JP2019211995A - Asset calculation system, asset calculation method, and asset calculation program - Google Patents

Abstract

To provide an asset calculation system, a method and a program thereof, for collecting asset states for customers, enhancing prediction accuracy in future asset states for the customers while taking into account external environmental factors including macro and micro indexes and the like, and also capable of giving effective advises to the customers.SOLUTION: A system comprises: a customer information storage section which obtains and stores customer total asset information including income information relating to income of a customer, expense information relating to expenses of the customer, asset information relating to assets of the customer, and debt information relating to debts of the customer; an asset income expense state calculation section which calculates the asset income expense state of the customer on the basis of the customer total asset information while obtaining life information of the customer; a compensation calculation section which reflects compensation information to the asset income expense state for calculating the future asset income expense state of the customer; and an output section which calculates and outputs a deviation amount between the asset income expense state and the future asset income expense state.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an asset calculation system, an asset calculation method, and an asset calculation program. More particularly, the present invention relates to an asset calculation system, an asset calculation method, and an asset calculation program for predicting a future economic situation based on a customer's economic situation.

  In the life of an individual, various lifestyle changes such as employment, marriage, real estate purchase, family increase, retirement, and other illnesses come. In such a case, in order to live safely in monetary terms, it is desirable to secure income and control spending as well as appropriately manage assets and reduce liabilities. However, individuals remain passive operations, such as simply saving or purchasing financial products. As a practical matter, it is difficult for one individual to grasp various financial products, tax systems, economic statistics, various economic indicators and manage assets accurately, and also to manage the adjustment of income and expenditure, etc. The burden of is also great.

  In recent years, with the improvement of computer performance, complex processing and calculation can be executed quickly, and from the development of information communication equipment, it is becoming easier to grasp the status of personal assets and predict aging. For example, an asset prediction system that predicts a future real estate value from the current real estate value has been proposed (see Patent Document 1).

  However, personal assets are not limited to real estate, but rather are formed by a combination of various financial assets. Then, when comprehensively grasping various assets, liabilities, etc. possessed by individuals and calculating their future value, the elements to be considered in the prediction increase dramatically. For this reason, in the current prediction system, (1) it is necessary to set a large number of variables for prediction of income and expenditure. (2) When inputting a large number of variables, it is often difficult to obtain accurate numerical values, and estimated values and approximate values are used. (3) On the premise of numerical values assumed from various variables, it is necessary to calculate individual predicted values from the top down by using macro numerical values, statistical values, and other databases. (4) Prediction at a certain point is limited to that point in time, and in general, past predictions and actual positions are hardly considered in the next prediction. For this reason, sufficient accuracy cannot be obtained in the conventional prediction, and the advice to individuals using the system tends to be ambiguous.

  Therefore, (1) It is possible to input accurate numerical values regardless of conventional rough estimates and estimations, (2) From the prediction to correspond to the characteristics of individual customers, (3) Use the system to ensure continuity in the prediction Asset calculation system and method that collects the asset status of individuals who perform and enhances the accuracy of future predictions while taking into account external environmental factors such as macro and micro indicators, and also enables effective advice to individuals using the system And a program was desired.

JP 2017-117378 A

  The present invention has been made in view of the above points, collects the asset status of customers accessing and using the system, and predicts the customer's future asset status while taking into account external environmental factors such as macro and micro indicators. Provided are an asset calculation system, a method and a program for improving accuracy and enabling effective advice to customers.

  A customer information storage unit that acquires and stores customer total asset information including revenue information about customer revenue, expenditure information about customer expenditure, asset information about customer assets, and debt information about customer liabilities; An asset balance status calculation unit that calculates the customer's asset balance status based on the customer total asset information, and reflects the correction information on the asset balance status to determine the customer's future asset balance status. The present invention relates to an asset calculation system comprising: a correction calculation unit for calculating; and an output unit for calculating and outputting a deviation amount between the asset balance state and the future asset balance state.

  According to the asset calculation system of the present invention, the customer asset status is collected, and external environmental factors such as macro and micro indicators are added to the asset status to improve the forecast accuracy of the customer's future asset status. Effective advice can also be made possible. Similarly, in the asset calculation method and the asset calculation program, it is possible to provide effective advice to customers.

