Method and device for measuring and calculating financial data, and computing equipment

Abstract

The embodiment of the invention provides a financial data measuring and calculating method and device and computing equipment. The method comprises the following steps: firstly, obtaining related contract data and accounting data, and then dividing a measurement project into a first type project and a second type project with different change frequencies; for the first type of items with relatively low change frequency, accurately calculating by adopting a calculation-by-calculation mode; and for the second type of items with relatively high change frequency, summarizing calculation is carried out by adopting a packaging measurement and calculation mode. And finally, based on results of one-by-one measurement and packaging measurement, a conclusion of an overall measurement project is comprehensively obtained. In conclusion, according to the embodiment of the invention, the measurement and calculation items are divided through fine granularity, and accurate target measurement and calculation are realized by adopting a mode of combining one-by-one measurement and calculation and packaging measurement and calculation.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device and computing equipment for calculating financial data. Background Art

[0002] Budget management (also known as annual budget plan) is a series of quantitative planning arrangements for the overall business activities of financial enterprises. It is an effective tool for implementing the strategy of financial enterprises and an important part of corporate financial management. Target estimation is the starting point of budget management and the key to the effective implementation of subsequent budget targets.

[0003] Target estimation refers to the estimation of the future financial activities of banks or other financial enterprises based on historical resources of financial activities and realistic requirements and conditions. For example, if the interest income is to increase by a certain amount within a year, the increase in business scale and the fluctuation range of business interest rates within the year must be determined. How to achieve accurate target estimation has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide a method, device and computing equipment for calculating financial data, which can achieve accurate target calculation.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for calculating financial data, the method comprising: obtaining contract data and accounting data related to a business; dividing the calculation items into fine-grained categories to obtain a plurality of small-grained calculation items; the plurality of small-grained calculation items include a first category of items and a second category of items, the account change frequency of the first category of items being less than the account change frequency of the second category of items; using the contract data to calculate each of the first category of items, and using the accounting data to perform packaged calculations on the second category of items; obtaining the calculation results of the calculation items based on the individual calculation results and the packaged calculation results.

[0007] Optionally, the use of the contract data to perform transaction-by-transaction calculations on the first category of projects includes: for businesses with expired contracts in the first category of projects, using a static calculation algorithm and static calculation parameters to perform transaction-by-transaction calculations on the businesses with expired contracts; the interest rate of the static calculation algorithm is the latest benchmark interest rate, and the non-interest rate data of the static calculation algorithm is the non-interest rate data of previous contracts; the static calculation parameters are related to reference incremental data; for businesses with unexpired contracts in the first category of projects, using the interest rate and non-interest rate data of the contract to perform transaction-by-transaction calculations on the businesses with unexpired contracts.

[0008] Optionally, the method for obtaining the reference incremental data includes: inputting the target data during the measurement period into a trained neural network model to obtain the reference incremental data; wherein the target data is non-contractual data and non-accounting data.

[0009] The trained neural network model is a neural network model trained based on a pre-constructed time series data set; the pre-constructed time series data set is the incremental data of historical measurement projects, historical central bank monetary policy data and historical macroeconomic data, and is a time series data set constructed based on the incremental scale of the measurement projects.

[0010] Optionally, the time series data is constructed based on macroeconomic data at a time point before the current time point.

[0011] Optionally, the static measurement parameters include an incremental scale, and the incremental scale is allocated according to the proportion of the stock structure of the measurement items. The method includes: based on the incremental details, adjusting the incremental scale for measurement items of the same category but different terms; for the business with expired contracts in the first category of projects, using the static measurement algorithm and static measurement parameters, calculating the business with expired contracts one by one, including: for the business with expired contracts in the first category of projects, using the static measurement algorithm and the adjusted incremental scale, calculating the business with expired contracts one by one.

[0012] Optionally, after calculating the expired business on a transaction-by-transaction basis, the method further includes: obtaining dynamic calculation parameters, the dynamic calculation parameters including adjusted incremental data, target interest rate and / or additional interest rate floating range; for the expired business in the first category of projects, calculating the expired business on a transaction-by-transaction basis using the dynamic calculation data.

[0013] Optionally, the dynamic calculation parameters are obtained, and the dynamic calculation parameters include adjusted incremental data, target interest rate and / or additional interest rate floating range, including: when the business with expired contracts is calculated transaction by transaction, if the calculation result does not meet the financial target requirements, and the adjusted incremental data meets the incremental data requirements, the dynamic calculation parameters are obtained.

