Accounting abnormality visualization apparatus, accounting abnormality visualization method, and accounting abnormality visualization program

Abstract

To provide an accounting abnormality visualization apparatus, accounting abnormality visualization method, and accounting abnormality visualization program which, based on tendency in allocation of an expenditure amount relating to each of organizations such as companies, can visualize injustice of individuals such as employees which deviates from the tendency.SOLUTION: A method according to the present invention has a step of acquiring an applicant determination result for each applicant based on statistical hypothesis verification for evaluation data including expenditure data set by associating expenditure amount, expenditure item, applicant, and application date of an evaluation target with one another, and evaluation degree data set with observation degree of a number of a first digit of each of expenditure amounts of the evaluation target, and a step of displaying, based on the applicant determination result, an applicant expenditure allocation state screen for enabling confirmation of a composition ratio of each of the items in the observation degree of the number of the first digit of the expenditure amount of the evaluation target of the applicant whose abnormality is detected.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an accounting anomaly visualization device, an accounting anomaly visualization method, and an accounting anomaly visualization program.

Background Art

[0002] Patent Document 1 discloses a configuration that enables confirmation of the deviation between Benford's law and the actual value by displaying the difference from Benford's law in the journal amounts of the entire subsidiary.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, as fraud that can be committed by individuals, fraud in falsifying expense amounts is actually being carried out. In the case of simple amount falsification, it is easy to detect by comparing evidence such as receipts, and prevention is also easy. However, there are many cases where it is falsified from the evidence, and in reality, it is difficult to detect fraud. In the invention described in Patent Document 1 above, there is a problem that it is not possible to visualize the expense trend of one employee detected as abnormal by Benford's law.

[0005] The present invention has been made in view of the above problems, and an object thereof is to provide an accounting anomaly visualization device, an accounting anomaly visualization method, and an accounting anomaly visualization program that can visualize the fraud of individuals such as employees who deviate from the accounting trend of each organization such as a company from the recorded trend of expense amounts.

Means for Solving the Problems

[0006] In order to solve the above-described problems and achieve the object, an accounting anomaly visualization apparatus according to the present invention is an accounting anomaly visualization apparatus including a storage unit and a control unit, wherein the control unit associates and sets an expense amount, an expense item, an applicant, and an application date of an evaluation target, and an evaluation frequency data in which the observed frequency of each leading digit number of the expense amount of the evaluation target is set. A determination result acquisition means for obtaining an applicant determination result for each applicant by statistical hypothesis testing for the evaluation data including the above, and based on the applicant determination result, in the observed frequency of each leading digit number of the expense amount of the evaluation target of the applicant for which an anomaly is detected, a screen display means for displaying an applicant expense recording status screen that enables confirmation of the composition ratio of each expense item. It is characterized by having.

[0007] Further, in the accounting anomaly visualization apparatus according to the present invention, the screen display means further includes, based on the applicant determination result, in the observed frequency of each leading digit number of the expense amount of the evaluation target of all other applicants other than the applicant for which an anomaly is detected, It is characterized by displaying an expense recording status screen for all other applicants that enables confirmation of the composition ratio of each expense item.

[0008] Further, in the accounting anomaly visualization apparatus according to the present invention, when the expense item is selected on the applicant expense recording status screen or the expense recording status screen for all other applicants, the screen display means further includes the applicant for which an anomaly is detected. And a transition comparison screen that enables comparison of the time-series transition of the expense amount of the evaluation target of the expense item of all other applicants.

[0009] Further, in the accounting anomaly visualization apparatus according to the present invention, the expense data is further associated and set with an approver, and when the expense item is selected on the applicant expense recording status screen, the screen display means further includes the applicant for which an anomaly is detected. It is characterized by displaying an approval record screen that enables confirmation of the expense amount of the evaluation target of the expense item by approver.

[0010] Further, in the accounting anomaly visualization device according to the present invention, the expense data is further set with an applicant comment associated therewith, and the screen display means further displays a detailed list screen in which the expense amount of the evaluation target of the applicant detected as abnormal, the application date, the item of expense, the approver, and the applicant comment are set in association with each other based on the expense data.

[0011] Further, in the accounting anomaly visualization device according to the present invention, the evaluation frequency data is further set with the expected frequency of each leading digit number, and the screen display means further displays an expected frequency comparison screen that enables comparison of the observed frequency of each leading digit number of the expense amount of the evaluation target of the applicant detected as abnormal, the observed frequency of each leading digit number of the expense amount of the evaluation target of all other applicants, and the expected frequency of each leading digit number based on the applicant determination result.

[0012] Further, in the accounting anomaly visualization device according to the present invention, the determination result acquisition means acquires the evaluation data including the expense data and the evaluation frequency data in which the observed frequency of each leading digit number of the expense amount of the evaluation target and the expected frequency of each leading digit number are set, and based on the evaluation data, sets the expected frequency of each leading digit number of the expense amount of the evaluation target and the degree of deviation between the observed frequency of each leading digit number of the expense amount of the evaluation target for each applicant and the expected frequency to obtain an anomaly determination target data set associated therewith, and obtains the test statistic and / or p-value of each leading digit number of the expense amount of the evaluation target for each applicant by the statistical hypothesis test for the anomaly determination target data set, thereby obtaining the applicant determination result for each applicant with respect to the evaluation data.

[0013] Further, in the accounting anomaly visualization device according to the present invention, the expected frequency is the Benford expected frequency.

[0014] Also, in the accounting anomaly visualization device according to the present invention, the storage unit includes an anomaly determination definition master in which a determination item indicating the type of the expense amount of the evaluation target, a Benford's law non-use flag indicating whether to use Benford's law for anomaly determination, a determination range indicating the period for defining the evaluation target, and a significance level are associated; and a Benford's law theoretical value master in which the leading digit number and the Benford's law expected frequency are associated. The determination result acquisition means acquires the evaluation data based on the anomaly determination definition master and the Benford's law theoretical value master, and acquires the applicant determination result for each applicant by the statistical hypothesis test on the evaluation data.

[0015] Also, in the accounting anomaly visualization device according to the present invention, when an instruction to change the aggregation unit from the applicant unit to the organization unit is set, the determination result acquisition means further acquires the organization determination result for each organization by the statistical hypothesis test on the evaluation data including the expense data in which the expense amount of the evaluation target, the expense item, the organization, the applicant, and the application date are associated, and the evaluation frequency data in which the observed frequency of each leading digit number of the expense amount of the evaluation target is set. The screen display means further displays an organization expense recording status screen that enables confirmation of the composition ratio of each expense item in the observed frequency of each leading digit number of the expense amount of the evaluation target of the organization in which the anomaly is detected, based on the organization determination result.

