Examination task support device, examination task support method, and examination task support program

Abstract

To provide an examination task support device, method, and program that detect client companies suspected of deteriorating financial conditions and support lending operations at financial institutions.SOLUTION: In an examination task support system, an examination task support server includes: a transaction data acquisition unit configured to acquire transaction data of fluidity transaction details for past window dressing target companies and non-window dressing target companies and transaction data of fluidity transaction details for client companies; an index data calculation unit configured to calculate index data based on the transaction data for each of the window dressing target companies and non-window dressing target companies and index data of characteristic indexes based on the transaction data for the client companies; a characteristic index identification unit configured to identify characteristic indexes that show data characteristics of the window dressing target companies, based on data difference between the index data for the window dressing target companies and the index data for the non-window dressing target companies; an association determination unit configured to determine an association between the index data for the characteristic indexes for the client companies and the index data for the characteristic indexes for the window dressing target companies; and an output unit configured to output client companies that are determined to be associated.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an examination operation support device, an examination operation support method, and an examination operation support program. [Background technology]

[0002] In corporate accounting, window dressing refers to the deliberate manipulation of figures in financial statements and other documents to distort the calculation of a company's financial profits in order to make the company's financial position and business performance appear better or worse than they actually are in order to favor a bank loan decision.

[0003] As a related technology, for example, Patent Document 1 describes a financial analysis support system that identifies the characteristics of data in financial statements that are known to be window dressing, and automatically determines on a PC whether or not the financial data of the financial statements being diagnosed has these data characteristics.

[0004] Furthermore, for example, Patent Document 2 describes a data analysis device that provides a data analysis device that compares the financial data of multiple companies, enabling data obtained as a result of window dressing or the like to be identified as abnormal values.

[0005] For example, Patent Document 3 describes a fraudulent financial transaction detection program for detecting fraudulent financial transactions (money laundering), which has a computer execute an information acquisition step of acquiring difference pattern information regarding the transaction history in the bank account of a new detection target person for whom fraudulent transactions are to be newly detected, and a determination step of referring to previously acquired reference difference pattern information regarding the transaction history in the bank account of a past detection target person and three or more levels of correlation between the possibility of fraudulent transactions by the past detection target person, and determining the possibility of fraudulent transactions by the new detection target person based on the difference pattern information acquired in the information acquisition step. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2002-312580 A [Patent Document 2] JP 2006-252259 A [Patent Document 3] JP 2021-144356 A Summary of the Invention [Problem to be solved by the invention]

[0007] However, the data used in the inventions described in Patent Documents 1 and 2 above is financial data from annual financial statements for each business year, which means that there is a problem of a lack of timeliness in discovering window dressing. It is also said that the current accounting system allows companies to conveniently record profits, and it has been pointed out that there is a limit to what can be learned from financial statements regarding window dressing methods.

[0008] On the other hand, the data used in the invention described in the above Patent Document 3 is transaction history in a bank account, and therefore has superior timeliness in detecting window dressing compared to financial data in an annual financial statement. However, the invention described in Patent Document 3 has a different purpose in that it is not corporate window dressing but rather detects money laundering. In addition, in the invention described in the above Patent Document 3, the reference difference pattern information related to the past transaction history in the bank account of the person to be detected, which is previously acquired, is an extraction of the difference in transaction history between two or more terms, but since fraudulent transactions are not necessarily included between two or more terms, in this case, it is difficult to detect fraudulent financial transactions (money laundering) even if the reference difference pattern information is referred to.

[0009] Recently, for example, loan screening department staff at financial institutions are required to perform more sophisticated yet efficient screening work, not only to detect accounting fraud but also to check credit ratings (downgrades) due to deterioration of financial conditions, etc. The present invention has been proposed in view of the above, and in one aspect, it is an object of the present invention to detect client companies suspected of having a worsening financial condition and support the loan operations of financial institutions. [Means for solving the problem]

[0010] In order to solve the above problems, a screening operation support device according to the present invention is a screening operation support device for a financial institution, and includes: an acquisition means for acquiring first financial data of a competitor company belonging to the same industry as a client company; an acquisition means for acquiring first third party data related to the industry; a calculation means for calculating a first feature indicating a relationship between the first financial data and the first third party data; an identification means for correlating and identifying characteristic financial data and characteristic third party data in which a relationship feature between the financial data and third party data of the competitor company is expressed based on the first feature; an acquisition means for acquiring second financial data of the client company identified as the characteristic financial data; an acquisition means for acquiring second third party data identified as the characteristic third party data; a calculation means for calculating a second feature indicating a relationship between the second financial data and the second third party data; an association determination means for determining an association between the second feature related to the client company and the first feature related to the competitor company; and an output means for outputting the client companies determined to be related by the association determination means. Effect of the Invention

[0011] According to one aspect of the embodiment of the present invention, it is possible to detect a client company suspected of having a deteriorating financial situation and to support the lending operations of a financial institution. [Brief description of the drawings]

[0012] [Figure 1] 1 is a diagram illustrating an example of the configuration of an examination operation support system according to an embodiment of the present invention. [Diagram 2] A diagram showing an example of the hardware configuration of the screening operation support server according to this embodiment. [Diagram 3] A diagram showing an example of the software configuration of the screening business support server related to this embodiment. [Figure 4A] 1 shows an example of customer information held by the financial institution system 10 according to this embodiment. [Figure 4B] 1 shows an example of account information and transaction details information held by the financial institution system 10 according to this embodiment. [Diagram 5] FIG. 13 is a diagram showing an overall process of detecting a possible destination of embellishment according to the present embodiment. [Figure 6] FIG. 11 is a diagram illustrating calculation of a feature index according to the present embodiment. [Figure 7] A figure showing the characteristic indicator identification process of the potential falsification destination of the examination business support server 20 in this embodiment. [Figure 8] A diagram showing the detection process of possible destinations of window dressing by the inspection business support server 20 in this embodiment. [Figure 9] FIG. 13 is a diagram for explaining a comparison of index data relating to transaction periodicity and transaction suddenness according to the present embodiment. [Figure 10] FIG. 13 is a diagram showing a correlation matrix between indexes based on transaction data according to the present embodiment. [Figure 11] A figure showing the difference in correlation matrices between non-window dressing target companies and window dressing target companies in this embodiment. [Figure 12] FIG. 13 is a diagram showing an example of a detection list of a client company according to the embodiment. [Figure 13] FIG. 13 is a diagram showing an example of a detection list of a client company according to the embodiment. [Figure 14] A diagram showing an example of the software configuration of the screening business support server in this embodiment. [Figure 15] 1 is a diagram showing an overall process for detecting a potential credit downgrade destination according to this embodiment; [Figure 16] A figure showing the process of identifying characteristic indicators of recipients of possible credit downgrades by the screening business support server 20-2 in this embodiment. [Figure 17] A diagram showing the process of detecting a recipient of a credit downgrade by the screening business support server 20-2 in this embodiment. [Figure 18] A diagram showing an example of the software configuration of the screening business support server in this embodiment. [Figure 19] A diagram showing the characteristic financial data and characteristic third-party data identification process of the screening business support server 20-3 in this embodiment. [Figure 20] 13 is a diagram showing a process for detecting anomalies in financial data declaration values ​​according to the present embodiment. FIG. [Figure 21] FIG. 13 is a diagram showing an example of abnormality detection of raw material market price data declaration values ​​according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described in detail with reference to the drawings. [Embodiment 1] <System configuration> Fig. 1 is a diagram showing an example of the configuration of a screening operation support system according to the present embodiment. The screening operation support system 100 in Fig. 1 includes a financial institution system 10, a screening operation support server 20, a processing DB 30, and a terminal 40, which are connected via a network 70.

[0014] The financial institution system 10 includes various systems and DBs (databases) owned by financial institutions such as banks. The financial institution system 10 includes, for example, a core system for managing various deposits such as ordinary deposits and fixed term deposits, a sales management system for managing sales records of financial products, a front compliance system for managing and proposing financial products according to the asset management needs of each customer, a history system (CRM) for managing negotiation history, and each DB associated with each system. Each DB according to this embodiment includes, for example, a DB of customer master, transaction data, and financial data of a client company in a financial institution. The financial data includes not only financial statements (balance sheets, profit and loss statements, cash flow statements, etc.) for each business year of the client company, but also data related to the client company's finances, such as trial balance data for the period showing the progress of profit and loss for the year.

[0015] The screening business support server (hereinafter simply referred to as the support server) 20 is a server device that detects client companies suspected of possible window dressing of financial data among client companies that hold accounts at financial institutions based on the processed data in the processing DB 30. The support server 20 also creates (generates) and displays a detection list based on values ​​(relevance / score) indicating the signs / possibility of window dressing of financial data on a terminal 40 of a financial institution's staff member (e.g., a staff member in the loan screening department). Note that window dressing according to this embodiment is not limited to window dressing based on financial statements for each business year, but includes window dressing of financial data in general (i.e. window dressing accounting).

