A method, apparatus, device, and readable storage medium for predicting the value of banking business.

Abstract

This invention provides a method, apparatus, device, and readable storage medium for predicting the value of banking business, relating to the field of computer technology. The method includes: acquiring historical product data and demand analysis, whereby the demand analysis includes demand names, directories, associated systems, and demand rules; calculating a demand data set based on the historical product data and demand analysis, where each element in the demand data set represents the number of transaction demand modifications, the set of demand rules, and the transaction credit value corresponding to a transaction; establishing a mathematical model of product value based on the historical product data, using the demand data set as input, and solving the mathematical model to obtain a product value set, where each element in the product value set represents the number of product demand modifications, the proportion of new business rules, and the product credit value corresponding to a product; and calculating the product value prediction result based on the product value set. By establishing a value chain relationship between products and demands, the method quantitatively reflects the value attributes of a product, displays the direction of business changes and the quality of demands in real time, assesses the direction of business changes, thereby improving customer experience, reducing operating costs, and forming a positive cycle.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method, apparatus, device, and readable storage medium for predicting the value of banking business. Background Technology

[0002] The application of financial technology in the banking industry is developing rapidly and penetrating all business lines. Transaction banking, mobile banking, and smart teller services have been fully launched, improving customer experience and reducing operating costs. However, with the multi-dimensional development of the banking industry and the increasing complexity of business scenarios, business personnel are finding it difficult to intuitively judge the value of a business or control the direction of business changes. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, device, and readable storage medium for predicting banking business, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for predicting the value of banking business, comprising: acquiring historical product data and demand analysis, wherein the demand analysis includes demand name, catalog, associated systems, and demand rules; calculating a demand data set based on the historical product data and the demand analysis, wherein each element in the demand data set represents the number of transaction demand modifications, the set of demand rules, and the transaction credit value corresponding to a transaction; establishing a product value mathematical model based on the historical product data, using the demand data set as input values, and solving the product value mathematical model to obtain a product value set, wherein each element in the product value set represents the number of product demand modifications, the proportion of new business rules, and the product credit value corresponding to a product; and calculating a product value prediction result based on the product value set.

[0005] Secondly, this application also provides a banking business value prediction and recommendation device, comprising: a data acquisition unit for acquiring historical product data and demand analysis, wherein the demand analysis includes demand name, catalog, associated systems, and demand rules; a data processing unit for calculating a demand data set based on the historical product data and the demand analysis, wherein each element in the demand data set is the number of transaction demand modifications, the set of demand rules, and the transaction credit value corresponding to a transaction; a data analysis unit for establishing a product value mathematical model based on the historical product data, using the demand data set as input values, and solving the product value mathematical model to obtain a product value set, wherein each element in the product value set is the number of product demand modifications, the proportion of new business rules, and the product credit value corresponding to a product; and a data generation unit for calculating a product value prediction result based on the product value set.

[0006] Thirdly, this application also provides a banking business value prediction device, comprising:

[0007] Memory, used to store computer programs;

[0008] A processor is used to implement the steps of the banking business value prediction method when executing the computer program.

[0009] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps described above based on the banking business value prediction method.

[0010] The beneficial effects of this invention are as follows:

[0011] The banking business value prediction method provided by this invention can obtain the number of product demand modifications through data processing; by combining historical data and demand analysis, this invention calculates the credit value of products and the proportion of new business rules; this invention establishes a correlation between products and demands through the value chain as an intermediate bridge, quantitatively reflects the value attributes of a product, and displays the direction of business changes and the quality of demands in real time. It provides a tool for project management to assess the direction of business changes, correctly introduce resources, and promote product innovation, thereby improving customer experience, reducing operating costs, and forming a positive cycle.

[0012] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show the present invention.

[0014] These are certain embodiments of the invention and should not be considered as limiting the scope. For those skilled in the art, other related drawings can be obtained from these drawings without any inventive effort.

[0015] Figure 1 This is a schematic diagram of the banking business value prediction method described in this embodiment of the invention;

[0016] Figure 2 This is a schematic diagram of the banking business value prediction device described in this embodiment of the invention.

[0017] Figure 3This is a schematic diagram of the banking business value prediction device described in an embodiment of the present invention;

[0018] Figure 4 This refers to the weight judgment matrix in the banking business value prediction method described in this embodiment of the invention.

