Cost management system, cost management method, and cost management program

Abstract

The purpose of the present invention is to provide a cost management system, a cost management method, and a cost management program that enable early detection of fraudulent manipulation of costs for loss-making construction projects at low cost and with high accuracy. The cost management system according to the present embodiment acquires cumulative construction data including the cumulative order amount and cumulative cost amount for each fiscal year and month and construction project, based on business data including order entry data having the business establishment, department, and order amount for each fiscal year and month and construction project, and construction cost entry history data having the business establishment, department, and cost amount for each fiscal year and month and construction project, and calculates the cumulative order amount.

Description

[Technical Field]

[0001] The present invention relates to a cost management system, a cost management method, and a cost management program. [Background technology]

[0002] Recently, there have been many cases of fraudulent manipulation (concealment) of costs for unprofitable construction work. Cost anomalies can only be detected by aggregating and comparing the order amount and cost amount for each construction work by month. Furthermore, since various data collection and calculation processes are required before fraudulent manipulation is discovered, there is a risk that a lot of work time will be required, and that work errors and oversights will occur. Conventional cost management systems include, for example, Patent Document 1. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-45819 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 does not describe anything about detecting fraudulent manipulation of costs for loss-making construction projects.

[0005] The present invention has been made in consideration of the above, and aims to provide a cost management system, cost management method, and cost management program that enable early detection of fraudulent manipulation of costs of loss-making construction projects at low cost and with high accuracy. [Means for solving the problem]

[0006] A cost management system equipped with a control unit, the control unit being configured to be able to access business data including order booking data having the business establishment, department, and order amount for each fiscal year / month and each construction project, and construction cost booking history data having the business establishment, department, and cost amount for each fiscal year / month and each construction project, and is characterized by having a detection means for obtaining construction cumulative data including the cumulative order amount and cumulative cost amount for each fiscal year / month and construction project based on the business data, detecting as an abnormality when the cumulative order amount is less than the cumulative cost amount and detecting the fiscal year / month and construction project, and a display control means for displaying analysis data for the detected construction project before and after the detected fiscal year / month on an analysis screen based on the business data.

[0007] Furthermore, according to one aspect of the present invention, the display control means may display a message in a specified area of ​​the analysis screen, including the construction work detected as an anomaly, the accounting year and month, the cost amount, and the detection method.

[0008] Furthermore, according to one aspect of the present invention, the display control means may display a graph in a predetermined area of ​​the analysis screen showing the monthly change in order amount and cost amount for construction work in which an abnormality has been detected based on the cumulative construction work data.

[0009] According to one aspect of the present invention, the business data further includes basic construction information data having the contract month, construction work, business establishment, department, and planned construction period, and the display control means, based on the business data, acquires cost rate data by progress rate for the department in charge of the construction work in which the abnormality was detected, including the construction period progress rate calculated using the elapsed construction month / planned construction period, and the cost rate calculated using the construction work, business establishment, department, cumulative cost amount / cumulative order amount, and may display a graph showing the trend in cost rate by construction period progress rate for each construction work in a specified area of ​​the analysis screen based on the acquired cost rate data by progress rate.

[0010] According to one aspect of the present invention, the business data includes cost adjustment data having a cost adjustment number, accounting year and month, source work, destination work, and cost amount, and the display control means, based on the business data, sets the work in which an abnormality was detected as the source work and acquires detailed cost allocation data for the destination work, including the cost adjustment number, business establishment, department, and cost amount, and for the destination work of the record with the largest cost amount in the acquired detailed cost allocation data, acquires cumulative cost allocation destination work data, including the accounting year and month, business establishment, department, cumulative order amount, and cumulative cost amount, based on the business data, and displays a graph showing the monthly order amount and cost amount trends in a specified area of ​​the analysis screen based on the acquired cumulative cost allocation destination work data.

[0011] According to one aspect of the present invention, the business data further includes basic construction information data having the contract month, construction work, business establishment, department, and planned construction period, and for the department in charge of the construction work to be allocated to that organization with the largest cost amount in the acquired cost allocation detail data, cost rate data by progress rate of the cost allocation destination organization is acquired based on the business data, including the construction period progress rate calculated using the construction month elapsed / planned construction period, and the cost rate calculated using the construction work, business establishment, department, cumulative cost amount / cumulative order amount, and a graph showing the cost rate trends by construction period progress rate for each construction work based on the acquired cost rate data by progress rate of the cost allocation destination organization is displayed in a specified area of ​​the analysis screen.

[0012] Furthermore, according to one aspect of the present invention, the business data includes basic construction information data having the contract month, construction work, business establishment, department, and planned construction period, and cost adjustment data having the cost adjustment number, accounting year and month, source construction work, destination construction work, and cost amount, and for the construction work in which the abnormality was detected, the construction work in which the abnormality was detected is treated as the source construction, and cost allocation detail data including the cost adjustment number, business establishment, department, and cost amount is obtained for the destination construction work, and a table based on the obtained cost allocation detail data is displayed in a specified area of ​​the analysis screen.

[0013] Furthermore, according to one aspect of the present invention, the analysis screen is an area for setting data extraction conditions, and includes an extraction condition area in which the aggregation unit can be specified by business establishment or department, and the display control means may switch the aggregation unit of the graph or table based on the business establishment or department specified in the extraction condition area.

[0014] In addition, in order to solve the above-mentioned problems and achieve the object, the present invention provides a cost management method executed by an information processing device equipped with a control unit, wherein the control unit is configured to be able to access business data including order accounting data having the business establishment, department, and order amount for each fiscal year / month and each construction project, and construction cost accounting history data having the business establishment, department, and cost amount for each fiscal year / month and each construction project, and the method includes a detection process executed by the control unit to acquire construction cumulative data including the accumulated order amount and accumulated cost amount for each fiscal year / month and each construction project based on the business data, detect as an abnormality when the accumulated order amount is less than the accumulated cost amount, and detect the fiscal year / month and construction project, and a display control process to display analysis data for the detected construction project before and after the detected fiscal year / month on an analysis screen based on the business data.

[0015] In addition, in order to solve the above-mentioned problems and achieve the object, the present invention is a cost management program to be executed by an information processing device equipped with a control unit, wherein the control unit is configured to be able to access business data including order accounting data having the business establishment, department, and order amount for each fiscal year / month and each construction project, and construction cost accounting history data having the business establishment, department, and cost amount for each fiscal year / month and each construction project, and the control unit is configured to execute a detection process to acquire construction cumulative data including the cumulative order amount and cumulative cost amount for each fiscal year / month and each construction project based on the business data, detect as an abnormality when the cumulative order amount is less than the cumulative cost amount, and detect the fiscal year / month and construction project, and a display control process to display analysis data for the detected construction project before and after the detected fiscal year / month on an analysis screen based on the business data. [Effects of the Invention]

[0016] The present invention has the effect of enabling early detection of fraudulent manipulation of costs of loss-making construction projects at low cost and with high accuracy. [Brief explanation of the drawings]

[0017] [Figure 1] Figure 1 is a diagram to explain the concept of loss-making construction. [Figure 2] FIG. 2 is a diagram for explaining an image of cost anomaly detection. [Figure 3] FIG. 3 is a diagram showing an example of the display of the initial analysis screen. [Figure 4] FIG. 4 is a diagram showing an example of the display of the analysis screen. [Figure 5] FIG. 5 is a block diagram showing a hardware configuration of the cost management system according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of the structure of order entry data. [Figure 7] FIG. 7 is a diagram showing an example of the configuration of construction cost accounting history data. [Figure 8] FIG. 8 is a diagram illustrating an example of the configuration of cost adjustment data. [Figure 9] FIG. 9 is a diagram showing an example of the configuration of the construction basic information data. [Figure 10] FIG. 10 is a diagram illustrating an example of the configuration of the automatic detection execution schedule data. [Figure 11] FIG. 11 is a diagram showing an example of the configuration of acquisition range condition data for construction data. [Figure 12] FIG. 12 is a diagram showing an example of the structure of the abnormality determination result data. [Figure 13] FIG. 13 is a diagram showing an example of the structure of the abnormality determination result message data. [Figure 14] FIG. 14 is a diagram showing an example of the structure of detailed data of an abnormality determination result message. [Figure 15] FIG. 15 is a flowchart for explaining an outline of the overall processing of the control unit of the cost management system of this embodiment. [Figure 16]FIG. 16 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 17] FIG. 17 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 18] FIG. 18 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 19] FIG. 19 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 20] FIG. 20 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 21] FIG. 21 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 22] FIG. 22 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 23] FIG. 23 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 24] FIG. 24 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 25] FIG. 25 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 26] FIG. 26 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 27] FIG. 27 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 28] FIG. 28 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 29] FIG. 29 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 30] FIG. 30 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 31] FIG. 31 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 32] FIG. 32 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 33] FIG. 33 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 34] FIG. 34 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 35] FIG. 35 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 36] FIG. 36 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 37] FIG. 37 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 38] FIG. 38 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 39] FIG. 39 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 40] FIG. 40 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 41] FIG. 41 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 42] FIG. 42 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 43]FIG. 43 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 44] FIG. 44 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 45] FIG. 45 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 46] FIG. 46 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 47] FIG. 47 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. [Figure 48] FIG. 48 is a diagram for explaining a specific example of the processing of the control unit of the cost management system in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0018] A cost management system according to an embodiment of the present invention will be described in detail below with reference to the drawings.

[0019] [1. Overview] The outline of the present invention will be explained in the following order: (Background and premise), (Problem), (Measures and effects), and (Analysis screen).

[0020] (background / premise) In recent years, scandals such as corporate fraud and embezzlement by employees and executives have been on the rise. The impact of COVID-19 has compounded this trend, further increasing the number of cases. These types of frauds are now beyond the scope of human detection, creating a need for systems that can detect and deal with them early using non-human means.

[0021] As a measure for early detection and response to fraud, and as a measure that does not require human intervention, it is desirable to create fraud scenarios and build a system that detects them automatically on a regular basis. There are many scenario patterns, as shown below, and it is necessary to build a system that can handle each one.

[0022] ·Fraud regarding sales results: Falsifying sales figures → Inflating results · Fraud regarding purchasing records: fictitious payments made in collusion with business partners → collusion Inventory fraud: Inflating end-of-period inventory amounts → Profit manipulation · Cost fraud: Reassignment of construction costs → Manipulation of profits for each construction project

[0023] In this embodiment, attention is focused on a fraud scenario related to costs. The following are possible purposes of fraud related to costs.