[0016] In addition, the accounting anomaly visualization method according to the present invention is an accounting anomaly visualization method for causing an accounting anomaly visualization device including a storage unit and a control unit to execute. The method includes a determination result acquisition step of acquiring, by means of a statistical hypothesis test, an applicant determination result for each applicant with respect to evaluation data including expense data in which an expense amount, an item of expense, an applicant, and an application date of an evaluation target are associated and set, and evaluation frequency data in which the observed frequencies of the leading digit numbers of the expense amounts of the evaluation target are set; and a screen display step of displaying an applicant expense recording status screen that enables confirmation of the composition ratio of each item of expense in the observed frequencies of the leading digit numbers of the expense amounts of the evaluation target of the applicant in which an anomaly is detected, based on the applicant determination result.

[0017] In addition, the accounting anomaly visualization program according to the present invention is an accounting anomaly visualization program for causing an accounting anomaly visualization device including a storage unit and a control unit to execute. In the control unit, the program causes execution of a determination result acquisition step of acquiring, by means of a statistical hypothesis test, an applicant determination result for each applicant with respect to evaluation data including expense data in which an expense amount, an item of expense, an applicant, and an application date of an evaluation target are associated and set, and evaluation frequency data in which the observed frequencies of the leading digit numbers of the expense amounts of the evaluation target are set; and a screen display step of displaying an applicant expense recording status screen that enables confirmation of the composition ratio of each item of expense in the observed frequencies of the leading digit numbers of the expense amounts of the evaluation target of the applicant in which an anomaly is detected, based on the applicant determination result.

Advantages of the Invention

[0018] According to the present invention, from the monetary tendency anomalies, it is possible to capture expense anomalies in a large range, smoothly execute the analysis of "employees, organizations, expense items, approvers, application comments, etc." where the characteristics of fraud and anomalies are likely to appear, and quickly detect even the hardly noticeable anomalies and lead them to analysis. Further, according to the present invention, it is possible to automatically detect and notify data registered fraudulently within the transaction data. Further, according to the present invention, it is possible to capture and detect the tendency anomalies in the amount of expenses accounted for in the past. Further, according to the present invention, it is effective that a person in charge without statistical, analytical, and business knowledge can detect expense anomalies and improve them early. Further, according to the present invention, even a person in charge without specialized knowledge can capture the tendency related to fraud on one screen and quickly detect anomalies. Further, according to the present invention, it is possible to quickly confirm at once the range of abnormal expenses and the approval information that seems suspicious, and it becomes possible to quickly confirm from the viewpoints such as whether the operation is a problem, whether the approver is a problem, or whether the content of the applied expenses is a problem, so that regular checks are also facilitated. Further, according to the present invention, since it is possible to grasp the amount pattern that seems abnormal, it is possible to list up and confirm the associated vouchers, and since the target is narrowed down, subsequent tendency analysis of anomalies becomes easy, and it is possible to quickly lead to fraud confirmation. Further, according to the present invention, it is effective that the confirmation can be started by limiting the target to the range where anomalies are likely to be included. Further, according to the present invention, from the viewpoint that "monetary fraud may be characterized by the amount", based on the monetary tendency analysis, it is possible to pick up and notify the tendency that seems abnormal, prompt the user to conduct an investigation, and assist until the fraud is detected.

Brief Description of the Drawings

[0019]

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Embodiments for Carrying Out the Invention

[0020] Embodiments of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited by these embodiments.

[0021] [1. Overview] First, the overview of the present invention will be described.

[0022] Conventionally, with the revision of corporate internal control standards, the demand for control strengthening and monitoring has been increasing. Fraud in corporate management still shows an increasing trend, and the aging and replacement of personnel have also occurred. Even personnel without skills need to check data. Also, conventionally, there has been an actual situation where expense fraud has occurred. In particular, as fraud that can be committed by individuals, fraud involving falsification of expense amounts has actually been carried out. Also, conventionally, in the approval operation as a preventive measure, fraud in collusion with approvers has also occurred, and it is difficult to say that it is sufficient for prevention. There has been an actual situation where fraud due to amount falsification has passed through.

[0023] Here, conventionally, regarding signs of financial fraud, there has been a problem (1) that it is difficult to determine whether there is an abnormality by simply checking on a single voucher, and a comprehensive judgment is required after analyzing by the characteristics of various expenses. That is, conventionally, there are multiple expense vouchers, and in many cases, approval operations are carried out for fraud prevention. Therefore, in order to conduct an abnormality investigation, it is necessary to check a large amount of expense data, and it may be necessary to conduct an investigation while associating information outside the scope of a single voucher. Conventionally, information related to abnormalities and fraud has appeared in points such as "a sudden increase in the expense amount in expense items that are easily misused (rarely occurring items or items without detailed descriptions: entertainment and hospitality expenses, etc.)", "occurrence of expense items that have increased abnormally compared to the past in terms of the occurrence ratio for each expense item", "an increase in the approval performance of some approvers due to the occurrence of an operation of approving fraudulent vouchers in collusion with some approvers", "an increase in vouchers with unclear application reasons", or "multiple vouchers with the same date or content being recorded".

[0024] Therefore, in the present embodiment, by using the anomaly detection based on Benford's law, those that seem to be abnormal in terms of amount are picked up, suspicious vouchers are grasped and visualized on a list basis, and the approver information that has approved the detected expenses is also visualized together, so that it is possible to confirm together whether there is any problem in approval, and by visualizing the occurrence ratio of the expense items used, a mechanism is provided that enables confirmation of expense items that may be misused.

[0025] In addition, conventionally, fraud due to falsification of amounts has been difficult to detect from evidence such as receipts, and there has been a problem (2) that it is difficult to detect signs of fraud from simple amount confirmation because there are many amounts and digit numbers with different amount patterns for the expenses to be falsified. That is, conventionally, expense applications have generally been operated by attaching evidence such as receipts, so there are almost no cases of simply falsifying the application amount alone, and since it has been falsified together with the evidence, in order to confirm whether it is abnormal, it is necessary to analyze and investigate features other than the evidence, and what to check varies depending on the case, making it difficult to determine the investigation method and the investigation is difficult.

[0026] Therefore, in the present embodiment, by using Benford's theory defined as "natural numbers in the world have a certain occurrence frequency", for the leading digit of the amount of expense information that shows a natural occurrence tendency and is not much affected by business or industry in terms of amount pattern, since it most often shows the most natural occurrence tendency, by using this tendency, based on the amount of the voucher for which the expense has been applied, Benford anomaly detection is executed based on the occurrence frequency of the leading digit of the amount, and the points with different frequencies are picked up as anomalies and led to a detailed investigation.

