JP6395966B1 - Journal rule creation support device, journal rule creation support method, and journal rule creation support program - Google Patents

Abstract

An object of the present invention is to provide a technique for supporting creation of a journal entry rule by a user.
A journal rule creation support device journals transaction contents into a predetermined account item by using transaction data representing transaction contents and past journal data including account items for journalizing the transaction contents. Support the creation of journal rules that express the correspondence for In the journal entry rule creation support device, the item name included in the data structure of the transaction data includes any of the first keyword group, and the value stored in the corresponding field includes any of the second keyword group. An analysis processing unit that uses items to determine the relevance between accounts included in past journal data and values that can be registered in transaction data, and extracts combinations of values with high relevance and account items as journal rule candidates And a rule creation unit that acquires a journal rule selected by the user from among the candidates and stores it in a storage device.
[Selection] Figure 2

Description

  The present invention relates to a journal rule creation support device, a journal rule creation support method, and a journal rule creation support program.

  Conventionally, an accounting processing apparatus for performing accounting processing by cloud computing in a web server has been proposed. The web server identifies the web detail data for each transaction, and each transaction is generated based on the keyword included in the transaction description of each transaction, with one or more account items associated with each keyword. -Automatically refer to a specific account by referring to the database.

JP 2017-16696 A

  Conventionally, a method for journalizing by learning the relationship between keywords and account items has been proposed. However, when the journal rules are created by machine learning, there is a problem that the validity of the journals cannot be guaranteed.

  An object of this invention is to provide the technique which assists creation of the journal rule by a user.

  The journal entry rule creation support device according to the present invention uses the transaction data representing the transaction content and the past journal data including the account item that journaled the transaction content to journal the transaction content to a predetermined account item. Support the creation of journal rules that express the correspondence for In the journal entry rule creation support device, the item name included in the data structure of the transaction data includes any of the first keyword group, and the value stored in the corresponding field includes any of the second keyword group. An analysis processing unit that uses items to determine the relevance between accounts included in past journal data and values that can be registered in transaction data, and extracts combinations of values with high relevance and account items as journal rule candidates And a rule creation unit that acquires a journal rule selected by the user from among the candidates and stores it in a storage device.

  In this way, information that is a candidate for the user to create a journal entry rule can be output, and the user can define a journal entry rule whose content is not a black box.

  Further, as the item name and the value stored in the corresponding field, information described in the natural language in the data definition may be used, and the transaction data may include the contents of transactions in a plurality of organizations. In this way, it is possible to extract characteristics common to a plurality of organizations, not information unique to the organization, and it is possible to realize group accounting including, for example, overseas affiliates.

In addition, the past journal data includes account items that journal the contents of transactions under multiple accounting standards, and the machine learning department records the values registered in the field of the item selected by the user for each accounting standard and the past journal entries. You may make it machine-learn the correspondence with the account item of data. In this way, it is possible to easily output accounting data such as journal entries and reference balances according to different accounting standards.

  The contents described in the means for solving the problems can be combined as much as possible without departing from the problems and technical ideas of the present invention. The contents of the means for solving the problems can be provided as a device such as a computer or a system including a plurality of devices, a method executed by the computer, or a program executed by the computer. A recording medium that holds the program may be provided.

  It is possible to provide a technique that supports creation of journal entry rules by the user.

It is a figure which shows an example of a structure of a system. It is a block diagram which shows an example of a structure of a base server, an accounting server, and a user terminal. It is a processing flow figure showing an example of journal entry rule creation processing. It is a figure which shows an example of transaction data. It is a figure which shows an example of conditions. It is a figure which shows an example of the keyword for classifying an item from a 5W3H viewpoint. It is a figure which shows an example of the reference | standard for evaluating identification performance. It is a figure which shows an example of the identification key produced | generated from transaction data. It is a figure which shows an example of the estimation result of an account item. It is a figure which shows the other example of the candidate of a journal entry rule. It is a figure which shows an example of the account item according to a Japanese accounting standard. It is a figure which shows an example of the account item according to IFRS criteria. It is a processing flow figure showing an example of journalizing processing.

  Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings.