[0016] The processed DB 30 is a DB that sequentially acquires raw data (so called because it is unprocessed) from each DB of the financial institution system 10 operated by the financial institution, and stores the processed data that the support server 20 processes the acquired raw data into a format that can be calculated. In machine learning methods, raw input data such as text and time series data often cannot be handled as is, so such unstructured data is converted into vectors through conversion processing. Note that although the formats of raw data and processed data are different, the content and meaning of the data itself are the same.

[0017] The terminal 40 is, for example, a PC (personal computer), a smartphone, a tablet terminal, or the like, and is a user terminal used by, for example, a person in charge of a loan screening department. The person in charge uses the terminal 40 to access the support server 20 and acquire and display the detection list. As described above, the detection list is a list of client companies that have accounts at financial institutions and are predicted to have signs or possibilities of window dressing. By checking the clients on the detection list in order of priority, the person in charge can efficiently check for signs or possibilities of window dressing even in a limited time. In addition, the person in charge can check for signs or possibilities of window dressing by searching for a specific client company for which a loan screening is to be performed, for example. A predetermined application program or a general-purpose web browser, etc. for acquiring and displaying the detection list from the support server 20 is pre-installed on the terminal 40.

[0018] The network 70 is a communication network including wired and wireless networks, and includes, for example, the Internet, a public line network, and WiFi (registered trademark).

[0019] <Hardware configuration> 2 is a diagram showing an example of the hardware configuration of the screening operation support server according to the present embodiment. As shown in FIG. 2, the screening operation support server 20 includes a CPU (Central Processing Unit) 21, a ROM (Read Only Memory) 22, a RAM (Random Access Memory) 23, a HDD (Hard Disk Drive) 24, and a communication device 25.

[0020] The CPU 21 executes various programs and performs arithmetic processing. The ROM 22 stores programs required at startup, etc. The RAM 23 is a work area for temporarily storing the processing by the CPU 21 and storing data. The HDD 24 includes a RAID (Redundant Array of Independent Disks) and stores various data and programs. The communication device 25 communicates with other devices via a network 70.

[0021] <Software configuration> 3 is a diagram showing an example of the software configuration of the screening operation support server according to this embodiment. The screening operation support server 20 has, as main functional units, a transaction data acquisition unit 201, an index data calculation unit 202, a feature index identification unit 203, an association determination unit 204, an output unit 205, and a storage unit 206.

[0022] The transaction data acquisition unit 201 has a function of acquiring transaction data of liquidity transaction details of past window dressing target companies and non-window dressing target companies. The transaction data acquisition unit 201 also has a function of acquiring transaction data of liquidity transaction details of client companies.

[0023] The index data calculation unit 202 has a function of calculating index data based on the transaction data of each of the window dressing target companies and the non-window dressing target companies. The index data calculation unit 202 also has a function of calculating index data of characteristic indicators based on the transaction data of the client companies.

[0024] The characteristic index identifying unit 203 has a function of identifying characteristic indexes that reflect the data characteristics of a window dressing company based on the data difference between the index data of the window dressing company and the index data of a non-window dressing company.

[0025] The association determination unit 204 has a function of determining the association between the index data of the characteristic indicators of the client company and the index data of the characteristic indicators of the window dressing victim company.

[0026] The output unit 205 has a function of outputting the customer companies that are determined to be related by the relatedness determination unit 204 .

[0027] The memory unit 206 has a function of storing information on the characteristic indicators of the window dressing target companies identified by the characteristic indicator identification unit 203 in the HDD 24.

[0028] Each functional unit is realized by a computer program executed on hardware resources such as the CPU, ROM, and RAM of the computer constituting the support server 20. These functional units may be interpreted as "means," "module," "unit," or "circuit." Furthermore, each DB may be placed in the HDD 24 of the support server 20 or an external storage device on the network 70. Furthermore, each functional unit of the support server 20 may not only be realized by a single server device, but may also be realized as a system consisting of multiple devices with distributed functions.

[0029] (Examples of data from each database) 4 shows an example of data items in the DB of the financial institution system 10. However, it goes without saying that the data items shown in Fig. 4 are merely an example, and other data items may be present.

[0030] 4A shows an example of customer information held by the financial institution system 10 according to this embodiment. This is customer attribute information for each customer, and is registered mainly based on an application from the customer when opening an account, etc. Depending on the type of customer, there are individual customers and corporate customers, and in this embodiment, a corporate customer will be particularly used as an example, but individual customers such as sole proprietors may also be used.

[0031] 4B shows an example of account information and transaction statement information held by the financial institution system 10 according to this embodiment. For example, information on each account held by each customer, such as a regular savings account, a current account, a fixed term deposit account, and a foreign currency deposit account, credit card information, and transaction statement information including foreign exchange transactions (liquidity transaction statement information), and information on loans made in the past. All past transaction statements, not to mention the most recent ones, are recorded.

[0032] For example, customer information is static and unlikely to change, while transaction data, such as transaction details for each deposit account and credit card details, is relatively dynamic and prone to change, and is referred to as "transaction data." Generally, transaction data is one type of data handled by corporate information systems, and refers to data that records the details of events that occur in the course of business.

[0033] The transaction data according to this embodiment refers to data that records details of events that occur in a financial institution when a client company uses financial services, such as liquidity transaction detail information. Transaction data is data that changes relatively frequently on a daily basis (update frequency) compared to information such as customer information, which does not change very frequently from the time of registration. However, the update frequency, when viewed on an individual customer basis, also depends on the frequency of use of financial services by that customer, so the specific update frequency is not an issue.

[0034] The support server 20 sequentially acquires raw data from the DB of the financial institution system 10 operated by the financial institution, processes and converts the acquired raw data into a format that can be calculated by the support server 20, and updates the processed data in the processed DB 30. The update cycle can be set arbitrarily depending on the operation, for example, every second, every few seconds to tens of seconds, every minute, every few minutes to tens of minutes, every hour, every few hours, every day, or every specified day, but the shorter the update interval, the better, and it is desirable to update in real time together with updates of the raw data in the DB of the financial institution system 10. As a result, the processed DB 30 updates data equivalent to that in the DB of the financial institution system 10 at the above update intervals, although the raw data and the processed data have different formats.

[0035] <Detection process for potential recipients of window dressing> Fig. 5 is a diagram showing the overall process of detecting a possible destination of embellishment according to this embodiment. Fig. 6 is a diagram showing the calculation of characteristic indexes according to this embodiment. Fig. 7 is a diagram showing the process of identifying characteristic indexes of a possible destination of embellishment by the examination business support server 20 according to this embodiment.

[0036] S1: The support server 20 acquires transaction data of past window dressing target companies and non-window dressing target companies in financial institutions as learning data (big data) from the processed DB 30 (originally a transaction data DB). The transaction data according to this embodiment is transaction data of transaction items that have frequent daily information changes (update frequency) in financial institutions, and is each transaction item data that can be acquired from liquidity transaction details information of, for example, ordinary savings accounts and current accounts.

[0037] S2: The support server 20 calculates features (indicator features) showing the characteristics of a predetermined indicator based on the transaction data of the window dressing target company and the non-window dressing target company. For example, the support server 20 calculates the features of indicators A, B, C, etc. based on the transaction data of the window dressing target company, and calculates the features of the same indicators A, B, C, etc. based on the transaction data of the non-window dressing target company.

[0038] For example, examples of indicator data based on transaction item data for a deposit account include a total deposit amount calculated by tallying up deposit amounts, a total withdrawal amount calculated by tallying up withdrawal amounts, a total transfer amount calculated by tallying up transfer amounts, the number of deposits calculated by tallying up the number of deposits, the number of withdrawals calculated by tallying up the number of withdrawals, and the number of transfers calculated by tallying up the number of transfers.

[0039] S3: The support server 20 identifies indicators (hereinafter referred to as characteristic indicators) with large differences in index features between window dressing target companies and non-window dressing target companies. Characteristic indicators with large differences between window dressing target companies and non-window dressing target companies are indicators that stand out in window dressing target companies as represented in data by transaction data analysis, and are indicators that are characteristic of window dressing target companies. In addition, the number of characteristic indicators identified is not limited to one, and multiple indicators may be identified. Information on the characteristic indicators of the identified window dressing target companies (including, for example, the indicator names and index feature values) is stored in the HDD 24.

[0040] Although the method for determining the degree of difference varies depending on each indicator, when there is a difference (deviation) between the index feature value between the two companies from a certain reference value (predetermined value), the indicator can be identified as a characteristic indicator with a large difference. Also, for example, when the index feature value range group of the window dressing company does not belong to the index feature value range group of the non-window dressing company by a certain ratio or more, the indicator can be identified as a characteristic indicator with a large difference.

[0041] FIG. 8 is a diagram showing the process of detecting possible destinations of window dressing by the examination business support server 20 according to this embodiment. S21: The support server 20 acquires transaction data of the client company to be monitored from the processed DB 30 (originally the transaction data DB). The transaction data according to this embodiment is, for example, each transaction item data that can be acquired from the liquidity transaction details information of the client company.

