Financial data processing method and device, medium, electronic equipment and program product

Abstract

The invention discloses a financial data processing method and device, a medium, electronic equipment and a program product. The method relates to the field of artificial intelligence, and comprises the steps that a first data set of a first object is acquired, and the first data set comprises attribute data of the first object and M kinds of financial data generated when the first object purchases financial products in a historical time period; based on the first data set, a first prediction model is screened out from N hybrid models, and the model types of the N hybrid models comprise a random slope intercept model; and inputting the first data set into the first prediction model, and outputting first financial data, the first financial data including financial data related to the target financial account of the first object in the financial institution, and the financial data including consumption data. Through the method and the device, the problem that the accuracy is low when consumption data of financial accounts of all customers are output by adopting a single regression equation in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and more specifically, to a method, device, medium, electronic device and program product for processing financial data. Background Art

[0002] In the related art, the independent variables input into the classic multivariate linear regression model and the stepwise regression model are fixed, and the total amount of consumption of the financial account (for example, credit card) is outputted, and the parameters estimated by the same batch of data are also fixed values. However, the consumption habits of different individual customers are different, and some customers are affected by different factors and at different times. Using the same regression equation can only accurately output the total amount of credit card consumption of the customer as a whole, and cannot distinguish the differences between individual customers. Therefore, the accuracy of the total amount of consumption of a single customer's financial account outputted by a single regression equation is low.

[0003] Currently, no effective solution has been proposed to address the problem of low accuracy in the related art of using a single regression equation to output consumption data of all customers' financial accounts. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, medium, electronic device and program product for processing financial data to solve the problem of low accuracy in the related art of using a single regression equation to output consumption data of all customers' financial accounts.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for processing financial data is provided. The method comprises: obtaining a first data set of a first object, wherein the first data set comprises: attribute data of the first object and M types of financial data generated by the first object purchasing financial products in a historical time period, where M is a positive integer; based on the first data set, screening out a first prediction model from N hybrid models, wherein the model types of the N hybrid models comprise: a random slope intercept model, wherein the random slope intercept model comprises: a model constructed based on a random slope and a random intercept, and N is a positive integer; inputting the first data set into the first prediction model, and outputting first financial data, wherein the first financial data comprises: financial data involved in a target financial account of the first object in a financial institution, wherein the financial data comprises: consumption data.

[0006] Furthermore, based on the first data set, a first prediction model is screened out from N hybrid models, including: obtaining N second data sets, wherein the N second data sets include: attribute data of each second object among the N second objects and M types of financial data generated by the second object purchasing financial products; matching the first data set with the N second data sets to obtain a first matching result, wherein the first matching result is used to indicate a second data set among the N second data sets that successfully matches the first data set; and determining the hybrid model associated with the second data set indicated by the matching result as the first prediction model.

[0007] Furthermore, the N mixed models are obtained in the following manner: determining S linear models, wherein the model types of the S linear models include at least one of the following: a random slope model, a random intercept model, and a random slope intercept model, and S is a positive integer; performing data fitting on the N second data sets using each of the linear models to obtain a fitting result of each of the linear models, and determining the N mixed models based on the fitting results, wherein the data fitting is used to determine the parameter values ​​of unknown parameters in the linear model.

[0008] Furthermore, based on the fitting results, N mixed models are determined, including: verifying each of the fitting results based on a target verification strategy to obtain a verification result of each of the linear models, wherein the target verification strategy includes at least one of the following: significance test, regression coefficient test and goodness of fit test; when the verification results of the S linear models all meet the preset conditions, N mixed models are determined based on the fitting results of the random slope intercept model.

[0009] Furthermore, after determining the N hybrid models based on the fitting results of the random slope-intercept model, it also includes: extracting the random slope and random intercept of each of the N hybrid models to obtain a third data set; based on the third data set, constructing a second prediction model, wherein the second prediction model includes: a model with random slope and random intercept as dependent variables and financial data generated by the target financial account as independent variables.

[0010] Furthermore, the financial data of the target financial account includes: the total transaction amount of the target financial account in a target time period. After constructing the second prediction model based on the third data set, it also includes: acquiring the financial data of the target financial account of the third object at the financial institution to obtain second financial data; inputting the second financial data into the second prediction model, and outputting a target slope and a target intercept, wherein the target slope is used to determine the degree to which the second financial data is affected by the attribute data of the third object or the purchase of financial products by the third object, and the target intercept is used to determine the minimum total transaction amount of the third object at multiple time points in the target time period.