[0024] - The cost of a construction project that has become unprofitable is transferred to another construction project that is making a profit, and the construction project is revised to make a profit. → The occurrence of a construction project that is making a loss = This causes the company's credibility to be damaged, so they try to cover it up. Avoidance of recording a construction loss reserve → (Construction accounting standards) A construction loss reserve must be recorded when a loss-making construction project is recognized. This is easily recognized by external parties as a management risk for the company, which can lead to an incentive to falsify accounting and tax settlements. This can easily lead to malicious profit manipulation on an organizational level. Avoiding managerial responsibility: This incentive is likely to work at the project manager level. When construction profits are viewed on a company-wide basis, total profits remain unchanged. For this reason, there are cases where employees do not realize that re-allocation of costs constitutes fraudulent processing.

[0025] In this embodiment, a system is constructed and proposed that can be used as one of the measures to prevent fraudulent practices related to costs.

[0026] In this scenario, loss-making construction projects are defined as follows: Loss-making construction projects are projects where the costs involved exceed the contract amount. The following are characteristics of fraud:

[0027] 1. Costs exceeding the contract amount are incurred during the construction period. 2. An operation occurs in which the cost is transferred to another project. 3. At the end of the construction period, the cost price will be as close as possible to the order amount.

[0028] Figure 1 is a diagram to explain the image of a loss-making project. In Figure 1, we will explain as an example the case where orders for Project A and Project B have been received and costs have been recorded. Project A is a loss-making project because its costs (cost amount) exceed its sales (order amount). If part of the costs of Project A are transferred to Project B, Project A will be recognized as a profitable project. The total profit, which is the sum of the profits from Project A and Project B, remains unchanged before and after the cost transfer.

[0029] This section explains the difference between normal and fraudulent patterns in cost rebalancing. Normal pattern Operational image: Allocation of common costs → It is assumed that costs are incurred within the range of profits, and this applies to cases where items (expense items) are corrected or errors in the destination of cost allocation are corrected. Fraudulent patterns Operational image: Cost re-allocation to profitable construction → This occurs for construction that is in the red, and after the cost re-allocation, the cost rate is just enough to make a profit.

[0030] By checking the cost fluctuations before and after the cost transfer for the original construction work, it is possible to determine whether or not there is any fraud.

[0031] We will explain how to detect abnormalities. If everything is normal, there will be no deficit costs recorded in the history. The following steps are considered to be cases where fraud may occur. (1) Costs incurred during the construction period are recorded in association with the construction work. (2) Check the cost of each project and recognize that the project is in the red. (3) To conceal the deficit, the costs were transferred to another project.

[0032] Therefore, in this scenario, we focus on whether there are any construction projects that have historically resulted in losses. Next, the user checks the detected data using the analysis screen. They check the cost trends and the situation where a loss has been corrected to a profit, and determine whether the data is abnormal.

[0033] Figure 2 is a diagram to explain an example of cost anomaly detection. Figure 2 shows an example where an order was received for Construction A for 10,000 yen and Construction B for 20,000 yen. For Construction A, orders from 2021 / 05 to 2021 / 07 were for 12,000 yen, resulting in a deficit. On the other hand, orders for Construction B from 2021 / 04 to 2021 / 7 were for 10,000 yen, resulting in a surplus.

[0034] Here, on August 5, 2021, a cost transfer of 3,000 yen is made from Construction A to Construction B. This is because Construction A has been corrected from a deficit to a surplus, making this a fraudulent pattern (fraudulent processing) in this scenario.

[0035] When the construction is completed, the profit from Construction A is 1,000 yen (profit), and the profit from Construction B is 5,000 yen (profit), for a total profit of 6,000 yen, which remains unchanged before and after the cost transfer.

[0036] Furthermore, to make fraud more difficult to detect, it is conceivable that costs may be spread across multiple construction projects and reassigned.

[0037] (assignment) When detecting fraudulent patterns, the following issues (1) and (2) arise.

[0038] (1) It is necessary to aggregate and compare multiple combinations, and the aggregation process takes time. The data to be checked is all order data and cost data related to the construction work, and the required aggregation unit is monthly using the following unit " ".

[0039] "Construction" → It is necessary to check the status of construction costs for each month and the increasing trend. "Construction x Organization" -> In order to detect organizations that are committing fraudulent transactions, it is necessary to confirm the increasing trend in the cost of each "construction x organization." "Order amount / cost amount" → It is necessary to confirm the actual situation where a loss in costs is occurring. "Construction from which costs are being reassigned and construction to which costs are being reassigned" → It is necessary to confirm that the cost of the construction to which costs are being reassigned continues to be in the black, and that the cost amount has not temporarily increased sharply due to the cost reassignment.

[0040] How to check: It is necessary to aggregate the monthly order amount and cost amount for each of the above "required aggregation units." This requires multiple aggregations from a huge amount of data, which takes time. Since it is necessary to compare data monthly within the same aggregation unit, oversights and mistakes can occur.

[0041] (2) It is necessary to tally up the cumulative cost amount for each construction project by month, which takes time and is prone to errors and omissions. Checking the history of negative costs = It is necessary to check when the cost amount exceeds the order amount. The latest cost information is the positive cost after adjustment, so it is necessary to check back to the state before the cost adjustment. In addition, just aggregating the costs for each construction project is expected to require a considerable amount of data, and since it is also necessary to aggregate each cost accounting history, an even larger amount of data must be aggregated. As such, aggregation takes time and is prone to human errors such as aggregation errors and incorrect selection of comparison targets.

[0042] (Measures and Effects) In order to solve the above problems, the cost management system of this embodiment supports fraud detection by taking the following measures (1) to (6).

[0043] (1) Automatically detects construction projects that have a history of recording costs that result in a loss. Automatically calculates costs for each month during the construction period, and detects history where costs exceed the order amount → reduces calculation costs and eliminates calculation and calculation errors. Prevents overlooking of historical periods where there are losses.

[0044] (2) The monthly order amount and cost amount for construction work for which an abnormality was detected are compiled, and the increasing trend in cost amount over the construction period is visualized. It allows you to check past cost amounts that exceed the order amount in a monthly time series. The cost trends have the following patterns: Costs increase towards the end of the construction period → Normal cost trends · The moment when costs exceed the order amount → the construction becomes unprofitable · The cost was reduced the following month to just below the order amount → Evidence of concealing unprofitable construction work This eliminates the cost of aggregating orders and cost amounts for each month, eliminating omissions and mistakes in monthly comparisons.

[0045] (3) Focusing on the information of the organization that manages the construction work for which an abnormality has been detected, the cost rate for each construction work managed by the organization is compiled and visualized by progress rate unit. The purpose of adjusting construction costs is often to conceal unprofitable construction projects (protecting the reputation of the organization / company). It is unlikely that fraud is committed by an individual person in charge. Fraud is assumed to be committed on an organizational level: it is highly likely that fraud is being committed across multiple construction projects. This eliminates the cost of calculating cost rates for each progress rate of construction managed by the organization, eliminating oversights and mistakes in checking each construction project. Furthermore, by checking the situation by organization, suspicious organizations can be identified and the scope of the impact can be quickly grasped.

[0046] (4) Focus on the construction to which the cost of the construction work for which an abnormality was detected is to be re-allocated. Aggregate order amounts and cost amounts by month and visualize the trend of increasing cost amounts over the construction period. It may be possible to identify a trend of sudden increases in costs at specific times. The cost may increase in the following cases: Costs increase towards the end of the construction period → Normal cost trends · Costs shifted from other construction projects → sudden increase in costs (cost increase different from previous trends) This eliminates the cost of tallying up orders and costs for each month, eliminating omissions and mistakes in monthly comparisons. By checking the "past cost status" and "post-cost status" of the cost-allocation-targeted construction, it is possible to identify evidence of part of the profit being used to conceal fraud.

[0047] (5) Focusing on the information of the organization that manages the construction to which the cost of the construction work for which an abnormality has been detected is transferred, the cost rate for each construction work managed by the organization is compiled and visualized by progress rate unit. Prerequisites for cost reimbursement: Both the source and destination of the reimbursement can be controlled. Increased costs = decreased profits. If the environment is not conducive to profit control, fraud is likely to be discovered. The appearance of fraud will change depending on the construction management organization to which the reimbursement is made. - The same organization as the original organization → Fraudulent operations are being performed within the organization (fraud in a single organization). · An organization different from the original organization → Fraudulent manipulation is being carried out through collusion between organizations (fraud by multiple organizations). This eliminates the cost of aggregating organizational information for "source construction" and "resource construction." It also allows you to quickly understand the scope of the need to investigate the cause of fraud.

[0048] (6) Visualize the details of the cost allocation data in a list. It is possible to analyze the characteristics of fraud from data before and after cost reassignment. Example of a characteristic: The person in charge of cost reassignment processing (responsible person) is different from the person who registered the data = evidence of misuse of other people's information. Even if there are multiple reassignment destinations, information limited to the reassignment destination can be quickly confirmed.

[0049] (Analysis screen) The analysis screen of the cost management system of this embodiment will be described below. The analysis screen outputs "order and cost amount by project" and "cost rate of project from which cost was allocated and to which cost was allocated." Information for which an anomaly has been detected is highlighted by changing the color and font size so that the anomaly can be recognized at a glance. The anomaly detection data that can be checked targets the history of cost amounts exceeding the order amount for each project (history of deficit projects), and projects with a history of being deficit projects are detected.

[0050] The analysis screen consists of an initial analysis screen as shown in Fig. 3 and an analysis screen as shown in Fig. 4. The analysis screen switches from Fig. 3 to Fig. 4 in that order.

[0051] The initial analysis screen in Figure 3 displays a list of messages resulting from detected anomalies, and outputs summary messages of detected anomalies.

[0052] Area A1 is the area that displays messages related to the anomaly detection process. It displays the timing of the anomaly detection, the construction work, and the cost amount for the month in which it was detected. Since the output is on an overview basis, you must select the message and switch screens to see the detailed detection method. If there are multiple pieces of information detected as anomalies, they will be displayed in a vertical row.

[0053] The analysis screen in Figure 4 displays the graphs necessary for analyzing the message resulting from the detection of an anomaly. This screen changes when a message is selected on the initial analysis screen in Figure 3. The graphs required for analysis are displayed, and the message outputs detailed information related to detection. The analysis screen in Figure 4 has multiple display areas, Area A1 to Area A7.

[0054] (1) Area A1 is an area that displays messages related to the anomaly detection process. It displays the "detection method used to detect the anomaly" and "key information for the data that detected the anomaly." If multiple pieces of information are detected as an anomaly, multiple messages are displayed for each anomaly. Clicking on a displayed message will highlight the portion of the graph in areas A2 to A6 that is related to the anomaly detection information you clicked on.