It is a schematic diagram which shows the structure of an asset calculation system. It is a schematic diagram which shows the flow of the information acquired and calculated. It is a schematic diagram explaining the transition of an asset balance state and the relationship between prediction and correction information. It is a schematic diagram which shows the deviation | shift amount of an asset balance state. It is a schematic diagram which shows the mutual comparison between customers. It is a schematic diagram which shows the future asset balance state between customers. It is a flowchart explaining the process of an asset calculation method.

  The configuration of the asset calculation system 1 of the present invention is represented as a schematic diagram of FIG. In the asset calculation system 1, customer terminals 11 and 12 that receive services from a business operator that operates (operates) the asset calculation system 1 are connected to the Internet line 10. Statistical data provider 21 and 22 are also connected to the Internet line 10. A computer unit 100 for executing the main processing of the present invention is connected to the Internet line 10. In this way, in the asset calculation system 1, various devices, customers (terminals), service providers, and statistical material data providers are connected through a wired or wireless network. Further, although not shown, a server for storing various information is also connected to the Internet line 10. For convenience of illustration, the customer terminal and the provider of statistical material data are two. Actually, the number of connections to the Internet line 10 is not limited to the figure and is plural.

  The terminals 11 and 12 are information equipment terminals of customers who contract with a business operator who operates the asset calculation system 1 and provides services. Specifically, the terminals 11 and 12 are devices such as personal computers (PCs), smartphones, and tablets. . Therefore, the customer can freely access the business operator and input necessary information, and obtain various calculation results from the business operator. In other words, customers can easily receive advice and support for their own financial life planning.

  The statistical data providers 21 and 22 are mainly government agencies (Ministry of Finance, Ministry of Economy, Trade and Industry, Ministry of Land, Infrastructure, Transport and Tourism, Ministry of Internal Affairs and Communications, National Tax Agency, etc.), Central Bank (Bank of Japan), Private Financial Institutions (Banks, Securities Companies, etc.) ), Think tanks, statistical research institutions, exchanges, stock exchanges, etc. These include various organizations, organizations, organizations, companies, universities, graduate schools, etc. that have published various economic statistics and economic indicators including macro indicators and micro indicators on the website of the institution.

  The computer unit 100 connected to the Internet line 10 is configured with a CPU, a main memory, other LSIs, a ROM, a RAM, and the like in hardware. In terms of software, it is realized by an asset calculation program or the like loaded in the main memory. The computer unit 100 can be realized using various electronic computers (calculation resources) such as a personal computer (PC), a mainframe, a workstation, a cloud computing system, and the like.

  When the function units of the computer unit 100 in FIG. 1 are realized by software, the computer unit 100 is realized by executing instructions of a program that is software for realizing each function. As a recording medium for storing the program, a “non-temporary tangible medium” such as a CD, a DVD, a semiconductor memory, a programmable logic circuit, or the like can be used. In addition, this program may be supplied to the computer unit 100 of the asset calculation system 1 via any transmission medium (such as a communication network or a broadcast wave) that can transmit the program.

  Various storage units in the computer unit 100 are known storage devices such as an HDD or an SSD. In addition, each functional unit that executes various calculations and calculations is an arithmetic element such as a CPU. In addition, the appropriate computer unit 100 includes an input / output interface, an input device such as a keyboard and a mouse, a display device such as a display, an output device for outputting data, and the like.

  The computer unit 100 corresponds to the customer information storage unit 110, the asset balance state calculation unit 120, the data aggregation unit 130, the extraction generation unit 140, the correction calculation unit 150, the mutual comparison unit 160, the reference presentation unit 170, and the output unit 180. The storage unit provided in the computer unit 100 stores various data, information, the asset calculation program, data necessary for executing the program, and the like.

  The asset calculation system 1 (computer unit 100) will be described with reference to FIGS. The customer information storage unit 110 includes revenue information (D1) related to the customer's income, expenditure information (D2) related to the customer's expenditure, asset information (D3) related to the customer's assets, and debt information (D4) related to the customer's debt. The customer total asset information (D5) including is acquired and stored. The customer can input the revenue information (D1), the expenditure information (D2), the asset information (D3), and the liability information (D4) through the terminal 11 in FIG. For the input, the data aggregation unit 130 described later is also used.