[0027] In addition, conventionally, there has been a problem (3) that it is difficult to determine which axis to check the expense information for abnormality judgment and to select the optimal analysis perspective for grasping the abnormality. That is, conventionally, as in problem (2), it is difficult to detect an abnormality in expenses. For example, when there is a possibility that the expense amount is abnormal, it is difficult to determine from which perspective the investigation should be started, and it tends to be a vague investigation such as looking at the expense details first and seeing who recorded and who approved it. Since the number of expense vouchers can be extremely large for a company, there are many strict cases in such a vague investigation.

[0028] Therefore, in the present embodiment, by the measure of problem (2), information on the leading digit of the amount that seems to be abnormal is retained and used for analysis, and the analysis target is narrowed down to the expense information corresponding to the retained leading digit of the amount, providing a mechanism that enables the relevant information to be analyzed.

[0029] [2. Configuration] An example of the configuration of the accounting abnormality visualization device 100 according to the present embodiment will be described with reference to FIGS. 1 to 6. FIG. 1 is a block diagram showing an example of the configuration of the accounting abnormality visualization device 100 in the present embodiment.

[0030] As shown in FIG. 1, the accounting abnormality visualization device 100 is a commercially available desktop personal computer. Note that the accounting abnormality visualization device 100 is not limited to a stationary information processing device such as a desktop personal computer, and may be a portable information processing device such as a commercially available notebook personal computer, a PDA (Personal Digital Assistants), a smartphone, or a tablet personal computer.

[0031] The accounting abnormality visualization device 100 includes a control unit 102, a communication interface unit 104, a storage unit 106, and an input / output interface unit 108. Each unit included in the accounting abnormality visualization device 100 is communicably connected via an arbitrary communication path.

[0032] The communication interface unit 104 communicably connects the accounting anomaly visualization device 100 to the network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 has a function of communicating data with other devices via a communication line. Here, the network 300 has a function of communicably connecting the accounting anomaly visualization device 100 and the server 200 to each other, and is, for example, the Internet or a LAN (Local Area Network).

[0033] An input device 112 and an output device 114 are connected to the input / output interface unit 108. As the output device 114, in addition to a monitor (including a touch panel), a speaker or a printer can be used. As the input device 112, in addition to a keyboard, a mouse, and a microphone, a monitor that cooperates with the mouse to realize a pointing device function can be used. In the following, the output device 114 may be described as the monitor 114 or the printer 114, and the input device 112 may be described as the keyboard 112 or the mouse 112.

[0034] Various databases, tables, files, etc. are stored in the storage unit 106. A computer program for giving instructions to the CPU (Central Processing Unit) in cooperation with the OS (Operating System) to perform various processes is recorded in the storage unit 106. As the storage unit 106, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, and an optical disk can be used. The storage unit 106 includes an accounting database 106a, a definition master for anomaly determination 106b, and a Benford's law value master 106c.

[0035] The accounting database 106a stores accounting data including expense data (business data). Here, the accounting database 106a may store evaluation data including expense data in which the expense amount, expense item, applicant, and application date to be evaluated are associated and set, and evaluation frequency data in which the observed frequency of each leading digit number of the expense amount to be evaluated is set. Here, the expense data may be associated and set by the approver. Also, the expense data may be associated and set with the applicant comment. Also, the evaluation frequency data may have the expected frequency of each leading digit number set. Here, the expected frequency may be the Benford expected frequency. Also, the accounting database 106a may store the determination result (abnormality determination result) for the evaluation data. Also, the accounting database 106a may store the determination result message.

[0036] Here, with reference to FIG. 2, an example of the expense data in the present embodiment will be described. FIG. 2 is a diagram showing an example of the expense data in the present embodiment.

[0037] As shown in FIG. 2, the expense data in the present embodiment is held in an expense data table that manages the expense data entered by the employee. When it is necessary to obtain teacher data to calculate the expected frequency, past data is referred to based on the application date, and information on the organization to which the employee belongs is held regarding the information of the business office / department.

[0038] Also, with reference to FIG. 3, an example of the abnormality determination result in the present embodiment will be described. FIG. 3 is a diagram showing an example of the abnormality determination result in the present embodiment.

[0039] As shown in FIG. 3, the abnormality determination result in the present embodiment is held in an abnormality determination result table that manages the results of the abnormality determination.

[0040] Also, with reference to FIG. 4, an example of the abnormality determination result message in the present embodiment will be described. FIG. 4 is a diagram showing an example of the abnormality determination result message in the present embodiment.

[0041] As shown in FIG. 4, the abnormality determination result message in the present embodiment is held in an abnormality determination result message table that manages information obtained by linguistically processing the abnormality determination result so that it can be easily understood by humans.

[0042] Returning to FIG. 1, the definition master 106b for abnormality determination is a master in which a determination item indicating the type of the expense amount to be evaluated, a Benford's law non-use flag indicating whether or not to use Benford's law for abnormality determination, a determination range indicating the period for defining the evaluation target, and a significance level are associated and set.

[0043] Here, with reference to FIG. 5, an example of the definition master 106b for abnormality determination in the present embodiment will be described. FIG. 5 is a diagram showing an example of the definition master 106b for abnormality determination in the present embodiment.

[0044] As shown in FIG. 5, the definition master 106b for abnormality determination in the present embodiment is held as a definition master table for abnormality determination that manages the pre-defined information required when executing abnormality determination. Here, as shown in FIG. 5, the size of the pre-information set in the definition master table for abnormality determination is an item for setting the weighting as information on the theoretical value (Benford's theoretical value) according to Benford's law. For example, in the present embodiment, when the pre-information size = 10 and the number of teacher data = 10, the Benford's theoretical value and the information on the teacher data are determined at a ratio of 1:1, and when the pre-information size = 10 and the number of teacher data = 10,000, the Benford's theoretical value and the information on the teacher data are determined at a ratio of 1:1,000.

[0045] Returning to FIG. 1, the Benford's theoretical value master 106c is a master in which the leading digit number and the Benford's expected frequency are associated and set.

[0046] Here, with reference to FIG. 6, an example of the Benford's theoretical value master 106c in the present embodiment will be described. FIG. 6 is a diagram showing an example of the Benford's theoretical value master 106c in the present embodiment.

[0047] As shown in FIG. 6, the Benford's law theoretical value master 106c in the present embodiment is held as a Benford's law theoretical value master data table that manages theoretical values conforming to Benford's law.

[0048] Returning to FIG. 1, the control unit 102 is a CPU or the like that comprehensively controls the accounting anomaly visualization device 100. The control unit 102 has an internal memory for storing control programs such as an OS, programs defining various processing procedures, and required data, and executes various information processes based on these stored programs. Functionally conceptually, the control unit 102 includes a determination result acquisition unit 102a and a screen display unit 102b.