[0042] S22: The support server 20 calculates the feature amounts of the feature indicators of the client company identified in S3 based on the transaction data of the client company. If there are multiple feature indicators, the support server 20 calculates the feature amounts of all the multiple feature indicators identified in S3 based on the transaction data. Specific examples of feature indicators will be described later.

[0043] S23: The support server 20 calculates a score indicating the possibility of window dressing for the customer company based on the feature amount of the feature indicator of the transaction data of the customer company and the feature amount of the feature indicator of the transaction data of the window dressing target company. The higher the correlation (distance) between the feature amount of the feature indicator of the customer company and the feature amount of the feature indicator of the window dressing target company, the higher the score is calculated.

[0044] S24: The support server 20 ranks the client companies in descending order of score and outputs the result to the terminal 40. The person in charge can view the output result using the terminal 40, and can confirm the presence or absence of window dressing by prioritizing client companies with a predetermined score or higher or a predetermined rank or higher that are suspected of window dressing, and by referring to the transaction data, financial data, etc. in more detail.

[0045] The process of detecting possible destinations of falsification (S21 to S24) may be executed when a request to obtain the detection list described below is received from the terminal 40, or may be executed every time the processed DB 20 is updated along with an update of the transaction data in the DB of the financial institution system 10, or may be executed periodically, for example, once a day.

[0046] <Analysis and evaluation of trade data differences> In order to find data-based characteristic indicators of transaction data between non-window dressing companies and window dressing companies, the applicant analyzed and evaluated the differences in transaction data between non-window dressing companies and companies that have previously been window dressing companies, using liquidity transaction detail information (transaction data) held by financial institutions for client companies.

[0047] (Feature Index Example 1) In the conventional qualitative analysis, the evaluation result of a window dressing target company is that the lack of financial management planning is one of the main factors. More specifically, a company that has window dressed in the past is weak in accounting management, weak in financial management, frequent financing, lack of ability to execute plans, etc., and this is one of the factors. In this embodiment, as a method of quantitatively analyzing the idea equivalent to the evaluation of the lack of financial management planning, the transaction data between a non-window dressing target company and a window dressing target company is analyzed in time series to calculate index data on the transaction periodicity and transaction suddenness of the client company, and the two are compared and evaluated.

[0048] Fig. 9 is a diagram for explaining a comparison of index data relating to transaction periodicity and transaction suddenness according to this embodiment. Specifically, the table shown in Fig. 9 tallyes up indexes relating to transaction relationships (e.g. remittance amount, number of remittances, credit payment amount, number of credit payment, deposit transfer amount, number of deposit transfers, etc.) based on the transaction items listed in the "summary" of the deposit transaction statement, and plots the variance of the indexes (indexes A, B, C, etc.) on the vertical axis against the time series on the horizontal axis. The larger the variance of each index, the more variation there is compared to past transactions, and the more drastic the change.

[0049] As shown in Figure 9, when comparing non-window dressing companies with window dressing companies, the overall trend is that non-window dressing companies have smaller variances in each indicator, while window dressing companies have larger variances in each indicator. In other words, non-window dressing companies have periodic transactions and are able to control their transactions in a planned manner, while window dressing companies have sudden transactions and many irregular transactions, so they are unable to control their transactions in a planned manner. Specifically, there is a large difference in variance values ​​for indicators A, B, and D between non-window dressing companies and window dressing companies, meaning that indicators A, B, and D can be said to be characteristic indicators.

[0050] In this way, the characteristic indicators A, B, and D are indicators seen in window dressing target companies that are revealed in data through transaction data analysis between window dressing target companies and non-window dressing target companies, and can be said to be characteristics seen in window dressing target companies. Therefore, by calculating, for example, characteristic indicators A, B, and D from the transaction data of the client company to be monitored, and calculating the relevance of the calculated characteristic indicators A, B, and D to the characteristic indicators A, B, and D of past window dressing target companies, it is possible to measure whether the client company also shows a lack of financial management planning seen in window dressing target companies, and thus the possibility of window dressing.

[0051] In S22, the support server 20 calculates index data (e.g., characteristic indexes A, B, D) related to transaction periodicity and transaction suddenness as the characteristic indexes identified in S3 based on the transaction data of the client company to be monitored. In S23, the support server 20 compares the variance value (index feature value) of the index data related to transaction periodicity and transaction suddenness of the client company and the index data related to transaction periodicity and transaction suddenness of the window dressing target company based on the index data, and calculates a score indicating the possibility of window dressing of the client company. In the case of the characteristic indexes A, B, and D, the higher the variance value of the characteristic indexes of the client company (e.g., the greater the variation of the indexes), the higher the score calculated, indicating that the client company has a high degree of association with the window dressing target company, as with past window dressing target companies.

[0052] (Feature Index Example 2) Typically, there is a relationship between certain transaction items. For example, if the number of deposits to a regular account increases, the total amount of deposits naturally increases, so there is a high correlation between them. On the other hand, it is difficult to intuitively say that the correlation between the number of deposits to a regular account and the number of withdrawals, the number of withdrawal transfers, or the amount of withdrawal transfer is high or low. Specifically, the level of correlation can be determined by calculating the correlation values ​​between the number of deposits and withdrawals to a regular account, the number of deposits and withdrawal transfers to a regular account, and the number of deposits and withdrawal transfer amounts to a regular account.

[0053] In other words, while there is a certain reasonable correlation between transaction items (between indicators based on transaction items) when viewed from the perspective of non-window dressing companies, in the case of window dressing companies, the correlation between these transaction items may be distorted in places. An easy example would be a window dressing company, where, for example, the number of deposits into a regular account is increasing, but the amount of the deposits is not increasing that much, and the indicators do not show a high correlation (an abnormal relationship is seen between the indicators).

[0054] FIG. 10 is a diagram showing a correlation matrix between indicators based on transaction data according to this embodiment. Specifically, the table shown in FIG. 10 shows correlation values ​​(-1.0 to 1.0) between each of the indicators (indicators a, b, c, etc.) that have been aggregated based on the transaction items described in the "summary" of the deposit transaction details (e.g., number of deposits, number of deposit transfers, deposit transfer amount, total deposit amount, number of withdrawals, number of withdrawal transfers, withdrawal transfer amount, total withdrawal amount, etc.). A larger correlation value means that the correlation between the two indicators is higher. Conversely, a smaller correlation value means that the correlation between the two indicators is lower, or if the correlation value is negative, the correlation is high.

[0055] 11 is a diagram showing the difference in correlation matrix between non-window dressing target companies and window dressing target companies according to this embodiment. Indicators with large absolute values ​​of difference are indicators seen in window dressing target companies that are expressed in data by transaction data analysis between window dressing target companies and non-window dressing target companies, and are indicators that can be said to be characteristics of window dressing target companies. Therefore, by calculating, for example, characteristic indicators (x1, y1)... from the transaction data of the client company being monitored and then calculating the degree of correlation between the calculated characteristic indicators (x1, y1)... and the characteristic indicators (x1, y1)... of companies that have engaged in window dressing in the past, it is possible to determine whether or not distortions have occurred between transaction items at the client company that are not seen in non-window dressing companies, and thus to gauge the possibility of window dressing.

[0056] In S22, the support server 20 calculates index data (e.g., characteristic index (x1, y1)··) relating to the relationship of transaction data as the characteristic index identified in S3, based on the transaction data of the client company to be monitored. In S23, the support server 20 compares the correlation value (index feature) of the index data relating to the relationship of transaction data at the client company and the index data relating to the relationship of transaction data at the window dressing target company, and calculates a score indicating the possibility of window dressing at the client company. In the case of the characteristic index (x1, y1)··, the larger the absolute value of the correlation difference value between the indicators is, as with past window dressing target companies (the less correlation there is to a certain reasonable degree), the higher the score calculated, indicating that the client company has a high degree of association with the window dressing target company.

[0057] Note that the indicator data on transaction periodicity and transaction suddenness and the indicator data on the relationship of transaction data are merely examples of characteristic indicators. The support server 20, through the processes of S1 to 3, identifies characteristic indicators found in window dressing target companies that are expressed in data form through transaction data analysis, including those that are not easily found by humans. That is, the support server 20 uses so-called AI (e.g., deep learning) to analyze and evaluate the characteristic amounts of transaction data indicators that have a large difference between non-window dressing target companies and window dressing target companies from the transaction data (big data) between non-window dressing target companies and window dressing target companies, and the support server autonomously discovers them, and focuses on the characteristics indicated by the discovered transaction data indicators to calculate a score based on the degree of relevance with the customer company to be monitored.

[0058] For the sake of convenience, in order to explain that the possibility of window dressing can be measured based on characteristic indicators, the explanation has been given on the premise that it is known in advance that indicator data related to transaction periodicity and transaction suddenness and indicator data related to the relationship of transaction data are characteristic indicators. However, in reality, they are identified as characteristic indicators seen in window dressing target companies that are expressed in data by the processing of S1 to S3 executed by the support server 20.