[0019] 5 Figure 5 This refers to the product value classification table in the banking business value prediction method described in this embodiment of the invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention.

[0021] This description merely illustrates selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Example 1:

[0024] This embodiment provides a method for recommending bank wealth management products.

[0025] See Figure 1 The figure shows that the method includes steps S100, S200, S300 and S400.

[0026] S100. Obtain historical product data and conduct requirement analysis. Requirement analysis includes requirement names, catalogs, associated systems, and requirement rules.

[0027] It should be noted that in this step, historical data refers to production events, business work orders, and other data stored in the banking business management system (such as the Tianhang system) within the past five years; requirements analysis refers to the analysis report of important business by bank staff; requirement name refers to the name of the analysis report; directory refers to the name of the business in the analysis report; relationship refers to the relationship between customers, business, and system in the analysis report; and requirement rules refer to the business transaction rules listed in the analysis report.

[0028] S200. Based on product historical data and demand analysis, a demand data set is calculated. Each element in the demand data set represents the number of times the transaction demand has been modified, the set of demand rules, and the transaction credit value corresponding to a transaction.

[0029] It should be noted that in this step, the number of transaction requirement modifications refers to the number of business modifications for each system transaction in the analysis report, the set of requirement rules is a set generated by the system by merging the business rules listed in the analysis report, and the transaction credit value is the credit value obtained through manual analysis for each transaction within the system.

[0030] S300. Establish a product value mathematical model based on historical product data, take the demand dataset as input value, solve the product value mathematical model to obtain the product value set, and each element in the product value set is the number of product demand modifications, the proportion of new business rules, and the product credit value corresponding to a product.

[0031] It should be noted that in this step, a requirement modification coefficient is calculated based on the number of requirement modifications within a certain period of time. Combined with the related systems of the product, the number of business modifications for each product is calculated. The percentage of new business rules is calculated by calculating the new business rules added in the requirement analysis and the business specifications in historical data. The product credit value is derived from the correlation and business credit value, showing the credit score of each product. Finally, a matrix is ​​created, where the first column is the product name, and the second to fourth columns are the number of requirement modifications for each product, the percentage of new business rules for each product, and the credit value for each product, respectively, resulting in the product value set. This calculation method organizes massive amounts of complex data into three sub-scores, quantitatively reflecting the three aspects of the product's value attributes.

[0032] S400. Calculate the product value prediction result based on the product value set.

[0033] It should be noted that in this step, the product's business modification frequency, the proportion of new business rules, and the product credit score are calculated using a preset formula to obtain the product and its corresponding value attributes. These value attributes are categorized into high, medium, and low levels according to preset thresholds. A matrix is ​​then constructed based on the product name, modification frequency, and value attributes. The first column of the matrix represents the product name, the second column represents the modification frequency, and the third column represents the corresponding value attributes. This matrix represents the product value prediction result. This approach allows managers to intuitively see data such as product name, modification frequency, and value attribute levels. Based on this data, they can leverage it in project management to assess the direction of business changes, correctly introduce resources, and drive product innovation.

[0034] In the specific embodiments disclosed in this application, the process of obtaining historical product data and demand analysis in step S100 includes extracting and manually analyzing the transaction records and demand analysis accumulated in the system over the past five years, dividing them into four parts for storage: a product pool, a value chain pool, a demand analysis pool, and a defect pool. This data processing method simplifies the difficulty of subsequent data extraction, increases the efficiency of system data processing, and improves the accuracy of final product value prediction through the processing of massive amounts of data.

[0035] In the specific embodiments disclosed in this application, step S200 includes steps S210, S220 and S230.

[0036] S210. Calculate the transaction set based on product historical data and demand analysis requests. Each element in the transaction set represents the number of times the transaction demand has been modified and the set of demand rules corresponding to a transaction.

[0037] It should be noted that in this step, the requirements analysis includes data such as requirement name, associated systems, requirement description, and rule name. The test requirements analysis is uploaded to the system, which extracts the requirement name, directory, and rules. It then deduplicates the requirements based on the name and directory, generates a transaction set, maps the directory to the transaction name, and finally matches and accumulates this data with historically stored data to calculate the number of requirement modifications and the set of requirement rules for each transaction. This calculation method improves the accuracy of business forecasting.