<System configuration>
FIG. 1 is a diagram illustrating an example of a configuration of an accounting system according to the present embodiment. The accounting system 1 includes a base server 2 (21, 22,... In FIG. 1) provided in a group company or branch office of a company, an accounting server 3 that centrally manages accounting of a plurality of companies, and an accounting system 1. And a user terminal 4 used by a person in charge of a company that uses the accounting system 1 or a person in charge of the company that provides the accounting system 1. So that they can communicate with each other. The number of each device is not limited to the illustrated configuration. The accounting server 3 may be a device that provides a service on a so-called cloud, and the plurality of base servers 2 may include servers of different companies that perform core business.

The accounting server 3 acquires data such as slips representing the contents of transactions performed by companies such as production, order receipt, and purchase from the base server 2. Further, the accounting server 3 journalizes the contents of the transaction into account items based on a predetermined journal rule, and registers it in the journal details and the reference balance file. In this embodiment, a plurality of companies can be managed in a unified manner by applying the same journal entry rules to a plurality of organizations such as domestic and foreign group companies of the user company. In addition, the system can record journal entries for a transaction for a predetermined period, create a reference balance in which the journals are accumulated, and output the journal entries and the reference balance. Journal rules are established for each accounting standard such as J-GAAP (Generally Accepted Accounting Principles), US-GAAP, International Financial Reporting Standards (IFRS). The output may be made based on the selected accounting standard. The accounting server 3 corresponds to the journal entry rule creation support apparatus according to the present invention.

  The journal entry rule is proposed to the user by performing machine learning using a combination of data representing transaction contents and a teacher value of the account item. In this embodiment, the accounting server 3 extracts items used for the journal entry rule from the items (attributes) included in the data representing the contents of the transaction, and uses the characteristics of the value registered for the item as the journal rule. Suggest to the user as a candidate. In this way, it is possible to provide support for the user to create a journal entry rule.

  FIG. 2 is a block diagram illustrating an example of the configuration of the base server 2, the accounting server 3, and the user terminal 4.

<Base server>
The base server 2 is a general computer and includes a communication I / F (Interface) 21, a storage device 22, an input / output device 23, a processor 24, and a bus 25.

  The communication I / F 21 is, for example, a network card or a communication module, and communicates with other computers via the network 5 based on a predetermined protocol.

  The storage device 32 is a main storage device such as a random access memory (RAM) or a read only memory (ROM), and an auxiliary storage device such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory. The main storage device temporarily stores programs and data read by the processor and secures a work area for the processor. The auxiliary storage device stores programs executed by the processor, information transmitted to and received from other devices, and the like.

  The input / output device 23 is an input device such as a keyboard and a mouse, an output device such as a monitor, and a user interface such as a touch panel.

  The processor 24 is an arithmetic processing unit such as a CPU, and performs each process according to the present embodiment by executing a program. In the base server 2, functional blocks are shown in the processor 24. The processor 24 includes a transaction information processing unit 241.

  The transaction information processing unit 241 transmits information such as a slip representing the contents of a transaction generated at a base such as a company branch office to the accounting server 3 via the communication I / F 21.

  The components as described above are connected via the bus 25.

<Accounting server>
The accounting server 3 is also a general computer, and includes a communication I / F (Interface) 31, a storage device 32, an input / output device 33, a processor 34, and a bus 35.

  The communication I / F 31 is, for example, a network card or a communication module, and communicates with other computers via the network 5 based on a predetermined protocol.

The storage device 32 is a main storage device such as a RAM or a ROM, and an auxiliary storage device such as an HDD, an SSD, or a flash memory. The main storage device temporarily stores programs and data read by the processor and secures a work area for the processor. The auxiliary storage device stores programs executed by the processor, information transmitted to and received from other devices, and the like.

  The input / output device 33 is, for example, an input device such as a keyboard and a mouse, an output device such as a monitor, and a user interface such as a touch panel.

  The processor 34 is an arithmetic processing unit such as a CPU, and performs each process according to the present embodiment by executing a program. The accounting server 3 also shows functional blocks in the processor 34. The processor 34 includes a data analysis unit 341, a rule creation unit 342, and an operation processing unit 343.

  The data analysis unit 341 determines that the item name included in the data structure of the transaction data includes any one of the predetermined keyword group (also referred to as the first keyword group), or the value stored in the corresponding field is the predetermined keyword. An item including any one of a group (also referred to as a second keyword group) is extracted. In addition, the rule creating unit 342 performs machine learning on the relationship between the value registered for the extracted item and the account item (also referred to as “transaction pattern”). For example, by machine learning, a Bayesian network for estimating a journal account item based on a conditional probability from transaction data is created. In addition, the rule creating unit 342 presents to the user a candidate for a journal rule that includes a combination of the value registered in the item and the account item, and the journal rule to be adopted is determined based on the user's operation. The operation processing unit 343 performs processing for journalizing the contents of the transaction based on the created journal rule in the operation stage of the system 1.