[0011] Furthermore, after inputting the first data set into the first prediction model and outputting the first financial data, it also includes: obtaining a preset mapping rule, matching the first financial data with the preset mapping rule, and obtaining a second matching result, wherein the preset mapping rule includes: a mapping relationship between the financial data of the target financial account and T types of financial products, T is a positive integer; based on the second matching result, determining a target financial product, wherein the target financial product includes: a financial product to be recommended to the first object.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a financial data processing device is provided. The device includes: a first acquisition unit, used to acquire a first data set of a first object, wherein the first data set includes: attribute data of the first object and M types of financial data generated by the first object purchasing financial products in a historical time period, M is a positive integer; a screening unit, used to screen out a first prediction model from N hybrid models based on the first data set, wherein the model types of the N hybrid models include: a random slope intercept model, and the random slope intercept model includes: a model constructed based on a random slope and a random intercept, and N is a positive integer; a first processing unit, used to input the first data set into the first prediction model and output first financial data, wherein the first financial data includes: financial data involved in the target financial account of the first object in a financial institution, and the financial data includes: consumption data.

[0013] Furthermore, the screening unit includes: an acquisition subunit, used to acquire N second data sets, wherein the N second data sets include: attribute data of each second object among the N second objects and M types of financial data generated by the second object purchasing financial products; a matching subunit, used to match the first data set with the N second data sets to obtain a first matching result, wherein the first matching result is used to indicate a second data set among the N second data sets that successfully matches the first data set; and a first determination subunit, used to determine a hybrid model associated with the second data set indicated by the matching result as the first prediction model.

[0014] Furthermore, the N mixed models are obtained through the following subunits: a second determination subunit, used to determine S linear models, wherein the model types of the S linear models include at least one of the following: a random slope model, a random intercept model and a random slope intercept model, and S is a positive integer; a fitting subunit, used to perform data fitting on the N second data sets using each of the linear models to obtain a fitting result of each of the linear models, and based on the fitting results, determine the N mixed models, wherein the data fitting is used to determine the parameter values ​​of the unknown parameters in the linear model.

[0015] Furthermore, the fitting subunit includes: a verification module, which is used to verify each of the fitting results based on a target verification strategy to obtain a verification result of each of the linear models, wherein the target verification strategy includes at least one of the following: significance test, regression coefficient test and goodness of fit test; a determination module, which is used to determine N of the mixed models based on the fitting results of the random slope intercept model when the verification results of the S types of linear models all meet the preset conditions.

[0016] Furthermore, the financial data processing device also includes: an extraction unit, which is used to extract the random slope and random intercept of each of the N mixed models after determining the N mixed models based on the fitting results of the random slope-intercept model, so as to obtain a third data set; a construction subunit, which is used to construct a second prediction model based on the third data set, wherein the second prediction model includes: a model with random slope and random intercept as dependent variables and financial data generated by the target financial account as independent variables.

[0017] Furthermore, the financial data of the target financial account includes: the total transaction amount of the target financial account in the target time period, and the financial data processing device also includes: a second acquisition unit, used to acquire the financial data of the target financial account of the third object in the financial institution after building the second prediction model based on the third data set, so as to obtain second financial data; a second processing unit, used to input the second financial data into the second prediction model, and output a target slope and a target intercept, wherein the target slope is used to determine the degree to which the second financial data is affected by the attribute data of the third object or the purchase of financial products by the third object, and the target intercept is used to determine the minimum total transaction amount of the third object at multiple time points in the target time period.

[0018] Furthermore, the financial data processing device also includes: a third acquisition unit, which is used to obtain a preset mapping rule after inputting the first data set into the first prediction model and outputting the first financial data, and match the first financial data with the preset mapping rule to obtain a second matching result, wherein the preset mapping rule includes: a mapping relationship between the financial data of the target financial account and T types of financial products, where T is a positive integer; and a determination unit, which is used to determine a target financial product based on the second matching result, wherein the target financial product includes: a financial product to be recommended to the first object.

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the computer-readable storage medium is located is controlled to execute the method for processing financial data.