  The income information (D1) is information related to the amount that can be acquired by the labor consideration, stock dividend, real estate revenue, interest rate, etc. in the customer's one year (or within a predetermined period). This revenue information (D1) mainly corresponds to the past actual value of the customer. The expenditure information (D2) is information related to the amount paid (consumed) from the household budget in one year (or within a predetermined period) of the customer. Asset information (D3) is information about cash (deposits), stocks, bonds, movable property, real estate, etc. held by customers. The debt information (D4) is information relating to a customer's borrowing, movable property or real estate loan. Of course, these pieces of information are examples, and there may be other than the list.

  Therefore, the customer information storage unit 110 aggregates the revenue information (D1), the expenditure information (D2), the asset information (D3), and the liability information (D4), thereby obtaining the customer total asset information (D5) of the customer. Generate (see FIG. 2). Therefore, the customer total asset information (D5) corresponds to a list that summarizes the income, expenditure, assets, and liabilities of the individual customer.

  The asset balance state calculation unit 120 acquires the customer life information (D6) and calculates the customer asset balance state (D7) based on the customer total asset information (D5). Customer life information (D6) is information related to changes in lifestyle, for example, marriage, childbirth, attendance at school, employment, job change, retirement, illness, customer property (passenger car), real estate (housing) in customers and their families. This is information that causes a change in the customer's asset balance such as purchases. In other words, it is information necessary for accurate assessment of the asset balance. The asset balance state (D7) is information adjusted by adding customer life information (D6) to the customer total asset information (D5), and is information equivalent to a so-called individual income statement, balance sheet, etc. . The customer can also input life information (D6) through the terminal 11 of FIG.

  The data aggregation unit 130 aggregates revenue information (D1), expenditure information (D2), asset information (D3), debt information (D4), and living information (D6) acquired through input from a customer. In addition, the data aggregation unit 130 obtains necessary information from the websites of customer banks, insurance companies, various point companies, etc. by utilizing web scraping (web crawler), application programming interface (API), etc. And can be aggregated. By providing the data aggregation unit 130, it is not necessary for the customer to input information necessary for calculation one by one (so-called automatic input), and information relating to the customer is inherited as it is unless the previously input information is changed. . In addition, aggregation for each customer or for each item is possible. By providing the data aggregation unit 130, the accuracy of information aggregation can be improved. In addition, the centralization of customer information reduces management and further increases the security of information protection.

  The extraction and generation unit 140 detects the web pages 27 and 28 by crawling the websites 25 and 26 in FIG. 1, and the revenue information (D1), expenditure information (D2), asset information (D3), Statistical data (D11, D12) relating to the debt information (D4) and the life information (D6) is extracted to generate correction information (D20; individual correction information D21, D22) (see FIG. 2).

  The schematic diagram of FIG. 1 and the statistical data provider 21 and 22 described above have web pages 27 and 28 on the websites 25 and 26, respectively. On the web pages 27 and 28, various economic statistics and economic indicators including macro indicators and micro indicators are also released and updated as needed. In the past, when trying to use these various statistics, indicators, etc., there were many scenes where the necessary data was browsed, extracted, and input by customers. For this reason, the labor burden on the operator such as accurate input of materials and confirmation thereof is large, and it is not easy to eradicate input errors.

  On the other hand, the extraction generation unit 140 automatically acquires information (statistics and indicators) related to customers existing on the web pages 27 and 28 on the websites 25 and 26 by using the function of the crawler. Then, statistical data (D11, D12) is extracted as necessary information from the acquired information. For this reason, with the automation by the crawler function, the efficiency of the worker is reduced and the accuracy is improved and the information is reflected quickly. The type and number of statistical data (D11, D12) are not limited. Micro statistics, macro statistics, prices, land prices, demographics, household consumption expenditure, Engel coefficient, education costs, taxation (tax rate), depreciation, insurance It includes a wide range of statistics and indicators related to customer lives such as fees and disease treatment.