[0038] S220. Calculate the set of transaction event weights based on the production problem classification and summary data. Each element in the set of transaction event weights is the weight corresponding to a transaction event.

[0039] By accumulating nearly five years of historical data, including production events and business work orders, and by conducting categorized and root cause analysis for each issue, testers assigned different weights to each transaction event based on the number of events corresponding to each system and the level of each event. This weighting method objectively reflects the importance of transactions.

[0040] S230. Establish a credit value mathematical model based on the set of transaction event quantity and the set of transaction event weight, and solve the credit value mathematical model to obtain the transaction credit value set.

[0041] Based on the time quantity and event level of each system's corresponding transactions in the transaction quantity set, and according to the weight of each transaction event in the transaction event weight set, a preset calculation formula is used to obtain the final credit value of each transaction. These final credit values ​​are then compiled into a transaction credit value set. This calculation method, verified through a combination of computer systems and manual verification, results in a more accurate transaction credit value set.

[0042] In the specific embodiments disclosed in this application, step S300 includes steps S310 and S320.

[0043] S310. Based on historical product data, calculate the product set, related systems, and value chain. The value chain includes business domain stages and key business modules at each stage.

[0044] It should be noted that in this step, historical data is extracted and calculated, filtered, and used to generate product sets, relationships, and value chains. This calculation method extracts the necessary product and rule information from massive amounts of historical data, improving the efficiency of subsequent steps in producing the final result.

[0045] S320. Establish a mathematical model of product value based on the product set, related system, and value chain. Take the demand data set as input value and solve the mathematical model of product value to obtain the product value set.

[0046] It's important to note that in this step, there's no direct correspondence between the data and the products. Instead, a model is built around the product set, related systems, and value chain. Based on this model and the extracted demand data set, the product value set is calculated. This approach establishes a mapping between the data in the demand data set and the products in the product set, making the final prediction results more intuitive and clear.

[0047] In the specific embodiments disclosed in this application, step S400 includes steps S410, S420 and S430.

[0048] S410. The weight coefficients are calculated based on the product value set and the preset AHP hierarchy method. The weight coefficients include the weight of the number of transformations, the weight of the proportion of new business rules, and the weight of credit value.

[0049] like Figure 4 As shown, Figure 4 For the weighted judgment matrix, the AHP hierarchical method is used in this step, which mainly utilizes the relative size of numbers, with larger numbers having higher relative weights. Three factors are obtained from the product value set: the number of demand modifications, the proportion of new business rules, and the credit score. Considering the importance of each factor in the product value, the score for the number of demand modifications is set to 1, the score for the proportion of new business rules to 4, and the score for the credit score to 2. The coefficients of each factor are obtained from the weighted judgment matrix, and the weight coefficients are obtained from the coefficients of each factor.

[0050] S420. The product value set is calculated based on the weighting coefficients, the product value set, and the preset value classification rules.

[0051] In the specific embodiments disclosed in this application, step S420 includes steps S421, S422 and S423.

[0052] S421. Establish an impact factor mathematical model based on the product value set, weight coefficients and preset evaluation calculation formulas. Solve the impact factor mathematical model to obtain the impact factor score set. Each element in the impact factor score set is the score for the number of times of transformation, the score for new business rules and the credit value corresponding to a product.

[0053] It should be noted that in this step, the three factors—number of requirement modifications, percentage of new business rules, and credit score—are categorized. Taking the number of requirement modifications as an example, they are divided into three tiers (High A, Medium B, Low C): Tier A is when the product of the number of modifications and its weight is greater than or equal to 14; Tier B is when the product of the number of modifications and its weight is greater than or equal to 4 but less than 14; and Tier C is when the number of modifications is less than 4. Similarly, the percentage of new business rules is divided into three tiers (High A, Medium B, Low C), and the credit score is divided into three tiers (High A, Medium B, Low C).

[0054] S422. Calculate the product value classification table based on the set of impact factor scores and the preset value classification rules.

[0055] like Figure 5 As shown, Figure 5 To create a product value grading table, the three factors from the previous step are cross-combined to obtain a total of 27 subcategories, which are then recorded in a table to form a product value classification table. This method can intuitively express the level of product value prediction.

[0056] S423. Calculate the product value set based on the product value classification table.

[0057] It should be noted that in this step, the value attributes of the products are obtained through the product value classification table, and then these value attributes are matched with the product set to obtain the product value set. This design results in high accuracy of product value prediction.