[0049] The determination result acquisition unit 102a acquires the determination result (abnormality determination result) for the evaluation data. Here, the determination result acquisition unit 102a may acquire the determination result for each organization (for example, company, subsidiary, office, or department, etc.) or for each applicant (employee) with respect to the evaluation data. Further, the determination result acquisition unit 102a may acquire the determination result for each organization or for each applicant by means of a statistical hypothesis test with respect to the evaluation data including the expense data in which the expense amount, expense item, applicant, and application date of the evaluation target are associated and set, and the evaluation frequency data in which the observed frequency of each leading digit number of the expense amount of the evaluation target is set. Further, the determination result acquisition unit 102a acquires the evaluation data including the expense data and the evaluation frequency data in which the observed frequency and the expected frequency of each leading digit number of the expense amount of the evaluation target are set, and based on the evaluation data, associates and sets the expected frequency of each leading digit number of the expense amount of the evaluation target, and the degree of deviation between the observed frequency and the expected frequency of each leading digit number of the expense amount of the evaluation target for each organization or for each applicant to obtain an abnormality determination target data set, and obtains the test statistic and / or p-value of each leading digit number of the expense amount of the evaluation target for each organization or for each applicant by means of a statistical hypothesis test for the abnormality determination target data set, thereby acquiring the determination result for each organization or for each applicant with respect to the evaluation data. Further, the determination result acquisition unit 102a may acquire the evaluation data based on the abnormality determination definition master 106b and the Benford theory value master 106c, and acquire the determination result for each organization or for each applicant by means of a statistical hypothesis test with respect to the evaluation data. Further, when an instruction to change the aggregation unit from the applicant unit to the organization unit is set, the determination result acquisition unit 102a may acquire the organization determination result for each organization by means of a statistical hypothesis test with respect to the evaluation data including the expense data in which the expense amount, expense item, organization, applicant, and application date of the evaluation target are associated and set, and the evaluation frequency data in which the observed frequency of each leading digit number of the expense amount of the evaluation target is set. Further, the determination result acquisition unit 102a may acquire a determination result message based on the determination result. Further, the determination result acquisition unit 102a may register the determination result and the determination result message in the accounting database 106a.

[0050] The screen display unit 102b displays an analysis screen. Here, the analysis screen may include an applicant expense accounting status screen, an organization expense accounting status screen, an other all applicants' expense accounting status screen, an other all organizations' expense accounting status screen, a trend comparison screen, an approval achievement screen, a detail list screen, and / or an expected frequency comparison screen, etc. Also, based on the determination result, the screen display unit 102b may display an applicant expense accounting status screen that enables confirmation of the composition ratio of each item of expense in the observed frequency of the leading digit of the expense amount of the evaluation target of the applicant in which an anomaly is detected. Further, based on the determination result, the screen display unit 102b may display an other all applicants' expense accounting status screen that enables confirmation of the composition ratio of each item of expense in the observed frequency of the leading digit of the expense amount of the evaluation target of all other applicants other than the applicant in which an anomaly is detected. Also, when an item of expense is selected on the applicant expense accounting status screen or the other all applicants' expense accounting status screen, the screen display unit 102b may display a trend comparison screen that enables comparison of the time-series trend of the expense amount of the evaluation target of the item of expense between the applicant in which an anomaly is detected and all other applicants (or organizations). Also, when an item of expense is selected on the applicant expense accounting status screen, the screen display unit 102b may display an approval achievement screen that enables confirmation of the expense amount of the evaluation target of the item of expense of the applicant in which an anomaly is detected by approver. Also, based on the expense data, the screen display unit 102b may display a detail list screen in which the expense amount, application date, item of expense, approver, and applicant comment of the evaluation target of the applicant in which an anomaly is detected are linked and set. Also, based on the determination result, the screen display unit 102b may display an expected frequency comparison screen that enables comparison of the observed frequency of the leading digit of the expense amount of the evaluation target of the applicant in which an anomaly is detected, the observed frequency of the leading digit of the expense amount of the evaluation target of all other applicants, and the expected frequency of each leading digit. Also, based on the organization determination result, the screen display unit 102b may display an organization expense accounting status screen that enables confirmation of the composition ratio of each item of expense in the observed frequency of the leading digit of the expense amount of the evaluation target of the organization in which an anomaly is detected.

[0051] [3. Specific Example] A specific example of this embodiment will be described with reference to FIGS. 7 to 36.

[0052] [Accounting anomaly visualization process] Here, with reference to FIG. 7, an example of the accounting anomaly visualization process in the present embodiment will be described. FIG. 7 is a flowchart showing an example of the process of the accounting anomaly visualization device 100 in the present embodiment.

[0053] As shown in FIG. 7, the determination result acquisition unit 102a acquires evaluation data including expense data in which the expense amount, expense item, organization, applicant, and application date of the evaluation target are associated and set, and evaluation frequency data in which the observed frequency and expected frequency of each leading digit number of the expense amount of the evaluation target are set. Based on the evaluation data, the determination result acquisition unit 102a acquires an abnormal determination target data set in which the expected frequency of each leading digit number of the expense amount of the evaluation target and the degree of deviation between the observed frequency and the expected frequency of each leading digit number of the expense amount of the evaluation target for each applicant are associated and set. By performing a statistical hypothesis test on the abnormal determination target data set, the determination result acquisition unit 102a acquires the test statistic and / or p-value of each leading digit number of the expense amount of the evaluation target for each applicant, thereby obtaining the determination result for each applicant with respect to the evaluation data (step SA-1).

[0054] Then, based on the determination result, the screen display unit 102b causes the output device 114 to display an analysis screen including an applicant expense posting status screen in which the composition ratio of each expense item can be confirmed in the observed frequency of each leading digit number of the expense amount of the evaluation target of the applicant detected as abnormal (step SA-2).

[0055] Then, the determination result acquisition unit 102a determines whether a change instruction for the aggregation unit (from the applicant unit to the organization unit or from the organization unit to another organization unit) is set on the analysis method specification screen via the input device 112 by the user (step SA-3).

[0056] If the determination result acquisition unit 102a determines that the aggregation unit change instruction is set (step SA-3: Yes), the process proceeds to step SA-4.

[0057] Then, the determination result acquisition unit 102a acquires evaluation data including expense data in which the expense amount, item, organization, applicant, and application date of the evaluation target are associated and set, and evaluation frequency data in which the observed frequency and expected frequency of each leading digit of the expense amount of the evaluation target are set. Based on the evaluation data, an abnormal determination target data set is acquired in which the expected frequency of each leading digit of the expense amount of the evaluation target and the degree of deviation between the observed frequency and the expected frequency of each leading digit of the expense amount of the evaluation target for each organization are associated and set. By performing a statistical hypothesis test on the abnormal determination target data set, the test statistic and / or p-value of each leading digit of the expense amount of the evaluation target for each organization are obtained, and thus the determination result for each organization with respect to the evaluation data is obtained (step SA-4).