[0055] (2) Area A2 is an area that displays Graph 1, which outputs the order amount and cost amount associated with the construction work for which an abnormality was detected. Graph 1 is output, which allows you to check the "trend in the monthly order amount and cost amount." You can check the progress of the construction work for which an abnormality was detected until it became a loss-making construction, and the progress from loss to profit.

[0056] (3) Area A3 is an area that displays Graph 2, which outputs the cost rate for each construction project managed by the "organization managing the construction project for which an abnormality was detected." It outputs Graph 2, which allows you to compare and check the "trend in cost rate by progress rate for each construction project." You can check whether there are any construction projects other than the construction project for which an abnormality was detected that are showing unnatural trends. For example, when costs are transferred to another construction project under management, it may be possible to visualize the timing when the cost rate of the transferred construction project suddenly increases.

[0057] (4) Area A4 is an area that displays Graph 3, which outputs the order amount and cost amount linked to the cost-allocated construction project. Graph 3 is output, which allows you to check the "monthly order amount and cost amount occurrence trend." You can see that profits were high before the cost allocation, but costs increased sharply after the cost allocation.

[0058] (5) Area A5 is the area that displays Graph 4, which outputs the cost rate for each construction project managed by the "organization that manages the construction project to which costs are transferred." It outputs a graph that allows you to compare and confirm the "trend in cost rate by progress rate for each construction project." If construction projects managed within an organization are designated as the destination for cost transfer, the same information as Graph 2 in Area A3 is displayed. If a different organization is involved, there may be other construction projects that are also the destination for cost transfer.

[0059] (6) Area A6 is an area that outputs a table that lists the cost vouchers that have been reassigned. It outputs a table that allows you to check the "Details of each cost voucher" and "Information on the reassigned construction work." If costs are being distributed and adjusted, you can check the multiple reassigned construction works. If the cost reassignment is for the purpose of reassigning profits, you can check that the reassigned construction work has been completed or has a later scheduled completion date.

[0060] (7) Area A7 is an extraction condition specification area for specifying extraction conditions such as the base date, start of period, end of period, aggregation unit (department or business establishment), and construction type for the graphs and tables to be output in areas A2 to A6. By switching between selecting department and business establishment, you can switch the aggregation unit for the department or business establishment.

[0061] In this way, the cost management system of this embodiment (1) can periodically detect cost anomalies from a huge amount of business data, (2) the system automatically processes the data that needs to be compiled and compared under various conditions such as by project, order cost, organization, and fiscal year / month, thereby avoiding the risk of operational errors, and (3) there is a screen for analyzing and confirming anomalies and their reasons, allowing for reliable detection of cost fraud and enabling early detection of fraudulent manipulation of costs for loss-making projects with low cost and high accuracy.

[0062] [2. Configuration] Figure 5 is a block diagram showing an example of the configuration of cost management system 100 according to this embodiment. In Figure 5, cost management system 100 includes control unit 102, communication interface unit 104, memory unit 106, and input / output interface unit 108. Each unit included in cost management system 100 is connected to each other so as to be able to communicate via any communication path.

[0063] The communication interface unit 104 communicatively connects the cost management system 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 the communication line. Here, the network 300 has a function of connecting the cost management system 100 and the server 200 etc. so that they can communicate with each other, and is, for example, the Internet or a LAN (Local Area Network).

[0064] An input device 112 and an output device 114 are connected to the input / output interface unit 108. The output device 114 may be a monitor (including a home television), a speaker, or a printer. The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that functions as a pointing device in cooperation with a mouse. Note that hereinafter, the output device 114 may be referred to as the monitor 114.

[0065] Various databases, tables, files, etc. are stored in the storage unit 106. Computer programs that work in conjunction with an OS (Operating System) to issue commands to a CPU (Central Processing Unit) to perform various processes are recorded in the storage unit 106. The storage unit 106 can be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, etc.

[0066] The storage unit 106 includes a business database 106a, an abnormality detection execution data table 106b, and an abnormality determination result data table 106c.

[0067] The business database 106a is a database for storing business data. The business data includes order entry data, construction cost entry history data, cost correction data, construction basic information data, etc. Fig. 6 is a diagram showing an example of the configuration of order entry data, Fig. 7 is a diagram showing an example of the configuration of construction cost entry history data, Fig. 8 is a diagram showing an example of the configuration of cost correction data, and Fig. 9 is a diagram showing an example of the configuration of construction basic information data.

[0068] As shown in FIG. 6, the order entry data may include the accounting year and month, the name of the project, the business establishment, the department, the person in charge, and the order amount.

[0069] As shown in FIG. 7, the construction cost accounting history data may include the accounting year and month, construction name, business establishment, department, person in charge, cost amount, cost slip number, cost adjustment number, and cost adjustment amount.

[0070] As shown in FIG. 8, the cost adjustment data may include a cost adjustment number, accounting year and month, construction cost binding date, source construction, destination construction, person in charge, person in charge of registration, and cost amount.

[0071] As shown in FIG. 9, the construction basic information data may include the contract month, construction name, business establishment, department, planned construction period (month), planned completion date, completion date, and construction type.

[0072] The anomaly detection execution data table 106b is a table for storing automatic detection execution schedule data, construction data acquisition range condition data, etc. Fig. 10 is a diagram showing an example of the configuration of automatic detection execution schedule data, and Fig. 11 is a diagram showing an example of the configuration of construction data acquisition range condition data.

[0073] As shown in FIG. 10, the automatic detection execution schedule data may include a detection ID, a schedule ID, an execution condition, and an execution time. The detection ID and schedule ID serve as key information when referencing the data. The detection unit 102b automatically executes cost anomaly detection according to the automatic detection execution schedule data. In the example shown in the same figure, the first line contains the detection ID "AB001," the schedule ID "SH001," the execution condition "the fifth business day of every month," and the execution time "23:00." Here, the execution condition is set to "the fifth business day" assuming the timing when the monthly closing process for the previous month is finalized.

[0074] As shown in Figure 11, the construction data acquisition range condition data may include a detection ID, a schedule ID, a target column, a FROM condition, and a TO condition. The detection ID and schedule ID serve as key information when referencing data. The detection unit 102b automatically executes cost anomaly detection within the acquisition range specified by the construction data acquisition range condition data. In the example shown in the figure, the first line contains the detection ID "AB001", the schedule ID "SH001", the target column "fiscal year / month", the FROM condition "automatic detection processing month - 1 year", and the TO condition "automatic detection processing month - 1 month".

[0075] The abnormality determination result data table 106c is a table for storing the determination results of an abnormality detection execution, such as abnormality determination result data, abnormality determination result message data, and abnormality determination result message detail data. When the detection unit 102b detects a cost abnormality, it stores the abnormality determination result data, abnormality determination result message data, and abnormality determination result message detail data in the abnormality determination result data table 106c. Fig. 12 is a diagram showing an example of the configuration of the abnormality determination result data, Fig. 13 is a diagram showing an example of the configuration of the abnormality determination result message data, and Fig. 14 is a diagram showing an example of the configuration of the abnormality determination result message detail data.

[0076] The anomaly determination result data may include a detection ID, a JOB ID, a message ID, a fiscal year and month, a business establishment, a department, a construction project, a cumulative order amount, and a cumulative cost amount, as shown in Fig. 12. The detection ID and the JOB ID are key information when referencing the data.

[0077] The abnormality determination result message data may include a detection ID, a JOB ID, a message ID, an abnormality level, a definition name, an overview, and a detection target, as shown in Fig. 13. The detection ID and the JOB ID are key information when referencing the data.

[0078] The detailed data of the abnormality determination result message may include a detection ID, a JOB ID, a message ID, a detection method, a reference value name, a reference value, a comparison target name, a comparison target, and a determination method, as shown in Fig. 14. The detection ID and JOB ID are key information when referencing the data.

[0079] Returning to Figure 5, the control unit 102 is a CPU or the like that performs overall control of the cost management system 100. The control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing operations based on these stored programs.

[0080] The control unit 102 is configured to be able to access the business database 106a, the data table for abnormality detection execution 106b, the data table for abnormality determination result 106c, etc., stored in the storage unit 106. Note that the business database 106a, the data table for abnormality detection execution 106b, and the data table for abnormality determination result 106c may be provided in another location (for example, the server 200) as long as they are accessible by the control unit 102.

[0081] The control unit 102 conceptually includes a memory control unit 102a, a detection unit 102b, and a display control unit 102c.

[0082] The storage control unit 102a may, for example, acquire daily business data from a business system (not shown) connected via the network 300 and store it in the business database 106a, or may input daily business data in response to an operator's operation on an input screen (not shown) displayed on the monitor 114 and store it in the business database 106a.

[0083] In addition, the storage control unit 102a sets automatic detection execution schedule data and construction data acquisition range condition data in accordance with, for example, an operator's operation on a data setting screen (not shown) displayed on the monitor 114, and stores the data in the anomaly detection execution data table 106b.

[0084] The detection unit 102b executes cost anomaly detection in accordance with the automatic detection execution schedule data and construction data acquisition range condition data stored in the anomaly detection execution data table 106b, acquires construction cumulative data including the cumulative order amount and cumulative cost amount for each accounting year / month and construction project based on the business data stored in the business database 106a, detects an anomaly when the cumulative order amount < cumulative cost amount, and detects the accounting year / month and construction project in question. When the detection unit 102b detects an anomaly, it stores the anomaly determination result data, the anomaly determination result message data, and the anomaly determination result message detail data in the anomaly determination result data table 106c.

[0085] The display control unit 102c controls the display of analysis screens (initial analysis screen, analysis screen) on the monitor 114. Based on the business data stored in the business database 106a, the display control unit 102c displays analysis data (for example, graphs 1 to 4, tables, etc. in FIG. 4) before and after the accounting month of detection for a construction project in which an abnormality was detected by the detection unit 102b on the analysis screen.

[0086] The display control unit 102c may display a message including the construction work detected as an anomaly, the accounting year and month, the cost amount, and the detection method in a predetermined area of ​​the analysis screen (for example, area A1 in FIG. 4).

[0087] The display control unit 102c may display a graph (e.g., graph 1 in Figure 4) in a predetermined area of ​​the analysis screen (e.g., area A2 in Figure 4) showing the monthly change in order amount and cost amount for construction work in which an abnormality has been detected based on the cumulative construction work data.

