JP2024059318A - Guarantee fee revenue forecasting device, guarantee fee revenue forecasting method, and guarantee fee revenue forecasting program - Google Patents

Abstract

To provide a guarantee fee profit predicting device, a guarantee fee profit predicting method and a guarantee fee profit predicting program that enable a prediction for a guarantee fee profit in accordance with a future balance before repayment from information coordinated from a financial agency at a timing at which the contract of a guarantee is settled.SOLUTION: A guarantee fee profit predicting device according to this embodiment includes a control unit. The control unit is accessible to contract data and a guarantee fee managing master. The control unit includes: a guarantee fee calculating unit that calculates a lump-sum guarantee fee based on the contract data and on the guarantee fee managing master; an expanding unit that creates repayment schedule data based on the contract data and on applied-time guarantee contract data; a totalizing unit that calculates a balance cumulative number total from the balance cumulative number for each month in the repayment schedule data; a guarantee annual rate calculating unit that calculates a guarantee annual rate by dividing the lump-sum guarantee fee by the balance cumulative number total; and a predicting unit that predicts the guarantee fee profit based on the balance before repayment for each month in the repayment schedule data, and on the guarantee annual rate of the guarantee contract data.SELECTED DRAWING: Figure 2

Description

The present invention relates to a guarantee fee revenue forecasting device, a guarantee fee revenue forecasting method, and a guarantee fee revenue forecasting program.

Traditionally, guarantor companies have sometimes received the guarantee fee in advance from the financial institution in a lump sum, and the guarantee fee income recorded by the guarantor is determined pro rata according to the monthly balance. For example, the monthly balance is determined by file transfer once a month from the guaranteeing financial institution. However, the guarantee fee is calculated only for the relevant month based on the most recent month's actual balance transferred monthly from the financial institution, and future guarantee fee income cannot be calculated.

Patent Document 1 discloses a lease installment guarantee system that provides trade credit insurance to installment receivables at the time of lease installment contract to help guarantee the installment contract, and discloses a configuration in which the trade credit insurance premium is set for the remaining installment receivables at the time of lease installment contract.

JP 2002-312700 A

However, in the past, there was an issue that it was not possible to predict future guarantee fee revenues based on the pre-repayment balance from the information shared with financial institutions at the time the guarantee contract was finalized.

The present invention has been made in consideration of the above problems, and aims to provide a guarantee fee revenue prediction device, a guarantee fee revenue prediction method, and a guarantee fee revenue prediction program that can predict guarantee fee revenue according to future pre-repayment balances from information shared with financial institutions at the time the guarantee contract is finalized.

In order to solve the above-mentioned problems and achieve the object, the guarantee fee revenue forecasting device of the present invention is a guarantee fee revenue forecasting device equipped with a control unit, the control unit being capable of accessing contract data including at least customer identification information, loan amount, loan interest rate, first repayment date, and final repayment date, and a guarantee fee management master in which at least the number of years of borrowing, customer credit information, and a guarantee fee per unit yen are set, the control unit being configured to include a guarantee fee calculation unit that calculates a lump-sum guarantee fee based on the contract data and the guarantee fee management master, and creates application-time guarantee contract data including at least the guarantee period, the guarantee amount, the customer credit information, and the lump-sum guarantee fee, and a guarantee fee calculation unit that develops the pre-repayment balance for each month based on the contract data and the application-time guarantee contract data. The system is characterized by comprising: an expansion unit that creates scheduled repayment data including at least the number of repayments, the repayment date, the pre-repayment balance for each month, the number of days elapsed since the previous repayment date, and the accumulated balance for each month; a totalization unit that calculates a total accumulated balance from the accumulated balance for each month of the scheduled repayment data; a guaranteed annual rate calculation unit that calculates a guaranteed annual rate by dividing the lump-sum guarantee fee by the total accumulated balance, and creates guarantee contract data including at least customer identification information and the guaranteed annual rate; and a prediction unit that predicts guarantee fee income based on the pre-repayment balance for each month of the scheduled repayment data and the guaranteed annual rate of the guarantee contract data, and creates scheduled guarantee fee income data including at least the number of repayments and the guarantee fee income.