  Furthermore, the extraction generation unit 140 collects and extracts from the website the customer revenue information (D1), expenditure information (D2), asset information (D3), debt information (D4), and life information (D6). Various statistical data (D11, D12) are combined to generate correction information (D20; D21, D22) (see FIG. 2). As an example of the correction information (D20), for example, in the case of income information (D1), the annual income tends to increase with age. Therefore, when predicting future revenue, it is a numerical value such as a predetermined multiplier (1.05 times). Further, in the case of the expenditure information (D2), it is a multiplier derived from a change in food expenses and food prices within a predetermined period (one year). In the case of life information (D6), it is an estimated model of cost generation that takes into account the type of school (private, public, overseas study abroad), period (university advancement, graduate school advancement), etc. where the child of the customer is advanced. Of course, these are merely examples, and other matters are naturally included and reflected.

  The correction calculation unit 150 reflects the correction information (D20) on the customer asset balance state (D7) to calculate the customer's future asset balance state (D30). As described above, the customer's asset balance state (D7) includes the customer's income information (D1), expenditure information (D2), asset information (D3), debt information (D4), and living information (D6). That is, the asset balance state (D7) is information reflecting the asset balance of the customer including the present (input time by the customer, aggregation time by the data aggregation unit). In other words, the asset balance state (D7) is information serving as a reference point for predicting the future.

  Various types of correction information (D20) exist as described above, and correspond to individual information (D1 and the like) constituting the asset balance state (D7). Therefore, on the basis of the current asset balance state (D7), necessary information such as multiplier, addition, and subtraction is executed based on the correction information (D20) for each piece of information included in the asset balance state (D7). Is done. As a result, the future asset balance state (D30) after the elapse of a predetermined period such as one year, three years, and five years from the current asset balance state (D7) is calculated. It is also possible to predict the future asset balance over a long period (more than 30 years). In this case, both the adjustment based on the change in the statistical value described above or the correction based on the comparison between the previous prediction and the actual value described later are used for the prediction.

  For example, as shown in the schematic diagram of FIG. 4 below, the current asset balance state (D7) is on the left side of the bar graph. And as an example, the future asset balance state (D30) after five years is on the right side of the bar graph. This customer has acquired real estate, and in the future asset balance state (D30), the subsequent increase in land prices is a calculation result included in the prediction. In this case, the correction information (D20) in which the land price is added to the customer asset information (D3) is reflected. Or, for example, when the customer refinances the current borrowing, the future repayment interest rate decreases, the accumulation of cash and deposits increases from the present time, and the increase in the amount of assets can be predicted as a whole. In this case, the correction information (D20) in which the repayment interest rate of the financial institution is added to the debt information (D4) is reflected.

  Further, as a utilization form of the correction information, based on correction information (D20; individual correction information D21, D22) at a certain time {X time}, a future asset balance state at a future time {Y time} is predicted. Is done. Here, the predicted value (Sx) up to the time Y at the time X and the actual value (Sy) of the asset balance state actually found at the time Y are compared, and the difference between them is calculated. For example, in the case of the result of “Sy / Sx = 1.1 (that is, 1.1 times or 110%)”, the future calculated from the correction information (D20; individual correction information D21, D22) at the time Y A multiplier of 1.1 times or 110% is added to the asset balance state.

  As a specific example, the expenditure of educational expenses in the prediction at the time point X was 1 million yen. Next, at the time point Y, the actual value from the time point X to the time point Y was 1.1 million yen. The deviation amount is corrected for the future predicted value of 1.2 million yen at the time Y. In this case, 1.1 million yen / 1 million yen = 1.1 times (110%), or 1.1 million yen-1 million yen = 100,000 yen is the correction value. Therefore, by reflecting the correction value to the numerical value of the future prediction at the time Y (for example, 1,200,000 yen), the initial prediction value at the time Y is 1,200,000 × 1.1 = 1.32 million yen (the prediction value after the correction value is reflected). ), Or 1.2 million yen + (1.1 million yen-1 million yen) = 1.32 million yen (predicted value after reflecting the correction value), the calculation including the correction value can be performed.

  Further, generation of correction information (D20) in the correction calculation unit 150 will be described with reference to FIG. In FIG. 3A, the right direction on the page is the elapsed time, and the vertical direction (upward) on the page is the asset balance state (amount of money). In the figure, the thick solid line represents the time series of the asset balance (amount of money) of a customer.