[0058] S430. Calculate the product value set based on the product value classification table.

[0059] It should be noted that in this step, the value of the product is evaluated from multiple dimensions in the previous steps to obtain a product value set. The "high-value zone" can be identified as the products that the bank will focus on promoting in the future, and this can be used as a basis to assess the direction of business changes, correctly introduce resources, and promote product innovation.

[0060] Example 2:

[0061] like Figure 2 As shown, this embodiment provides a banking business value prediction and recommendation device, the device including...

[0062] Data acquisition unit 1 is used to acquire historical product data and demand analysis. The demand analysis includes demand name, catalog, related systems and demand rules.

[0063] Data processing unit 2 is used to calculate a demand data set based on product historical data and demand analysis. Each element in the demand data set is the number of times the transaction demand is modified, the set of demand rules, and the transaction credit value corresponding to a transaction.

[0064] Data analysis unit 3 is used to build a product value mathematical model based on historical product data. The demand data set is used as input value to solve the product value mathematical model to obtain the product value set. Each element in the product value set is the number of product demand modifications, the proportion of new business rules, and the product credit value corresponding to a product.

[0065] Data generation unit 4 is used to calculate the product value prediction result based on the product value set.

[0066] In some specific embodiments, the data processing unit 2 includes:

[0067] The first calculation unit 21 is used to calculate a transaction set based on product historical data and demand analysis requests. Each element in the transaction set is the number of transaction demand modifications and the set of demand rules corresponding to a transaction.

[0068] The second calculation unit 22 is used to calculate a set of transaction credit values ​​based on product historical data, where each element in the set of transaction credit values ​​is the transaction credit value corresponding to a transaction.

[0069] The third calculation unit 23 is used to calculate the demand data set based on the transaction set and the transaction credit value set.

[0070] In some specific embodiments, the second computing unit 22 includes:

[0071] The fourth calculation unit 221 is used to calculate the set of transaction event quantities and the summary data of production problem classification based on the product historical data. Each element in the set of transaction event quantities is the number of transaction events corresponding to a transaction.

[0072] The fifth calculation unit 222 is used to calculate the set of transaction event weights based on the production problem classification and summary data. Each element in the set of transaction event weights is the weight corresponding to a transaction event.

[0073] The sixth calculation unit 223 is used to establish a credit value mathematical model based on the set of transaction event quantity and the set of transaction event weight, and solve the credit value mathematical model to obtain the transaction credit value set.

[0074] In some specific embodiments, the data analysis unit 3 includes:

[0075] The seventh calculation unit 31 is used to calculate the product set, related system and value chain based on the product's historical data. The value chain includes business domain stages and key business modules at each stage.

[0076] The eighth calculation unit 32 is used to establish a product value mathematical model based on the product set, related system and value chain, take the demand data set as input value, and solve the product value mathematical model to obtain the product value set.

[0077] In some specific embodiments, the data generation unit 4 includes:

[0078] The ninth calculation unit 41 is used to calculate the weight coefficients based on the product value set and the preset AHP hierarchical method. The weight coefficients include the weight of the number of transformations, the weight of the proportion of new business rules, and the weight of credit value.

[0079] The tenth calculation unit 42 is used to calculate the product value set based on the weight coefficient, the product value set, and the preset value classification rules;

[0080] The eleventh calculation unit 43 is used to calculate the product value prediction result based on the product value set.

[0081] In some specific embodiments, the tenth computing unit 42 includes:

[0082] The twelfth calculation unit 421 is used to establish an impact factor mathematical model based on the product value set, weight coefficients and preset evaluation calculation formula, and solve the impact factor mathematical model to obtain the impact factor score set. Each element in the impact factor score set is the score of the number of times of transformation, the score of new business rules and the credit value corresponding to a product.

[0083] The thirteenth calculation unit 422 is used to calculate the product value classification table based on the set of impact factor scores and the preset value classification rules.

[0084] The fourteenth calculation unit 423 calculates the product value set based on the product value classification table.

[0085] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0086] Example 3:

[0087] Corresponding to the above method embodiments, this embodiment also provides a banking business value prediction device. The banking business value prediction device described below and the banking business value prediction method described above can be referred to each other.