[0059] (Indicator example) Below are examples of indicators that are compiled based on the liquidity transaction details information of ordinary savings accounts and current accounts. Each indicator is index data that is compiled and calculated from one or more transaction data. At the client company, each transaction data (transaction data) changes according to the use of each company's account, and the index data also changes.

[0060] Deposit system Regular_Deposit_Number, Regular_Deposit_Amount, Regular_Deposit_Transfer_Number, Regular_Deposit_Transfer_Amount, Current_Deposit_Number, Current_Deposit_Amount, Current_Deposit_Transfer_Number, Current_Deposit_Transfer_Amount Withdrawal system Regular_Withdrawal_Number, Regular_Withdrawal_Amount, Regular_Withdrawal_Transfer_Number, Regular_Withdrawal_Transfer_Amount, Regular_Withdrawal_Number of Funds Transfers, Regular_Withdrawal_Amount of Funds Transfers, Current_Withdrawal_Number, Current_Withdrawal_Amount Current_withdrawal_transfer_number, Current_withdrawal_transfer_amount, Current_withdrawal_funds_transfer_number, Current_withdrawal_funds_transfer_amount <Client company scoring output> 12 is a diagram showing an example of a detection list of client companies according to this embodiment. For example, a person in charge of loan screening uses a terminal 40 to access the support server 20, and acquires and displays the detection list 500 on the screen. The person in charge can also narrow down the search for client companies in the detection list by selecting and inputting narrowing conditions 501 such as the fiscal year end and industry type as necessary.

[0061] The detection list 500 shown in Fig. 12 is a list of client companies that have accounts at financial institutions and are predicted to have signs or the possibility of window dressing among client companies whose fiscal year ends soon in "March 2023". The client company detection list 500 has items such as priority 500a, branch number, customer number, corporate name, industry, score 500b, loan balance, deposit balance, and this bank ratio.

[0062] Priority 500a indicates the priority order in which the person in charge should check the financial situation (financial data) of the client company. Score 500b is a score calculated in S23 indicating the possibility of window dressing for the client company. The higher (closer) the correlation (distance) between the characteristic amount of the client company's characteristic indicator and the characteristic amount of the window dressing target company's characteristic indicator, the higher the calculated score. The person in charge can grasp the degree of window dressing possibility for each client company using specific numerical values.

[0063] 13 is a diagram showing an example of a detection list of client companies according to this embodiment. By selecting one client company from the detection list 500, the person in charge can display financial status information 503 detected for that client company.

[0064] In particular, the financial status information 503 shown in FIG. 13 includes information 503a on indicator characteristics. For example, an "abnormal transaction" has been detected in the client company, and "C" indicating a certain level of abnormal transaction and an alert message saying "Comparative indicator (characteristic indicator) "Regular_Withdrawal_Number of Funds Transfers": Caution is required as the characteristics are similar to those of past window dressing target companies" (indicator data on transaction periodicity and transaction suddenness are close to those of past window dressing target companies). Also, for example, "D" indicating a certain level of abnormal transaction and an alert message saying "Comparative indicator (characteristic indicator) "Number of deposits and withdrawals in ordinary accounts": Caution is required as the characteristics are similar to those of past window dressing target companies" (indicator data on the relationship of transaction data are close to those of past window dressing target companies). And "C" indicating the degree of the possibility of window dressing is displayed as an "overall evaluation".

[0065] In this way, according to the detection list of client companies according to the present embodiment, the person in charge can check the presence or absence of signs and possibility of window dressing efficiently even in a limited time by checking the clients on the detection list in order of priority. Also, the person in charge can check the presence or absence of signs and possibility of window dressing by searching for a specific client company for which a loan screening is being conducted, for example.

[0066] As described above, the transaction data according to this embodiment is used in financial institutions to monitor companies and calculate a score to measure the possibility of window dressing, using transaction data from the deposit accounts of client companies. Since transaction data cannot be tampered with by client companies, is frequently acquired and updated, and contains a large amount of information, it enables staff in the loan screening department of financial institutions to detect client companies that are at risk (possible window dressing, etc.) in a timely manner and in line with the actual transaction situation before the end of the fiscal year.

[0067] Although annual financial statements are easy to quantitatively evaluate from the perspective of traditional financial analysis, data can be easily falsified and false reports can be made, and data is not collected or updated frequently, making it difficult to detect client companies that may be engaging in window dressing in a timely manner.

[0068] In addition, from among the vast number of client companies, the person in charge can focus on only those clients that exceed a certain scoring risk level. This allows for efficient screening and corporate monitoring, for example by prioritizing screening of high-risk clients and quickly noticing the existence of risks.

[0069] (supplement) As explained above, when a customer company determines that the characteristic parameters of the characteristic indicators of the transaction data of the customer company are highly related to the characteristic parameters of the characteristic indicators of the transaction data of the fraudulent company, the customer company is suspected of fraud and a score higher than a certain standard is calculated for the customer company (S23).

[0070] However, when the score is calculated again thereafter, there are cases where the calculated score is not as high as the previous one. It can be said that the client company whose score has dropped in this way has a lowered possibility of window dressing, and depending on the score, the possibility of window dressing has already been eliminated. The support server 20 holds the score history calculated each time, and when the current score is significantly lower than the previous score (for example, by a certain reference value) compared with the past scores, it can explicitly display this on the terminal 40 of the person in charge. In addition, if a flag is set for a client company whose high score has been calculated as a monitored company, if the current score drops significantly from the past score, the client company may be automatically excluded from the managed companies.

[0071] Furthermore, for a client company whose current score has dropped significantly from its past score, the support server 20 may determine the degree of association between the characteristic indices of the transaction data of the client company and the characteristic indices of the transaction data of a "non-window dressing client company." This is a "white" judgment. If the degree of association is determined to be high (if a high score is calculated), it is confirmed that the client company does not exhibit the same indicator characteristics as the window dressing client company with respect to the characteristic indices seen in the window dressing client company, and furthermore, that the client company exhibits characteristic indices like the non-window dressing client company. This allows the client company to be said with even greater certainty that the possibility of window dressing has decreased, and depending on the score, the possibility of window dressing has already been eliminated.

[0072] [Embodiment 2] In this embodiment, the present invention is applied to credit rating examination in financial institutions. Credit rating (also called credit rating) is a criterion for banks and the like to decide whether to provide a loan. For example, when a bank receives a loan application from a company, the bank must decide whether to provide the loan to the company and, if so, how to set the interest rate, and the criterion for this decision is the rating of the company. Credit rating is determined by comprehensively analyzing a company's repayment capacity and financial situation, and is mainly determined by two types of evaluation: an evaluation based on the analysis results of financial statements (quantitative evaluation) and an evaluation of the attitude of the management and management policy (qualitative evaluation).

[0073] However, in evaluation based on the analysis results of financial statements (quantitative evaluation), if there is falsification or false reporting such as window dressing, it is difficult to perform an appropriate rating evaluation, and since the frequency of acquiring and updating financial data is low (every business year), it is difficult to perform a timely rating evaluation of a company. Therefore, the screening operation support system 100-2 (screening operation support server 20-2) according to the second embodiment supports credit rating screening by, for example, a person in charge of a loan screening department.

[0074] Here, the standards for credit ratings differ depending on the bank or company that issues the rating, but they are generally expressed as a hierarchical ranking based on the creditworthiness using letters and numbers. In this embodiment, for the sake of convenience, it is assumed that there are six rating ranks, A to E, as an example. However, in reality, the creditworthiness of a company is often further subdivided and divided into around 10 rating ranks. A: (Normal) Financial condition is good and ability to repay is high B: (Caution required) Customers with poor or unstable performance or financial problems, etc., who require careful management in the future. C: (Insecure) Currently, the company is not in a state of bankruptcy, but there is a high possibility that its ability to repay will decrease in the future. D: (effectively bankrupt) Currently has problems with ability to repay E: (Bankrupt company) Already in default on debt In this embodiment, companies with a credit rating of A are referred to as credit-rated normal companies (hereinafter simply referred to as normal companies), and companies with a credit rating other than A are referred to as credit-rated abnormal companies (hereinafter simply referred to as abnormal companies).

[0075] <Software configuration> 14 is a diagram showing an example of the software configuration of the screening operation support server according to this embodiment. The screening operation support server 20-2 has, as main functional units, a transaction data acquisition unit 201-2, an index data calculation unit 202-2, a feature index identification unit 203-2, an association determination unit 204-2, an output unit 205-2, and a memory unit 206-2.

[0076] The transaction data acquisition unit 201-2 has a function of acquiring transaction data of liquidity transaction details of non-normal and normal counterparties with past credit ratings. The transaction data acquisition unit 201-2 also has a function of acquiring transaction data of liquidity transaction details of client companies.

[0077] The index data calculation unit 202-2 has a function of calculating index data based on the transaction data of each of the abnormal and normal companies. The index data calculation unit 202-2 also has a function of calculating index data of characteristic indicators based on the transaction data of the client companies.