[0020] According to another aspect of the present application, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the method for processing financial data is executed when the program is running.

[0021] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, which implement the steps of the method for processing financial data when executed by a processor.

[0022] In the present application, a first data set of a first object is obtained, wherein the first data set includes: attribute data of the first object and M types of financial data generated by the first object purchasing financial products in a historical time period, where M is a positive integer; based on the first data set, a first prediction model is screened out from N hybrid models, wherein the model types of the N hybrid models include: a random slope intercept model, and the random slope intercept model includes: a model constructed based on a random slope and a random intercept, and N is a positive integer; the first data set is input into the first prediction model, and first financial data is output, wherein the first financial data includes: financial data involved in a target financial account of the first object in a financial institution, and the financial data includes: consumption data, thereby solving the technical problem of low accuracy in the related art of using a single regression equation to output consumption data of all customers' financial accounts. In the present invention, according to the data set of the first object, a first prediction model associated with the first object is screened out from N hybrid models, and the consumption data of the target financial account of the first object is output using the first prediction model, thereby avoiding the situation in the related art of using a single regression equation to output consumption data of all customers' target financial accounts and being unable to distinguish individual differences, thereby achieving the technical effect of accurately determining the consumption data of a single customer's financial account. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for processing financial data is shown;

[0025] Figure 2 is a flowchart of a method for processing financial data provided in an embodiment of the present application;

[0026] Figure 3 It is a relationship diagram between individuals and groups provided according to an embodiment of the present application;

[0027] Figure 4 is a schematic diagram of a first coordinate system provided according to an embodiment of the present application;

[0028] Figure 5 is a schematic diagram of a second coordinate system provided according to an embodiment of the present application;

[0029] Figure 6 is a schematic diagram of a financial data processing device provided according to an embodiment of the present application;

[0030] Figure 7 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0034] Embodiment 1

[0035] According to an embodiment of the present application, a method embodiment of a method for processing financial data is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for processing financial data is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (shown as 102a, 102b, ..., 102n in the figure) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0037] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0038] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the method for processing financial data in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned method for processing financial data is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0039] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0040] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0041] Under the above operating environment, this application provides Figure 2 The method of processing financial data shown. Figure 2 It is a flowchart of a method for processing financial data according to the first embodiment of the present application.

[0042] Step S201, obtaining a first data set of a first object, wherein the first data set includes: attribute data of the first object and M types of financial data generated by the first object purchasing financial products in a historical time period, where M is a positive integer.

[0043] The attribute data of the first object may include, but are not limited to, basic information such as the first object's occupation, work unit, work department, work location (e.g., work city), salary, years of work, etc. The first object may include an object that has established a target financial account in a financial institution, and the first data set may include: the attribute data of the first object and M types of financial data generated by the first object purchasing financial products during a historical period, and the M types of financial data may include: consumption data generated by using the first object's target financial account to pay for transactions to purchase financial products.

[0044] Step S202, based on the first data set, screen out a first prediction model from N hybrid models, wherein the model types of the N hybrid models include: random slope intercept model, the random slope intercept model includes: a model constructed based on a random slope and a random intercept, and N is a positive integer.

[0045] The N hybrid models mentioned above are used for financial data related to target financial accounts of different objects, for example, the total consumption amount in a historical period.

[0046] In this embodiment, a second data set of objects involved in each hybrid model can be obtained, wherein the second data set includes: attribute data of the second object and M types of financial data generated by the second object purchasing financial products. The model type of the above-mentioned hybrid model may include: a random slope-intercept model, and the hybrid model may include multiple variables, random slopes and random intercepts.

[0047] By matching the first data set with the second data set, the first prediction model can be screened out from the N hybrid models according to the matching result.

[0048] Step S203, inputting the first data set into the first prediction model, and outputting first financial data, wherein the first financial data includes: financial data related to the target financial account of the first object in the financial institution, and the financial data includes: consumption data.

[0049] The above-mentioned first financial data may include: financial data related to the target financial account of the first object in a financial institution, the financial data includes: consumption data (for example, total consumption amount), and the above-mentioned target financial account may include a credit card account. In this embodiment, the first data set can be input into the first prediction model, and the variables in the first prediction model are replaced with the data in the first data set to calculate the first financial data. It should be noted that in order to avoid the situation where non-numeric data cannot be calculated, in this embodiment, non-numeric data can also be converted into numerical data, and the conversion method may include: conversion using a one-hot encoding method.