[0058] Then, based on the determination result, the screen display unit 102b causes the output device 114 to display an analysis screen including an organizational expense recording status screen that enables confirmation of the composition ratio of each item in the observed frequency of each leading digit of the expense amount of the evaluation target of the abnormally detected organization (step SA-5), and shifts the process to step SA-3.

[0059] On the other hand, when the determination result acquisition unit 102a determines that the aggregation unit change instruction is not set (step SA-3: No), the process ends.

[0060] Here, with reference to FIGS. 8 to 32, an example of the accounting anomaly visualization process in the present embodiment will be described. FIGS. 8 to 27 are diagrams showing an example of the accounting anomaly visualization process in the present embodiment. FIG. 28 is a diagram showing an example of an expected frequency comparison screen in the present embodiment. FIG. 29 is a diagram showing an example of an expense recording status screen in the present embodiment. FIG. 30 is a diagram showing an example of a trend comparison screen in the present embodiment. FIG. 31 is a diagram showing an example of an approval record screen in the present embodiment. FIG. 32 is a diagram showing an example of a detail list screen in the present embodiment.

[0061] As shown in FIG. 8, in the present embodiment, [1] as the abnormal detection execution process, when a definition ID to be executed at the time of definition execution is specified, an abnormal determination definition master table used for each definition ID is acquired, and all Benford's law theoretical value master data is acquired. Here, in the present embodiment, abnormal detection is performed on a per-employee basis. However, when the group unit is changed to an organization such as a business office or a department, data is aggregated on an organizational unit basis, and an organization that seems to be abnormal is detected.

[0062] Then, as shown in FIG. 9, in the present embodiment, detection data is acquired based on the information registered in the abnormal determination definition master table. Here, as shown in FIG. 9, in the present embodiment, based on the previous month of the day when the abnormal determination was executed, the abnormal determination definition master table is compared with the expense data table, and data is extracted according to the setting of the range target and the determination range of the abnormal determination definition master table.

[0063] Then, as shown in FIG. 10, in the present embodiment, from the data obtained in "2", the column of the determination items registered in the abnormal determination definition master table is referred to, the appearance frequency of the value of the first digit of the amount is calculated, and the calculation result is held as evaluation data.

[0064] Then, as shown in FIG. 11, in the present embodiment, referring to the setting value of the Benford's law non-use FLG in the abnormal determination definition master table, an abnormal determination is executed using the data calculated in "3", and abnormal data is detected. Here, as shown in FIG. 11, in the present embodiment, for the use of Benford's law, the Benford's law theoretical value master data is used as it is.

[0065] Then, as shown in FIG. 12, in the present embodiment, since the set value of the significance level in the definition master table for abnormality determination is 5%, the threshold value for normal determination is 15.5. Since the total deviation (chi-square estimated value) exceeds the threshold value, it is determined as abnormal. In order to pick up the leading digit number determined as abnormal, the test statistic is calculated for each leading digit number. Here, as shown in FIG. 12, in the present embodiment, in the case of a significance level of 5%, those with a p-value less than 0.05 can be determined as abnormal. Therefore, the numbers 1 and 9 are determined as abnormal (P(|z|≧1.96)=0.05).

[0066] Then, as shown in FIG. 13, in the present embodiment, the table is updated based on the result of the abnormality determination. When there is data determined as abnormal, message data verbalized for easy human understanding is generated and the table is updated. Note that, as shown in FIG. 13, in the present embodiment, when the group unit in the definition master table for abnormality determination is an organization, the abnormality determination is made for each organization unit.

[0067] Then, as shown in FIG. 14, in the present embodiment, as the [2] analysis screen output process, the data in the abnormality determination result table and the abnormality determination result message table are acquired, and the "chi-square estimated value" and the "chi-square threshold value" are replaced with the "statistical test quantity" and the "normal reference value", which are more conceptually understandable words, and are displayed in a list on the screen.

[0068] Then, as shown in FIG. 15, in the present embodiment, when the message on the screen is clicked, the analysis screen is transitioned based on the abnormality determination result data associated with the clicked message.

[0069] Then, as shown in FIG. 16, in the present embodiment, in this scenario, since six charts are used in total at the time of analysis, the screen with the printing areas for six charts secured is redrawn for analysis.

[0070] Then, as shown in FIG. 17, in this embodiment, the aggregation unit corresponding to the (initial setting) group unit of the abnormality determination definition master table is in a selected state (when the aggregation unit is selected on the analysis method specification screen by the user, the said aggregation unit is in a selected state), data necessary for analysis is acquired, and the display range of the accounting year and month is set based on the determination range of the abnormality determination definition master table as the initial display (for example, when executed on 2023 / 04 / 05, the determination range: 2022 / 04 to 2023 / 03 is set as the initial value of the accounting year and month From to To). Here, in this embodiment, when values are set for the office / department / employee of the extraction conditions, extraction is performed when acquiring data.

[0071] Then, as shown in FIGS. 18 and 19, in this embodiment, based on the abnormality determination results obtained in "1", data is extracted respectively to analyze the differences between the employees with detected abnormalities and all other employees, and chart data is acquired (when the aggregation unit (employee or organization) is selected on the analysis method specification screen by the user, based on the abnormality determination results obtained in "1", data is extracted respectively to analyze the differences between the employees or organizations with detected abnormalities and all other employees or organizations, and chart data is acquired). Here, as shown in FIG. 18, in Chart (1), data of employees other than the employee (Employee A) with detected abnormalities is aggregated, and the value divided by the number of target employees is calculated as "Total". Also, as shown in Chart (2) of FIG. 18, in this embodiment, the achievements of employees other than the employee (Employee A) with detected abnormalities are the acquisition targets, and in the situation without expense item selection, they are sorted and acquired in ascending order of expense item CD. Also, as shown in Chart (3) of FIG. 18, in this embodiment, the achievements of the employee (Employee A) with detected abnormalities are the acquisition targets, and in the situation without expense item selection, they are sorted and acquired in ascending order of expense item CD. Also, as shown in FIG. 19, in this embodiment, since it is displayed after an expense item is selected in Chart (2) or Chart (3), no data acquisition is performed for Chart (4) and Chart (5).

[0072] Then, as shown in FIG. 20, in the present embodiment, based on the abnormality determination result obtained in "1", the employees and the leading digit of the amount for which an abnormality was detected are emphasized on the chart. Here, as shown in FIG. 20, in the present embodiment, since it is displayed after the expense item is selected in Chart (2) or Chart (3), there is no chart output for Chart (4) and Chart (5). As shown in FIG. 21, in the present embodiment, when the aggregation unit is changed on the analysis method specification screen by the user, the data is acquired by switching the part obtained in employee units to the selected aggregation unit.

[0073] Then, in the present embodiment, as shown in FIG. 22, on the screen where the printing area for six charts shown in FIG. 23 is secured, the chart drawing data obtained in "3" is superimposed and the initial display is completed.