[0078] The characteristic index identifying unit 203-2 has a function of identifying a characteristic index in which the data characteristics of an abnormal company appear based on the data difference between the index data of an abnormal company and the index data of a normal company.

[0079] The association determination unit 204-2 has a function of determining association between the index data of the characteristic index at the customer company and the index data of the characteristic index at the abnormal company.

[0080] The output unit 205-2 has a function of outputting the customer companies that are determined to be related by the relatedness determination unit 204.

[0081] The storage unit 206-2 has a function of storing, in the HDD 24, information on the characteristic indicators of the abnormal companies identified by the characteristic indicator identification unit 203, and the like.

[0082] Each functional unit is realized by a computer program executed on hardware resources such as the CPU, ROM, and RAM of the computer constituting the support server 20-2. These functional units may be interpreted as "means," "module," "unit," or "circuit." Each DB may be placed in the HDD 24 of the support server 20-2 or an external storage device on the network 70. Each functional unit of the support server 20-2 may not only be realized by a single server device, but may also be realized as a system consisting of multiple devices with distributed functions.

[0083] In the second embodiment, the configuration example of the screening operation support system shown in Fig. 1 and the hardware configuration example of the screening operation support server shown in Fig. 2 can be applied similarly to the first embodiment, so that the repeated explanation will be omitted. However, the screening operation support server 20 will be referred to as the screening operation support server 20-2 in the second embodiment.

[0084] <Detection process for potential credit downgrades> Fig. 15 is a diagram showing the overall process of detecting potential credit downgrades according to this embodiment. Fig. 16 is a diagram showing the process of identifying characteristic indicators of potential credit downgrades by the screening operation support server 20-2 according to this embodiment. Here, the target companies that should be detected immediately by the person in charge in the screening operation are client companies that may be downgraded to a lower rating than the current credit rating, and more specifically, companies that previously had a credit rating of A or B, but have suddenly fallen into a financial state equivalent to a rating of B or C or lower in the near future.

[0085] S31: The support server 20-2 acquires past transaction data of non-normal credit rating companies and normal credit rating companies in financial institutions as learning data (big data) from the processed DB 30 (originally a transaction data DB). Note that non-normal credit rating companies are, for example, companies with credit ratings of B, C, D, and / or E, and normal credit rating companies are, for example, companies with credit ratings of A.

[0086] S32: The support server 20-2 calculates features (index features) indicating the features of a predetermined index based on the transaction data of the non-normal and normal counterparties with credit ratings. For example, the support server 20-2 calculates the features of the indexes A, B, C, etc. based on the transaction data of the non-normal counterparties with credit ratings, and calculates the same features of the indexes A, B, C, etc. based on the transaction data of the normal counterparties with credit ratings.

[0087] S33: The support server 20-2 identifies characteristic indicators with large differences in index features between credit-rated abnormal and normal companies. Characteristic indicators with large differences between abnormal and normal companies are indicators that stand out in abnormal companies that are represented in data by transaction data analysis, and are indicators that are characteristic of abnormal companies. The number of characteristic indicators identified is not limited to one, and multiple indicators may be identified. Information on the characteristic indicators of the identified abnormal companies (including, for example, the indicator names and index feature values) is stored in the HDD 24.

[0088] In addition, if the abnormal company in terms of credit rating in S31 is, for example, a company with a credit rating of C, the normal company may be, for example, a company with a credit rating of A or B. In other words, if a company is ranked higher than the abnormal company, it is relatively a normal company.

[0089] Furthermore, characteristic indicators with large differences between abnormal and normal counterparties may be relatively specified for each credit rating. For example, characteristic indicators with large differences between abnormal counterparties with credit rating B and normal counterparties with credit rating A, characteristic indicators with large differences between abnormal counterparties with credit rating C and normal counterparties with credit rating A, or characteristic indicators with large differences between abnormal counterparties with credit rating C and relatively normal counterparties with credit rating B. In this case, information on the characteristic indicators of the specified abnormal counterparties is stored in HDD 24 as a data set of credit rating information for both abnormal and normal counterparties.

[0090] However, for example, characteristic indicators with large differences between abnormal counterparties with credit ratings D and normal counterparties with credit ratings A may be used, and characteristic indicators with large differences between abnormal counterparties with credit ratings E and normal counterparties with credit ratings A may be used, but the financial status of such two counterparties is obviously very different in the first place. For this reason, in consideration of identifying characteristic indicators that are found in abnormal counterparties that are expressed in data by transaction data analysis, including those that are not easily found by humans, the closer the credit ratings between the counterparties are, the better (for example, abnormal counterparties with credit ratings B and normal counterparties with credit ratings A, abnormal counterparties with credit ratings C and normal counterparties with credit ratings A). Furthermore, characteristic indicators with large differences between counterparties with credit ratings that differ by one rank (companies with credit ratings that are adjacent by one rank) may be identified (for example, abnormal counterparties with credit ratings B and normal counterparties with credit ratings A, abnormal counterparties with credit ratings C and normal counterparties with credit ratings B).

[0091] FIG. 17 is a diagram showing the process of detecting a recipient of a credit downgrade by the screening operation support server 20-2 according to this embodiment.

[0092] S41: The support server 20-2 acquires the credit rating (FIG. 4B) of the client company to be monitored from the client master DB. The credit rating according to this embodiment indicates any one of A to E.

[0093] S42: Next, the support server 20-2 acquires transaction data of the client company to be monitored from the processed DB 30 (originally the transaction data DB). The transaction data according to this embodiment is, for example, each transaction item data that can be acquired from the liquidity transaction details information of the client company.

[0094] S43: The support server 20-2 calculates the feature amounts of the feature indicators of the client company identified in S33 based on the transaction data of the client company. If there are multiple feature indicators, the support server 20-2 calculates the feature amounts of all the multiple feature indicators identified in S33 based on the transaction data.

[0095] S44: The support server 20-2 calculates a score indicating the possibility of credit downgrade of the client company based on the feature amount of the feature index of the transaction data of the client company and the feature amount of the feature index of the transaction data of the abnormal counterparty company. The higher the correlation (distance) between the feature amount of the feature index of the client company and the feature amount of the feature index of the abnormal counterparty company, the higher the score is calculated.

[0096] In addition, if the credit rating of the client company is A, the abnormal counterparty company may be a company with a credit rating of B or below B. In this case, the support server 20-2 calculates a score indicating the possibility of downgrading the credit rating of the client company based on the characteristic indicators of the transaction data of the client company and the characteristic indicators of the transaction data of companies with credit ratings of B or below.

[0097] However, if the credit rating of the client company acquired in S41 is, for example, B, then abnormal companies from the client company's perspective are companies with a credit rating of C or lower. In this case, the support server 20-2 can calculate a score indicating the possibility of a downgrade in the client company's credit rating based on the characteristic index of the transaction data of the client company and the characteristic index of the transaction data of companies with a credit rating of C or lower.

[0098] S45: The support server 20-2 ranks the client companies in descending order of score and outputs the result to the terminal 40. The person in charge can view the output result using the terminal 40, and can check the credit ratings by prioritizing client companies with scores above a predetermined score or client companies above a predetermined rank that are considered to be at risk of being downgraded, and by referring to transaction data, financial data, and the like in more detail.

[0099] The process of detecting recipients of credit downgrades (S41 to S45) may be executed when a request to obtain a detection list of recipients of possible credit downgrades is received from terminal 40, or it may be executed every time the processed DB 20 is updated along with an update of the transaction data in the DB of the financial institution system 10, or it may be executed periodically, for example, once a day.

[0100] As described above, the transaction data according to this embodiment is used in financial institutions to monitor companies and calculate a score to measure the possibility of credit downgrades. Since transaction data cannot be tampered with by client companies, is frequently acquired and updated, and contains a large amount of information, it enables financial institutions' loan screening department staff to detect client companies that are at risk (possible credit downgrades, etc.) in a timely manner and in line with the actual transaction situation before the end of the fiscal year.

[0101] Although annual financial statements are easy to quantitatively evaluate from the perspective of traditional financial analysis, data can be falsified and false reports can be made, and data is not collected or updated frequently, making it difficult to detect client companies whose credit ratings may be downgraded in a timely manner.

[0102] In addition, from among the vast number of client companies, the person in charge can focus on only those clients that exceed a certain scoring risk level. This allows for efficient screening and corporate monitoring, for example by prioritizing screening of high-risk clients and quickly noticing the existence of risks.

[0103] (Supplementary Note 1) As explained above, when a client company determines that the feature values ​​of the feature indicators of the transaction data of the client company are highly related to the feature values ​​of the feature indicators of the transaction data of the abnormal client company, the client company is suspected of being subject to a credit downgrade and a score higher than a certain standard is calculated for the client company (S43).