  The components as described above are connected via the bus 35.

<User terminal>
The user terminal 4 is a general computer, and includes a communication I / F 41, a storage device 42, an input / output device 43, a processor 44, and a bus 45.

  The communication I / F 41 is, for example, a network card or a communication module, and communicates with other devices based on a predetermined protocol.

  The storage device 42 is a main storage device such as a RAM or a ROM, and an auxiliary storage device such as an HDD, an SSD, or a flash memory. The main storage device temporarily stores programs and data read by the processor and secures a work area for the processor. The auxiliary storage device stores programs executed by the processor and information transmitted to and received from other devices.

  The input / output device 43 is, for example, an input device such as a keyboard and a mouse, an output device such as a monitor, and a user interface such as a touch panel.

  The processor 44 is an arithmetic processing unit such as a CPU, and performs each process according to the present embodiment by executing a program. Also for the user terminal 4, functional blocks are shown in the processor 44. The processor 44 includes, for example, a rule setting unit 441 and a display processing unit 442.

The rule setting unit 441 receives journal rule candidates proposed by the rule creation unit 342 of the accounting server 3 via the communication I / F 41 and causes the input / output device 43 to output them. In addition, the rule setting unit 441 receives selection of a journal entry rule adopted by the user via the input / output device 43. For example, the user may be able to inquire about the processing result output from the accounting server 3 via the Internet browser installed in the user terminal 4 and to set the journal entry rule. Further, the rule setting unit 441 transmits the journal entry rule adopted by the user to the accounting server 3 via the communication I / F 41. The display processing unit 442 acquires accounting data such as financial statements requested by the user from the accounting server 3 and causes the input / output device 43 to output the accounting data.

  The components as described above are connected via the bus 45.

<Journal rule creation process>
FIG. 3 is a process flow diagram illustrating an example of a journal rule creation process for proposing journal rule candidates to the user and supporting the user's operation for creating the journal rule. In addition, the storage device 32 of the accounting server 3 includes a combination of transaction data representing the contents of the transaction and data representing the account item that the user has entered as a machine learning teacher value, the data structure of the transaction data, It is assumed that information such as data definitions including item names (attribute names) is registered in advance.

  The data analysis unit 341 of the accounting server 3 reads transaction data from the storage device 42 (FIG. 3: S1). In this step, the data analysis unit 341 reads transaction data representing the contents of various orders, production, sales, and other transactions that occur in the user company of this system.

  FIG. 4 is a diagram illustrating an example of transaction data. The table of FIG. 4 includes items (attributes) of file ID, transaction classification, transaction type,..., Total classification code, processing date, transaction amount, and number of transactions. Also, predetermined values are registered in the fields corresponding to the respective items. The registered value may be a predetermined alphanumeric character such as a so-called identification code, but in the example of FIG. 4, the content represented by each code is shown in parentheses in natural language (for example, Japanese). Yes. For example, in the data of the first record, the identification code “1” is registered in the field corresponding to the item “transaction classification”, and “1” represents “credit” as shown in parentheses. The contents indicated by the values included in such transaction data can be determined based on data definition information stored in the storage device 32 in advance. Further, it is assumed that a name described in a natural language as shown in the item name of FIG. 4 is also included in the data definition information.

  Further, the data analysis unit 341 extracts combinations of transaction data and account items as journal rule candidates (FIG. 3: S2). In this step, the data analysis unit 341 first extracts candidate items used for identifying a transaction pattern among a plurality of items included in the transaction data. Specifically, candidate items are extracted based on predetermined conditions. For example, a condition such that the item name includes any one of a plurality of keywords (also referred to as a first keyword group) serving as a clue for identifying a transaction may be used.