[0050] In this embodiment, through the above steps, according to the data set of the first object, the first prediction model associated with the first object is screened out from N mixed models, and the consumption data of the target financial account of the first object is output using the first prediction model, thereby avoiding the situation in which a single regression equation is used in the related technology to output the consumption data of the target financial accounts of all customers and individual differences cannot be distinguished, thereby achieving the technical effect of accurately determining the consumption data of the financial account of a single customer, and further solving the technical problem of low accuracy in the related technology of using a single regression equation to output the consumption data of the financial accounts of all customers.

[0051] Optionally, in the financial data processing method provided in the embodiment of the present application, based on the first data set, a first prediction model is screened out from N hybrid models, including: obtaining N second data sets, wherein the N second data sets include: attribute data of each second object among the N second objects and M types of financial data generated by the second object purchasing financial products; matching the first data set with the N second data sets to obtain a first matching result, wherein the first matching result is used to indicate a second data set among the N second data sets that successfully matches the first data set; and determining the hybrid model associated with the second data set indicated by the matching result as the first prediction model.

[0052] The above-mentioned N mixed models may include independent variables of fixed effects and independent variables of random effects. The parameters of the independent variables of fixed effects of the N mixed models (i.e., fixed parameters) are the same, and the parameters of the independent variables of random effects of the N mixed models (random slopes and random intercepts) may be different.

[0053] In this embodiment, the first data set and N second data sets can be matched. For example, the content corresponding to the random variables (i.e., random effects) in the first data set can be matched with the content corresponding to the random variables in the N second data sets. For example, if the first data set indicates that the working city corresponding to the first object is A and the occupation is B, and a second data set indicates that the working city corresponding to the second object is also A and the occupation is also B, then it can be determined that the first data set and the second data set are matched successfully, and then the mixed model associated with the second data set can be determined as the first prediction model.

[0054] In this embodiment, different objects can input corresponding mixed models according to different random effects, and output the total consumption amount of the target financial account of the object, thereby achieving the technical effect of improving the calculation accuracy of the total consumption amount of the target financial account.

[0055] Optionally, in the financial data processing method provided in the embodiment of the present application, N mixed models are obtained in the following manner: determining S linear models, wherein the model types of the S linear models include at least one of the following: a random slope model, a random intercept model, and a random slope intercept model, and S is a positive integer; using each linear model to perform data fitting on N second data sets to obtain a fitting result of each linear model, and based on the fitting results, determining N mixed models, wherein the data fitting is used to determine the parameter values ​​of unknown parameters in the linear model.

[0056] In this embodiment, the model types of the S linear models include but are not limited to: a general linear model, a random slope model, a random intercept model, and a random slope intercept model.

[0057] General linear model: y i=β0+β1x i +ε i

[0058] Random intercept model: y i =β0+γ0+β1x i +ε i

[0059] Random slope model: y i =β0+(β1+γ1)x i +ε i

[0060] Random intercept and random slope model: y i =β0+γ0+(β1+γ1)x i +ε i

[0061] Among them, β i and γ i represents the estimated parameter, ε i Represents random error.

[0062] In this embodiment, the attribute information of multiple customers (corresponding to N second objects) and the consumption history information of the target financial account (M types of financial data generated by the second object purchasing financial products) can be collected. The customer information data can be processed and model fitting can be used to achieve the purpose of outputting the total credit card consumption Y of each customer according to the model.

[0063] Input data: N (for example, 61) customers with independent variables X1, X2, X3, X4, X5, and X6. Each customer has ni (3, or 4) observations of total credit card consumption Y and independent variables X2 and X3. These ni different observations are recorded at different times t (1 to 4 weeks). The number of observations for each customer may not be the same, the observation interval may not be the same, and the number of observations may not be large. This is different from the classic time series model that requires a large number of equally spaced observations. Output: Predict the total credit card consumption Y for each customer, calculate the fixed parameters b0, b1, b2, b3, b4, b5, b6, and the random parameter random intercept γ N0 and the random slope γ N2 and γ N3 , the following is an explanation of the linear model fitting process to the data set:

[0064] 1. General linear model:

[0065] Use the general linear model: y = Xβ + ε, i = 1, 2, ..., N, to fit the data set. The maximum likelihood method can be used to obtain the estimated parameter β and the variance of the model, and then perform statistical verification. The statistical test includes the significance test of the regression equation, the regression coefficient test and the goodness of fit test. Multivariate linear model: N customers' independent variables X1, X2, X3, X4, X5, X6, the total amount of customer credit card consumption Y,

[0066]

[0067] It can also be expressed as:

[0068]

[0069] If the linear model is a random intercept slope model: y i =X i β i +Z i γ i +ε i , i = 1, 2, ..., N, and fitting the N second data sets can obtain the above N mixed models, where the estimated parameter β i is a fixed parameter, and the estimated parameter γ i are random parameters (i.e., random slope and random intercept), and when the estimated parameters of N mixed models are not calculated, they can be expressed as:

[0070]

[0071] The dependent variables X1, X2, X3, X4, X5, and X6 of N customers are fixed effects. The independent variables X2 and X3 are added to the model as random effects. Each customer has ni observations of total credit card consumption Y and independent variables X2 and X3. These ni different observations are recorded at different times t. i0 .

[0072] A random intercept and random slope model is built for each customer, resulting in different intercept estimates (random intercepts) and slope estimates (random slopes). The intercept and slope are estimated for each customer individually.

[0073] If the coefficient of the fixed effect estimate is (the value of x1 is 1, the system cannot be estimated, and the default β1 = 0):

[0074] β0=1.0960, β2=-2.1106, β3=0.4291, β4=0.1790, β5=-0.5643, β6=1.2720

[0075] If the coefficients (or parameters) of the random effect estimate of the mixed model (y1) of the first second object are:

[0076] γ 20 =-5.1817,γ 21 =-1.8940,γ 23 =1.2389

[0077] Then the mixed model of the first and second objects is:

[0078] y1=(β 10 +β 11 x 11 +...+β 16 x 16 )+(γ 10 +γ 12 x 12 +γ 13 x 13 )+ε1

[0079] y1=(1.0960 -2.1106x 12 +0.4291x 13 +0.1790x 14 -0.5643x 15 +1.2720x 16 )

[0080] +(-5.1817-1.8940x 12 +1.2389x 13 )

[0081] If the mixed model of the second object corresponds to y2,γ 20 =-4.3688,γ 21 =-0.3656,γ 23 =0.0622, then the mixed model of the second object is:

[0082] y2=(β 20 +β 21 x 21 +...+β 26 x 26 )+(γ 20 +γ 22 x 22 +γ 23 x 23 )+ε2

[0083] y2=(1.0960-2.1106x 22 +0.4291x 23 +0.1790x 24 -0.5643x 25 +1.2720x 26 )+(-4.3688-0.3656x22 +0.0622x 23 )

[0084] It should be noted that the random intercept and slope allow for differences in level (intercept) and association (slope) between individuals. For a particular individual (unit), there is only one intercept and one slope, even though there may be repeated measurement data. Therefore, although the random intercept and slope are obtained from repeated test data (individual level), the intercept and slope themselves are repeated measurement data at the population level.

[0085] In this embodiment, the random effect mixed model can be used for fitting, including:

[0086] (1) Observed value y of random effect i , ε i are all independent random variables. i is an unobservable random error.

[0087] (2) Observation value y i and the fixed effect independent variable X i , random effect independent variable Z i There is an obvious linear relationship.

[0088] (3) Random errors ε1, ε2, …, ε n There is a linear relationship between ε and independent and identically distributed samples, ε i ~N(0,σ 2 ),i=1,2,…,n.

[0089] (4) Random effect variable Z i It follows a normal distribution.

[0090] (5) Observation value y i Obey the expected distribution, normal distribution, Poisson distribution, etc.

[0091] The general form of a linear random effects mixed model (corresponding to a mixed model) is:

[0092] y i =X i β i +Z i γ i +ε i ,i=1,2,...,N,

[0093] Among them, y i n i ×r-dimensional, X i n i ×p-dimensional, β i For p×r dimensions, Z i ni ×q-dimensional, γ i For q×r dimensions, ε i n i ×r-dimensional.