In addition, according to one aspect of the present invention, the guarantee fee per unit yen is a guarantee fee per 1 million yen, and the guarantee fee calculation unit calculates the lump-sum guarantee fee by (guarantee fee per 1 million yen) x (the loan amount) ÷ 1 million.

According to one aspect of the present invention, the expansion unit calculates the balance product for each month by (the balance before repayment) × (the number of days elapsed) ÷ 365.

According to one aspect of the present invention, the prediction unit predicts the guarantee fee income for each month by (the balance before repayment for each month) x (the annual guarantee rate) ÷ 12.

According to one aspect of the present invention, the customer credit information is a course selected based on the customer's creditworthiness, and the guarantee fee per unit yen is set according to the loan term and the course.

The guarantee fee revenue forecasting method according to the present invention includes the steps of: a guarantee fee revenue forecasting device accessing contract data including at least customer identification information, loan amount, loan interest rate, first repayment date, and final repayment date; the guarantee fee revenue forecasting device accessing a guarantee fee management master in which at least the number of years of borrowing, customer credit information, and a guarantee fee per unit yen are set; the guarantee fee revenue forecasting device calculating a lump-sum guarantee fee based on the contract data and the guarantee fee management master, and creating guarantee contract data at the time of application including at least the guarantee period, the guarantee amount, the customer credit information, and the lump-sum guarantee fee; and the guarantee fee revenue forecasting device expanding the pre-repayment balance for each month based on the contract data and the guarantee contract data at the time of application, and calculating the number of repayments, the repayment date, and the prepayment date. The method includes the steps of: creating scheduled repayment data including at least the pre-repayment balance for each month, the number of days elapsed since the last repayment date, and the accumulated balance for each month; the guarantee fee revenue prediction device calculating a total accumulated balance from the accumulated balance for each month of the scheduled repayment data; the guarantee fee revenue prediction device calculating a guaranteed annual rate by dividing the lump-sum guarantee fee by the total accumulated balance, and creating guarantee contract data including at least customer identification information and the guaranteed annual rate; and the guarantee fee revenue prediction device predicting guarantee fee revenue based on the pre-repayment balance for each month of the scheduled repayment data and the guaranteed annual rate of the guarantee contract data, and creating scheduled guarantee fee revenue data including at least the number of repayments and the guarantee fee revenue.

In addition, the guarantee fee revenue forecasting program of the present invention includes the steps of: accessing contract data including at least customer identification information, loan amount, loan interest rate, first repayment date, and final repayment date in a computer; accessing a guarantee fee management master in which at least the number of years of borrowing, customer credit information, and a guarantee fee per unit yen are set; calculating a lump-sum guarantee fee based on the contract data and the guarantee fee management master, and creating application-time guarantee contract data including at least the guarantee period, guarantee amount, customer credit information, and the lump-sum guarantee fee; and expanding the pre-repayment balance for each month based on the contract data and the application-time guarantee contract data, and calculating the number of repayments, the repayment date, and each of the steps. The system is characterized by executing the steps of: creating scheduled repayment data including at least the pre-repayment balance for each month, the number of days elapsed since the last repayment date, and the accumulated balance for each month; calculating a total accumulated balance from the accumulated balance for each month of the scheduled repayment data; calculating the annual guaranteed rate by dividing the lump-sum guarantee fee by the total accumulated balance, and creating guarantee contract data including at least customer identification information and the annual guaranteed rate; and predicting guarantee fee revenue based on the pre-repayment balance for each month of the scheduled repayment data and the annual guaranteed rate of the guarantee contract data, and creating scheduled guarantee fee revenue data including at least the number of repayments and the guarantee fee revenue.

The present invention has the effect of making it possible to predict future guarantee fee income based on the pre-repayment balance from information shared with financial institutions at the time the guarantee contract is finalized.