[0014] Optionally, the method also includes: if the calculation result does not meet the financial target requirements, and the adjusted incremental data does not meet the incremental data requirements, continue to adjust the incremental data; for the business with expired contracts in the first category of projects, use a static calculation algorithm and the incremental data after continued adjustment to re-calculate the business with expired contracts one by one.

[0015] In a second aspect, an embodiment of the present application further provides a device for calculating financial data, the device comprising:

[0016] An acquisition unit is used to acquire contract data and accounting data related to the business; a configuration unit is used to divide the measurement items into fine-grained groups to obtain multiple small-grained measurement items; the multiple small-grained measurement items include first-category items and second-category items, and the account change frequency of the first-category items is less than the account change frequency of the second-category items; a calculation unit is used to use the contract data to perform item-by-item measurement on the first-category items, and use the accounting data to perform packaged measurement on the second-category items; a determination unit is used to obtain the measurement results of the measurement items based on the item-by-item measurement results and the packaged measurement results.

[0017] In a third aspect, an embodiment of the present application provides a computing device, including a memory and a processor.

[0018] The memory is used to store the computing program;

[0019] The processor is used to execute the calculation program to implement the method for calculating financial data as described in any one of the first aspects.

[0020] In a fourth aspect, an embodiment of the present application provides a computer storage medium for storing a computer program, which, when executed, is used to implement the method as described in any one of the first aspects.

[0021] In a fifth aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on at least one computing device, enables the at least one computing device to implement a method as described in any one of the first aspects.

[0022] The embodiments of the present application provide a method, device and computing equipment for calculating financial data. When executing the method: first, relevant contract data and accounting data are obtained, and then the measurement items are subdivided into first-category items and second-category items with different change frequencies. For first-category items with low change frequencies, a method of item-by-item measurement is used for accurate calculation; and for second-category items with high change frequencies, a packaged measurement method is used for summary calculation. Finally, based on the results of item-by-item measurement and packaged measurement, a conclusion of the overall measurement project is drawn. In summary, the embodiments of the present application achieve accurate target measurement by dividing the measurement items into fine-grained categories and combining item-by-item measurement with packaged measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0024] Figure 1 A system architecture diagram of a method for calculating financial data provided in an embodiment of the present application;

[0025] Figure 2 A flow chart of a method for calculating financial data provided in an embodiment of the present application;

[0026] Figure 3 A flow chart of a method for implementing a calculation provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the structure of a financial data calculation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] First, the professional terms involved in the embodiments of the present application are introduced.

[0030] Budget: A formal, quantitative expression of a company's business plan. Commercial bank budgets make a series of quantitative plans for the bank's overall business activities by refining strategic planning and annual business plans, helping banks achieve their strategic goals, monitoring the progress of strategic goal implementation, and providing banks with a benchmark for measuring actual performance.

[0031] Target estimation: refers to making scientific forecasts and estimates of the future financial activities and results of banks or other financial institutions based on historical data of financial activities and taking into account realistic requirements and conditions. For example, if the interest income from loans is required to increase by a certain amount within a year, it is necessary to calculate the increase in the size of the loan and the fluctuation range of the loan interest rate within that year.

[0032] Neural network model: a computational model that simulates the interconnection and information transmission between neurons in the human brain. It consists of multiple neuron layers, each of which contains a certain number of neurons. Neural network models can be used for various machine learning and artificial intelligence tasks, such as image recognition, speech recognition, natural language processing, prediction, etc.

[0033] Static calculation: refers to the system matching the latest benchmark interest rate for the business after maturity, and other terms are automatically renewed by default according to the original contract (product, term, floating range, repricing method, etc.). Based on the calculation parameters of each calculation item, the static calculation rules are applied to the existing business to obtain the calculation results under static calculation.

[0034] Dynamic calculation: refers to treating the business after maturity as new business. According to management requirements, the product structure, term structure, interest rate fluctuation range and other parameters can be adjusted accordingly to calculate the interest income and expenditure after the increase of management requirements. Based on the static calculation results, dynamic calculation rules are adopted for the existing business, dynamic calculation parameters are maintained, and the calculation results under dynamic calculation are obtained.

[0035] Faced with the gradual marketization of interest rates, stricter supervision and increasingly fierce competition among peers, banks or financial enterprises have an increasingly urgent need to seek benefits from management, which has put forward new requirements for the refinement of target measurement. Refined target measurement is based on precise quantitative analysis, involving the processing, storage and analysis of massive contract-level detailed data, and requires rich and flexible measurement algorithms to adapt to different measurement projects. Faced with massive data and the complexity of scenarios, the target measurement work of many banks or financial enterprises is currently more of a combination of manual and system, using the statistical data of each measurement project for rough estimation. This measurement method is relatively primitive and cumbersome, and the accuracy of the measurement results is poor.