[0074] Then, as shown in FIG. 24, in the present embodiment, when the expense item (accommodation expense) output in Chart (2) or Chart (3) is selected, the chart drawing data for Chart (2) and Chart (3) is acquired with the accommodation expense as the selected expense item. Here, the expense item selection can be done by selecting from the legend or by selecting from the corresponding item on the bar graph. As shown in FIG. 24, in the present embodiment, it is selected from the legend, but if it is clicked on the bar graph, the corresponding expense item will be selected in the same way. Also, as shown in Chart (5) of FIG. 25, in the present embodiment, the total amount of the settlement amount of the employee (Employee A) for whom an abnormality was detected from April 2022 to March 2023 is the acquisition target. Here, in Chart (5) of FIG. 25, the approved amount for each approver (the person who approved the application expenses of Employee A) is stored, and the approved amount of "Employee A" shows the case of "self-approval".

[0075] And in this embodiment, as shown in FIG. 26, charts (2)-(5) are connected, and as shown in FIG. 27, charts (2)-(5) are superimposed and displayed on the analysis screen. Here, as shown in FIG. 27, in the analysis method specification screen in this embodiment, from the selection of the aggregation unit, for charts (1) and (3)-(6), it is possible to switch to the tendency of the organizational unit of the office / department for analysis. That is, in this embodiment, by switching the aggregation unit, it is possible to switch the analysis axis between the tendency analysis for each employee and the tendency analysis for each organization. For chart (1), it can be switched to the unit selected as the object to be compared with the expected frequency and the whole, and output (for example, when the unit of the head office: Tokyo Head Office is selected, the expected frequency and the whole of employee A and the Tokyo Head Office can be compared). For charts (3)-(6), it can be switched to the unit selected as the output target unit and output (for example, for chart (3), when the unit of the head office: Tokyo Head Office is selected, the performance of the Tokyo Head Office from April 2022 to March 2023 can be output).

[0076] Here, as shown in FIG. 28, in chart (1) in this embodiment, the expected frequency, the expense tendency of the whole company, and the expense tendency of one employee can be visualized. By arranging the values of the leading digit of the expense amount on the X-axis side, the tendency by value can be visualized. Therefore, it is possible to detect a location with a large tendency difference as an exception deviating from the general occurrence tendency and the occurrence tendency of the whole company. Here, as shown in FIG. 28, in this embodiment, by paying attention to the leading digit number of the amount determined to be abnormal for the detected employee (employee A), it is possible to confirm that the detected number is clearly large or small. That is, as shown in FIG. 28, in this embodiment, when checking the whole except employee A, it can be seen that the data of employee A is clearly heterogeneous from the point that it is almost similar to the expected frequency.

[0077] Also, as shown in FIG. 29, in the chart (2) in this embodiment, except for the employees with anomalies detected, for the composition ratio with the total being 100% by the value of the first digit of the amount of the expenses accounted for across the entire company, the ratio of how much each expense item occurs can be visualized. Therefore, by checking the expense item ratio of the value for which a tendency anomaly was confirmed in chart (1), it is possible to focus on and analyze from the expense items with high and low ratios. Also, as shown in FIG. 29, in the chart (3) in this embodiment, for the composition ratio with the total being 100% by the value of the first digit of the amount of the expenses accounted for by the employees with anomalies detected, the ratio of how much each expense item occurs can be visualized. Therefore, by checking the expense item ratio of the value for which a tendency anomaly was confirmed in chart (1), it is possible to focus on and analyze from the expense items with high and low ratios.

[0078] Here, as shown in FIG. 29, in this embodiment, for a specific expense item (accommodation expenses in this example), the ratio of the first digit number: 1 is large and the ratio of 9 is small across all employees. However, for the employee with anomalies detected (Employee A), the ratio of the first digit number being 9 is clearly large, and the ratio of 1 is less than that of all employees. From this, the possibility that what was originally the first digit number 1 has been rewritten to 9 emerges. Also, as shown in FIG. 29, in the chart (3) in this embodiment, compared with chart (2), it enables analysis focusing on the expense items that deviate from the overall company trend. Note that, as shown in FIG. 29, in this embodiment, when the expense items to be analyzed in charts (2) and (3) are selected, the data regarding the selected expense items are output to charts (4) and (5), and the selected expense items are represented at the bottom of the stacked bar graph (it is also possible to compare the magnitudes of the ratios of the expense items for each value of the first digit of the amount).

[0079] Also, as shown in FIG. 30, in the chart (4) in the present embodiment, after a person determines and confirms the expense items that may be abnormal in the charts (2) and (3), when the expense item is selected by clicking on the legend in the chart (2) or (3), the time-series change of the actual expenses related to the selected expense item can be visualized, and it is possible to confirm how the detected employee compares with the overall average of other employees excluding the detected employee (for example, whether there are characteristics of high or low levels, or whether there are sudden changes in the change). Here, as shown in FIG. 30, in the present embodiment, since the amount of the detected employee (Employee A) is clearly at a high level compared to other employees, it is possible to confirm the situation where high expenses are continuously being used.

[0080] Also, as shown in FIG. 31, in the chart (5) in the present embodiment, it is possible to confirm the actual performance of each employee who approved the expenses related to the chart (4). Since it is common for expenses to be sandwiched between basic approval operations, if illegal or abnormal data occurs, there may be a problem with the approval operation. If the approver is the detected employee himself / herself, there may be a possibility of fraud due to self-approval. Here, as shown in FIG. 31, in the present embodiment, since it is possible to visualize the employees who have approved the expenses of the detected employee (Employee A) in a biased manner, if a specific employee has approved, it is possible to confirm the possibility that an employee colluding with Employee A has approved. If Employee A himself / herself has approved, it is possible to confirm the possibility that Employee A is passing illegal expenses through self-approval.