FIG. 1 is a diagram showing an example of the conventional method of recording guarantee fee income. FIG. 2 is a block diagram illustrating an example of a configuration of a guarantee fee revenue prediction device according to an embodiment. FIG. 3 is a diagram illustrating an example of contract data according to the embodiment. FIG. 4 is a diagram illustrating an example of the guarantee fee management master according to the embodiment. FIG. 5 is a diagram illustrating an example of application guarantee contract data according to the embodiment. FIG. 6 is a diagram illustrating an example of repayment schedule data according to the embodiment. FIG. 7 is a diagram illustrating an example of the warranty contract data according to the embodiment. FIG. 8 is a diagram illustrating an example of guarantee fee income forecast data according to the embodiment. FIG. 9 is a diagram illustrating an example of a data flow of the guarantee fee revenue prediction process according to the embodiment. FIG. 10 is a flowchart illustrating an example of a guarantee fee income prediction process according to the embodiment.

An embodiment of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to this embodiment.

[1. Overview]
First, an overview of the present invention will be described.

Figure 1 shows an example of how guarantee fee revenue is recorded in the past. Each month, financial institutions share information for the previous month, including the repayment period, loan interest rate, loan date, and loan balance, with the guarantor company. Each month, the guarantor company calculates the monthly guarantee fee revenue based on the loan balance for the target month shared by the financial institution, by multiplying the pre-repayment balance (actual balance) by the guarantee fee rate.

To calculate future guarantee fee revenue, it is necessary to know the monthly balance for future installments as well. However, in most cases, it was not possible to obtain repayment schedules from financial institutions because the financial institutions did not retain the repayment schedule data, so previously, guarantor companies only retained balance information for the month shared by the financial institution. In other words, previously, since repayment schedules were not retained, it was only possible to calculate guarantee fee revenue for the most recent month shared by the financial institution.

The guarantee fee revenue forecasting device of this embodiment holds the future planned balance by developing a repayment schedule from the loan amount, repayment period, and interest rate shared by the financial institution when the guarantee contract is finalized. The guarantee fee revenue forecasting device of this embodiment then calculates future guarantee fee revenue from the future planned balance and the annual guarantee rate (= guarantee fee ÷ total accumulated balance).

According to the guarantee fee revenue forecasting device of this embodiment, by developing a repayment schedule based on information received from a financial institution, the expected monthly balance can be grasped and the guarantee fee can be calculated according to that balance. Since guarantor companies cannot obtain repayment schedules from financial institutions, it has been difficult for them to grasp future revenues, but by developing the repayment schedule, it is possible to simulate future revenues. Understanding future revenues using this simulation function (future prediction function for guarantee fee revenues) is useful for management decisions.

2. Configuration
An example of the configuration of the guarantee fee revenue prediction device 100 according to this embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of the guarantee fee revenue prediction device 100.

The guarantee fee revenue prediction device 100 is a commercially available desktop personal computer. Note that the guarantee fee revenue prediction device 100 is not limited to a stationary information processing device such as a desktop personal computer, but may be a portable information processing device such as a commercially available notebook personal computer, PDA (Personal Digital Assistant), smartphone, or tablet personal computer.

The guarantee fee revenue prediction device 100 comprises a memory unit 1, a control unit 2, a communication interface unit 3, and an input/output interface unit 4. Each unit of the guarantee fee revenue prediction device 100 is connected to be able to communicate with each other via any communication path.

The storage unit 1 stores data such as various databases, tables, and files. The storage unit 1 records computer programs that work with the OS (Operating System) to give instructions to the CPU (Central Processing Unit) to perform various processes. The storage unit 1 can be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, or an optical disk.

The memory unit 1 also stores contract data 1a, guarantee fee management master 1b, guarantee contract data at the time of application 1c, repayment schedule data 1d, guarantee contract data 1e, and guarantee fee revenue forecast data 1f. Details of the contract data 1a, guarantee fee management master 1b, guarantee contract data at the time of application 1c, repayment schedule data 1d, guarantee contract data 1e, and guarantee fee revenue forecast data 1f will be described later with reference to Figures 3 to 8.

The control unit 2 is a CPU or the like that provides overall control of the guarantee fee revenue prediction device 100. The control unit 2 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing operations based on these stored programs.

The control unit 2 includes a guarantee fee calculation unit 2a, an expansion unit 2b, a totalization unit 2c, a guaranteed annual rate calculation unit 2d, and a prediction unit 2e.