[0036] In view of this, an embodiment of the present application provides a method for calculating financial data, first obtaining relevant contract data and accounting data, and then subdividing the measurement items into first-category items and second-category items with different change frequencies. For first-category items with low change frequencies, a method of accurate calculation is adopted for each item; and for second-category items with high change frequencies, a packaged calculation method is adopted for summary calculation. Finally, based on the results of the item-by-item calculation and the packaged calculation, a conclusion of the overall measurement project is comprehensively drawn. The embodiment of the present application achieves accurate target measurement by dividing the measurement items into fine-grained categories and adopting a combination of item-by-item calculation and packaged calculation.

[0037] First, the application scenarios of the financial data calculation method provided in the embodiments of the present application are introduced.

[0038] Attached Figure 1 A system architecture diagram of a method for calculating financial data provided in an embodiment of the present application. The system architecture 100 is a diagram of a calculation system architecture, and the system architecture 100 includes a data source device 101, a network 102, and a computing device 103. The network 102 is used to provide a medium for a communication link between the data source device 101 and the computing device 103. The network 102 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0039] The user can use the data source device 101 to interact with the computing device 103 through the network 102 to receive or send messages, etc. The data source device 101 can be an in-bank system such as a deposit system, a loan system, a bond system, and an accounting system.

[0040] The computing device 103 may be a computing device that provides various services, such as a background management computing device (only an example) that provides support for the data source device 101. The background management computing device may process the received data and perform target measurement.

[0041] It should be noted that the financial data calculation method provided in the embodiment of the present invention is generally executed by the computing device 103, and accordingly, the computing device is generally arranged in the computing device 103. It should be understood that Figure 1 The number of data source systems, networks, and computing devices in the embodiment is only illustrative. Any number of data sources, networks, and computing devices may be provided as required.

[0042] A method for calculating financial data provided in an embodiment of the present application is described in detail below.

[0043] Attached Figure 2 A flowchart of a method for calculating financial data provided in an embodiment of the present application. The method can be performed by a financial data calculation device, which can be implemented by software and / or hardware and configured in Figure 1 In the computing device shown. The method includes the following contents:

[0044] S210. Configure the granularity of the measurement items and the scope of the product accounts under their jurisdiction.

[0045] The granularity of subdivision refers to the degree to which the measurement project is subdivided when managing the measurement project. The higher the granularity of subdivision, the more detailed the subdivision is and the more detailed the information provided. The scope of the product account refers to the scope of the specific products or accounts included in the measurement project.

[0046] In one example, the configured measurement items include the first business, the second business, the third business, the fourth business, and the fifth business. Among them, the first business is related to the user storage business, such as the deposit business. The second business is related to the bank lending business, such as the user loan business. The third business is related to financial management, such as the bond business, and the fourth business is the business with other institutions, such as the central bank business. The fifth business is other business, such as the integration and lending business.

[0047] The measurement items can be further subdivided, and the higher the subdivision granularity, the more detailed the subdivision, and the more detailed the information included. In one example, the measurement items can be divided according to user objects, time, and / or execution type.

[0048] Exemplary explanation: The first business can be subdivided into the first personal business and the first corporate business. The first personal business can be further divided into personal demand deposits, personal notice deposits, personal time deposits, personal margin deposits and personal non-interest bearing deposits. Among them, the product account scope of personal demand deposits includes personal general demand deposits and personal fixed and flexible deposits. The product account scope of personal notice deposits includes personal one-day notice deposits and personal seven-day notice deposits. The product account scope of personal time deposits includes personal lump-sum deposits (three months), personal lump-sum deposits (six months), personal lump-sum deposits (one year), personal lump-sum deposits (two years), personal lump-sum deposits (three years), personal lump-sum deposits (five years) other personal time deposits, personal margin deposits and personal non-interest bearing deposits.

[0049] The first part of the corporate business is further divided into corporate demand deposits, corporate notice deposits, corporate time deposits and corporate other time deposits. Among them, the product account scope of corporate demand deposits includes corporate general demand deposits and corporate agreement deposits. The product account scope of corporate notice deposits includes corporate one-day notice deposits and corporate seven-day notice deposits. The product account scope of corporate time deposits includes: corporate time deposits (three months), corporate time deposits (six months), corporate time deposits (one year), corporate time deposits (two years), corporate time deposits (three years) and corporate time deposits (five years). The product account scope of corporate other time deposits includes: corporate agreement deposits, corporate margin deposits and corporate non-interest-bearing deposits.