[0081] Also, as shown in FIG. 32, in the chart (6) of the present embodiment, it is possible to visualize the breakdown list of expenses incurred within the period specified by the accounting year and month of the extraction conditions, and it is possible to finely visualize points that seem to be illegal or abnormal. Therefore, it is possible to confirm the possibility of irregularities or abnormalities such as the same date, frequent use of specific expense items, and the applicant's comments being abstract, ambiguous, or appropriate. Here, as shown in FIG. 32, in the present embodiment, since the possibility of "vouchers with the same date = double counting" emerges, it is possible to check other items (such as expense items, amounts, or application comments) and confirm whether there are duplicates. Also, as shown in FIG. 32, in the present embodiment, when the applicant's comment is unclear or simplistic, there is a possibility that it is not a normal expense. Therefore, from the perspective of operational safety (it is easier for the approver side to grasp the content), it is possible to check whether the correct content is clearly stated. Here, in the present embodiment, when the applicant is the same and the approver is different depending on the expense item, there may be a problem with the approval operation. For example, as shown in FIG. 32, in the present embodiment, when employee A is the accounting manager, there is a case of fraudulently recording expenses by abusing expenses that allow self-approval, that is, a case where expenses that require third-party checks due to user involvement, etc., are approved by another accounting manager member, or a case where expenses such as transportation expenses, which do not involve users, are allowed to be approved with the applicant's own accounting manager authority. It is possible to confirm fraud cases that take advantage of this environment. Also, as shown in FIG. 32, in the present embodiment, when employee A is the president of a subsidiary company, there is a case of fraud using the parts that are subject to the parent company's check and those that are not, that is, a case where expenses related to user involvement or transactions with the parent company pass through the head office accounting check, or a case where expenses such as transportation expenses, which are not directly related to users or the parent company, are entrusted to the subsidiary company officers for checking. It is possible to confirm fraud cases that take advantage of this environment.

[0082] Also, with reference to FIGS. 33 to 36, an example of the analysis switching process in the present embodiment will be described. FIGS. 33 to 36 are diagrams showing an example of the analysis switching process in the present embodiment.

[0083] In this embodiment, as shown in FIG. 33, when "department" is selected as the aggregation unit on the analysis method specification screen, the determination results for each department are obtained. Then, as shown in FIG. 34, based on the determination results, data for chart drawing of the department where an abnormality is detected (department 1) and data for chart drawing of all other departments (other departments) except the department where an abnormality is detected (department 1) are obtained. Here, as shown in FIG. 34, in this embodiment, data other than the department where an abnormality is detected (department 1) is obtained as the data for chart (2), and the data of the department where an abnormality is detected (department 1) is obtained as the data for charts (3) and (5). Note that, as shown in FIG. 34, in charts (2) to (5), the data obtained when accommodation expenses are selected as the expense item is sorted. In charts (2) to (3), the selected expense item is sorted first, and the remaining expense items are sorted in ascending order of expense CD.

[0084] Then, as shown in FIG. 35, in this embodiment, the data is bound to the chart and drawn on the screen. Here, as shown in FIG. 36, in chart (5) in this embodiment, when there is an organizational abnormality, it is possible to confirm the possibility of approval by the organizational upper management (such as the organization head). In chart (6) in this embodiment, it is possible to detect abnormal trends such as a plurality of employees using biased expenses for accounting by checking a plurality of items.

[0085] Also, with reference to FIG. 37, an example of the business usage image in this embodiment will be described. FIG. 37 is a diagram showing an example of the business usage image in this embodiment.

[0086] As shown in FIG. 37, in this embodiment, regarding the analysis in this system, as the axes to be confirmed, mainly the current deviation between the expected frequency and the actual results, and the grasping of factors can be mentioned. Taking the occurrence status of the leading digit of the amount of expenses as the axis, the analysis range is narrowed by capturing abnormal occurrence trends in terms of amount, and points where abnormal characteristics are likely to appear in the expenses are checked to find the basis for the abnormality. That is, as shown in FIG. 37, in this embodiment, in order to confirm the current deviation between the expected frequency and the actual results, a confirmation is performed by comparing the expected frequency with the actual results for each detection target (employee or organization). In order to confirm the grasping of factors, it is necessary to visually confirm the viewpoints of the usage item, approver, and voucher details, which are the parts likely to be the abnormal factors of the expenses. However, it is desirable that these information can be detected, visualized, and analyzed. In this scenario, it is a system assuming that the detection and visualization of information are performed so that these information can be confirmed.

[0087] [4. Contribution to the Sustainable Development Goals (SDGs) Led by the United Nations] According to this embodiment, it is possible to contribute to promoting business efficiency and appropriate business judgment of the enterprise, so it is possible to contribute to Goals 8 and 9 of the SDGs.

[0088] Also, according to this embodiment, it is possible to contribute to reducing waste loss and promoting paperless and digitalization, so it is possible to contribute to Goals 12, 13, and 15 of the SDGs.

[0089] Also, according to this embodiment, it is possible to contribute to strengthening control and governance, so it is possible to contribute to Goal 16 of the SDGs.

[0090] [5. Other Embodiments] The present invention may be implemented in various different embodiments within the scope of the technical idea described in the claims, in addition to the above-described embodiments.

[0091] For example, among the processes described in the embodiments, all or part of the processes described as being automatically performed can be manually performed, or all or part of the processes described as being manually performed can be automatically performed by a known method.

[0092] In addition, the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions for each process, screen examples, and database configurations shown in this specification and the drawings can be arbitrarily changed unless otherwise specified.

[0093] Regarding the accounting anomaly visualization device 100, each of the illustrated components is a functional concept and does not necessarily have to be physically configured as shown in the figure.

[0094] For example, regarding the processing functions provided by the accounting anomaly visualization device 100, particularly each processing function performed by the control unit 102, all or any part of them may be realized by a CPU and a program interpreted and executed by the CPU, or may be realized as hardware by wired logic. Note that the program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing an information processing apparatus to execute the processes described in this embodiment, and is mechanically read by the accounting anomaly visualization device 100 as necessary. That is, in a storage unit such as a ROM or an HDD (Hard Disk Drive), a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded. This computer program is executed by being loaded into the RAM and constitutes the control unit in cooperation with the CPU.

[0095] In addition, this computer program may be stored in an application program server connected to the accounting anomaly visualization device 100 via an arbitrary network, and all or part of it can be downloaded as necessary.

[0096] Also, a program for executing the processes described in this embodiment may be stored in a non-transitory computer-readable recording medium, or may be configured as a program product. Here, this "recording medium" includes any "portable physical medium" such as a memory card, a USB (Universal Serial Bus) memory, an SD (Secure Digital) card, a flexible disk, a magneto-optical disk, a ROM, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (registered trademark) (Electrically Erasable and Programmable Read Only Memory), a CD-ROM (Compact Disk Read Only Memory), an MO (Magneto-Optical disk), a DVD (Digital Versatile Disk), and a Blu-ray (registered trademark) Disc.

[0097] Also, the "program" is a data processing method described in any language or description method, and is not limited to a form such as source code or binary code. Note that the "program" is not necessarily limited to being configured singly, and also includes those that are distributedly configured as a plurality of modules or libraries, or those that achieve their functions in cooperation with another program represented by an OS. Note that for the specific configuration, reading procedure, and installation procedure after reading for reading the recording medium in each device shown in this embodiment, well-known configurations and procedures can be used.

[0098] The various databases and the like stored in the storage unit 106 are storage means such as a memory device such as a RAM or a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk, and store various programs, tables, databases, and web page files used for various processes and website provision.