[0104] However, when the score is calculated again thereafter, it may not be as high as the previous score. It can be said that a client company whose score has dropped in this way has had its credit downgraded, or that, depending on the score, there is a possibility that its credit may be upgraded. The support server 20 holds a history of scores calculated each time, and when the current score is significantly lower than the previous score (for example, by a certain reference value) compared to past scores, it can explicitly display this on the terminal 40 of the person in charge. In addition, if a flag is set for a client company whose score has been calculated as high as a monitored company, the client company may be automatically excluded from the managed companies if its current score drops significantly from its past score.

[0105] Furthermore, for a client company whose current score has dropped significantly from its past score, the support server 20-2 may determine the degree of association between the characteristic indices of the transaction data of the client company and the characteristic indices of the transaction data of a "normal client company" (e.g., a company with a credit rating of A). This is a "white" judgment. If the degree of association is determined to be high (if a high score is calculated), it is confirmed that the client company does not exhibit the characteristic indices seen in abnormal clients, and furthermore exhibits characteristic indices like normal clients. This makes it possible to say with even greater certainty that the client company has a lower possibility of being downgraded, or that, depending on the score, there is a possibility of being upgraded.

[0106] (Supplementary Note 2) As a variation of S44, when the credit rating of a client company is an abnormal client company (e.g., credit rating B or lower), the support server 20-2 can also calculate a score indicating the "likelihood of credit upgrade" of the client company based on the feature amount of the feature index of the transaction data of the client company and the feature amount of the feature index of the transaction data of a "normal client company" (e.g., credit rating A) with a higher credit rating than the client company. The higher the relevance (distance) of the feature amount of the client company's feature index to the feature amount of the normal client company's feature index, the higher the calculated score. The support server 20-2 outputs the client companies to the terminal 40 in order of highest score.

[0107] This allows the staff member to view the output results using the terminal 40, prioritize client companies with a certain score or higher that are considered likely to be upgraded, and review and confirm the credit ratings by referring to transaction data, financial data, etc. in more detail. In addition, the staff member can focus on only those clients that are below a certain scoring risk level among a huge number of client companies. For example, by prioritizing the screening of clients with reduced risk, the staff member can quickly notice the reduction in risk, and thus carry out efficient screening work and corporate monitoring.

[0108] [Embodiment 3] The screening operation support server according to the above-mentioned first and second embodiments uses transaction data of client companies at financial institutions to calculate a score for measuring the possibility of window dressing and credit downgrade of client companies. Compared with financial data such as financial statements, transaction data cannot be tampered with by client companies, data is acquired and updated frequently, and data information is large. This allows the staff in the loan screening department at financial institutions to detect risky client companies' possibility of window dressing and credit downgrade in a timely manner before the accounting period, in line with the actual transaction situation.

[0109] However, there is a problem that it is difficult to calculate scores for client companies with little daily transaction data among the client companies of financial institutions. The person in charge of the loan screening department wants to predict and detect risky companies without relying on the amount of transaction data of the client companies. In view of this, in the third embodiment, the range of learning data (big data) available to the screening operation support server is expanded, and third-party data is also used.

[0110] Here, the third-party data according to this embodiment refers collectively to data provided by third parties other than the parties involved, such as financial institutions and client companies, and more specifically, for example, statistical data provided by government agencies and research institutions, stock price data and exchange data in financial markets, etc. These data can be said to be market data that objectively indicates the market environment of client companies, and the data itself has not been tampered with and is a highly reliable data source.

[0111] (Example of third-party data) Examples of third-party data include statistical data from e-Stat, a comprehensive portal for government statistics,<URL:https: / / www.e-stat.go.jp / statistics-by-theme / > The macro statistical indicator data on this site is classified into 17 fields: "Land and Weather," "Population and Households," "Labor and Wages," "Agriculture, Forestry and Fisheries," "Mining and Manufacturing," "Commerce and Services," "Businesses, Households and Economy," "Housing, Land and Construction," "Energy and Water," "Transportation and Tourism," "Information and Communications, Science and Technology," "Education, Culture, Sports and Life," "Administration and Finance," "Judicial Affairs, Safety and Environment," "Social Security and Sanitation," "International," and "Other," and statistical surveys are provided for each field. Among these, the macro statistical indicator data particularly related to the industry and business sector to which the client company belongs are shown below. Needless to say, the relevant macro statistical indicator data differs depending on the industry and business sector to which the client company belongs, and it is difficult to list all the macro statistical indicator data, so the following is only a partial example.

[0112] Agriculture, forestry and fisheries Agricultural Management Statistics Survey Agriculture and Forestry Census Fisheries Census Crop Statistics Survey Marine Fisheries Production Statistics Survey Milk and dairy product statistics survey Mining and manufacturing Pharmaceutical Industry Production Trends Survey Industrial Statistics Survey Ministry of Economy, Trade and Industry Production Statistics Survey Production Statistics Industrial Production, Shipment and Inventory Index Manufacturing Capacity and Utilization Rate Index Statistical Survey of Ore Reserves Shipbuilding and Machinery Construction Statistical Survey Railway Vehicle Production Statistics Survey · Commercial and service industry Commercial Statistics Survey Current Commerce Survey Survey of specific service industries Petroleum Products Supply and Demand Trend Survey ·Business ·Household ·Economy National Accounts Individual Business Economic Survey Economic Census - Basic Survey Economic Census - Activity Survey Economic Structure Survey Household Survey National Household Structure Survey (formerly the National Survey of Family Income and Expenditure) Retail Price Survey National Price Survey Input-Output Table Corporate Enterprise Statistics Survey Ministry of Economy, Trade and Industry Basic Survey of Business Activities ·Housing ·Land ·Construction Housing and Land Survey Construction Starts Statistics Survey Construction Work Statistics Survey Corporate Land and Building Basic Survey Energy Water Ministry of Economy, Trade and Industry: Survey on Oil Consumption by Specified Industries Gas Industry Production Trend Survey Ministry of Economy, Trade and Industry Statistical Survey on Oil Consumption by Specified Industries ·Transportation ·Tourism Port Survey Motor Vehicle Transport Statistics Survey Domestic Shipping Statistics Survey ·Social security / sanitation School Health Statistics Survey Medical Facility Survey Patient Survey Social security cost statistics <Software configuration> 18 is a diagram showing an example of the software configuration of the screening operation support server according to this embodiment. The screening operation support server 20-3 has, as main functional units, a financial data acquisition unit 201-3a, a third-party data acquisition unit 201-3b, a relationship feature amount calculation unit 202-3, a feature financial data identification unit 203-3, a relevance determination unit 204-3, an output unit 205-3, and a memory unit 206-3.

[0113] The financial data acquisition unit 201-3a has a function of acquiring first financial data of other companies in the same industry as the customer company, and a function of acquiring second third-party data identified as characteristic third-party data by the characteristic financial data identification unit 203-3.

[0114] The third-party data acquisition unit 201-3b has a function of acquiring first third-party data related to the industries of the client company and other companies in the same industry. The third-party data acquisition unit 201-3b also has a function of acquiring second third-party data identified as characteristic third-party data by the characteristic financial data identification unit 203-3.

[0115] The relationship feature amount calculation unit 202-3 has a function of calculating a first feature amount indicating a relationship between the first financial data and the first third party data, and also has a function of calculating a second feature amount indicating a relationship between the second financial data and the second third party data.

[0116] The characteristic financial data identification unit 203-3 has a function of identifying, in association with each other, characteristic financial data and characteristic third-party data in which a relationship characteristic appears between the financial data of other companies in the same industry and the third-party data, based on the first characteristic amount.

[0117] The association determination unit 204-3 has a function of determining an association between the second characteristic amount related to the client company and the first characteristic amount related to another company in the same industry.

[0118] The output unit 205-3 has a function of outputting the customer company determined to be related by the relatedness determination unit 204-3.

[0119] The storage unit 206-3 has a function of storing, in the HDD 24, information on the characteristic financial data and the characteristic third party data identified by the characteristic financial data identification unit 203-3.

[0120] Each functional unit is realized by a computer program executed on hardware resources such as the CPU, ROM, and RAM of the computer constituting the support server 20-3. These functional units may be interpreted as "means," "module," "unit," or "circuit." Each DB may be placed in the HDD 24 of the support server 20-3 or an external storage device on the network 70. Each functional unit of the support server 20-3 may not only be realized by a single server device, but may also be realized as a system consisting of multiple devices with distributed functions.

[0121] In the present embodiment 3, the configuration example of the screening operation support system shown in Fig. 1 and the hardware configuration example of the screening operation support server shown in Fig. 2 can be applied similarly to the embodiment 1, so that the repeated explanation will be omitted. However, the screening operation support server 20 will be referred to as the screening operation support server 20-3 in the present embodiment 3.

[0122] <Detection of abnormalities in financial data declaration values> 19 is a diagram showing the characteristic financial data and characteristic third party data identification process of the screening business support server 20-3 according to this embodiment. Note that the support server 20-3 according to this embodiment 3 differs from the above-mentioned embodiments 1 and 2 in that it does not use the transaction data of the client company, but uses the financial data of the client company.