[0050] The second business is further subdivided into the second corporate business and the second personal business.

[0051] Among them, the second corporate business is further subdivided into corporate loans, discounts and corporate overdrafts. The second personal business is further subdivided into personal housing loans, other personal loans and personal card overdrafts.

[0052] The product categories of corporate loans include: short-term corporate loans, medium-term corporate loans and long-term corporate loans.

[0053] The product categories of personal housing loans include: short-term personal housing loans, medium-term personal housing loans and long-term personal housing loans. The product categories of other personal loans include: short-term other personal loans, medium-term other personal loans and long-term other personal loans. The product categories of personal card overdrafts include: installments and other personal overdrafts.

[0054] The third business is further divided into reorganization and non-reorganization.

[0055] Among them, the product accounts of the restructuring category include: receivables from the Ministry of Finance and special government bonds. The product accounts of the non-restructuring category include: government-issued bonds, People's Bank of China bonds, policy bank bonds, interbank and other financial institutions bonds, public entity bonds and corporate bonds.

[0056] The fourth business is further divided into deposits with the central bank and borrowing from the central bank.

[0057] Among them, the product categories of deposits with the central bank include: deposits with the central bank's reserves, deposits with the central bank's excess reserves, and deposits with the central bank's fiscal deposits. The product categories of borrowing from the central bank include: liquidity re-loans, special policy re-loans, and other re-loans.

[0058] Integration and outflow are further subdivided into outflow and integration.

[0059] The product accounts of lending include: deposits with peers, funds on loan, financial assets purchased under resale agreements, and bills purchased under resale agreements. The product accounts of financing include: deposits with peers and other financial institutions, funds on loan, financial assets sold under repurchase agreements, and bills sold under repurchase agreements.

[0060] It should be noted that the granularity of the measurement items and the scope of the product accounts configured above are only for illustration and can be adjusted as needed in actual use.

[0061] By configuring the subdivision granularity of the measurement items and the scope of the product accounts under their jurisdiction, the refinement of the target measurement work can be improved and the measurement accuracy can be improved.

[0062] S220. Obtain contract data and accounting data.

[0063] Basic account data refers to account-related data in banks or other financial institutions. For example, basic account data can be the account detail data required for target measurement. Specifically, basic account data can include account detail data such as deposits, loans, credit card overdrafts, bonds, overdue non-accruals, money markets, and other interest-bearing and non-interest-bearing asset and liability data. These account detail data come from various customer-related transaction systems of banks or other financial institutions. For example, customer-related transaction systems of banks include deposit systems, loan systems, credit card systems, bond systems, and financial market systems. In an embodiment of the present invention, the above account detail data is backed up to a data warehouse according to a set period. In the data warehouse, the storage and calculation of massive and fine-grained basic account data are realized in a distributed database manner.

[0064] Among them, basic account data includes contract data and accounting data. Contract data refers to transaction data agreed with users, including but not limited to customer information, contract terms, transaction details, and special agreements (such as early repayment penalty interest, liquidated damages, etc.). Accounting data refers to data summarized by accounting subjects, including but not limited to: overall balance, increase or decrease changes, and classified statistics.

[0065] Furthermore, in order to improve the calculation accuracy of the measurement project, the computing device screens, classifies and summarizes the contract data. Specifically, the contract data related to the specific product account caliber can be screened out according to the product account caliber configured for the measurement project, and the screened contract data can be classified according to the category of the measurement project to match the measurement algorithm of each measurement project.

[0066] The accounting data is aggregated according to the project dimensions. For example, the data can be aggregated according to the data period, such as the cumulative aggregated data, or the monthly aggregated data, or the quarterly aggregated data. The aggregated data includes the balance, daily average balance, income and expenditure, structural ratio and other data.

[0067] S230. Determine the incremental data during the measurement period.

[0068] Incremental data refers to the data added during the measurement period, and should reflect the growth of the measurement business. In the embodiment of the present application, incremental data includes but is not limited to incremental scale, incremental detail adjustment or incremental daily average balance.

[0069] Determining the incremental data during the measurement period can facilitate the measurement personnel to adjust the incremental data during the measurement period, so as to perform target measurement based on the adjusted incremental data and improve the measurement accuracy. The embodiment of the present application can determine the reference incremental data of the measurement project during the measurement period by constructing a time series data set, training a neural network model and model prediction, specifically including:

[0070] Step 1: Build a time series dataset.