[0099] Further, the accounting anomaly visualization device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which an arbitrary peripheral device is connected. Further, the accounting anomaly visualization device 100 may be realized by installing software (including programs or data, etc.) for realizing the processing described in the present embodiment in the device.

[0100] Furthermore, the specific form of the distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various additions or according to the functional load. That is, the above-described embodiments may be arbitrarily combined and implemented, or the embodiments may be selectively implemented.

Industrial Applicability

[0101] The present invention is useful in various industries that require accounting anomaly visualization.

Explanation of Signs

[0102] 100 Accounting anomaly visualization device 102 Control unit 102a Determination result acquisition unit 102b Screen display unit 104 Communication interface unit 106 Storage unit 106a Accounting database 106b Definition master for anomaly determination 106c Benford's theoretical value master 108 Input / output interface unit 112 Input device 114 Output device 200 Server 300 Network

Claims

1. An accounting anomaly visualization device comprising a memory unit and a control unit, wherein the control unit, judgment result acquisition means for acquiring, by means of a statistical hypothesis test, an applicant judgment result for each applicant with respect to evaluation data including expense data in which an expense amount, item, applicant, and application date to be evaluated are associated and set, and evaluation frequency data in which the observed frequencies of the leading digit numbers of each of the expense amounts to be evaluated are set; screen display means for displaying an applicant expense recording status screen that enables confirmation of the composition ratio of each item in the observed frequencies of the leading digit numbers of the expense amounts to be evaluated for the applicant for whom an anomaly has been detected, based on the applicant judgment result; An accounting anomaly visualization device, characterized by comprising the above.

2. The screen display means, further, based on the applicant judgment result, displays an expense recording status screen for all other applicants that enables confirmation of the composition ratio of each item in the observed frequencies of the leading digit numbers of the expense amounts to be evaluated for all other applicants other than the applicant for whom an anomaly has been detected. The accounting anomaly visualization device according to claim 1, characterized by this.

3. The screen display means, further, when an item is selected on the applicant expense recording status screen or the expense recording status screen for all other applicants, displays a transition comparison screen that enables comparison of the time series transitions of the expense amounts to be evaluated for the item for the applicant for whom an anomaly has been detected and all other applicants. The accounting anomaly visualization device according to claim 2, characterized by this.

4. The expense data, further, is associated and set by an approver, The screen display means, further, when an item is selected on the applicant expense recording status screen, displays an approval performance screen that enables confirmation of the expense amounts to be evaluated for the item for the applicant for whom an anomaly has been detected by approver. The accounting anomaly visualization device according to claim 1, characterized by this.

5. The expense data, further, is associated and set with an applicant comment, The screen display means, further, based on the expense data, displays a detailed list screen in which the expense amount to be evaluated, the application date, the item, the approver, and the applicant comment for the applicant for whom an anomaly has been detected are associated and set. The accounting anomaly visualization device according to claim 4, characterized by this.

6. The evaluation frequency data, further, the expected frequencies of the respective leading digit numbers are set, The screen display means, Furthermore, based on the applicant determination result, a frequency comparison screen is displayed that enables comparison of the observed frequencies of the leading digit numbers of the expense amounts of the evaluation target of the abnormally detected applicant, the observed frequencies of the leading digit numbers of the expense amounts of the evaluation target of all other applicants, and the expected frequencies of the leading digit numbers. The accounting anomaly visualization device according to claim 2, characterized in that.

7. The determination result acquisition means acquires the expense data and the evaluation data including the evaluation frequency data in which the observed frequencies of the leading digit numbers of the expense amounts of the evaluation target and the expected frequencies of the leading digit numbers are set. Based on the evaluation data, the expected frequencies of the leading digit numbers of the expense amounts of the evaluation target and the deviation degrees between the observed frequencies and the expected frequencies of the leading digit numbers of the expense amounts of the evaluation target for each applicant are associated and set to obtain an anomaly determination target data set. By obtaining the test statistic and / or p-value of the leading digit numbers of the expense amounts of the evaluation target for each applicant through the statistical hypothesis test on the anomaly determination target data set, the applicant determination result for each applicant with respect to the evaluation data is obtained. The accounting anomaly visualization device according to any one of claims 1 to 5, characterized in that.

8. The expected frequency is the Benford expected frequency. The accounting anomaly visualization device according to claim 7, characterized in that.

9. The storage unit includes an anomaly determination definition master in which a determination item indicating the type of the expense amount of the evaluation target, a Benford non-use flag indicating whether to use the Benford's law for anomaly determination, a determination range indicating the period for defining the evaluation target, and a significance level are associated and set, a Benford theoretical value master in which the leading digit number and the Benford expected frequency are associated and set, and is provided with The determination result acquisition means acquires the evaluation data based on the anomaly determination definition master and the Benford theoretical value master, and obtains the applicant determination result for each applicant by the statistical hypothesis test with respect to the evaluation data. The accounting anomaly visualization device according to any one of claims 1 to 6, characterized in that.

10. The determination result acquisition means Furthermore, when an instruction to change the aggregation unit from the applicant unit to the organization unit is set, the organization determination result for each organization by the statistical hypothesis test is obtained for the evaluation data including the expense amount of the evaluation target, the expense item, the organization, the applicant, and the expense data set by associating the application date, and the screen display means furthermore, based on the organization determination result, an organization expense recording status screen that enables confirmation of the composition ratio of each expense item in the observed frequency of each leading digit number of the expense amount of the evaluation target of the organization where the abnormality is detected is displayed. The accounting abnormality visualization device according to any one of claims 1 to 6.

11. An accounting abnormality visualization method for causing an accounting abnormality visualization device including a storage unit and a control unit to execute, executed in the control unit, a determination result acquisition step of obtaining an applicant determination result for each applicant by statistical hypothesis testing for evaluation data including expense data set by associating the expense amount of the evaluation target, the expense item, the applicant, and the application date, and the observed frequency of each leading digit number of the expense amount of the evaluation target, and a screen display step of displaying an applicant expense recording status screen that enables confirmation of the composition ratio of each expense item in the observed frequency of each leading digit number of the expense amount of the evaluation target of the applicant where the abnormality is detected based on the applicant determination result. An accounting abnormality visualization method characterized by including.

12. An accounting abnormality visualization program for causing an accounting abnormality visualization device including a storage unit and a control unit to execute, in the control unit, a determination result acquisition step of obtaining an applicant determination result for each applicant by statistical hypothesis testing for evaluation data including expense data set by associating the expense amount of the evaluation target, the expense item, the applicant, and the application date, and the observed frequency of each leading digit number of the expense amount of the evaluation target, and a screen display step of displaying an applicant expense recording status screen that enables confirmation of the composition ratio of each expense item in the observed frequency of each leading digit number of the expense amount of the evaluation target of the applicant where the abnormality is detected based on the applicant determination result. An accounting abnormality visualization program for causing the above to execute.

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