Asset balance state at the time _{T 0} (past time) is a _{M o.} Point in time T ₀ time from T ₁ asset balance state at the time of the transition in due course to the _(current) is a M _p. However, according to the customer, the increase in the balance of assets remains lower than originally planned. Reality of the asset balance state is M _1. That is, the difference amount Δ ₁ between M _p and M ₁ is determined at time T ₁ . Furthermore, based on the actual asset balance state M ₁ at the time T ₁ of the the customer, future asset balance state M ₂ at the time T ₂ is predicted. Then, the future of the asset balance state M _q of the time T ₂ of the prediction of as planned, also the amount of difference Δ ₂ between the future of the asset balance state M ₂ at the time T ₂ which is an extension of the current state of life to turn out. Therefore, if taken no measures, over time, the amount of difference delta ₁ is expanded to the amount of difference delta _2, the difference between the extension of the transition and current plan spreads.

As is clear from this, there is a need for a more accurate prediction of the future asset balance state of the forecast as planned and the future asset balance state of the prediction of the extension of the current life. If accurate prediction is possible, the review and advice of customers' lives will be efficient and accurate. Therefore, being based asset balance state (D7) at the current time point T _1, as a future asset balance state (D30), and future asset balance condition M _q at time T ₂ of the prediction of the planned, the current state of life future both asset balance state M ₂ at the time T ₂ of the case lasted is predicted, including the correction information (D20).

In FIG. 3 (b), in the current time T _1, when the review of the customer's life can be achieved suitably, shows how to reach the future asset balance condition M _r at the time T ₃ of the prediction planned Yes. A broken line K is a transition prediction of the asset balance state when it is assumed that an appropriate review is performed. And Changes to the asset balance condition and which at the current time point T ₁ is grasped, the future asset balance state in the near future are accurately predicted with correct information. Therefore, the asset balance status based on daily life is compared with the forecasted future asset balance status as planned, and whether or not the asset balance status is smooth or has room for improvement is determined and reviewed at successive points in time series. Is possible. By using the asset calculation system, the customer can check the state of asset balance at any time, so that the customer can continue to review their lives and help to form an asset without forcing the customer to make an excessive effort.

  In the near future prediction using the asset calculation system, as a method of future prediction using the correction information (D20) generated by the correction calculation unit 150, first, the average value of the asset balance status for a plurality of customers is calculated. Used. The asset balance statuses of a plurality of customers who are other than one target customer and whose lifestyle is relatively close to the one customer are aggregated, and the average value of the asset balance status is obtained from these. This average value is incorporated in the correction information (D20). If the customers are relatively close to each other, the tendency of the asset balance state is considered to be similar. Therefore, fluctuations in asset balance status for each customer are alleviated.

  Second, the average value of a plurality of asset balance states of the same customer is used. Specifically, multiple asset balance states at the time of past time series up to the present are extracted. The average, especially the moving average value, is obtained. For example, it is a monthly moving average value for the most recent 6 months, the most recent 12 months, etc. every month. This average value (moving average value) is incorporated in the correction information (D20). When the average value is used, factors such as seasonal fluctuations in the balance of payments for the same customer are alleviated, and the tendency of the asset balance state is easily grasped.

  Third, the planned value of the asset balance status of the same customer is used. In this case, the asset balance status of the customer is planned by self-reporting etc. in time series, and is set as a planned value at each time point. This plan value is incorporated in the correction information (D20). By using the planned value, it is possible to make the customer conscious of life design separately from the future asset balance state (D30) calculated by the asset calculation system. Therefore, the customer can easily adjust the increase / decrease in expenditure within an appropriate range. When the customer knows the difference between the planned value and the actual value, the customer gets motivation such as reviewing the planned value or improving the actual life. When setting plan values, the types of scenario models related to lifetime design, such as the purchase of real estate and the conversion to independent business owners, can be included in the selection factors.

  The output unit 180 calculates and outputs the amount of deviation (D40) between the asset balance state (D7) and the future asset balance state (D30) after the lapse of a predetermined period mainly at the present time. The future asset balance state (D30) is not limited to a single numerical value, but can be calculated as a certain range. The output of the divergence amount (D40) is a notification from the business operator using the asset calculation system 1 to the terminals 11 and 12 of the customers who receive service provision through the Internet line 10. Appropriate methods such as e-mail, chat, and guidance for browsing the dedicated website. The transition of the time-series asset balance state in FIG. 3 and the display of the divergence amount in FIG. 4 include visual figures such as graphs as well as specific numerical values.