[0071] The embodiment of the present application constructs a time series data set for the measurement project based on the incremental data of historical measurement projects, historical data such as the central bank's monetary policy data (such as the reserve requirement ratio, rediscount rate, reloan rate, benchmark interest rate) and macroeconomic data (such as GDP growth rate, CPI increase, PPI increase).

[0072] It should be noted that the central bank's monetary policy data and macroeconomic data can be screened based on the project characteristics and data relevance of the measurement items to improve the accuracy of determination. For example, the project characteristics of personal time deposits are greatly affected by factors such as interest rate levels, economic conditions, and residents' income levels. Data related to personal time deposits include benchmark interest rates, CPI growth rates, GDP growth rates, etc. For example, the project characteristics of corporate loans are greatly affected by factors such as corporate investment intentions, market interest rates, and macroeconomic policies. Data related to corporate loans include re-loan rates, PPI growth rates, and industrial added value.

[0073] Furthermore, since the macroeconomic data-related indicators are lagging indicators, that is, the macroeconomic data at the current time point reflects the indicators of the economic situation in the past period. Therefore, when constructing a time series data set, the macroeconomic indicator value at the previous time point should be selected to construct the sample value at the current time point.

[0074] Step 2: Model training.

[0075] The embodiment of the present application uses a neural network model to predict reference incremental data.

[0076] Among them, the neural network model is a network model trained using the above time series data set.

[0077] In one example, the time series data set described in step 1 is divided into a test set and a training set. In one example, 20% of the data set can be randomly selected from the time series data set as the test set, and the remaining 80% of the data set can be used as the training set. The neural network model is trained using the training set, and the performance of the trained neural network model is tested using the test set, so as to improve the prediction accuracy of the neural network model.

[0078] Step 3: Model prediction.

[0079] In the embodiment of the present application, the reference incremental data within the measurement period is predicted using a trained neural network model. The specific prediction method is as follows: the central bank's monetary policy data and current macroeconomic data at the current time point are input into the trained neural network model to obtain incremental data.

[0080] It should be noted that in the embodiment of the present application, the neural network model only needs to be trained once each time it is used.

[0081] S240: Configure a measurement algorithm.

[0082] The calculation algorithm is a method of calculating the stock data and incremental data of each measurement item according to the characteristics of the smallest-granularity measurement item and the business scale.

[0083] The calculation algorithm is divided into two types: transaction-by-transaction calculation algorithm and package calculation algorithm based on the use of data. For the first type of project, the transaction-by-transaction calculation algorithm is usually used, and for the second type of project, the package calculation algorithm is usually used.

[0084] Among them, the frequency of change of the first type of items is less than that of the second type of items. For example, the first type of items are measurement items with large scale, long-term unchanged contracts or fixed repayment frequency (such as time deposits, housing loans, bonds, etc.). The second type of items are measurement items with frequent changes in some accounts (such as demand deposits, overdrafts, etc.).

[0085] The calculation algorithm for each transaction refers to a detailed and individual calculation for each specific customer or transaction contract. Specifically, the interest income or expenditure of each contract is calculated according to the contract data, interest calculation method and actual interest rate. The interest income or expenditure of all contracts is aggregated to obtain the total interest income or expenditure. Package calculation refers to the calculation of the overall data of a certain type of project (such as the balance of the calculated project after statistical aggregation, the average daily balance and other accounting data) to calculate the total interest income or expenditure.

[0086] Furthermore, for the transaction-by-transaction calculation algorithm, when the contract expires during the calculation period, a static calculation algorithm and a dynamic calculation algorithm are set. The static calculation algorithm means that after the contract expires during the calculation period, the calculation still uses the original contract's term, term and other non-interest terms and the latest benchmark interest rate.

[0087] A dynamic calculation algorithm refers to adding price management requirements to the calculation process, adjusting the interest rate and amount of contracts after expiration, or adding an interest rate fluctuation range to incremental data.

[0088] S250. Based on the contract data, the first category of items are calculated item by item, and based on the accounting data, the second category of items are calculated in packages.

[0089] In the embodiment of the present application, the computing device can use the contract data and the calculation algorithm to calculate the first type of items in the calculation items one by one to obtain the calculation results. The computing device can also use the package calculation algorithm based on the accounting data to perform package calculation on the second type of items in the calculation items to obtain the package calculation results.

[0090] Therefore, the computing device uses the transaction-by-transaction calculation algorithm to perform accurate calculations on the first type of items in the measurement project one by one through the contract data, and can obtain the result of each single transaction; at the same time, the computing device also uses the packaged calculation algorithm based on the accounting data to perform overall packaged calculations on the second type of items in the measurement project, so as to quickly obtain the overall measurement results, realizing accurate and efficient dual measurement of different types of projects, ensuring that the measurement results are completed accurately and quickly, and improving the overall measurement efficiency and the measurement capabilities of financial data.