[0088] Figure 3 This is a block diagram illustrating a banking business value prediction device 800 according to an exemplary embodiment. Figure 3 As shown, the banking business value prediction device 800 may include: a processor 801 and a memory 802. The banking business value prediction device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0089] The processor 801 controls the overall operation of the banking value prediction device 800 to complete all or part of the steps in the aforementioned banking value prediction method. The memory 802 stores various types of data to support the operation of the banking value prediction device 800. This data may include, for example, instructions for any application or method operating on the banking value prediction device 800, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the bank wealth management product recommendation device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0090] In an exemplary embodiment, the banking business value prediction device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned bank wealth management product recommendation method.

[0091] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the banking business value prediction method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the banking business value prediction device 800 to complete the banking business value prediction method described above.

[0092] Example 4:

[0093] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the banking business value prediction method described above.

[0094] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the banking business value prediction method described in the above method embodiments.

[0095] The readable storage medium can specifically be a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or any other readable storage medium capable of storing program code.

[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0097] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A banking value prediction method characterized by, The method comprises the following steps: acquiring product historical data and demand analysis, wherein the demand analysis comprises demand name, catalog, correlation system and demand rule; calculating demand data set according to the product historical data and the demand analysis, wherein an element in the demand data set is transaction demand transformation times corresponding to a transaction, demand rule set and transaction credit value; establishing product value mathematical model according to the product historical data, taking the demand data set as input value, solving the product value mathematical model to obtain product value set, wherein an element in the product value set is product demand transformation times corresponding to a product, proportion of newly added business rule and product credit value; calculating product value prediction result according to the product value set; the step of establishing product value mathematical model according to the product historical data, taking the demand data set as input value, solving the product value mathematical model to obtain product value set, comprises the following steps: calculating demand transformation coefficient through demand transformation times in a certain time period; calculating demand transformation times corresponding to each product based on the correlation system corresponding to the product; calculating proportion of newly added business rule through calculation of newly added business rule in demand analysis and business specification in historical data; calculating credit value score corresponding to each product through correlation and business credit value; establishing matrix, wherein the matrix comprises product name, demand transformation times corresponding to each product, proportion of newly added business rule corresponding to each product and credit value corresponding to each product.

2. The banking value prediction method according to claim 1, characterized by the step of calculating demand data set according to the product historical data and the demand analysis, comprises the following steps: calculating transaction set according to the product historical data and the demand analysis, wherein an element in the transaction set is transaction demand transformation times and demand rule set corresponding to a transaction; calculating transaction credit value set according to the product historical data, wherein an element in the transaction credit value set is transaction credit value corresponding to a transaction; calculating demand data set according to the transaction set and the transaction credit value set.

3. The banking value prediction method according to claim 2, characterized by the step of calculating transaction credit value set according to the product historical data, comprises the following steps: calculating transaction event quantity set and production problem classification summary data according to the product historical data, wherein an element in the transaction event quantity set is transaction event quantity corresponding to a transaction; calculating transaction event weight set according to the production problem classification summary data, wherein an element in the transaction event weight set is weight corresponding to a transaction event; establishing credit value mathematical model according to the transaction event quantity set and the transaction event weight set, solving the credit value mathematical model to obtain the transaction credit value set.

4. The banking value prediction method according to claim 1, characterized by the step of establishing product value mathematical model according to the product historical data, taking the demand data set as input value, solving the product value mathematical model to obtain product value set, comprises the following steps: calculating product set, correlation system and value chain according to the product historical data, wherein the value chain comprises business field stage and key business module in each stage. A product value mathematical model is established according to the product set, the correlation system and the value chain, the demand data set is taken as an input value, the product value mathematical model is solved to obtain a product value set.

5. The banking value prediction method of claim 1, characterized by The product value prediction result is calculated according to the product value set. A weight coefficient is calculated according to the product value set and a preset AHP hierarchical method, and the weight coefficient includes a transformation frequency weight, a new business rule proportion weight and a credit value weight. The product value set is calculated according to the weight coefficient, the product value set and a preset value classification rule. The product value prediction result is calculated according to the product value set.

6. The banking value prediction method according to claim 5, characterized in that The product value set is calculated according to the weight coefficient, the product value set and a preset value classification rule. An influence factor mathematical model is established according to the product value set, the weight coefficient and a preset evaluation calculation formula, an influence factor score set is obtained by solving the influence factor mathematical model, and one element in the influence factor score set is a transformation frequency score, a new business rule score and a credit value score corresponding to one product. A product value classification table is calculated according to the influence factor score set and a preset value classification rule. The product value set is calculated according to the product value classification table.