[0088] The business data may further include basic construction information data having the contract month, construction project, business establishment, department, and planned construction period, and the display control unit 102c may acquire, based on the business data, cost rate data by progress rate for the department in charge of the construction project in which the abnormality was detected, including the construction period progress rate calculated using the elapsed construction month / planned construction period, and the cost rate calculated using the construction project, business establishment, department, cumulative cost amount / cumulative order amount, and display a graph (e.g., graph 2 in Figure 4) showing the trend in cost rate by construction period progress rate for each construction project based on the acquired cost rate data by progress rate in a specified area of ​​the analysis screen (e.g., area A3 in Figure 4).

[0089] The business data may include cost adjustment data having a cost adjustment number, accounting year and month, source work, destination work, and cost amount, and the display control unit 102c, based on the business data, may treat the work in which an abnormality was detected as the source work and acquire cost allocation detail data for the destination work, including the cost adjustment number, business establishment, department, and cost amount, and for the destination work of the record with the largest cost amount in the acquired cost allocation detail data, acquire cumulative cost allocation destination work data, including the accounting year and month, business establishment, department, cumulative order amount, and cumulative cost amount, based on the business data, and display a graph (e.g., graph 3 in Figure 4) showing the monthly order amount and cost amount trends based on the acquired cumulative cost allocation destination work data in a specified area of ​​the analysis screen (e.g., area A4 in Figure 4).

[0090] The business data may further include basic construction information data having the contract month, construction work, business establishment, department, and planned construction period, and the display control unit 102c may acquire, based on the business data, cost rate data by progress rate of the cost transfer recipient organization, including the construction period progress rate calculated using the construction elapsed month / planned construction period, and the cost rate calculated using the construction work, business establishment, department, cumulative cost amount / cumulative order amount, for the department in charge of the transfer recipient construction work of the record with the largest cost amount from the acquired cost transfer detail data, and display a graph (e.g., graph A4) showing the cost rate trends by construction period progress rate for each construction work, based on the acquired cost rate data by progress rate of the cost transfer recipient organization, in a specified area of ​​the analysis screen (e.g., area A5 in Figure 4).

[0091] The business data may include basic construction information data having the contract month, construction work, business establishment, department, and planned construction period, and cost adjustment data having the cost adjustment number, accounting year and month, source construction work, destination construction work, and cost amount.The display control unit 102c may acquire detailed cost allocation data for a construction work in which an abnormality has been detected, including the cost adjustment number, business establishment, department, and cost amount, for the destination construction work, with the construction work in which the abnormality was detected as the source construction, and display a table based on the acquired detailed cost allocation data in a specified area of ​​the analysis screen (for example, area A6 in Figure 4).

[0092] In addition, the analysis screen is an area for setting data extraction conditions, and includes an extraction condition area (e.g., area A7 in Figure 4) in which the aggregation unit can be specified by business establishment or department, and the display control unit 102c may switch the aggregation unit of the graph or table based on the business establishment or department specified in the extraction condition area.

[0093] [3. Specific Examples] Specific examples of processing by control unit 102 of cost management system 100 in this embodiment will be described with reference to FIGS.

[0094] (3-1. Overall processing) FIG. 15 is a flowchart for explaining an outline of the overall processing of the control unit 102 of the cost management system according to this embodiment.

[0095] An overview of the overall processing of the control unit 102 of the cost management system 100 in this embodiment will be described with reference to Figure 15. In Figure 15, the detection unit 102b executes an abnormality detection process (step S1). Specifically, in the abnormality detection process, the detection unit 102b executes cost abnormality detection in accordance with the automatic detection execution schedule data and construction data acquisition range condition data stored in the abnormality detection execution data table 106b, acquires construction cumulative data including the accumulated order amount and accumulated cost amount for each accounting year and month and construction project based on the business data stored in the business database 106a, detects a case where the accumulated order amount < the accumulated cost amount as an abnormality, and detects the accounting year and month and construction project.

[0096] The display control unit 102c executes an analysis screen display process (step S2). Specifically, in the analysis screen display process, for the construction work in which an abnormality has been detected by the detection unit 102b, analysis data (for example, graphs 1 to 4, tables, etc. in FIG. 4) before and after the accounting month of the detection are displayed on the analysis screen based on the business data stored in the business database 106a.

[0097] In this case, the display control unit 102c may display a message in area A1 of the analysis screen, including the construction work detected as an anomaly, the accounting year and month, the cost amount, and the detection method.

[0098] In addition, the display control unit 102c may display graph 1 in area A2 of the analysis screen, which shows the monthly change in order amount and cost amount for construction work in which an abnormality has been detected, based on the cumulative construction work data.

[0099] Furthermore, the display control unit 102c may acquire, based on business data, cost rate data by progress rate for the department in charge of the construction work in which the abnormality was detected, including the construction period progress rate calculated using the number of months elapsed since construction / planned construction period, and the cost rate calculated using the construction work, business establishment, department, cumulative cost amount / cumulative order amount, and display Graph 2 showing the cost rate trends by construction period progress rate for each construction work in area A3 of the analysis screen based on the acquired cost rate data by progress rate.

[0100] Furthermore, the display control unit 102c may, based on business data, designate the construction work in which an abnormality was detected as the source construction work, acquire detailed cost allocation data for the destination construction work, including the cost adjustment number, business establishment, department, and cost amount, and, based on business data, acquire cumulative cost allocation data for the destination construction work of the record with the largest cost amount from the acquired detailed cost allocation data, including the fiscal year and month, business establishment, department, cumulative order amount, and cumulative cost amount, and display graph 3 in a specified area A4 of the analysis screen, showing the monthly order amount and cost amount trends, based on the acquired cumulative cost allocation data for the destination construction work.

[0101] Furthermore, the display control unit 102c may acquire, based on business data, cost rate data by progress rate of the cost transfer destination organization for the department in charge of the transfer destination construction work of the record with the largest cost amount from the acquired cost transfer detail data, including the construction period progress rate calculated using the construction elapsed months / planned construction period, and the cost rate calculated using the construction work, business establishment, department, cumulative cost amount / cumulative order amount, and display graph 4 showing the cost rate trends by construction period progress rate for each construction work in area A5 of the analysis screen based on the acquired cost rate data by progress rate of the cost transfer destination organization.

[0102] For a construction project in which an abnormality has been detected, the display control unit 102c may acquire detailed cost allocation data for the construction project to which the abnormality has been detected, including the cost adjustment number, business establishment, department, and cost amount, with the construction project in which the abnormality has been detected being the source construction, and display a table based on the acquired detailed cost allocation data in area A6 of the analysis screen.

[0103] In addition, the display control unit 102c is an area for setting data extraction conditions, and an extraction condition area in which the aggregation unit can be specified by business establishment or department is displayed in area A7 of the analysis screen, and the aggregation unit of the graph or table can be switched based on the specified business establishment or department.

[0104] (3-2. Sample data) 16 to 48 are diagrams showing sample data for explaining a specific example of the processing by the control unit 102 of the cost management system 100 in this embodiment. A specific example of the processing by the control unit 102 of the cost management system 100 in this embodiment will be explained with reference to FIGS. 16 to 48.

[0105] The cost management system 100 has specifications that focus on the elapsed months of a construction project. The definition of the elapsed months is as follows: Elapsed months: The month that has passed since the first order registration month within the construction project. The first order registration month = the accounting year and month of the first registration slip for each construction project in the "order entry data."

[0106] (S1: Abnormality detection process) A specific example of the anomaly detection process will be described with reference to Figures 16 to 19. The detection unit 102b executes cost anomaly detection in accordance with the automatic detection execution schedule data and construction data acquisition range condition data stored in the anomaly detection execution data table 106b, acquires construction cumulative data including the accumulated order amount and accumulated cost amount for each accounting year and month and construction project based on the business data stored in the business database 106a, detects as an anomaly a case where the accumulated order amount < the accumulated cost amount, and detects the accounting year and month and construction project.

[0107] 1. Processing to detect construction work exceeding the order amount as an anomaly based on the order entry data and construction cost entry history data in the business database 106a

[0108] (1) Pre-settings (1-1) The storage control unit 102a stores information necessary for anomaly detection in advance in the anomaly detection execution data table 106b (data provided in advance). Specifically, automatic detection execution schedule data and construction data acquisition range condition data are set in the anomaly detection execution data table 106b.

[0109] Fig. 16(A) is a diagram showing an example of automatic detection execution schedule data. In the example shown in the figure, automatic detection is executed at 23:00 on the fifth business day of each month. The fifth business day is assumed to be the timing when monthly closing processing for the previous month is finalized. The detection ID and schedule ID are used as key information when referencing the data.

[0110] Figure 16(B) shows an example of construction data acquisition range condition data. In the example shown in the figure, the construction data acquisition range for the target column "fiscal year and month" is set to "automatic detection processing month - 1 year" to "automatic detection processing month - 1 month." The detection ID and schedule ID are used as key information when referencing data.

[0111] (2) The detection unit 102b automatically executes anomaly detection. (2-1) First, acquire timing information for detecting an abnormality. Specifically, acquire automatic detection execution schedule data set in the abnormality detection execution data table 106b as shown in Figures 17(A) and 17(B).

[0112] (2-2) Determine whether the timing of automatic execution is the timing to detect an abnormality. Figure 17(C) is a diagram for explaining the automatic execution timing determination process. In the example shown in the figure, the start timing is "2021 / 04 / 07", the fifth business day, and the automatic detection execution schedule data is "the fifth business day of every month", so the execution determination result is "execute."

[0113] Depending on the execution determination result, the process branches to whether or not to perform subsequent processing. If the execution determination result is "execute," the process from (2-3) onwards is executed. If the execution determination result is "do not execute," the process ends.

[0114] The method of determining whether a day is a business day or a holiday is to refer to a calendar master (not shown) stored in the business database 106a. The calendar master is based on the sales business calendar and is always updated.

[0115] (2-3) Obtain the range conditions of the data for detecting anomalies. (2-3-1) Specifically, as shown in FIGS. 17(D) and 17(E), the acquisition range condition data for the construction data set in the abnormality detection execution data table 106b is acquired.

[0116] (2-4) The order entry data and construction cost entry history data in the business database 106a are referenced to detect construction work for which the cost amount has exceeded the order amount. (2-4-1) First, as shown in Figure 18(A), using the extraction condition column "Fiscal Year / Month", the FROM condition "2020 / 04", and the TO condition "2021 / 03" as parameters, the cumulative construction data is obtained from the order entry data and construction cost entry history data in the business database 106a.

[0117] Figure 18(B) shows an example of cumulative construction data. The cumulative construction data may include the accounting year and month, construction name, business location, department, person in charge, order amount, cumulative order amount, cost amount, and cumulative cost amount. Figure 18(C) shows an example of order posting data. Figure 18(D) shows an example of construction cost posting history data.