[0091] Furthermore, to ensure the accuracy of the calculation, the contract data can be screened according to the product account scope, and the contract data related to the specific product account scope can be screened out. The screened contract data can be classified according to the category of the calculation item in order to match the calculation algorithm of each calculation item.

[0092] For accounting data, the data is summarized according to the measurement project dimensions. For example, the data is summarized according to the data period, such as the cumulative summary data, or the monthly summary data, or the quarterly summary data. The summarized data includes balance, daily average balance, income and expenditure amount, structure ratio and other data.

[0093] Furthermore, the computing device can further refine the calculation algorithm for each transaction. First, determine whether the contract corresponding to the business is due. If it is due, for example, Figure 3 The flowchart of a method for implementing the calculation provided in the embodiment of the present application is used for processing. If it has not expired, the data of the current contract is used to calculate the business one by one to obtain the calculation result.

[0094] like Figure 3 As shown, the method includes the following contents:

[0095] S310: Obtain static measurement parameters.

[0096] Static measurement parameters include incremental data or adjusted incremental data. If the adjusted incremental data is incremental scale, since the incremental scale is allocated according to the proportion of the stock structure of the measurement items, the incremental scale of the measurement items of the same category with different terms is fine-tuned based on the incremental details on the basis of proportional allocation.

[0097] It should be noted that the computing device pre-creates the calculation tasks, as well as the calculation period and data reference date corresponding to the calculation personnel. The annual, quarterly or monthly financial target calculation work is completed based on the calculation tasks.

[0098] In addition, the computing equipment is also used to perform statistical processing on the contract data of the measurement projects based on the measurement period and data base date corresponding to the measurement tasks, and calculate reference indicators such as the weighted interest rate, weighted interest rate fluctuation range, and average daily balance of existing maturities of the measurement projects one by one, to provide a reference benchmark for adding management requirements such as incremental scale structure proportion adjustment and additional interest rate fluctuation range during measurement.

[0099] S320. Calculate the business with expired contracts using the static calculation algorithm and static calculation parameters corresponding to the calculation items, and summarize the calculation results.

[0100] The computing device uses a static computing algorithm and static computing parameters to compute the contract-expiring businesses and obtains the computing results of each contract-expiring business. The computing device aggregates the computing results of each contract-expiring business and obtains aggregated computing results, such as an average revenue growth rate or an average annual revenue profit rate.

[0101] S330: Determine whether the summarized measurement results meet the target requirements. If they do, save the static measurement results. If not, execute S340.

[0102] Target requirements refer to the results that a bank or financial enterprise hopes to achieve, such as profit targets.

[0103] S340: Determine whether the configuration of the incremental data meets the preset requirements, if not, execute S350. If yes, execute S360.

[0104] The preset requirement means that the configuration of incremental data follows the structural proportion of the existing items. If the preset requirement is met, the configuration of incremental data follows the structural proportion of the existing items.

[0105] S350, adjust the incremental data, and execute S310.

[0106] S360: Obtain dynamic measurement parameters.

[0107] Dynamic measurement parameters include not only incremental data or adjusted incremental data, but also price management requirements, which include target interest rates and additional interest rate fluctuation ranges.

[0108] S370. Utilize the dynamic calculation algorithm and dynamic calculation parameters corresponding to the calculation items to calculate the business with expired contracts, and summarize the calculation results.

[0109] S380: Determine whether the summarized calculation results meet the target requirements. If they do, save the dynamic calculation results. If not, adjust the incremental data and interest rate parameters, and re-execute S350.

[0110] The present invention proposes a calculation scheme that combines static calculation and dynamic calculation. First, static calculation is performed to roughly determine the incremental data of the calculation project and evaluate the financial goals. Then, based on the static calculation, the incremental data structure details are adjusted and the interest rate fluctuation is added to initiate dynamic calculation. The calculation process supports the addition of management requirements and detailed adjustments to meet the refined requirements of budget management work.

[0111] S260. Based on the item-by-item calculation results and the packaged calculation results, obtain the calculation results of the calculation items.

[0112] All the calculation results of each transaction are summarized to obtain the overall financial indicators (such as total revenue, total cost, total profit, etc.).

[0113] Compare the results of each transaction and the results of the packaged transaction: Compare the summary data of the transaction-by-transaction calculation results with the packaged transaction results to ensure that the difference between the two is within an acceptable range. Combine the transaction-by-transaction calculation results with the packaged transaction results to obtain the final calculation results.