7. A banking value prediction device characterized by comprising: The data collection unit is configured to acquire product historical data and demand analysis, and the demand analysis includes demand names, categories, correlation systems and demand rules. The data processing unit is configured to calculate a demand data set according to the product historical data and the demand analysis, and one element in the demand data set is a transaction demand transformation frequency, a demand rule set and a transaction credit value corresponding to one transaction. The data analysis unit is configured to establish a product value mathematical model according to the product historical data, take the demand data set as an input value, solve the product value mathematical model to obtain a product value set, and one element in the product value set is a product demand transformation frequency, a new business rule proportion and a product credit value corresponding to one product. The data generation unit is configured to calculate a product value prediction result according to the product value set. The data analysis unit is further configured to calculate a demand transformation coefficient based on a demand transformation frequency in a certain time period, calculate a demand transformation frequency corresponding to each product based on a correlation system corresponding to the product, calculate a new business rule proportion by calculating a new business rule in the demand analysis and a business specification in the historical data, calculate a credit value score corresponding to each product based on a correlation relationship and a business credit value, and establish a matrix including product names, the demand transformation frequency corresponding to each product, the new business rule proportion corresponding to each product and the credit value corresponding to each product. The data processing unit includes:

8. The banking value prediction apparatus according to claim 7, characterized by The first calculation unit is configured to calculate a transaction set according to the product historical data and the demand analysis request, and one element in the transaction set is a transaction demand transformation frequency and a demand rule set corresponding to one transaction. ​ a second calculation unit configured to calculate a transaction credit value set according to the product history data, wherein an element in the transaction credit value set is a transaction credit value corresponding to a transaction; a third calculation unit configured to calculate a demand data set according to the transaction set and the transaction credit value set.

9. The banking value prediction apparatus according to claim 8, characterized by The second calculation unit comprises: a fourth calculation unit configured to calculate a transaction event quantity set and production problem classification summary data according to the product history data, wherein an element in the transaction event quantity set is a transaction event quantity corresponding to a transaction; a fifth calculation unit configured to calculate a transaction event weight set according to the production problem classification summary data, wherein an element in the transaction event weight set is a weight corresponding to a transaction event; a sixth calculation unit configured to establish a credit value mathematical model according to the transaction event quantity set and the transaction event weight set, and solve the credit value mathematical model to obtain the transaction credit value set.

10. The banking value forecasting apparatus according to claim 7, characterized by The data analysis unit comprises: a seventh calculation unit configured to calculate a product set, a correlation system and a value chain according to the product history data, wherein the value chain comprises a business field stage and a key business module of each stage; an eighth calculation unit configured to establish a product value mathematical model according to the product set, the correlation system and the value chain, input the demand data set into the product value mathematical model, and solve the product value mathematical model to obtain a product value set.

11. The banking value forecasting apparatus according to claim 7, characterized by, The data generation unit comprises: a ninth calculation unit configured to calculate a weight coefficient according to the product value set and a preset AHP hierarchical method, wherein the weight coefficient comprises a transformation frequency weight, a new business rule proportion weight and a credit value weight; a tenth calculation unit configured to calculate a product value set according to the weight coefficient, the product value set and a preset value classification rule; an eleventh calculation unit configured to calculate a product value prediction result according to the product value set.

12. The banking value prediction apparatus according to claim 11, characterized by, The tenth calculation unit comprises: a twelfth calculation unit configured to establish an influence factor mathematical model according to the product value set, the weight coefficient and a preset evaluation calculation formula, solve the influence factor mathematical model to obtain an influence factor score set, and wherein an element in the influence factor score set is a transformation frequency score, a new business rule score and a credit value score corresponding to a product; a thirteenth calculation unit configured to calculate a product value classification table according to the influence factor score set and a preset value classification rule; a fourteenth calculation unit configured to calculate a product value set according to the product value classification table.

13. A banking value prediction device characterized by comprising: comprises: a memory configured to store a computer program; a processor configured to implement the steps of the bank business value prediction method according to any one of claims 1 to 6 when the computer program is executed.

14. A readable storage medium characterized by: The readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the bank business value prediction method according to any one of claims 1 to 6.

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