. [Explanation of Symbols]

[0051] 1 User terminal 2 Server devices 3. Communication Network 10 Systems 11 Control Unit 12 RAM 13 Storage Section 14 Input section 15 Display 16 Communication Interfaces 21 Control Unit 22 RAM 23 Storage Section 24 Communication Interfaces

Claims

1. A system comprising at least one computer device, A loan eligibility determination tool that determines whether or not to provide a loan to a business based on the business's financial information and the costs incurred by the business in implementing measures to reduce greenhouse gas emissions. A system that includes these features.

2. The system according to claim 1, wherein the loan eligibility determination means further determines whether to provide a loan to the business operator based on the revenue derived from carbon credits obtained by the business operator implementing measures to reduce greenhouse gas emissions.

3. The system according to claim 1 or 2, wherein the loan eligibility determination means takes the financial information of another business as input data, and uses a machine learning-developed predictive model to determine whether or not to provide a loan to the other business, using the information regarding the eligibility of providing a loan to the other business determined based on the financial information of the other business as output data.

4. A system comprising at least one computer device, A means of identifying the amount of money that can be loaned to a business and / or the interest rate on such loans, based on the business's financial information and the costs incurred by the business in implementing measures to reduce greenhouse gas emissions. A system that includes these features.

5. The system according to claim 4, wherein the means for determining the amount of loans available to the business operator determines the amount of loans available to the business operator and / or the interest rate on the loans based on the revenue derived from carbon credits obtained by the business operator as a result of implementing measures to reduce greenhouse gas emissions.

6. The system according to claim 4 or 5, wherein the means for determining the amount of loans available to another business operator takes the financial information of another business operator as input data, and uses a machine learning-trained predictive model to determine the amount of loans available to the other business operator and / or the interest rate on loans, which are determined based on the financial information of the other business operator, as output data.

7. An input method for receiving input on measures to reduce greenhouse gas emissions, Based on the emissions reduction measures that have been accepted as input, a cost identification method is used to identify the costs of implementing greenhouse gas emission reduction measures. The system according to claim 1, 2, 4, or 5, comprising:

8. An input method for receiving input on measures to reduce greenhouse gas emissions, Based on the emissions reduction measures that have been accepted as input, a revenue identification method identifies revenue derived from carbon credits obtained by implementing greenhouse gas emission reduction measures. The system according to claim 1, 2, 4, or 5, comprising:

9. The system according to claim 1, 2, 4, or 5, wherein the financial information is information relating to the business operator's balance sheet and / or information relating to the business operator's income statement.

10. A method performed in a system comprising at least one computer device, A loan eligibility determination step that determines whether or not to provide a loan to a business based on the business's financial information and the costs incurred by the business in implementing measures to reduce greenhouse gas emissions. A method having

11. A method performed in a system comprising at least one computer device, Loan eligibility determination step: Based on the business operator's financial information and the costs incurred by the business operator in implementing measures to reduce greenhouse gas emissions, the loan amount that can be provided to the business operator and / or the interest rate on the loan. A method having

12. Computer equipment, A loan eligibility determination tool that determines whether or not to provide a loan to a business based on the business's financial information and the costs incurred by the business in implementing measures to reduce greenhouse gas emissions. A program that makes something function as such.

13. Computer equipment, A means of identifying the amount of money that can be loaned to a business and / or the interest rate on such loans, based on the business's financial information and the costs incurred by the business in implementing measures to reduce greenhouse gas emissions. A program that makes something function as such.

14. A system comprising at least one computer device, A loan eligibility determination tool that determines whether or not to provide a loan to a business based on the business's financial information. Equipped with, A system for determining whether to provide a loan to another business, using financial information of another business as input data and information regarding the feasibility of providing a loan to that other business, determined based on that financial information, as output data, and employing a machine learning-developed predictive model to determine whether to provide a loan to that business.

15. A means of identifying the amount of loans that can be provided to a business operator and / or the interest rate on those loans, based on the business operator's financial information. Equipped with, The system according to claim 14, wherein the loan eligibility determination means and the loan amount identification means take the financial information of another business as input data, and use a machine learning-developed predictive model to determine whether a loan can be made to the other business, and if it is determined that a loan can be made to the business, to identify the loan amount and / or loan interest rate to the business.

16. A system comprising at least one computer device, A means of identifying the amount of loans that can be provided to a business operator and / or the interest rate on those loans, based on the business operator's financial information. Equipped with, A system for determining the amount of loans available to another business, which uses the financial information of another business as input data, and uses a machine learning-developed predictive model to determine the amount of loans available to that other business and / or the interest rate on loans, which are identified based on the financial information of the other business, as output data.

17. A method performed in a system comprising at least one computer device, Loan eligibility determination step: Based on the financial information of the business operator, determine whether or not to provide a loan to the business operator. It has, A method for determining whether to provide a loan to a business operator, in the loan eligibility determination step, in which financial information of other business operators is used as input data, and information regarding the eligibility of providing a loan to said other business operator, determined based on the financial information of said other business operators, is used as output data, and a machine learning-developed predictive model is used to determine whether to provide a loan to said business operator.

18. A method performed in a system comprising at least one computer device, Loan eligibility step: Based on the financial information of the business operator, determine the amount of loan that can be provided to the business operator and / or the interest rate on the loan. It has, A method for determining the loan amount, in which the financial information of another business is used as input data, and the loan amount and / or interest rate to the other business identified based on the financial information of the other business is used as output data to determine the loan amount and / or interest rate to the said business using a machine learning-developed predictive model.

19. Computer equipment, A loan eligibility determination tool that determines whether or not to provide a loan to a business based on the business's financial information. To make it function as, A program that uses a machine learning-based predictive model to determine whether or not to provide a loan to a business, using the financial information of another business as input data and information regarding the feasibility of providing a loan to that other business, determined based on the financial information of the other business, as output data.

20. Computer equipment, A means of identifying the amount of loans that can be provided to a business operator and / or the interest rate on those loans, based on the business operator's financial information. To make it function as, A program that uses a machine learning-developed predictive model to identify the loan amount and / or lending interest rate to another business, using the financial information of another business as input data and the loan amount and / or lending interest rate to the other business identified based on the financial information of the other business as output data.

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