[0114] In summary, by involving the contract data and accounting data of the business in the target calculation, the calculation items are divided into fine granularity and the contract data is used to calculate each item (such as time deposits and housing loans) in combination with the characteristics of the calculation items. The data of other items (such as demand deposits and overdrafts) are packaged and calculated, and the neural network model is introduced to predict the incremental scale of the calculation items to improve the accuracy of the calculation results. At the same time, through the calculation method combining static calculation and dynamic calculation, the static calculation is carried out under the assumption that the expired contract is automatically renewed and the interest rate remains unchanged, in order to facilitate the calculation personnel to find a target benchmark; the dynamic calculation adds management requirements to the calculation on the basis of the static calculation results, and ensures the refinement of the target calculation by adjusting the corresponding product structure, term structure, interest rate fluctuation range and other parameters. The two methods are step-by-step, and can quickly and accurately achieve the corresponding budget goals, helping the calculation personnel to quickly determine the management requirements that need to be added and the financial indicators that need to be completed during the budget cycle.

[0115] In addition, an embodiment of the present application also provides a device for calculating financial data.

[0116] Attached Figure 4 A schematic diagram of a financial data measurement device provided in an embodiment of the present application. The device 400 includes: an acquisition unit 401, which is used to acquire contract data and accounting data related to the business;

[0117] A configuration unit 402 is used to divide the measurement items into fine-grained categories to obtain a plurality of small-grained measurement items; the plurality of small-grained measurement items include first-category items and second-category items, and the account change frequency of the first-category items is less than the account change frequency of the second-category items;

[0118] The calculation unit 403 is used to calculate the first-category items one by one using the contract data, and to calculate the second-category items in a package using the accounting data;

[0119] The determination unit 404 is used to obtain the calculation results of the calculation items based on the calculation results of each item and the packaged calculation results.

[0120] Optionally, the calculation unit 403 is specifically used for: for the businesses with expired contracts in the first category of projects, calculating the expired businesses one by one using a static calculation algorithm and static calculation parameters; the interest rate of the static calculation algorithm is the latest benchmark interest rate, and the non-interest rate data of the static calculation algorithm is the non-interest rate data of the previous contract; the static calculation parameters are related to the reference incremental data; for the businesses with unexpired contracts in the first category of projects, calculating the contracts one by one using the interest rate and non-interest rate data of the contracts.

[0121] Optionally, the method for obtaining the reference incremental data includes:

[0122] Input the central bank's monetary policy data and macroeconomic data during the measurement period into the trained neural network model to obtain reference incremental data;

[0123] The trained neural network model is a neural network model trained based on a pre-constructed time series data set; the pre-constructed time series data set is the incremental data of historical measurement projects, historical central bank monetary policy data and historical macroeconomic data, and a time series data set constructed based on the incremental scale of the measurement projects.

[0124] Optionally, time series data is constructed based on macroeconomic data at time points before the current time point.

[0125] Optionally, the static measurement parameters include incremental scale, which is allocated according to the proportion of the stock structure of the measurement items, and the incremental scale is adjusted for the measurement items of the same category but different terms based on the incremental details;

[0126] The calculation unit 403 is also used to: for the businesses with expired contracts in the first category of projects, use a static calculation algorithm and an adjusted incremental scale to calculate each of the businesses with expired contracts.

[0127] Optionally, the calculation unit 403 is also used to: obtain dynamic calculation parameters, the dynamic calculation parameters include adjusted incremental data, target interest rate and / or additional interest rate floating range; for the business with expired contracts in the first category of projects, use the dynamic calculation data to calculate the business with expired contracts one by one.

[0128] Optionally, the calculation unit 403 is also used to: obtain dynamic calculation parameters, the dynamic calculation parameters include adjusted incremental data, target interest rate and / or additional interest rate floating range, when the business with expired contracts is calculated one by one, if the calculation result does not meet the financial target requirements, and the adjusted incremental data meets the incremental data requirements, the dynamic calculation parameters are obtained.

[0129] Optionally, the calculation unit 403 is also used to: if the calculation result does not meet the financial target requirements and the adjusted incremental data does not meet the incremental data requirements, continue to adjust the incremental data; for the business with expired contracts in the first category of projects, use the static calculation algorithm and the incremental data after continued adjustment to re-calculate the business with expired contracts one by one.