  According to the schematic diagram of FIG. 4, the difference (indicated by an arrow) between the left and right bar graphs corresponds to the divergence amount (D40) here. That is, the difference between the present and the predicted future. In this way, by incorporating a large number of micro indexes and the like, the prediction accuracy of the customer's future asset balance state can be improved. Therefore, the customer can clearly recognize the current and future comparisons and the pros and cons of the interests and can reflect them in daily life. Also, multiple types can be assumed depending on the operational pattern of financial assets for the future. Therefore, what kind of management should be selected (low risk / low return conservative operation, high risk / high return active management combination), customer demand, balance of assets balance based on customer lifestyle, Portfolio proposals are also possible. Further, the time series transition of the asset balance state, the asset balance state prediction, and the difference amount information of FIG. 3 described above are also included in the deviation amount (D40).

  Furthermore, the output unit 180 outputs alert information (D45) to the customer according to the deviation amount (D40). For example, it is possible to predict the future such as the imbalance of the balance when the customer continues the current lifestyle, the risk of being unable to repay due to excessive borrowing. In other words, if the amount of increase in assets etc. is different, it is profitable for the customer. On the other hand, if the future asset balance state (D30) is smaller than the current asset balance state (D7), the customer should review the current life. In other words, there is room for improvement here. Therefore, the alert information (D4) is output as a review of the current life. For example, if you are going to purchase real estate (housing) with a small amount of down payment, you may want to wait for a predetermined cash deposit to increase before purchasing. Alternatively, if the trend of deterioration in the future asset situation is predicted, a suggestion for saving a certain amount every month is also included in the alert information. In addition, as mentioned above, lump sum repayment of borrowings, refinancing to lower interest rates of borrowings, etc. are also included in the alert information.

  As shown in FIG. 1 and as described above, the operator who operates the asset calculation system 1 can collect various customer information (D1 and the like) through the customer terminals 11 and 12, and further, in the aggregation unit 130 of the asset calculation system 1 Centralized management of customer information is possible. For centralized management of customer information, in addition to acquisition through a terminal, for example, authentication information or the like can be stored on a cloud server and acquired directly from the cloud server. Therefore, comparison between customers or users of business operators who operate the asset calculation system 1 is also possible.

  Therefore, the intercomparison unit 160 aggregates the asset balance states (D7) for a plurality of customers, and executes a mutual comparison of the asset balance states (D7) of a plurality of customers. For example, it can be expressed as the schematic diagram of FIG. The figure shows the asset balance status (D7; individual D7a, D7b, D7c) for each customer for a plurality of customers a, b, and c as a pie chart. The circle is divided into revenue information (D1), expenditure information (D2), asset information (D3), liability information (D4), etc. of each customer included in the asset balance state (D7). The size of the circle is the total amount of the asset balance state (D7).

  The intercomparison unit 160 is effective for comparison (relativeization) between customers belonging to a common tendency hierarchy after the hierarchy classification such as customer generation, family structure, income, and occupation. From the schematic diagram of FIG. 5, first, the total amount of the asset balance state (D7) is grasped by the size of the circle for each customer. Furthermore, the asset balance status (D7) can be grasped as income dependency (labor income type) or asset dependency (non-labor income advantage type) through the divisional balance in the circle. In addition, since some of the spending and liabilities between customers can be ascertained, it is also possible to analyze waste trends or saving trends.

  These mutual comparison information is output to the individual customers through the output unit 180 described above. However, from the viewpoint of personal information protection, information and illustrations are anonymized and output.

  Furthermore, it can be extended to the mutual comparison of future asset balance status by developing the mutual comparison function. Therefore, the reference presentation unit 170 aggregates the asset balance state (D7) for a plurality of customers, calculates the future asset balance state (D30) of the plurality of customers, and also calculates the future asset balance state ( D30) Extract and present the asset balance state of the customer as a reference or the future asset balance state of the customer as a reference from D30).