The guarantee fee calculation unit 2a calculates the guarantee fee when application information and loan information are linked from the financial institution to the guarantee fee revenue prediction device 100 of the guarantor company. The application information linked from the financial institution to the guarantor company includes, for example, the customer name, loan date, repayment period, and loan interest rate. In addition, the loan information linked from the financial institution to the guarantor company includes, for example, the number of years of loan, course, guarantee amount, number of years of loan, guarantee fee type, and approval/disapproval.

The method of linking the application information and loan information from the financial institution to the guarantee fee revenue prediction device 100 may be arbitrary. For example, the guarantee fee revenue prediction device 100 may accept the linking of the application information and loan information by an operator's operational input. Also, for example, the guarantee fee revenue prediction device 100 may accept the linking of the application information and loan information by data communication via the network 200.

First, the guarantee fee calculation unit 2a updates the contract data 1a based on the application information and loan information linked to the guarantee fee revenue prediction device 100 from the financial institution.

Figure 3 is a diagram showing an example of contract data 1a in an embodiment. Contract data 1a in an embodiment includes a customer number, a branch number, a loan date, a loan amount, a loan interest rate, a first repayment date, and a final repayment date.

The customer number (an example of customer identification information) is a number that identifies a customer. The subnumber is a number that identifies multiple pieces of data when there are multiple pieces of data identified by the customer number. The loan date is the date on which the loan is executed (the date on which the loan amount is deposited). The loan amount is the amount loaned (loan amount). The loan interest rate (loan interest rate) is the ratio of interest paid to the loan amount (borrowed principal). The first repayment date is the date of the first repayment. The final repayment date is the date of the last repayment.

Then, the guarantee fee calculation unit 2a calculates the guarantee fee based on the contract data 1a and the guarantee fee management master 1b, and updates the guarantee contract data at the time of application 1c.

Figure 4 is a diagram showing an example of the guarantee fee management master 1b of the embodiment. The guarantee fee management master 1b of the embodiment is set as a master that holds the guarantee fee per 1 million yen for each course and each number of years of borrowing. Specifically, the guarantee fee management master 1b of the embodiment includes the number of years of borrowing, course, guarantee fee type, normal or excess, revision date, and guarantee fee (yen) per 1 million yen.

The number of years of loan is the number of years for which the loan amount is to be borrowed. The course (an example of customer credit information) is selected based on the creditworthiness of the customer who is the guarantor. The set value of the guarantee fee per 1 million differs depending on the course. The type of guarantee fee indicates a classification of lump sum (lump sum advance guarantee fee) or installment (installment guarantee fee). In the example embodiment, the type of guarantee fee is set to "lump sum."

Normal or excess is set to "normal" or "excess." "Normal" indicates a guarantee fee for an amount that does not exceed the collateral amount held in the guarantee fee revenue prediction device 100 of the embodiment. "Excess" indicates a guarantee fee for an amount that exceeds the collateral amount held in the guarantee fee revenue prediction device 100 of the embodiment. The revision date is the date on which the data in the guarantee fee management master 1b was revised.

Guarantee fee (yen) per 1 million (an example of the guarantee fee per yen) is the guarantee fee (yen) per 1 million. The guarantee fee (yen) per 1 million is set according to the loan term and course.

Figure 5 is a diagram showing an example of application guarantee contract data 1c in an embodiment. Application guarantee contract data 1c in an embodiment includes customer number, sub-number, application customer number, acceptance/rejection result, guarantee period, guarantee amount, course, guarantee fee type, and guarantee fee. The customer number and sub-number are the same as those explained in Figure 3, so their explanations are omitted. The course and guarantee fee type are the same as those explained in Figure 4, so their explanations are omitted.

The applicant customer number is a number that identifies the customer applying for the loan. In the example of the applicant customer number in Figure 5, it is set to the same value as the customer number. The approval result is set to whether the loan is approved or not (OK or NG). The guarantee period is a period equivalent to the loan period described above. The guarantee amount is an amount equivalent to the loan amount (borrowing amount) described above.