  FIG. 5 is a diagram illustrating an example of a condition. The table in FIG. 5 includes items (attributes) for classification and determination conditions. In the field corresponding to each item, a condition for extracting a transaction data item as a candidate is registered. In the present embodiment, an item whose value included in the transaction data matches “number of digits”, “literal”, “variation” among the conditions shown in FIG. 5 and whose item name matches at least one of “keyword”. Is extracted. In the example of FIG. 5, the value stored in the field corresponding to an item of transaction data is 5 digits or less, the data type of the item is alphanumeric or numeric, and the stored value variation (ie, item If the value) is within 50 types, and the item name of the item definition includes any of the keywords, the item is extracted. The item data type is a data type of a code value stored as an item value, and a name described in a natural language is not used. For example, among the items shown in FIG. 4, the file ID, transaction classification, transaction type, total classification code, transaction amount, and number of transactions are extracted.

In addition, the data analysis unit 341 classifies the extracted items from the viewpoint of 5W3H based on the values registered for the extracted items and predetermined keywords. 5W3H means WHAT (what) that represents a task, WY (why) that represents motivation, WHEN (when) that represents time, WHERE (where) that represents place, WHO (where) that represents object, and HO that represents method
W (how), HOW MUCH (how much) that represents money, and HOW MANY (how much) that represents scale. By performing classification, it is possible to define the meaning of accounting for each item.

  FIG. 6 is a diagram illustrating an example of keywords for classifying items from the viewpoint of 5W3H. The table in FIG. 6 includes classification and keyword items. Any of 5W3H viewpoints is registered in the field corresponding to the classification item. In the field corresponding to the keyword item, keywords to be classified into each viewpoint are registered. Using the table as shown in FIG. 6, when the value included in the transaction data includes a keyword, the data analysis unit 341 associates the item of transaction data in which the value is registered with the keyword in FIG. It classifies into the viewpoint of 5W3H.

  For example, if the items extracted from FIG. 4 are classified according to the example of FIG. 6, the item of file ID is not classified from any viewpoint. Since the item of transaction classification includes the keyword “credit” in FIG. 6, it is classified into the viewpoint “WHY”. Similarly, the item of transaction type is classified as HOW, the item of transaction amount is classified as HOW MUCH, and the number of transactions is classified as HOW MANY. The classification from the viewpoint of 5W3H may also be used when determining the layout of output data.

  In addition, the data analysis unit 341 uses a keyword (also referred to as a second keyword group) for evaluating performance for identifying a predetermined account item for each viewpoint, and includes items included in the transaction data. The correct answer rate of the items classified from the viewpoints of “WHAT”, “WHY”, and “HOW” is obtained.

  FIG. 7 is a diagram illustrating an example of a criterion for evaluating identification performance. The table in FIG. 7 includes items of a discrimination target and analysis contents. In the field corresponding to the item to be discriminated, an object to be evaluated among the transaction data is registered. Criteria for evaluating the target are registered in the analysis content item. In the example of FIG. 7, keywords for obtaining the correct answer rate (also referred to as “match rate” or “relevance”) are registered. For each item of transaction data, the keywords shown in FIG. The ratio of records including either is calculated as the correct answer rate.

  And the data analysis part 341 employ | adopts the item whose correct answer rate is more than a predetermined threshold value as an identification key for identifying a transaction pattern. The identification key includes a combination of an item and one or more hit keywords.

  FIG. 8 is a diagram showing an example of an identification key generated from the transaction data of FIG. In the example of FIG. 8, values included in the transaction data are registered for each item of transaction classification and transaction type. The value included in the transaction data shown in FIG. 4 is not a so-called code value but a matching rate with a predetermined keyword using a name written in a natural language. It is possible to create general journal rule candidates that can be applied to a plurality of companies. In addition, for example, by creating transaction rule candidates using transaction data of a plurality of companies included in a group company, journal rule candidates that can be applied to the entire group company can be created.

  Further, the data analysis unit 341 uses a learned model obtained by machine learning of the relationship between the transaction data and the journal item of the journal, and causes the account item to be estimated using the identification key. The learned model is a model obtained by machine learning of the relationship between the identification key included in the transaction data generated in the company (or group company) in the past and the account item of the journal.

The method of machine learning is not particularly limited, and for example, a Bayesian network can be used. That is, a value represented by an effective graph with each variable as a node and a conditional probability (weight) between the nodes are determined using values that can be stored in the item adopted as the identification key as variables. The model and the conditional probability can be determined using existing techniques. According to the Bayesian network, the learning data represented by the character string is used as it is, and for example, it can be statistically processed without being digitized as preprocessing. Therefore, the Bayesian network is suitable for the journalizing process performed by the present system.