[0118] The "cumulative order amount" for cumulative construction data is calculated by aggregating the cumulative amount for each month when obtaining the "order amount" from the order posting data. For example, the "cumulative order amount" for construction E for the accounting month "2020 / 04" is calculated as follows: order amount "800,000" for the accounting month "2020 / 02" of the order posting data + order amount "300,000" for the accounting month "2020 / 04" = 1,100,000.

[0119] The "cumulative cost amount" of the construction cumulative data is calculated by aggregating the cumulative amount for each month when obtaining the cost amount from the construction cost accounting history data. For example, the "cumulative cost amount" of construction A for the accounting month "2020 / 06" is calculated as follows: cost amount "200,000" for the accounting month "2020 / 05" in the [construction cost accounting history data] + cost amount "50,000" for the accounting month "2020 / 06" = 250,000.

[0120] (2-4-2) Compare the "cumulative order amount" and "cumulative cost amount" from the cumulative construction data obtained in (2-4-1). Specifically, use the following anomaly detection method (rule-based) to check for the existence of construction that is judged to be abnormal.

[0121] Anomaly detection method: rule-based Reference value: Cumulative order amount Compare to: Cumulative cost amount Abnormality determination method: Reference value < comparison target

[0122] Figure 19(C) shows an example of cumulative construction data to be detected (same as Figure 18(B)), Figure 19(A) shows an example of an abnormality determination result, and Figure 19(B) shows a diagram to explain the abnormality determination method (rule-based).

[0123] For the fiscal year and month "2021 / 02" and the work "Work A" in the cumulative data for the work, the cumulative order amount "1,000,000" < cumulative cost amount "1,300,000", so as shown in Figure 19 (A), the abnormality judgment result is "2021 / 02", the work "Work A", cumulative order amount "1,000,000", cumulative cost amount "1,300,000", and the judgment result is "abnormal".

[0124] In Figure 19(B), the anomaly determination method (rule-based) is a method for determining the position of the object (comparison object) to be determined as an anomaly by comparing it with a standard value (reference value). The reason for selecting the anomaly determination method (rule-based) is that in this scenario, we are focusing on the fact that construction work in which the cost amount exceeds the order amount = loss-making construction work, so we have adopted it because the standard value and comparison object are clear.

[0125] (2-4-3) The abnormality determination result data for the construction work determined to be abnormal in (2-4-2) is saved in the abnormality determination result data table 106c. At the same time, the abnormality determination result message data and the detailed abnormality determination result message data, which are messages to be displayed on the analysis screen, are saved in the abnormality determination result data table 106c.

[0126] FIG. 19(D) shows an example of abnormality determination result data, FIG. 19(E) shows an example of abnormality determination result message data, and FIG. 19(F) shows an example of abnormality determination result message detail data.

[0127] (S2: Analysis screen display processing) A specific example of the analysis screen display process will be described with reference to Figures 20 to 48. The display control unit 102c displays, on the analysis screen, analysis data (for example, Graphs 1 to 4, tables, etc. in Figure 4) before and after the accounting month of detection for a construction project in which an abnormality was detected by the detection unit 102b, based on the business data stored in the business database 106a.

[0128] 2. Execute a process to display the abnormal data detected in 1 and data related to the abnormal data referenced to detect it on the initial analysis screen. (1) Information automatically detected as abnormal from the abnormality determination result data table 106c is output to the initial analysis screen. (1-1) First, obtain the result data and result message of the detected abnormality. Specifically, as shown in Fig. 20(A), using the JOBID "Construction cost alert" as a parameter (key), abnormality determination result data as shown in Fig. 20(B), abnormality determination result message data as shown in Fig. 20(C), and abnormality determination result message detail data as shown in Fig. 20(D) are acquired from the abnormality determination result data table 106c. Note that in order to display the detected information in a list, the data is acquired without setting the detection ID.

[0129] (1-2) When an abnormality is detected, a message is displayed on the screen. Specifically, as shown in Figure 20(E), the abnormality level, definition name, summary, and detection target of the abnormality judgment result message data in Figure 20(C) are extracted and displayed in area A1 of the initial analysis screen as shown in Figure 20(F).

[0130] (1-3) Obtain the date when the initial analysis screen is launched and set it as the base date of the extraction conditions of the initial analysis screen. Specifically, as shown in Figures 21(A) and 21(B), set the base date "2021 / 04 / 10" as the base date of the extraction conditions of the analysis period screen.

[0131] (2) Select the result message of the detected abnormality on the initial analysis screen to launch the analysis screen. The following process is performed by referring to the abnormality judgment result data, abnormality judgment result message data, and abnormality judgment result message detailed data selected from the list in (1).

[0132] (2-1) Switch the message to detailed information display and secure the output area for graphs and tables. 1. Switch to detailed message display. Specifically, for the detailed data of the abnormality determination result message shown in Fig. 22(A), the detection method, reference value name, comparison target name, and determination method are referenced as shown in Fig. 22(B), and a detailed message display is performed as shown in Fig. 22(C) (the detection method and the reason for determination are added).

[0133] 2. Allocate an output area for graphs and tables on the analysis screen. In this system, for example, four graphs and one table are output. Therefore, an output area for four graphs and one table is allocated.

[0134] (2-2) Set the extraction conditions. 1. The range of data to be analyzed is obtained based on the accounting year and month of the abnormality determination result data linked to the abnormality determination result message data. (i) Obtain the accounting year and month within one year before and after the month in which the abnormality was detected. Specifically, as shown in Figure 23(A), obtain the accounting year and month "2021 / 02" of the month in which the abnormality was detected from the anomaly determination result data shown in Figure 23(B), and obtain the accounting year and month "2020 / 03" one year before and "2022 / 03" one year after.

[0135] Here, the setting of around one year is in accordance with the detection conditions for a one-year range in the "Construction Data Acquisition Range Condition Data" data provided in advance for anomaly detection. If you want to expand the range for analysis, you can do so by changing the start and end period of the extraction conditions.

[0136] (ii) Refer to the accumulated business data and obtain the range narrowed down to the range in which data exists. For simplicity of explanation, we will assume that data exists only in the range of "2020 / 04~2021 / 03". As shown in Figure 23(C), the analysis data acquisition range conditions are set as follows: extraction condition column "fiscal year / month", FROM condition "2020 / 04 (used from months after 2020 / 03)", TO condition "2021 / 03 (used from months before 2022 / 03)".

[0137] Set the extraction conditions with the information in 2.1 set as the initial display values. Specifically, as shown in Figure 23 (D), set the initial extraction condition values ​​as follows: base date "2021 / 04 / 10 (when the analysis screen is launched), start of period "2020 / 04 (FROM condition of the analysis data acquisition range conditions), and end of period "2021 / 03 (TO condition of the analysis data acquisition range conditions). As shown in Figures 23 (E) and (F), when switching to graph display state, display the extraction items for extracting data for graph output. For aggregation, set "Department" as the fixed initial value and display it.

[0138] (2-3) The cumulative construction data is acquired from the order entry data and construction cost entry history data in the business database 106a. Based on the conditions initially set in the extraction conditions, the "cumulative construction data" is acquired and used as data for displaying Graph 1.

[0139] Specifically, based on the extraction condition initial values ​​shown in Figure 24(A) and the work "Work A" in the abnormality determination result data shown in Figure 24(C), the period start "2020 / 04", period end "2021 / 03", and designated work "Work A" as parameters as shown in Figure 24(B) are used to obtain cumulative work data as shown in Figure 24(D). The calculation of the "cumulative order amount" and "cumulative cost amount" of the cumulative work data is the same as in Figure 18, so a detailed explanation will be omitted.

[0140] (2-4) The cost rate data by progress rate is acquired from the order entry data, construction cost entry history data, and construction basic information data in the business database 106a.

[0141] Figure 25(C) shows an example of abnormality determination result data, Figure 25(D) shows an example of cost rate data by progress rate, Figure 25(E) shows an example of basic construction information data, Figure 25(F) shows an example of order entry data, and Figure 25(G) shows an example of construction cost entry history data.

[0142] Based on the initial extraction condition values ​​shown in Figure 25(A) and the department "Department A" in the anomaly judgment result data shown in Figure 25(C), the start of the period "2020 / 04", the end of the period "2021 / 03", the aggregation "Department", and the designated organization "Department A" as parameters as shown in Figure 25(B) are used to obtain cost rate data by progress rate as shown in Figure 25(D) as data for display in Graph 2. The use of the period start to end is used when limiting the acquisition to construction work for which costs have been recorded within the specified period. In this example, the construction work for which costs have been recorded between "2020 / 04 and 2021 / 03" is the target to be acquired.

[0143] The cost rate data by progress rate includes the progress rate of the construction period, the name of the construction project, the business establishment, the department, and the cost rate, as shown in Figure 25(D). The cost rate data by progress rate is obtained based on the basic construction information data shown in Figure 25(E).

[0144] Based on the number of months since the initial registration month (contract month) of the basic construction information data and the planned construction period (months), the following formula is used to calculate the "construction period progress rate" of the cost rate data by progress rate.

[0145] Construction progress rate calculation formula: Construction progress rate = Months elapsed since construction / Planned construction period The month in which construction began is counted as the month in which the initial order was registered.

[0146] For example, in Figure 25 (E), for Project A of Department A, the contract month (month of initial order registration) is "2020 / 04" and the planned construction period is (12), so the accounting year and month is "2020 / 06", the elapsed months are "2", the planned construction period is "12", and the progress rate is 17% (= 2 / 12).

[0147] The "cost rate" of the cost rate data by progress rate is calculated based on the order posting data shown in Figure 25(F) and the construction cost posting history data shown in Figure 25(G). When obtaining the order amount from the order posting data, the "cumulative order amount" is calculated by aggregating the cumulative amount for each month that has passed. In addition, when obtaining the "cost amount" from the construction cost posting history data, the "cumulative cost amount" is calculated by aggregating the cumulative amount for each month that has passed. The calculated "cumulative order amount" and "cumulative cost amount" are used to calculate the cost rate using the following formula.

[0148] Cost rate calculation formula: Cost rate = cumulative cost amount / cumulative order amount

[0149] (2-5) The cost allocation detail data is acquired from the order entry data, construction cost entry history data, basic information data, and cost correction data in the business database 106a.

[0150] Figure 26(C) shows an example of abnormality determination result data, Figure 26(D) shows an example of cost allocation detail data, Figure 26(E) shows an example of basic construction information data, Figure 26(F) shows an example of order posting data, Figure 26(G) shows an example of construction cost posting history data, and Figure 26(H) shows an example of cost adjustment data.