[0123] S51: The support server 20-3 acquires financial data (public values) of client companies and competitor companies in the financial institution as learning data (big data) from the processed DB 30 (originally financial data). Note that financial data is acquired in multiple chronological order. In the case of annual financial statements, as many years' worth of data as possible is acquired on an annual basis.

[0124] Furthermore, a competing company is another company that is classified in the same industry as the client company, if the industry can be classified into, for example, manufacturing, agriculture, forestry, fishing, mining, construction, transportation, wholesale, retail, finance, insurance, or services. If the competing company is a client of the financial institution, the financial data may be financial data held by the financial institution, or if the competing company is not a client, the financial data may be financial data of the competing company that is publicly disclosed as a listed company. The industry of the client company is known information in the client information (FIG. 4A).

[0125] S52: The support server 20-3 acquires third-party data related to the industries of the client company and other companies in the same industry as learning data (big data) from the processed DB 30 (originally statistical data, etc.). The third-party data related to the industries of the client company and other companies in the same industry is statistical data related to the industry field, and for example, when the industries of the client company and other companies in the same industry are manufacturing, it is statistical data directly classified as "mining and manufacturing". However, even in the case of manufacturing, it may be statistical data classified as "business, household, economy", for example. This is because statistical data classified as "business, household, economy" includes, for example, the Retail Price Survey and the National Price Survey, and can be said to be third-party data related to all industries. Note that statistical data often includes data trends from the past to the latest in a chronological order, and in such cases, it is not necessary to acquire third-party data in multiple chronological orders.

[0126] S53: The support server 20-3 calculates a relationship feature value that indicates a relationship between the transition of the financial data of the other company in the same industry acquired in S51 and the transition of the third-party data acquired in S52. The relationship feature value can be calculated, for example, based on the correlation between the two transition changes.

[0127] For example, when the financial data is the purchase price of a raw material published in the financial statements of other companies in the same industry, and the corresponding third-party data is price statistical data showing the market price of the raw material, the market price of the raw material and the purchase price are usually highly correlated (e.g., have a positive correlation value). Conversely, when the financial data is the profitability rate published in the financial statements of other companies in the same industry, and the corresponding third-party data is price statistical data showing the market price of the raw material, the market price of the raw material and the profitability rate are usually highly correlated (e.g., have a negative correlation value).

[0128] On the other hand, when the financial data is the rent published in the financial statements of a competing company, and the corresponding third-party data is price statistical data showing the market conditions of the market price of the raw materials, there is usually a low correlation between the market price of the raw materials and the rent (for example, a correlation value close to 0).

[0129] S54: The support server 20-3 identifies financial data that has a strong relationship with the third-party data as characteristic financial data in relation to the third-party data based on the relationship characteristic amount calculated in S53. For example, in the case where characteristic financial data is represented by a correlation value, if the absolute value of the correlation value is greater than a predetermined value, the financial data is identified as characteristic financial data in relation to the third-party data. On the other hand, financial data that has a weak relationship with the third-party data is not identified as characteristic financial data.

[0130] S55: The support server 20-3 also identifies the third-party data corresponding to the characteristic financial data identified in S54 as characteristic third-party data. The identified characteristic financial data and characteristic third-party data are stored in the HDD 24 as a data set. The financial data includes not only one piece of financial data but also financial indicator data calculated from multiple pieces of financial data.

[0131] FIG. 20 is a diagram showing the process of detecting anomalies in financial data declared values ​​according to this embodiment. S61: The support server 20-3 acquires financial data (declared values) of the client company from the financial institution as learning data (big data) from the processed DB 30 (originally financial data). The financial data is acquired in multiple chronological order. The financial data of the client company acquired here is the financial data identified as characteristic financial data in S54. In other words, it means financial data of the same item type as that of other companies in the same industry that are deemed to have a high relationship with the characteristic third-party data identified in S55. In the above example, it is data on purchase prices and profit rates declared in the financial statements of the client company.

[0132] S62: The support server 20-3 acquires the third-party data identified in S55 as the characteristic third-party data. The acquired third-party data here refers to third-party data that is considered to have a high correlation with the financial data acquired as the characteristic financial data in S61. In the above example, it is price statistical data showing the market conditions of the market prices of raw materials.

[0133] S63: The support server 20-3 calculates a relationship feature value that indicates a relationship between the transition of the financial data of the client company acquired in S61 and the transition of the third-party data acquired in S62. The relationship feature value can be calculated, for example, based on the correlation between the two transition changes.

[0134] S64: The support server 20-3 calculates a score indicating the possibility of an anomaly in the financial data declaration value based on the relationship feature of the client company calculated in S63 and the relationship feature of the competitor company calculated in S53. The lower the degree of association (distance) between the relationship of the client company and the relationship of the competitor company, the lower the score is calculated.

[0135] In principle, since other companies in the same industry tend to have a strong relationship with the characteristic third-party data regarding financial data identified as characteristic financial data, it is expected that customer companies in the same industry will also tend to have a strong relationship with the characteristic third-party data regarding the same financial data.

[0136] For example, it has been determined that the purchase price of raw materials on a financial statement (financial data identified as characteristic financial data) and price statistical data showing the market price of the raw materials (characteristic third-party data) are highly correlated (e.g., have a positive correlation value A) for companies in the same industry (S53 to S55).

[0137] On the other hand, for client companies in the same industry, the purchase price of raw materials on their financial statements (financial data identified as characteristic financial data) is highly correlated with the price statistical data showing the market conditions of the market price of the raw materials (characteristic third-party data) (for example, has a positive correlation value B).

[0138] Then, in S64, the support server 20-3 compares the relationship feature of the client company (e.g., positive correlation value B) with the relationship feature of another company in the same industry (e.g., positive correlation value A), and calculates a higher score the closer the two relationship feature values ​​(e.g., positive correlation values ​​A and B) are, and calculates a lower score the further apart they are. Examples of low relevance and distance include cases where the correlation value of the client company is 0 (no correlation) or a negative correlation value (inverse correlation). Among companies in the same industry, certain financial data would tend to be affected by market conditions in the same way, but if it is not affected at all by market conditions or is affected in the opposite way to market conditions, it can be said that the financial data of such a client company may have an abnormality in the declared value.

[0139] S65: The support server 20-3 outputs the client companies in ascending order of score to the terminal 40. The person in charge can view the output result using the terminal 40, and can check for window dressing by giving priority to client companies with a score below a predetermined score or a predetermined rank that are suspected of having anomalies in the declared values ​​of financial data, by referring to the transaction data, financial data, etc. in more detail.

[0140] In addition, the process of detecting possible destinations of falsification (S61 to S65) may be executed when a request to obtain a detection list is received from the terminal 40, or may be executed every time the processed DB 20 is updated along with updates to third-party data in the DB of the financial institution system 10.

[0141] In addition, it is preferable that the other companies in the same industry in S51 are listed companies. In the first place, it is assumed that the other companies in the same industry are companies that do not window dress, that is, companies that have no abnormalities in their financial data (declared values), so if the other companies in the same industry are listed companies, this assumption is more reliably guaranteed. Also, there may be multiple other companies in the same industry. If there are multiple other companies in the same industry, this assumption is more reliably guaranteed.

[0142] (Examples of characteristic financial data and characteristic third-party data) FIG. 21 is a diagram showing an example of abnormality detection of raw material market price data declared values ​​according to this embodiment. First, FIG. 21(a) shows that, for client company X, which is engaged in metal manufacturing, and competitor company Y, there is a strong correlation between the raw material costs (characteristic financial data) in the financial statement declaration and the metal market price market (characteristic third-party data). In other words, the raw material costs of competitor company Y are seen to move in a positive correlation with the metal market price market (characteristic third-party data). In other words, for metal manufacturing companies, the raw material costs are seen to move in a positive correlation with the metal market price market, so if the metal market price market is on a downward trend, it can be said that it is natural that the raw material costs declared by client company X will also be on a downward trend.

[0143] On the other hand, according to FIG. 21(b), the raw material cost (characteristic financial data) in the financial statement declaration and the metal market price (characteristic third-party data) do not show a positive interlocking movement, but rather show a negative movement against the metal market price (characteristic third-party data). Therefore, in this case, the support server 20-3 compares the relationship feature of the client company (e.g., positive correlation value B) with the relationship feature of the other company in the same industry (e.g., positive correlation value A), and calculates a low score indicating that the two relationship feature values ​​(e.g., positive correlation values ​​A and B) are far apart. For a company engaged in metal manufacturing, raw material costs are naturally influenced by market conditions, but the client company X is influenced in a way that is opposite to the market conditions rather than being influenced by the market conditions. It can be said that the raw material cost in the financial statement declaration of such a client company X may be abnormal in the declared value (and thus may be window dressed).