  FIG. 9 is a diagram illustrating an example of the account item estimation result. The table of FIG. 9 includes each item of the transaction category and transaction type extracted as the identification key, the correct answer rate described above, and each item of the debit item and the credit item that are the estimation results. A plurality of subject estimation results may be output, and a priority order may be set based on the correct answer rate or the like. The combination of the identification key and the estimation result as shown in FIG. 9 (that is, the combination of the item value included in the transaction data and the candidate account item of the journal) is used as a candidate for a rule for journalizing the account item. Presented to the user.

  For example, regarding the combination of the value of the transaction data used as learning data that is smaller than the threshold and the journal account item, for example, a flag indicating that there is a possibility of irregular data is set and the user is informed. You may make it notify. In this way, it is possible to suppress the setting of journal entry rules based on data with low occurrence frequency. In addition, for combinations of field values included in flagged transaction data and journal account candidates, the user refers to the transaction data on which the journal rule candidate is generated (drill down). You may be able to do it. In this way, it is possible to provide the user with a material for determining whether or not to set a journal entry rule.

  FIG. 10 is a diagram illustrating another example of journal rule candidates. The table in FIG. 10 includes items classified as 5W3H among the items included in the transaction data in addition to the items in FIG. As described above, items classified from the viewpoint of 5W3H and values registered in the fields corresponding to the items may be output to the journal rule candidates. These items may be used as part of the journal entry rule, or may be used as part of the accounting data to be output.

  Returning to the description of FIG. 3, the rule creation unit 342 of the accounting server 3 transmits journal rule candidates to the user terminal 4 via the communication I / F 41 (FIG. 3: S3). On the other hand, the rule setting unit 441 of the user terminal 4 receives journal rule candidates via the communication I / F 41 (S4). For example, journal rule candidates are displayed on the Internet browser of the user terminal 4. In addition, the rule setting unit 441 outputs the received journal rule candidates to the input / output device 43 and accepts a selection based on a user operation (S5). In this step, the journal rule adopted by the user is selected. Then, the rule setting unit 441 transmits information indicating the adopted journal entry rule to the accounting server 3 via the communication I / F 41 (S6). For example, the user selects a journal entry rule via an Internet browser.

  On the other hand, the rule creation unit 342 of the accounting server 3 receives information indicating the adopted journal entry rule via the communication I / F 31 (S7) and stores it in the storage device 32 (S8). A journal entry rule is created as described above.

  Alternatively, journal rules may be created for each of a plurality of accounting standards so that journal details according to different accounting standards can be output according to the user's selection. Further, based on the classification of 5W3H, the layout of output accounting data (for example, journal details and reference balance) may be determined.

  FIG. 11 is a diagram showing an example of account items according to Japanese accounting standards. FIG. 12 is a diagram illustrating an example of an account item according to the IFRS standard. Each includes items of original data, accounting books, account items, and amount data for a plurality of details. As described above, account items in a plurality of accounting standards may be registered.

<Journal processing>
FIG. 13 is a process flow diagram illustrating an example of a journal entry process. It is assumed that the journal rule created by the journal rule creation process as shown in FIG. 3 is stored in the storage device 32 of the accounting server 3.

  The transaction information processing unit 241 of the base server 2 transmits transaction data indicating the contents of the generated transaction to the accounting server 3 via the communication I / F 21 (FIG. 11: S11). The processing in this step is performed at a predetermined timing such as the date and time, monthly, etc., whenever a transaction occurs. Further, transaction data as shown in FIG. 4 is transmitted.

  On the other hand, the operation processing unit 343 of the accounting server 3 receives the transaction data via the communication I / F 31 (S12) and performs a journalizing process (S13). In this step, a journal is performed based on a predetermined journal rule, and is registered in the journal details and reference balance data stored in the storage device 32.

  When the display processing unit 442 of the user terminal 4 receives an operation for requesting display of accounting data (journal details and reference balance) from the user via the input / output device 43, the accounting server 3 via the communication I / F 41. Information to request the desired accounting data is transmitted to (S14). The request may specify at least a part of a period to be aggregated, a range of group companies to be aggregated, a type of accounting book, an accounting standard to be applied, and the like.

  When receiving the request via the communication I / F 31 (S15), the operation processing unit 343 of the accounting server 3 reads the data in the journal entry book and outputs the accounting data (journal description and reference balance) in response to the request ( S16). Further, the operation processing unit 343 transmits the output accounting data to the user terminal 4 via the communication I / F 31 (S17).