The guarantee fee is calculated by (guarantee fee per 1 million yen) x (loan amount) ÷ 1 million. For example, in the case of loan amount (loan amount): 30,000,000 yen, loan period: 35 years, course: A, and guarantee fee type: lump sum payment, the guarantee fee is calculated by the following formula based on the guarantee fee management master 1b in FIG.
10,000 yen x (30,000,000 yen ÷ 1,000,000)
= 300,000 yen (rounded down to the nearest yen)

Next, the expansion unit 2b expands the repayment schedule based on the contract data 1a and the application guarantee contract data 1c, and creates the repayment schedule data 1d (expands the planned balance by month).

6 is a diagram showing an example of repayment schedule data 1d according to an embodiment of the present invention. The example in FIG. 6 shows a case where a repayment schedule is developed under the following lending conditions:
Loan amount: 30,000,000 yen Loan date: January 10, 2022 Repayment period: 35 years Loan interest rate: 0.5%
Lump sum guarantee fee: 300,000 yen

In the embodiment, the repayment schedule data 1d includes the number of repayments, the repayment date, the monthly repayment amount, the principal paid, the interest paid, the balance before repayment, the balance after repayment, the number of days elapsed, and the accumulated balance. The number of repayments is the number of repayments. The repayment date is the date on which the repayment is made. The monthly repayment amount is the monthly repayment amount. The principal paid is the amount of the monthly repayment that corresponds to the principal. The interest paid is the amount of the monthly repayment that corresponds to the interest.

The pre-payment balance is the balance before the monthly payment. The pre-payment balance is used, for example, to calculate the balance integral and guarantee fee income. The post-payment balance is the balance after the monthly payment. The number of days since the last payment.

The balance integral is the balance integral for the number of repayments, and is calculated by (balance before repayment) x (number of days elapsed) ÷ 365. For example, the balance integral for the first repayment is calculated by the following formula.
30,000,000 yen x 31 ÷ 365 = 2,547,945 (rounded down to the nearest yen)

Next, the totaling unit 2c calculates the total balance amount from the repayment schedule data 1d. In the example of FIG. 6, the total balance amount is 541,855,226 yen.

Next, the annual guarantee rate calculation unit 2d calculates the annual guarantee rate by dividing the lump sum guarantee fee in the guarantee contract data at the time of application 1c (Figure 5) and the total balance accumulated value calculated by the totaling unit 2c from the repayment schedule data 1d (Figure 6), and updates the guarantee contract data 1e.

Figure 7 is a diagram showing an example of guarantee contract data 1e in an embodiment. Guarantee contract data 1e in an embodiment includes customer N, branch number, application customer No., acceptance/rejection result, guarantee period, guarantee amount, course, guarantee fee type, and guarantee fee rate. Customer N, branch number, application customer No., acceptance/rejection result, guarantee period, guarantee amount, course, and guarantee fee type are the same as those explained in Figure 5, so explanations are omitted.

The guarantee fee rate is a value calculated by (lump sum guarantee fee)/(total balance product). For example, in the examples of Figures 5 and 6, the annual guarantee rate is calculated by the following formula.
(Annual rate of guarantee) = (lump sum guarantee fee) ÷ (total balance)
= 300,000 / 541,855,226
≒ 0.05537% (rounded up to the sixth decimal place)

Next, the prediction unit 2e predicts (expands) future revenues based on the monthly pre-repayment balance and the guaranteed annual rate, and creates the guarantee fee revenue forecast data 1f.

Figure 8 is a diagram showing an example of guarantee fee revenue forecast data 1f of an embodiment. The guarantee fee revenue forecast data 1f of the embodiment includes the number of repayments, repayment date, monthly repayment amount, principal paid, interest paid, balance before repayment, balance after repayment, number of days elapsed, cumulative balance, annual guarantee rate, and guarantee fee revenue. The number of repayments, repayment date, monthly repayment amount, principal paid, interest paid, balance before repayment, balance after repayment, number of days elapsed, and cumulative balance are the same as those explained in Figure 6, so their explanations are omitted. Also, the annual guarantee rate is the same as those explained in Figure 7, so their explanations are omitted.