  In the example of the schematic diagram of FIG. 6, the left column is the so-called asset balance state (D7; D7a, D7b, D7c) for the current customer a, customer b, and customer c. As in FIG. 4, the circle is divided into revenue information (D1), expenditure information (D2), asset information (D3), liability information (D4), etc. of each customer included in the asset balance state (D7). . The size of the circle is the total amount of the asset balance state (D7).

  The right column shows the asset balance state (D30; D30a, D30b, D30c) predicted for customer a, customer b, and customer c after aging (for example, five years later). There is also a change in the size of each circle and the division ratio inside it. As described above, the customer a, the customer b, and the customer c are customers that belong to a common trend hierarchy. In that case, it is supposed to converge to a similar tendency in nature. Therefore, if there is a relative lack of relative balance from other customers through a comparison of current and future asset balances, some problems may be pointed out in the life of the customer.

  In such a case, the asset balance state (D7) of the reference customer or the future asset balance state (D30) of the reference customer is presented through the reference presentation unit 170. Then, it is output through the output unit 180 described above. However, from the viewpoint of personal information protection, information and illustrations are anonymized and output. Then, the customer can improve the state of the current asset balance and review the life on the basis of the future asset balance information of other customers that is helpful from the present situation.

  Next, the asset calculation method of the present invention will be described together with an asset calculation program using the flowchart of FIG. The asset calculation method of the present invention is executed by the computer unit 100 of the asset calculation system 1 based on an asset calculation program. The asset calculation program has a customer information storage function, an asset balance state calculation function, a data aggregation function, an extraction generation function, a correction calculation function, a mutual comparison function, a reference presentation function, an output function, etc. for the computer unit 100 of FIG. The various functions are executed. Each of these functions is executed in the order shown. Since each function overlaps with the description of the asset calculation system 1 described above, the details are omitted.

  The flowchart of FIG. 7 shows the overall flow of the asset calculation method. The customer information storage step (S110), the asset balance state calculation step (S120), the data aggregation step (S130), the extraction generation step (S140), and the correction calculation step ( S150), a comparison step (S160), a reference presentation step (S170), and an output step (S180). In addition, the asset calculation method includes various steps (not shown) such as storage, storage, recall, calculation, and comparison.

  The customer information storage function includes customer information including revenue information (D1) related to customer income, expenditure information (D2) related to customer expenditure, asset information (D3) related to customer assets, and debt information (D4) related to customer liabilities. Total asset information (D5) is acquired and stored (S110; customer information storage step). The customer information storage function is executed by the customer information storage unit 110 (see FIG. 1) of the computer unit 100 of the asset calculation system 1.

  The asset balance state calculation function acquires the customer life information (D6) and calculates the customer asset balance state (D7) based on the customer total asset information (D5) (S120; asset balance state calculation step). The asset balance state calculation function is executed by the asset balance state calculation unit 120 of the computer unit 100.

  The data aggregation function aggregates income information (D1), expenditure information (D2), asset information (D3), debt information (D4), and living information (D6) acquired through input from the customer (S130; data Aggregation step). The data aggregation function is executed by the data aggregation unit 130 of the computer unit 100.

  The extraction and generation function detects the web pages 27 and 28 by crawling the websites 25 and 26, and the revenue information (D1), expenditure information (D2), asset information (D3), and debt information (D4) from the web pages 27 and 28. ) And statistical material data (D11, D12) related to life information (D6) are extracted to generate correction information (D20) (S140; extraction generation step). The extraction generation function is executed by the extraction generation unit 140 of the computer unit 100.

  The correction calculation function calculates the customer's future asset balance state (D30) by reflecting the correction information (D20) on the asset balance state (D7) (S150; correction calculation step). The correction calculation function is executed by the correction calculation unit 150 of the computer unit 100.

  The mutual comparison function aggregates the asset balance status (D7) for a plurality of customers, and executes a mutual comparison of the asset balance statuses (D7) of a plurality of customers (S160; mutual comparison step). The mutual comparison function is executed by the mutual comparison unit 160 of the computer unit 100.