  Then, the display processing unit 442 of the user terminal 4 receives the accounting data via the communication I / F 41 (S18) and stores it in the storage device 42 or outputs it to the input / output device 43. As described above, transaction data can be journalized and journal details and reference balance data can be output in a desired format.

<Effect>
According to the present system, the user can assist in creating the journal entry rule, and the user can define a journal entry rule whose content is not a black box. In addition, when creating a candidate for a journal entry rule, information described in a natural language is used as the item name and value in the data definition, so that it can be applied in common to different organizations such as group companies. Journal rule candidates may be extracted. Further, a journal entry rule may be created for each accounting standard. This makes it possible to easily output accounting data such as financial statements according to different accounting standards.

<Others>
The above-described configuration is an example, and the present invention is not limited to the illustrated configuration. The matters described above can be implemented in appropriate combination within a range not departing from the problems and technical ideas of the present invention.

  The present invention also includes a computer program that executes the above-described processing. Furthermore, a computer-readable recording medium on which the program is recorded also belongs to the category of the present invention. With respect to the recording medium on which the program is recorded, the above-described processing can be performed by causing the computer to read and execute the program on the recording medium.

  Here, the computer-readable recording medium refers to a recording medium in which information such as data and programs is accumulated by electrical, magnetic, optical, mechanical, or chemical action and can be read from a computer. Examples of such a recording medium that can be removed from the computer include a flexible disk, a magneto-optical disk, an optical disk, a magnetic tape, and a memory card. In addition, examples of the recording medium fixed to the computer include a hard disk drive and a ROM.

1: Accounting system 2: Base server 21: Communication I / F
22: Storage device 23: Input / output device 24: Processor 241: Transaction information processing unit 25: Bus 3: Accounting server 31: Communication I / F
32: Storage device 33: Input / output device 34: Processor 341: Data analysis unit 342: Rule creation unit 343: Operation processing unit 35: Bus 4: User terminal 41: Communication I / F
42: storage device 43: input / output device 44: processor 441: rule setting unit 442: display processing unit 45: bus 5: network

Claims (5)

Creation of a journal rule that expresses the correspondence for journalizing the contents of the transaction to a predetermined account using transaction data representing the contents of the transaction and past journal data including the account that journalized the contents of the transaction Journal entry rule creation support device that supports
Using the item name included in the data structure of the transaction data including any of the first keyword group and the value stored in the corresponding field including any of the second keyword group, the past journal An analysis processing unit that obtains a degree of association between an account included in data and a value that can be registered in the transaction data, and extracts a combination of a value with a high degree of association and the account as a candidate for a journal entry rule;
Among the candidates, a rule creation unit that acquires a journal rule selected by the user and stores it in a storage device;
A journal rule creation support device comprising: As information stored in the item name and the corresponding field, information described in natural language in the definition of the data,
The journal entry rule creation support device according to claim 1, wherein the transaction data includes transaction contents in a plurality of organizations. The past journal data includes an account item that journals the contents of the transaction in a plurality of accounting standards,
The journal rule creation support device according to claim 1, wherein the rule creation unit acquires a combination of a value that can be registered in the transaction data and an account item for each accounting standard as a journal rule. Creation of a journal rule that expresses the correspondence for journalizing the contents of the transaction to a predetermined account using transaction data representing the contents of the transaction and past journal data including the account that journalized the contents of the transaction Journal entry rule creation support method for supporting
The item name included in the data structure of the transaction data is an item including any of the first keyword group, and the value stored in the corresponding field is an item including any of the second keyword group Learning the degree of association with an account included in the past journal data, and extracting a combination of a value with a high degree of association and the account as a candidate for a journal rule;
Obtaining a journal rule selected by the user among the candidates and storing it in a storage device;
The journal rule creation support method that the computer executes. Creation of a journal rule that expresses the correspondence for journalizing the contents of the transaction to a predetermined account using transaction data representing the contents of the transaction and past journal data including the account that journalized the contents of the transaction Journal entry rule creation support program for supporting
The item name included in the data structure of the transaction data is an item including any of the first keyword group, and the value stored in the corresponding field is an item including any of the second keyword group Learning the degree of association with an account included in the past journal data, and extracting a combination of a value with a high degree of association and the account as a candidate for a journal rule;
Obtaining a journal rule selected by the user among the candidates and storing it in a storage device;
A journal rule creation support program that causes a computer to execute

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