The guarantee fee income is calculated by (each month's pre-repayment balance) x (annual guarantee rate) ÷ 12. For example, in the case of the first repayment, the guarantee fee income is calculated by the following formula.
(Guarantee fee income) = (Balance before repayment for each month) x (Guarantee annual rate) ÷ 12
= 30,000,000 x 0.05537% ÷ 12
= 1,384.25
≒1,385 (rounded up to the nearest whole number)

In addition, if the contract is renewed, such as with partial early repayment, the repayment pattern will be recalculated each time and the correct guarantee fee income will be recalculated.

Returning to FIG. 1, the communication interface unit 3 communicatively connects the guarantee fee revenue prediction device 100 to the network 200 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 3 has a function of communicating data with other devices via the communication line. Here, the network 200 has a function of connecting the guarantee fee revenue prediction device 100 and other devices so that they can communicate with each other, and is, for example, the Internet or a LAN (Local Area Network).

The input/output interface unit 4 is connected to an input device 110 and an output device 120. The input device 110 may be a keyboard, a mouse, a microphone, or a monitor (including a touch panel) that cooperates with the mouse to realize a pointing device function. The output device 120 may be a monitor (including a touch panel), or a speaker or a printer. The output device 120 outputs, for example, the results of processing by the control unit 2.

Figure 9 is a diagram showing an example of the data flow of the guarantee fee revenue forecast process in an embodiment. First, based on the course and loan term entered on the application screen, the guarantee fee (normal guarantee fee) per 1 million yen is obtained from the guarantee fee management master 1b, and the guarantee fee (lump sum guarantee fee) is calculated. For example, the guarantee fee is calculated as (normal guarantee fee per 1 million yen) x (loan amount below the collateral valuation amount) ÷ 1 million. Then, the guarantee fee calculation unit 2a updates the application time guarantee contract data 1c.

Next, the expansion unit 2b expands the repayment schedule based on the application information and loan information shared by the financial institution, and creates repayment schedule data 1d. Next, the totaling unit 2c totals the balance accumulations in the repayment schedule data 1d to calculate the total balance accumulation.

Next, the annual guarantee rate calculation unit 2d calculates the annual guarantee rate from the guarantee fee (lump sum guarantee fee) in the guarantee contract data 1c at the time of application and the total balance accumulated amount calculated by the totaling unit 2c from the repayment schedule data 1d, and updates the guarantee contract data 1e.

Next, the prediction unit 2e performs a simulation of future guarantee fee revenue by calculating the guarantee fee revenue for each month. In other words, since the guarantee fee revenue prediction device 100 of the embodiment holds the pre-repayment balance (planned balance) for each month, it becomes possible to simulate future guarantee fee revenue according to the planned balance.

Figure 10 is a flowchart showing an example of a guarantee fee revenue forecasting process in an embodiment. First, the guarantee fee calculation unit 2a calculates a lump-sum guarantee fee from the contract data 1a and the guarantee fee management master 1b, and creates application time guarantee contract data 1c that includes at least the guarantee period, guarantee amount, course (an example of customer credit information), and lump-sum guarantee fee (step S1).

Then, the expansion unit 2b expands the pre-repayment balance for each month based on the contract data 1b and the application guarantee contract data 1c, and creates repayment schedule data 1d including at least the number of repayments, the repayment date, the pre-repayment balance for each month, the number of days since the last repayment date, and the accumulated balance for each month (step S2). Next, the totaling unit 2c calculates the total accumulated balance from the accumulated balance for each month in the repayment schedule data 1d (step S3).

Next, the annual guarantee rate calculation unit 2d calculates the annual guarantee rate by dividing the lump-sum guarantee fee by the total accumulated balance, and creates guarantee contract data 1e including at least the customer identification information and the annual guarantee rate (step S4). Next, the prediction unit 2e predicts the guarantee fee revenue for each month based on the pre-repayment balance for each month in the repayment schedule data 1d and the annual guarantee rate in the guarantee contract data 1e, and creates the guarantee fee revenue schedule data 1f including at least the number of repayments and the guarantee fee revenue (step S5).

As described above, according to the embodiment of the guarantee fee revenue prediction device 100, it is possible to predict the guarantee fee revenue according to the future pre-repayment balance from the information shared by the financial institution at the time the guarantee contract is finalized.