[0151] Based on the initial extraction condition values ​​shown in Figure 26(A) and the abnormality determination result data for the work "Work A" shown in Figure 26(C), the cost allocation detail data shown in Figure 26(D) is obtained as data for table display using the period start "2020 / 04", period end "2021 / 03", and designated work "Work A" as parameters as shown in Figure 26(B).

[0152] As shown in Figure 26(D), the cost allocation detail data includes the cost adjustment number, the work to be allocated to, the type of work, the order date, the expected completion date, the completion date, the business location, the department, the person in charge, the person in charge of registration, the work cost binding date, and the cost amount.

[0153] "Establishment / Department" retrieves the basic construction information data linked to the destination construction (cost allocation destination) of the designated construction (source construction) in the cost adjustment data. Person in charge / registration person retrieves the cost adjustment data.

[0154] We will explain the records linked to the "cost adjustment data" that exists in the construction cost accounting history data. In the example of construction cost accounting history data in Figure 26 (G), the cost of construction A was transferred to another construction as of March 2021. This data results in a process that reduces the cost from the perspective of construction A. Therefore, the amount of the cost adjustment record for construction A in the construction cost accounting history data is stored with the sign reversed.

[0155] In the following (2-6) and (2-7), data is retrieved based on the information of the record with the largest amount from the records retrieved in (2-5).

[0156] (2-6) The cumulative data of the cost allocation destination construction work is acquired from the order entry data and construction cost entry history data in the business database 106a.

[0157] Figure 27(C) shows an example of cost allocation detail data (data obtained in (2-5)), Figure 27(D) shows an example of cost allocation destination construction cumulative data, Figure 27(E) shows an example of order posting data, and Figure 27(F) shows an example of construction cost posting history data.

[0158] Based on the initial extraction condition values ​​shown in Figure 27(A) and the destination construction work "Construction D" in the cost allocation detail data shown in Figure 27(C), the cumulative cost allocation construction work data shown in Figure 27(D) is obtained as data for displaying Graph 3, using the period start "2020 / 04", period end "2021 / 03", and designated construction work "Construction D" as parameters, as shown in Figure 27(B).

[0159] As shown in Figure 27(D), the cumulative data on cost allocation destination construction projects includes the accounting year and month, construction name, business establishment, department, person in charge, order amount, cumulative order amount, cost amount, and cumulative cost amount.

[0160] The "cumulative order amount" of the cumulative data for cost transferee construction work is calculated by aggregating the cumulative amount for each month when the "order amount" is obtained from the order posting data. The "cumulative cost amount" of the cumulative data for cost transferee construction work is calculated by aggregating the cumulative amount for each month when the "cost amount" is obtained from the construction cost posting history data.

[0161] (2-7) The cost rate data by progress rate of the cost transfer destination organization is acquired from the order entry data, construction cost entry history data, and basic information data in the business database 106a.

[0162] Figure 28(C) shows an example of cost allocation detail data (data obtained in (2-5)), Figure 28(D) shows an example of cost rate data by progress rate for the cost allocation destination organization, Figure 28(E) shows an example of basic construction information data, Figure 28(F) shows an example of order posting data, and Figure 28(G) shows an example of construction cost posting history data.

[0163] Based on the initial extraction condition values ​​shown in Figure 28(A) and the department "Department B" in the cost allocation detail data shown in Figure 28(C), the cost rate data by progress rate of the cost allocation destination organization as shown in Figure 28(D) is obtained as data for displaying Graph 4, using the period start "2020 / 04", period end "2021 / 03", and specified organization "Department B" as parameters as shown in Figure 28(B).

[0164] The use from the start to the end of the period is limited to construction work whose costs were recorded within the specified period. In this example, construction work whose costs were recorded between April 2020 and March 2021 will be acquired.

[0165] As shown in FIG. 28(D), the cost rate data by progress rate of the cost transfer destination organization includes the progress rate of the construction period, the name of the construction project, the business establishment, the department, and the cost rate.

[0166] The cost rate data by progress rate of the cost transfer destination organization is obtained based on the basic construction information data. The "construction progress rate" is calculated using the following formula based on the number of months elapsed since the initial registration month (contract month) of the basic construction information data and the planned construction period.

[0167] Construction progress rate calculation formula: Construction progress rate = Months elapsed since construction / Planned construction period The month in which construction began is counted as the month in which the initial order was registered.

[0168] When obtaining the "order amount" from the order booking data, calculate the "cumulative order amount" by aggregating the cumulative amount for each month that has passed. When obtaining the "cost amount" from the construction cost booking history data, calculate the "cumulative cost amount" by aggregating the cumulative amount for each month that has passed. Using the calculated "cumulative order amount" and "cumulative cost amount", calculate the cost rate using the following formula.

[0169] Cost rate calculation formula: Cost rate = cumulative cost amount / cumulative order amount

[0170] (2-8) Bind (assign) the construction cumulative data, cost rate data by progress rate, cost allocation detail data, cost allocation recipient construction cumulative data, and cost rate data by progress rate of the cost allocation recipient organization to graphs 1 to 4 and the table. Binding is performed to a total of four graphs and one table.

[0171] 1. Bind the construction cumulative data to Graph 1. For example, according to the data binding information (X axis, Y axis, legend, title) shown in Fig. 29(A), the cumulative construction data for the construction project (construction A in the example shown in the figure) of the selected anomaly detection result data is bound to Graph 1, as shown in Fig. 29(B). In Graph 1, the title is the amount trend for construction A, the X axis is the accounting year and month, the Y axis is the cumulative order amount or cumulative cost amount, and the legend is the order amount and cost amount.

[0172] 2. Bind the cost rate data by progress rate to Graph 2. For example, according to the data binding information (X axis, Y axis, legend, title) shown in Fig. 29(C), the cost rate data by progress rate for the construction management department (Department A in the example shown in the figure) of the selected anomaly detection result data is bound to Graph 2, as shown in Fig. 29(E). In Graph 2, the title is "Cost trends by construction for the Construction A management department (Department A)," the X axis is the construction progress rate, the Y axis is the cost rate, and the legend is Construction A, Construction B, and Construction C.

[0173] 3. Bind the cumulative data of cost allocation destination construction work to Graph 3. For example, according to the data binding information (X axis, Y axis, legend, title) shown in Figure 29(D), the cumulative data for the cost transfer destination construction for the cost adjustment number / construction (in the example shown in the figure, cost adjustment number: NO0002, cost transfer destination construction: Construction D) of the record with the highest cost amount in the cost transfer detail data is bound to Graph 3, as shown in Figure 29(F). In Graph 3, the title is Cost adjustment number: NO0002, cost transfer destination construction: Amount trend for Construction D, the X axis is the accounting year and month, the Y axis is the cumulative order amount or cost amount, and the legend is order amount, cost amount.

[0174] 4. Bind the cost rate data by progress rate of the cost allocation destination organization to Graph 4. For example, according to the data binding information (X axis, Y axis, legend, title) shown in Figure 30(A), the cost rate data by progress rate of the cost transfer destination organization for the construction / department (in the example shown in the figure, the Construction D Management Department (Department B)) of the record with the highest cost amount in the cost transfer detail data, as shown in Figure 30(B), is bound to Graph 4. In Graph 4, the title is Construction D Management Department (Department B) Cost Rate Trends by Construction, the X axis is construction progress rate, the Y axis is cost rate, and the legend is Construction D, Construction E, Construction F.

[0175] 5. Bind the "Cost Allocation Detail Data" to the table. For example, according to the data binding information (column names, titles) shown in Figure 30(C), the cost allocation detail data for the construction / department of the record with the highest cost amount in the cost allocation detail data is bound to a table, as shown in Figure 30(D). In the table, the title is Construction A: List of cost allocated documents, and the column names are all the column names of the cost allocation detail data.

[0176] 6. Output the bound graphs 1 to 4 and tables to the analysis screen. The bound graphs 1 to 4 and table are output to areas A2 to A6 of the analysis screen, respectively, as shown in FIG. 30(E).

[0177] 7. Add click functionality to table records. This is set as a function for selecting the designated organization in the data acquisition parameters used when acquiring bind data for Graphs 3 and 4. It is set so that selection can be made by clicking on a record-by-record basis. For example, if you click on the record framed in red in the table shown in Figure 31(A), as shown in Figures 31(B) and (C), the "designated construction" parameter for acquiring the cumulative data of the cost transfer destination construction, which is the bind data for Graph 3, and the "department" parameter for acquiring the cost rate data by progress rate of the cost transfer destination organization, which is the bind data for Graph 4, will change to the contents of that record, and the display contents of Graphs 3 and 4 shown in Figure 31(D) will change.

[0178] An example of analysis using switching between graphs 3 and 4 is described in (3-2) Analysis by user operation (2).

[0179] 8. Based on the organization and construction information contained in the abnormality judgment result data linked to the message, the abnormal data is highlighted. FIG. 32(A) shows an example of graph coloring parameter data, FIG. 32(B) shows an example of abnormality determination result data, and FIG. 32(C) shows an example of an analysis screen display.

[0180] The graph lines and legends are highlighted according to the graph coloring parameters. As shown in Figure 32(A), the graph coloring parameters include the legend abnormality flag (True or False), reference information flag (True or False), legend color, and line color. The reference information is used to confirm how abnormal the object being judged to be abnormal (the cost amount in this job) is.

[0181] In Graph 2, the lines and legends are emphasized in red. Specifically, if the organization / construction in the legend is the same as the organization / construction in which an abnormality was detected, the color (red) will be applied to indicate that the abnormality flag in the legend is "True." For legends other than the organization / construction in which an abnormality was detected, the color will be applied to indicate that the abnormality flag in the legend is "False."

[0182] Graphs 1 and 3 display the order amount as reference information to determine whether the change in cost amount is normal or not. The color of the reference information flag True above is applied.

[0183] For graphs 1 and 3, the color of the abnormality flag True is applied to the cost amount (in this scenario, whether or not there is an abnormality is determined based on the cost amount, so it is highlighted in the same way as the abnormality detection result).

[0184] For Graph 4, the construction line and legend will display the construction work displayed in Graph 3 as reference information. The color of the reference information flag True above will be applied.

[0185] The points to note about the initial display of the analysis screen are as follows: (1) A message is displayed in area A1 of the analysis screen. It outputs detailed information including the detected anomaly, the detection method, and the standard value used to determine the anomaly. For the user checking the information, analysis can be performed from a state where the construction work that may have an anomaly is known.