  The reference presentation function aggregates the asset balance status (D7) for a plurality of customers, calculates the future asset balance status (D30) of the plurality of customers, and calculates the future asset balance status (D30) of the plurality of customers. The asset balance state (D7) of the reference customer or the future asset balance state (D30) of the reference customer is extracted and presented (S170; reference presentation step). The reference presentation function is executed by the reference presentation unit 170 of the computer unit 100.

  The output function calculates and outputs a deviation amount between the asset balance state (D7) and the future asset balance state (D30) (S180; output step). The output function is executed by the output unit 180 of the computer unit 100.

  According to the asset calculation system of the present invention, since the customer's asset status is collected and external environmental factors such as macro and micro indicators are taken into account, the customer's future asset status prediction accuracy is improved. Therefore, it contributes to life planning advice. In addition, similar effects are expected in the asset calculation method and the asset calculation program.

DESCRIPTION OF SYMBOLS 1 Asset calculation system 10 Internet line 11,12 Terminal 21,22 Statistical data provider 25,26 Website 27,28 Web page 100 Computer part 110 Customer information storage part 120 Asset balance state calculation part 130 Data aggregation part 140 Extraction Generation unit 150 Correction calculation unit 160 Intercomparison unit 170 Reference presentation unit 180 Output unit

Claims (11)

A customer information storage unit for acquiring and storing customer total asset information including revenue information on customer revenue, expenditure information on customer expenditure, asset information on customer assets, and debt information on customer liabilities;
An asset balance state calculation unit that obtains customer life information and calculates a customer's asset balance state based on the customer total asset information;
A correction calculation unit for calculating a future asset balance state of the customer by reflecting correction information on the asset balance state;
An asset calculation system comprising: an output unit that calculates and outputs a deviation amount between the asset balance state and the future asset balance state.   The asset calculation system according to claim 1, further comprising a data aggregation unit that aggregates the income information, the expenditure information, the asset information, the debt information, and the life information acquired through input from the customer.   Extraction that detects the web page by crawling the website and extracts the statistical information on the income information, the expenditure information, the asset information, the liability information, and the life information from the web page to generate the correction information The asset calculation system according to claim 1, wherein the generation unit is provided.   The asset output system according to any one of claims 1 to 3, wherein the output unit outputs alert information to a customer according to the amount of deviation.   The asset calculation system according to any one of claims 1 to 4, further comprising: an intercomparison unit that aggregates the asset balance states for a plurality of customers and executes a mutual comparison of the asset balance states of the plurality of customers. .   The asset balance status is aggregated for a plurality of customers, the future asset balance status of the plurality of customers is calculated, and the asset balance status or reference of the customer that is used as a reference from the future asset balance status of the plurality of customers The asset calculation system according to any one of claims 1 to 5, further comprising a reference presentation unit that extracts and presents a future asset balance state of the customer.   The asset calculation system according to any one of claims 1 to 6, wherein an average value of the asset balance states for a plurality of customers is used when generating the correction information in the correction calculation unit.   The asset calculation system according to any one of claims 1 to 6, wherein an average value of a plurality of the asset balance states of the same customer is used when generating the correction information in the correction calculation unit.   The asset calculation system according to any one of claims 1 to 6, wherein when the correction information is generated by the correction calculation unit, a plan value of the asset balance state of the same customer is used. Computer part
A customer information storage step of acquiring and storing customer total asset information including revenue information relating to customer revenue, expenditure information relating to customer expenditure, asset information relating to customer assets, and debt information relating to customer liabilities;
An asset balance state calculating step of obtaining customer life information and calculating a customer's asset balance state based on the customer total asset information;
A correction calculation step of calculating a future asset balance state of the customer by reflecting correction information on the asset balance state;
An asset calculating method comprising: executing an output step of calculating and outputting a deviation amount between the asset balance state and the future asset balance state. In the computer part,
A customer information storage function for acquiring and storing customer total asset information including revenue information relating to customer revenue, expenditure information relating to customer expenditure, asset information relating to customer assets, and debt information relating to customer liabilities;
An asset balance state calculation function for obtaining customer life information and calculating a customer's asset balance state based on the customer total asset information;
A correction calculation function for calculating a future asset balance status of the customer by reflecting correction information on the asset balance status;
An asset calculation program characterized by causing an output function to calculate and output a deviation amount between the asset balance state and the future asset balance state.

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