[0130] The embodiment of the present application provides a device for calculating financial data. The device first obtains relevant contract data and accounting data, and then subdivides the measurement items into first-category items and second-category items with different change frequencies. For the first-category items with low change frequencies, a method of accurate calculation is adopted for each item; and for the second-category items with high change frequencies, a packaged calculation method is adopted for summary calculation. Finally, based on the results of the item-by-item calculation and the packaged calculation, a conclusion of the overall measurement project is comprehensively drawn. Therefore, the embodiment of the present application achieves accurate target measurement by fine-grained division of the measurement items and a combination of item-by-item calculation and packaged calculation.

[0131] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0132] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0133] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0134] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0135] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0136] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data calculation method, characterized in that: The method comprises: Obtain contract data and accounting data related to the business; Fine-grained division of the measurement items to obtain a plurality of small-grained measurement items; the plurality of small-grained measurement items include first-category items and second-category items, and the change frequency of the first-category items is less than or equal to the account change frequency of the second-category items; Using the contract data to calculate the first category of items one by one, and using the accounting data to calculate the second category of items in a package; Based on the item-by-item calculation results and the packaged calculation results, the calculation results of the calculation items are obtained.

2. The method according to claim 1, characterized in that The using the contract data to calculate the first category of items one by one includes: For the businesses with expired contracts in the first category of projects, the static calculation algorithm and static calculation parameters are used to calculate the businesses with expired contracts one by one; the interest rate of the static calculation algorithm is the latest benchmark interest rate, and the non-interest rate data of the static calculation algorithm is the non-interest rate data of the previous contracts; the static calculation parameters are related to the reference incremental data; For the unexpired businesses in the first category of projects, the interest rate and non-interest rate data of the contracts are used to perform calculations on a case-by-case basis.

3. The method according to claim 2, characterized in that The method for obtaining the reference incremental data includes: Input the target data during the measurement period into the trained neural network model to obtain the reference incremental data, the target data fee contract data, and non-accounting data; The trained neural network model is a neural network model trained based on a pre-constructed time series data set; the pre-constructed time series data set is the incremental data of historical measurement projects, historical central bank monetary policy data and historical macroeconomic data, and is a time series data set constructed based on the incremental scale of the measurement projects.

4. The method according to claim 3, characterized in that The time series data is constructed based on macroeconomic data at a time point before the current time point.

5. The method according to claim 2, characterized in that: The static calculation parameters include an incremental scale, and the incremental scale is allocated according to the stock structure ratio of the calculation items. The method includes: adjusting the incremental scale for the calculation items of the same category with different terms based on the incremental details; For the businesses with expired contracts in the first category of projects, the static calculation algorithm and static calculation parameters are used to calculate the businesses with expired contracts one by one, including: For the businesses with expired contracts in the first category of projects, a static measurement algorithm and adjusted incremental scale are used to measure each of the businesses with expired contracts.

6. The method according to any one of claims 2 to 5, characterized in that: After calculating the business with expired contracts one by one, the method further includes: Obtaining dynamic calculation parameters, wherein the dynamic calculation parameters include adjusted incremental data, target interest rate and / or additional interest rate floating range; For the businesses with expired contracts in the first category of projects, dynamic measurement data is used to measure each of the businesses with expired contracts.

7. The method according to claim 6, characterized in that The obtaining of dynamic measurement parameters, which include the adjusted incremental data, target interest rate and / or additional interest rate floating range, includes: When the business with expired contracts is calculated one by one, if the calculation result does not meet the financial target requirements, and the adjusted incremental data meets the incremental data requirements, the dynamic calculation parameters are obtained.

8. The method according to claim 7, characterized in that The method further comprises: If the measurement result does not meet the target requirements, and the adjusted incremental data does not meet the incremental data requirements, continue to adjust the incremental data; For the businesses with expired contracts in the first category of projects, a static calculation algorithm and incremental data after continued adjustment are used to recalculate each of the businesses with expired contracts.

9. A financial data calculation device, characterized in that: The device comprises: An acquisition unit, used to acquire contract data and accounting data related to the business; A configuration unit is used to divide the measurement items into fine-grained categories to obtain a plurality of small-grained measurement items; the plurality of small-grained measurement items include a first category of items and a second category of items, and the account change frequency of the first category of items is less than the account change frequency of the second category of items; A calculation unit, used to calculate the first-category items one by one using the contract data, and to calculate the second-category items in a package using the calculation data; The determination unit is used to obtain the calculation result of the calculation item based on the calculation result of each item and the packaged calculation result.

10. A computing device, characterized in that: including memory and processor; The memory is used to store the computing program; The processor is used to execute the calculation program to implement the financial data calculation method according to any one of claims 1 to 8.

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