[0186] (2) Areas A2 to A6 on the analysis screen display graphs 1 to 4 and tables. Graph 1 shows the order amount and cost amount for the construction project where an anomaly was detected. Check how the cost amount changes over the course of the month. If there is a month in which the cost amount exceeds the order amount, this is considered an anomaly. In this example, we can see that as of February 2021, the cost amount was greater than the order amount. Furthermore, as of March 2021, the cost amount has been trending below the order amount. This suggests that cost manipulation through cost allocation may have occurred in March 2021.

[0187] Graph 2 shows the cost rate trends for all construction work in the organization that manages the construction work for which an abnormality was detected. Check the cost rate trends by construction progress rate calculated from the planned construction period and the number of months elapsed. Construction cost manipulation is often carried out on an organizational basis, as it is often aimed at manipulating external impressions. There is a possibility that other construction work may also have costs with cost rates exceeding 100%. In this example, we can see that only the construction work for which an abnormality was detected has a cost exceeding 100%.

[0188] Graph 3 shows the construction project to which the highest cost was allocated, based on the cost allocation data for the construction project where an abnormality was detected. As with Graph 1, the order amount and cost amount can be confirmed. In the case of fraudulent cost allocation, it may be possible to confirm a sharp increase in cost amount before and after the cost allocation process. In this example, a sharp increase in cost amount can be seen between 2020 / 06 and 2021 / 02. As of 2020 / 06, the order amount also increased, which is thought to be due to the impact of additional orders. As of 2021 / 02, the increase is not related to orders, so it is thought to be an unplanned increase. As 2021 / 02 is the same month as the cost allocation month, it is thought to be an fraudulent increase in cost.

[0189] Graph 4 allows you to check the cost rate trends for all construction work for the organization that manages the construction work that was the subject of Graph 3. As with Graph 2, check the cost rate trends by construction progress rate calculated from the planned construction period and elapsed months. The organization that manages the construction work to which costs are being transferred may be the same as the organization that manages the construction work from which the costs are being transferred, or an organization with a strong relationship (in the case of fraud: an organization involved in the fraud). In this case, since the organization that manages the construction work for which the abnormality was detected (Department A) is a different organization (Department B), there is a possibility that costs have been transferred between Departments A and B in accordance with their understanding. This raises the possibility that similar cost transfers are occurring not only in Construction A but also in other construction work, so this organization should be recognized as one requiring caution.

[0190] In the table, you can check a list of details for which costs were transferred from the construction work for which an abnormality was detected. Pay attention to the transferred cost amount, the construction to which it was transferred, and the number of recipients. If there are a large number of recipients, it is clear that the transferred costs have been distributed across multiple construction works. In this case, it is clear that the costs have been distributed across construction work B, construction work D, and construction work G. Since costs have been transferred across organizations, there is a possibility that fraud involving multiple organizations has occurred.

[0191] From this point onwards, operations must be performed by the user who operates the system. A case where analysis is performed by switching the graph display on the analysis screen will be described with reference to Figures 33 to 48.

[0192] (3) Switch the display of the analysis graph and perform the analysis The following processing is performed by referring to the abnormality determination result data, abnormality determination result message data, and abnormality determination result message detail data selected from the list in (1) above.

[0193] (3-1) Switch the output unit of the organization to check and analyze the cost rate trends by construction. Output data aggregated by organization type (e.g., business establishment).

[0194] Figure 33(A) shows the extraction conditions for the analysis screen, Figure 33(B) shows the parameters for extraction, Figure 33(C) shows an example of abnormality determination result data, Figure 33(D) shows cost rate data by progress rate, Figure 33(E) shows cost rate data by progress rate of the cost transfer destination organization, Figure 33(F) shows an example of basic construction information data, Figure 33(G) shows an example of order posting data, and Figure 33(H) shows an example of construction cost posting history data.

[0195] 1. In the aggregation item of the extraction conditions shown in Figure 33(A), click the business establishment button to switch the graph display.

[0196] 2. Based on the specified extraction conditions, cost rate data by progress rate and cost rate data by progress rate of the cost allocation destination organization are obtained from the business database 106a as data to be displayed in graphs 2 and 4. For graphs other than Graphs 2 and 4, parameters are set in the same way as in (2-3) to (2-7) above to obtain data. Data for Graphs 2 and 4 is obtained based on the information of the record with the largest amount from the records obtained in (2-5) above. In this case, where data is obtained on a business unit basis, Graphs 2 and 4 have the same conditions and the same data acquisition method, so the data obtained will be the same.

[0197] 3. As shown in Figures 34(A) and (B), bind the cost rate data by progress rate to Graph 2, and as shown in Figures 34(C) and (D), bind the cost rate data by progress rate of the cost allocation destination organization to Graph 4. Note that all graphs except for Graphs 2 and 4 are bound to the graphs in the same way as (2-8) above.

[0198] 4. As shown in FIG. 34(E), graphs 1 to 4 are output to areas A2 to A5 of the analysis screen.

[0199] 5. As shown in Figure 35, the data for abnormalities is highlighted based on the organization and construction information contained in the "Abnormality Judgment Result Data" linked to the message. The same processing as in Figure 32 is performed.

[0200] 6. Analyze the data from the displayed graph. Figure 36 is a diagram to explain the analysis image using Graphs 2 and 4. The point to check here is that the purpose may be to avoid unprofitable construction work, i.e., to avoid damaging the organization's external reputation, so we consider the possibility of organizational fraud.

[0201] In Graph 2 shown in Figure 36(A), (1) the cost rate has increased sharply, so it is unlikely that a sudden increase at the end of a construction period is normal, and the increase is likely due to cost re-allocation. (2) There are other unprofitable construction projects with cost rates exceeding 100%, so this will be subject to recording in the construction loss reserve. Therefore, there is a possibility that fraud will occur next month through cost re-allocation, so this is a construction project that requires review next month. (3) There is a construction project with a large profit, and there is another unprofitable construction project, so there is a possibility that this will be subject to cost re-allocation. Therefore, this is a construction project that requires review next month.

[0202] In Graph 4 shown in Figure 36(B), (4) as confirmed in (1) above, it can be seen that one of the construction projects whose cost rate has increased sharply is a cost re-allocation destination. It can also be confirmed that the other projects are also cost re-allocation destinations.

[0203] (3-2) Switch the construction work to which costs are allocated and check and analyze the order amount, cost amount trends, and cost rate trends of the construction work to which costs are allocated. 37 to 41 are diagrams for explaining how to switch the construction work to which costs are allocated and check and analyze the order amount, cost amount trends, and cost rate trends of the construction work to which costs are allocated to another construction work.

[0204] 1. From the table shown in Figure 37(A), click on the record of the details you want to display to switch the graph display.

[0205] Based on the construction and organization information of the record selected in 2.1, the cumulative data of the cost transfer recipient construction and the cost rate data by progress rate of the cost transfer recipient organization are obtained as data for display in Graphs 3 and 4.

[0206] (i) According to the parameters shown in FIG. 37(B), cost allocation destination construction cumulative data, which is data for displaying Graph 3 as shown in FIG. 37(D), is acquired from the business database 106a.

[0207] (ii) According to the parameters shown in FIG. 38(A), the cost rate data by progress rate of the cost transfer destination organization, which is the data for displaying Graph 4 shown in FIG. 38(C), is acquired from the business database 106a.

[0208] 3. As shown in Figures 39(A) and (B), bind the cumulative data of the cost transfer recipient's construction work to Graph 3, and as shown in Figures 39(C) and (D), bind the "cost rate data by progress rate of the cost transfer recipient organization" to Graph 4. Note that all graphs other than Graphs 3 and 4 are bound to the graphs in the same way as (2-8) above.

[0209] 4. As shown in FIG. 39(E), graphs 1 to 4 are output to the analysis screen.

[0210] 5. As shown in Figure 40, the data for abnormalities is highlighted based on the organization and construction information contained in the abnormality judgment result data linked to the message.

[0211] 6. Analyze the data from the displayed graph. Figure 41 is a diagram to explain an image of analysis using graphs 3 and 4. Figure 41(A) shows an example of graph 3, Figure 41(B) shows an example of graph 4, and Figure 41(C) shows an example of cost allocation detail data (data acquired in (2-5)).

[0212] The key point to check here is that costs may be distributed across multiple projects as a way to conceal the transfer of costs. When costs are distributed across multiple projects, a table record is created for each transfer, so each one must be checked. If the transaction amount of the transferred project is large, the transferred costs may be buried and difficult to discover.

[0213] As shown in Figure 41 (A) and (B), in graphs 3 and 4, the re-allocation amount is small compared to the cost amount that has already occurred, so it is hidden (the characteristics of the amount fluctuation before and after the re-allocation are not apparent). Therefore, this type of case can occur when cost re-allocation is performed between construction projects with a difference in amount. Similarly, when the cost amount is divided into smaller amounts and re-allocated, it can also look like this.

[0214] (3-3) Specify the type of construction work and check and analyze the cost status of the specific construction work. 42 to 48, a case where the cost status of a specific construction project is confirmed and analyzed by specifying the construction project type will be described.

[0215] 1. Enter a value in the construction type field of the extraction conditions on the analysis screen to switch the graph display. Figure 42 shows an example of extraction conditions on the analysis screen, and the following explains an example where "rebar work" is entered as the construction type.

[0216] 2. Based on the specified extraction conditions, detailed cost allocation data, cost rate data by progress rate, and cost rate data by progress rate of the cost allocation destination organization are obtained as data to be displayed in Graphs 2 and 4, and in the table.

[0217] (i) According to the parameters shown in Figure 43(A), cost rate data by progress rate, which is data for displaying Graph 2 as shown in Figure 43(C), is obtained from the business database 106a. At this time, as shown in Figure 43(C), "construction" is extracted based on the parameter "construction type" from the construction basic information data shown in Figure 43(D). In the example shown in the figure, "construction D" is extracted using the construction type "rebar work" as the key.

[0218] (ii) According to the parameters shown in Figure 44(A), cost allocation detail data, which is data for table display as shown in Figure 44(C), is obtained from the business database 106a. At this time, as shown in Figure 44(C), "construction" is extracted based on the parameter "construction type" from the construction basic information data shown in Figure 44(D). In the example shown in the figure, "construction D" is extracted using the construction type "rebar work" as a key.

[0219] (iii) According to the parameters shown in Figure 45(A), cost rate data by progress rate of the cost allocation destination organization, which is data for displaying Graph 4 as shown in Figure 45(C), is obtained from the business database 106a. At this time, as shown in Figure 45(C), "construction" is extracted based on the parameter "construction type" from the construction basic information data shown in Figure 45(D). In the example shown in the same figure, "construction D" is extracted using the construction type "rebar work" as a key.