[3. Contribution to the United Nations-led Sustainable Development Goals (SDGs)]
This embodiment can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby making it possible to contribute to goals 8 and 9 of the SDGs.

In addition, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, which can contribute to the achievement of SDGs goals 12, 13, and 15.

In addition, this embodiment can contribute to strengthening control and governance, making it possible to contribute to Goal 16 of the SDGs.

4. Other embodiments
The present invention may be embodied in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method.

In addition, the processing procedures, control procedures, specific names, registered data for each process, information including search conditions and other parameters, screen examples, and database configurations shown in this specification and drawings may be changed as desired unless otherwise specified.

Furthermore, with regard to the guarantee fee revenue prediction device 100, each component shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure.

For example, the processing functions of the guarantee fee revenue prediction device 100, particularly the processing functions performed by the control unit 2, may be realized in whole or in part by a CPU and a program interpreted and executed by the CPU, or may be realized as hardware using wired logic. The program is recorded on a non-transient computer-readable recording medium that contains programmed instructions for causing the information processing device to execute the processing described in this embodiment, and is mechanically read by the guarantee fee revenue prediction device 100 as necessary. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM, and cooperates with the CPU to form the control unit.

In addition, this computer program may be stored in an application program server connected to the guarantee fee revenue prediction device 100 via any network, and it is also possible to download all or part of it as needed.

In addition, the program for executing the processing described in this embodiment may be stored on a non-transitory computer-readable recording medium, or may be configured as a program product. Here, the term "recording medium" refers to a memory card, a USB (Universal Serial Bus) memory, a SD (Secure Digital) card, a flexible disk, a magneto-optical disk, a ROM, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (registered trademark) (Electrically Erasable and Programmable Read Only Memory), a CD-ROM (Compact Disk Read Only Memory), an MO (Magneto-Optical disk), a DVD (Digital Versatile This includes any "portable physical media" such as a DVD (registered trademark) or Blu-ray (registered trademark) Disc.

A "program" is a data processing method written in any language or description method, and may be in any format, such as source code or binary code. Note that a "program" is not necessarily limited to a single configuration, but also includes a distributed configuration consisting of multiple modules or libraries, and a program that works in conjunction with a separate program, such as an OS, to achieve its function. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in this embodiment, as well as the installation procedure after reading, can use well-known configurations and procedures.

The various databases stored in the memory unit 1 are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

The guarantee fee revenue prediction device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which any peripheral device is connected. The guarantee fee revenue prediction device 100 may be realized by implementing software (including programs or data, etc.) that causes the device to realize the processing described in this embodiment.

Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the figures, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various additions or functional loads. In other words, the above-mentioned embodiments can be implemented in any combination, or the embodiments can be implemented selectively.

The present invention is useful in credit guarantee services for loans from financial institutions, such as housing loans.

REFERENCE SIGNS LIST 100 Guarantee fee income prediction device 1 Memory unit 1a Contract data 1b Guarantee fee management master 1c Guarantee contract data at time of application 1d Repayment schedule data 1e Guarantee contract data 1f Guarantee fee income prediction data 2 Control unit 2a Memory control unit 2b Setting unit 2c Changing unit 3 Communication interface unit 4 Input/output interface unit 110 Input device 120 Output device 200 Network

Claims (7)