[0144] The raw material cost and metal market price in the financial statement declaration are merely examples of characteristic financial data and characteristic third-party data. In reality, the characteristic financial data and third-party data are identified as characteristic financial data and third-party data that are seen among companies in the same industry that are expressed in data terms by the processing of S51 to 55 executed by the support server 20-3. The support server 20-3 identifies characteristic financial data and characteristic third-party data that are expressed in data terms by third-party data analysis from various combination patterns of various types of financial data and various types of third-party data, including those that are not easily found by humans, by the processing of S51 to 55. That is, the support server 20-3 uses so-called AI (e.g., deep learning) to analyze and evaluate the relevance features from certain financial data of companies in the same industry and third-party data (big data) related to the same industry, and the support server autonomously discovers financial data that is recognized to be related to the third-party data, focuses on the features indicated by the discovered financial data, and calculates a score based on the degree of relevance with the financial data of the client company to be monitored.

[0145] As described above, the support server according to this embodiment uses third-party data to monitor companies and calculates a score for measuring the possibility of anomalies in financial data declarations. Third-party data cannot be tampered with by client companies, is highly reliable, and has a large amount of multifaceted data information. This allows financial institution loan screening department personnel to detect risky client companies in a timely manner in line with external market conditions, even for client companies with little transaction data. Furthermore, even for client companies with a sufficient amount of transaction data, it is possible to diversify prediction and detection by incorporating new perspectives by expanding the learning data to third-party data.

[0146] In addition, from among a huge number of client companies, the person in charge can focus on only those clients that fall below a certain scoring risk level. This allows for efficient screening and corporate monitoring, for example by prioritizing screening of high-risk clients and quickly noticing the existence of risks.

[0147] <Summary> As described above, the screening operation support system according to the present embodiment can detect client companies suspected of having a deteriorating financial situation and support the lending operation of financial institutions. Note that, although the present invention has been described using specific examples according to preferred embodiments of the present invention, it is clear that various modifications and changes can be made to these examples without departing from the broad spirit and scope of the present invention as defined in the claims. In other words, the present invention should not be construed as being limited by the details of the examples and the accompanying drawings.

[0148] <Inventions extracted from this embodiment> The following describes features of the inventions extracted from the above-described embodiments.

[0149] (Appendix A1) A screening operation support device for a financial institution, comprising: An acquisition means for acquiring transaction data of liquidity transaction details of past window dressing target companies and non-window dressing target companies; A calculation means for calculating index data based on the transaction data of each of the window dressing target companies and the non-window dressing target companies; A means for identifying characteristic indicators that represent data characteristics of the window dressing target company based on a data difference between the indicator data of the window dressing target company and the indicator data of the non-window dressing target company; An acquisition means for acquiring transaction data of liquidity transaction details at a client company; A calculation means for calculating index data of the characteristic index based on the transaction data of the client company; an association determination means for determining an association between index data of the characteristic indicators of the client company and index data of the characteristic indicators of the window dressing target company; an output means for outputting the customer companies determined to be related by the relatedness determination means; An examination operation support device comprising:

[0150] (Appendix B1) A screening operation support device for a financial institution, comprising: An acquisition means for acquiring transaction data of liquidity transaction details of non-normal and normal counterparties having past credit ratings; A calculation means for calculating index data based on the transaction data of each of the abnormal companies and the normal companies; A means for identifying a characteristic indicator that represents the data characteristics of the abnormal company based on a data difference between the index data of the abnormal company and the index data of the normal company; An acquisition means for acquiring transaction data of liquidity transaction details at a client company; A calculation means for calculating index data of the characteristic index based on the transaction data of the client company; an association determination means for determining an association between index data of the characteristic indicators of the customer company and index data of the characteristic indicators of the abnormal customer company; an output means for outputting the customer companies determined to be related by the relatedness determination means; An examination operation support device comprising:

[0151] (Appendix C1) A screening operation support device for a financial institution, comprising: An acquisition means for acquiring first financial data of other companies in the same industry as the client company; An acquisition means for acquiring first third-party data related to the business type; A calculation means for calculating a first feature amount indicating a relationship between the first financial data and the first third-party data; an identification means for identifying, in association with each other, characteristic financial data and characteristic third-party data in which a relationship characteristic between the financial data and third-party data of the competing company is expressed based on the first characteristic amount; an acquisition means for acquiring second financial data of the customer company identified as the characteristic financial data; An acquisition means for acquiring second third-party data identified as the characteristic third-party data; A calculation means for calculating a second feature amount indicating a relationship between the second financial data and the second third-party data; an association determination means for determining an association between the second feature amount related to the client company and the first feature amount related to the other company in the same industry; an output means for outputting the customer companies determined to be related by the relatedness determination means; An examination operation support device comprising: [Explanation of symbols]

[0152] 10 Financial Institutions System 20, 20-2, 20-3 Examination business support server 30 Processing DB 40 Terminals 70 Network 100, 100-2, 100-3 Examination support system 201, 201-2 Transaction data acquisition section 201-3a Financial Data Acquisition Department 201-3b Third Party Data Acquisition Department 202, 202-2 Index data calculation unit 202-3 Relationship feature calculation unit 203, 203-2 Feature index identification section 203-3 Specific Financial Data 204, 204-2, 204-3 Related Judgment Section 205, 205-2, 205-3 Output section 206, 206-2, 206-3 Storage section

Claims

1. A screening operation support device for a financial institution, comprising: An acquisition means for acquiring first financial data of other companies in the same industry as the client company; An acquisition means for acquiring first third-party data related to the business type; A calculation means for calculating a first feature amount indicating a relationship between the first financial data and the first third-party data; an identification means for identifying, in association with each other, characteristic financial data and characteristic third-party data in which a relationship characteristic between the financial data and third-party data of the other company in the same industry is expressed based on the first characteristic amount; an acquisition means for acquiring second financial data of the customer company identified as the characteristic financial data; An acquisition means for acquiring second third party data identified as the characteristic third party data; a calculation means for calculating a second feature amount indicating a relationship between the second financial data and the second third-party data; an association determination means for determining an association between the second feature amount related to the client company and the first feature amount related to another company in the same industry; an output means for outputting the customer companies determined to be related by the relatedness determination means; An examination operation support device comprising:

2. The acquisition means includes: Obtaining a plurality of types of the first third-party data; The calculation means is calculating a first feature amount indicating a relationship between the first financial data and the first third-party data for each combination pattern of the first financial data and the first third-party data; The identification means is identifying, in association with each other, characteristic financial data and characteristic third-party data in which a relationship characteristic between the financial data and third-party data of the competing company is expressed, based on the first characteristic amount calculated for each combination pattern from among combination patterns of the first financial data and the first third-party data; 2. The examination operation support device according to claim 1,

3. The third-party data is statistical data indicating market conditions related to the industry; 2. The examination operation support device according to claim 1,

4. The said competitors are listed companies.

2. The examination operation support device according to claim 1,

5. The screening support device for financial institutions An acquisition step of acquiring first financial data of a competitor company belonging to the same industry as the client company; an acquisition step of acquiring first third-party data related to the business type; a calculation step of calculating a first feature amount indicating a relationship between the first financial data and the first third-party data; an identification step of identifying, in association with each other, characteristic financial data and characteristic third-party data in which a relationship characteristic between the financial data and third-party data of the other company in the same industry is expressed based on the first characteristic amount; an acquisition step of acquiring second financial data of the customer company identified as the characteristic financial data; an acquisition step of acquiring second third-party data identified as the characteristic third-party data; a calculation step of calculating a second feature amount indicating a relationship between the second financial data and the second third-party data; an association determination step of determining an association between the second feature amount related to the customer company and the first feature amount related to another company in the same industry; an output step of outputting the customer companies determined to be related by the relatedness determination step; A method for supporting examination operations.

6. On the computer, An acquisition means for acquiring first financial data of other companies in the same industry as the client company; An acquisition means for acquiring first third-party data related to the business type; A calculation means for calculating a first feature amount indicating a relationship between the first financial data and the first third-party data; an identification means for identifying, in association with each other, characteristic financial data and characteristic third-party data in which a relationship characteristic between the financial data and third-party data of the other company in the same industry is expressed based on the first characteristic amount; an acquisition means for acquiring second financial data of the customer company identified as the characteristic financial data; An acquisition means for acquiring second third party data identified as the characteristic third party data; a calculation means for calculating a second feature amount indicating a relationship between the second financial data and the second third-party data; an association determination means for determining an association between the second feature amount related to the client company and the first feature amount related to another company in the same industry; an output means for outputting the customer companies determined to be related by the relatedness determination means; This is a screening support program that enables the system to function as a screening system.

7. A screening operation support device for a financial institution, comprising: a financial data acquisition means for acquiring first financial data that is influenced by market conditions among financial data declared by a client company, and second financial data among financial data of the client company and other companies in the same industry; A third-party data acquisition means for acquiring third-party data indicating market conditions related to the financial data; a relationship determination means for determining a first relationship between a transition of the first financial data and a transition of the third-party data, and a second relationship between a transition of the second financial data and a transition of the third-party data; an association determination means for determining an association between the first relationship and the second relationship; an output means for outputting the customer companies determined to be not related by the relatedness determining means; An examination operation support device comprising:

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