[0220] 3. As shown in Figure 46(A) and (B), bind the cost rate data by progress rate of the cost transfer destination organization to Graph 2, as shown in Figure 46(C) and (D), bind the "cost transfer detail data" to the table, as shown in Figure 46(E) and (F). Note that all data other than Graphs 2, 4, and the table are bound to the graphs in the same way as (2-8) above.

[0221] 4. As shown in Figure 47(A), graphs 1 to 4 and the table are output to areas A2 to A6 on the analysis screen.

[0222] 5. As shown in Figures 47(B) to (D), the data for abnormalities is highlighted based on the organization and construction information contained in the abnormality judgment result data linked to the message.

[0223] 6. Analyze the data from the displayed graph. Figure 48 is a diagram to explain the analysis image using Graphs 2 and 4 and the table. The point to check here is that it is unlikely that fraud would occur in construction projects that are strictly controlled, such as those that are checked by the government (e.g. dredging construction). Fraud is more likely to occur in construction projects where it is relatively easy to manipulate costs and profits.

[0224] As shown in Figure 48(A), Graph 2 (1) includes construction projects for which abnormalities were detected. (2) There are other construction projects that are also in the red, so there is a possibility that fraud through cost recalculation may occur in the same way.

[0225] As shown in Figure 48(B), Graph 4 (1) includes the construction work to which costs were re-allocated. (2) A similar trend of a sudden increase in cost rates can be seen in other construction work. In this example, given that both Department A and Department B belong to Business Unit A, it is possible that cost re-allocation is taking place for similar construction work.

[0226] As explained above, according to this embodiment, cumulative construction data including the cumulative order amount and cumulative cost amount for each accounting year and month and construction project is obtained based on the business data stored in the business database 106a, and a case where the cumulative order amount is less than the cumulative cost amount is detected as an abnormality, and a detection unit 102b detects the accounting year and month and construction project, and a display control unit 102c displays analysis data for the detected construction project before and after the detected accounting year and month on an analysis screen based on the business data, thereby enabling early detection of fraudulent manipulation of the cost of loss-making construction projects at low cost and with high accuracy.

[0227] [4. Contribution to the United Nations-led Sustainable Development Goals (SDGs)] This embodiment can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby contributing to the achievement of SDGs Goals 8 and 9.

[0228] Furthermore, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to the achievement of SDGs Goals 12, 13, and 15.

[0229] Furthermore, this embodiment can contribute to strengthening control and governance, which can contribute to Goal 16 of the SDGs.

[0230] 5. Other Embodiments The present invention may be implemented in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

[0231] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0232] Furthermore, 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 drawings can be changed as desired unless otherwise specified.

[0233] Furthermore, with regard to the cost management system 100, the components shown in the figures are functional concepts, and do not necessarily have to be physically configured as shown in the figures.

[0234] For example, all or any part of the processing functions of the cost management system 100, particularly those performed by the control unit, may be implemented by a CPU and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory, computer-readable recording medium containing programmed instructions for causing an information processing device to execute the processes described in this embodiment, and is mechanically read by the cost management system 100 as needed. That is, a computer program is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive) for working with the OS to issue instructions to the CPU and perform various processes. This computer program is executed by being loaded into RAM and cooperates with the CPU to form the control unit.

[0235] In addition, this computer program may be stored in an application program server connected to the cost management system 100 via any network, and all or part of it may be downloaded as needed.

[0236] Furthermore, the program for executing the processes described in this embodiment may be stored in a non-transitory computer-readable recording medium or configured as a program product. Here, the term "recording medium" includes any "portable physical medium" such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM (registered trademark)), a Compact Disk Read Only Memory (CD-ROM), a Magneto-Optical disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray (registered trademark) disc.

[0237] Furthermore, a "program" is a data processing method written in any language or description method, regardless of the format, such as source code or binary code. Note that a "program" is not necessarily limited to a single structure, but also includes a structure that is distributed as multiple modules or libraries, or a structure that achieves its function by cooperating with a separate program, such as an OS. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known configurations and procedures.

[0238] The various databases stored in the memory unit are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

[0239] The cost management system 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which any peripheral device is connected. The cost management system 100 may also be realized by installing software (including programs, data, etc.) that causes the device to perform the processing described in this embodiment.

[0240] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be configured by functionally or physically distributing and integrating them in any unit depending on various additions or functional loads. In other words, the above-described embodiments can be implemented in any combination, or embodiments can be implemented selectively. [Explanation of symbols]

[0241] 100 Cost Management System 102 Control section 102a Memory control unit 102b Detection unit 102c Display control unit 104 Communication interface unit 106 Storage section 106a Business Database 106b Data table for anomaly detection execution 106c Abnormality judgment result data table 108 Input / Output Interface Section 112 Input Device 114 Output Device 300 Network

Claims

1. A cost management system equipped with a control unit, The control unit, It is configured to provide access to business data including order recording data containing accounting month and business location, department, and order amount for each project, and construction cost recording history data containing accounting month and business location, department, and cost amount for each project. Based on the aforementioned business data, the cumulative order amount and cumulative cost amount for each construction project at the end of each accounting month are calculated, and cumulative construction data including the cumulative order amount and cumulative cost amount for each construction project in each accounting month is obtained. Construction projects where the cumulative order amount < cumulative cost amount in the accounting month during the period set in the cumulative construction data, and where the cumulative order amount ≥ cumulative cost amount in subsequent accounting months are detected as fraudulent, and a detection means for detecting accounting months where the cumulative order amount < cumulative cost amount is provided. Based on the aforementioned business data, a display control means displays analytical data for the accounting year and month in which the detected construction work was found on an analysis screen. A cost management system characterized by having the following features.

2. The cost management system according to claim 1, characterized in that the display control means displays a message including the construction work detected as abnormal, accounting year and month, cost amount, and detection method in a predetermined area of ​​the analysis screen.

3. The cost management system according to claim 1, characterized in that the display control means displays a graph showing the monthly trend of order amount and cost amount for construction work in a predetermined area of ​​the analysis screen, based on the construction cumulative data.

4. The aforementioned business data further includes basic construction information data, which includes the contract month, construction project, business location, department, and planned construction period. The cost management system according to claim 1, characterized in that the display control means acquires cost rate data by progress rate, including the progress rate calculated by the number of months elapsed in construction / scheduled construction period, the construction, business office, department, and cumulative cost amount / cumulative order amount, for the department in charge of the construction where an anomaly was detected based on the business data, and displays a graph showing the trend of cost rates by progress rate for each construction based on the acquired cost rate by progress rate in a predetermined area of ​​the analysis screen.

5. The aforementioned business data includes cost adjustment data having a cost adjustment number, accounting year and month, original project to be substituted, destination project to be substituted, and cost amount. The cost management system according to claim 1, characterized in that the display control means, based on the business data, acquires cost relocation detail data including cost adjustment number, business office, department, and cost amount for the relocation destination project, with the project in which an anomaly was detected as the relocation source project, and acquires cumulative cost relocation destination project data including accounting year and month, business office, department, cumulative order amount, and cumulative cost amount for the relocation destination project with the largest cost amount among the acquired cost relocation detail data, based on the business data, and displays a graph showing the monthly trend of order amount and cost amount based on the acquired cumulative cost relocation destination project data in a predetermined area of ​​the analysis screen.

6. The aforementioned business data further includes basic construction information data, which includes the contract month, construction project, business location, department, and planned construction period. The cost management system according to claim 5, characterized in that the display control means acquires cost rate data by progress rate of the cost transfer destination organization, including the construction progress rate calculated by construction months elapsed / scheduled construction period, construction, business office, department, and cumulative cost amount / cumulative order amount, based on the business data, for the department that oversees the transfer destination construction for the record with the largest cost amount among the acquired cost transfer detail data, and displays a graph showing the trend of cost rates by construction progress rate for each construction based on the acquired cost rate data by progress rate of the cost transfer destination organization in a predetermined area of ​​the analysis screen.

7. The aforementioned business data includes basic construction information data having contract month, construction, business location, department, and planned construction period, and cost adjustment data having cost adjustment number, accounting year and month, source construction, destination construction, and cost amount. The cost management system according to claim 1, characterized in that the display control means, for construction work in which an anomaly has been detected, acquires cost relocation detail data including cost adjustment number, business office, department, and cost amount for the relocation destination construction work, with the construction work in which the anomaly was detected as the relocation source construction work, and displays a table based on the acquired cost relocation detail data in a predetermined area of ​​the analysis screen.

8. The cost management system according to any one of claims 2 to 7, wherein the analysis screen is an area for setting data extraction conditions, and includes an extraction condition area in which the aggregation unit can be specified as a business office or department, and the display control means switches the aggregation unit of the graph or table according to the business office or department specified in the extraction condition area.

9. A cost management method performed by an information processing device equipped with a control unit, The control unit, It is configured to provide access to business data including order recording data containing accounting month and business location, department, and order amount for each project, and construction cost recording history data containing accounting month and business location, department, and cost amount for each project. The control unit is executed as follows: Based on the aforementioned business data, the cumulative order amount and cumulative cost amount for each construction project at the end of each accounting month are calculated, and cumulative construction data including the cumulative order amount and cumulative cost amount for each construction project in each accounting month is obtained. Construction projects where the cumulative order amount < cumulative cost amount in the accounting month within the period set in the cumulative construction data, and where the cumulative order amount ≥ cumulative cost amount in subsequent accounting months are detected as fraudulent, and a detection process to detect accounting months where the cumulative order amount < cumulative cost amount is performed, Based on the aforementioned business data, a display control process is performed to display analytical data for the detected construction work, specifically for the accounting year and month before and after the detection, on the analysis screen. A cost management method characterized by including the following.

10. A cost management program to be executed by an information processing device equipped with a control unit, The control unit, It is configured to provide access to business data including order recording data containing accounting month and business location, department, and order amount for each project, and construction cost recording history data containing accounting month and business location, department, and cost amount for each project. In the control unit, Based on the aforementioned business data, the cumulative order amount and cumulative cost amount for each construction project at the end of each accounting month are calculated, and cumulative construction data including the cumulative order amount and cumulative cost amount for each construction project in each accounting month is obtained. Construction projects where the cumulative order amount < cumulative cost amount in the accounting month within the period set in the cumulative construction data, and where the cumulative order amount ≥ cumulative cost amount in subsequent accounting months are detected as fraudulent, and a detection process to detect accounting months where the cumulative order amount < cumulative cost amount is performed, Based on the aforementioned business data, a display control process is performed to display analytical data for the detected construction work, specifically for the accounting year and month before and after the detection, on the analysis screen. A cost management program to implement this.

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