A guarantee fee revenue prediction device including a control unit,
The control unit is
Contract data including at least customer identification information, loan amount, loan interest rate, first repayment date, and final repayment date;
The customer can access a guarantee fee management master in which at least the loan term, customer credit information, and guarantee fee per unit yen are set.
The control unit is
a guarantee fee calculation unit that calculates a lump-sum guarantee fee based on the contract data and the guarantee fee management master, and creates application time guarantee contract data including at least a guarantee period, a guarantee amount, customer credit information, and the lump-sum guarantee fee;
an expansion unit that expands the unpaid balance for each month based on the contract data and the guarantee contract data at the time of application, and creates repayment schedule data including at least the number of repayments, the repayment date, the unpaid balance for each month, the number of days since the last repayment date, and the accumulated balance for each month;
a totaling unit for calculating a total balance integrated value from the balance integrated value of each month of the repayment schedule data;
a guarantee annual rate calculation unit that calculates a guarantee annual rate by dividing the lump-sum guarantee fee by the total balance product and creates guarantee contract data including at least customer identification information and the guarantee annual rate;
a prediction unit for predicting a guarantee fee income based on the pre-repayment balance for each month of the repayment schedule data and the annual guarantee rate of the guarantee contract data, and creating guarantee fee income schedule data including at least the number of repayments and the guarantee fee income;
A guarantee fee revenue prediction device comprising: The guarantee fee per unit yen is the guarantee fee per 1 million yen,
The guarantee fee revenue prediction device according to claim 1, characterized in that the guarantee fee calculation unit calculates the lump-sum guarantee fee by (guarantee fee per 1 million yen) x (the loan amount) ÷ 1 million. The guarantee fee revenue prediction device according to claim 1 or 2, characterized in that the expansion unit calculates the balance product for each month by (the balance before repayment) x (the number of days elapsed) ÷ 365. The guarantee fee revenue prediction device according to claim 1 or 2, characterized in that the prediction unit predicts the guarantee fee revenue for each month by (the balance before repayment for each month) x (the annual guarantee rate) ÷ 12. The customer credit information is a course selected according to the customer's creditworthiness,
3. The guarantee fee revenue prediction device according to claim 1 or 2, wherein the guarantee fee per unit yen is set according to the number of years of the loan and the course. A step in which the guarantee fee revenue prediction device accesses contract data including at least customer identification information, a loan amount, a loan interest rate, a first repayment date, and a final repayment date;
A step in which the guarantee fee income prediction device accesses a guarantee fee management master in which at least the number of years of borrowing, customer credit information, and a guarantee fee per unit yen are set;
The guarantee fee revenue prediction device calculates a lump-sum guarantee fee based on the contract data and the guarantee fee management master, and creates application time guarantee contract data including at least a guarantee period, a guarantee amount, customer credit information, and the lump-sum guarantee fee;
The guarantee fee revenue prediction device develops the pre-repayment balance for each month based on the contract data and the guarantee contract data at the time of application, and creates repayment schedule data including at least the number of repayments, the repayment date, the pre-repayment balance for each month, the number of days elapsed since the last repayment date, and the accumulated balance for each month;
a step of the guarantee fee income prediction device calculating a total balance integrated value from the balance integrated value of each month of the repayment schedule data;
the guarantee fee revenue prediction device calculates a guarantee annual rate by dividing the lump-sum guarantee fee by the total balance product, and creates guarantee contract data including at least customer identification information and the guarantee annual rate;
The guarantee fee income prediction device predicts the guarantee fee income based on the pre-repayment balance for each month of the repayment schedule data and the annual guarantee rate of the guarantee contract data, and creates guarantee fee income schedule data including at least the number of repayments and the guarantee fee income;
A method for predicting guarantee fee revenue, comprising: On the computer,
accessing contract data including at least customer identification information, loan amount, loan interest rate, first repayment date, and final repayment date;
A step of accessing a guarantee fee management master in which at least the loan term, customer credit information, and guarantee fee per unit yen are set;
A step of calculating a lump-sum guarantee fee based on the contract data and the guarantee fee management master, and creating application time guarantee contract data including at least a guarantee period, a guarantee amount, customer credit information, and the lump-sum guarantee fee;
A step of developing the balance before repayment for each month based on the contract data and the guarantee contract data at the time of application, and creating repayment schedule data including at least the number of repayments, the repayment date, the balance before repayment for each month, the number of days elapsed since the previous repayment date, and the cumulative balance for each month;
A step of calculating a total balance integrated value from the balance integrated value of each month of the repayment schedule data;
a step of calculating a guarantee annual rate by dividing the lump-sum guarantee fee by the total balance product, and creating guarantee contract data including at least customer identification information and the guarantee annual rate;
A step of predicting a guarantee fee income based on the pre-repayment balance for each month of the repayment schedule data and the annual guarantee rate of the guarantee contract data, and creating guarantee fee income schedule data including at least the number of repayments and the guarantee fee income;
A guarantee fee revenue forecasting program for carrying out the above.

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