[0094]

[0095] For all i,ε i ~N(0,Σ), and is independent of γ i , usually X i and Z i The first column is a constant, Z i Contains X i The subset to be estimated is β i ,Σ,ψ. X in the formula i β i is the fixed utility part, and Z i γ i is the random utility part. If the data is written as follows

[0096]

[0097] as well as:

[0098]

[0099] Model i =X i β i +Z i γ i +ε i (i=1,2,...,N) can be written as:

[0100] Y=Xβ+Zγ+ε.

[0101] Where Y is n i ×r-dimensional, in the case of univariate, r = 1, for each i∈{1,2,…,N}, there are n i Observed values.

[0102] More generally:

[0103] y i =X i β i +Z i γ i +ε i ,i=1,2,...,N

[0104] can be written as:

[0105] y i =f(X1,X2,...,X p )+Zi γ i +ε i ,i=1,2,...,N,

[0106] Among them, f(X1,X2,...,X p ) is used to explain the fixed effect part, which can be linear or nonlinear. i γ i is the random utility part. This modeling formula greatly improves the adaptability and flexibility of the model.

[0107] Linear random effects mixed model results analysis:

[0108] By performing regression analysis on customer credit card data, we can obtain estimated parameters, variance, equation significance test and parameter significance verification, and predict the total credit card consumption amount Y of N customers. This avoids the situation in related technologies where the same regression equation is used and only the total credit card consumption amount y of the entire customer base can be accurately output, but the differences between individual customers cannot be distinguished, resulting in inaccurate output of the total credit card consumption amount of each customer.

[0109] Optionally, in the financial data processing method provided in the embodiment of the present application, N hybrid models are determined based on the fitting results, including: verifying each fitting result based on a target verification strategy to obtain a verification result of each linear model, wherein the target verification strategy includes at least one of the following: significance test, regression coefficient test and goodness of fit test; when the verification results of S linear models all meet preset conditions, N hybrid models are determined based on the fitting results of the random slope intercept model.

[0110] For example, (1) a general linear model: y = Xβ + ε, i = 1, 2, ..., N, is used to fit the data set (i.e., the second data set). The maximum likelihood method can be used to obtain the estimated parameter β and the variance of the model. Statistical tests include significance test of the regression equation, regression coefficient test, and goodness of fit test.

[0111] (2) When the verification results of the general linear model meet the preset conditions, the random intercept model can be used: i =X i β i +γ i +ε i The data set is refitted to obtain estimated parameters β and γ, variance and statistical test value, wherein the preset condition may mean that the accuracy of the prediction result of consumption data prediction by the fitted model can reach a preset threshold, for example, the variance is less than a preset variance threshold.

[0112] (3) When the verification results of the random intercept model meet the preset conditions, the random slope model can be used: i =X i β i +ε i , refit the data set, estimate the parameters, and obtain the estimated parameters β and γ, variance and statistical test quantity.

[0113] (4)) When the verification result of the random slope model meets the preset conditions, the random intercept slope model is adopted: y i =X i β i +Z i γ i +ε i , i=1,2,...,N, fit the data set, estimate the parameters, and obtain the estimated parameters β and γ, variance and statistical test quantity. Add random vector independent variables X2 and X3 as random effects to the model, update the model, and re-estimate the parameters β and γ, variance and statistical test quantity.

[0114] In an optional example, the model with a significant regression equation and the smallest variance can be selected based on the estimated parameters, variance and statistical test of the regression equation, regression coefficient test and goodness of fit test of each model, thereby achieving the technical effect of improving the accuracy of the output results of the hybrid model.

[0115] Optionally, in the method for processing financial data provided in the embodiment of the present application, after determining N hybrid models based on the fitting results of the random slope-intercept model, it also includes: extracting the random slope and random intercept of each of the N hybrid models to obtain a third data set; based on the third data set, constructing a second prediction model, wherein the second prediction model includes: a model with the random slope and random intercept as dependent variables and the financial data generated by the target financial account as independent variables.

[0116] In this embodiment, the intercept and slope can also be used as dependent variables to fit a second prediction model. The random intercept and slope allow for differences in level (intercept) and association (slope) between individuals. For a specific individual (unit), there is only one intercept and one slope, even if there may be repeated measurement data. Therefore, although the random intercept and slope are obtained from repeated test data (individual layer), the intercept and slope themselves are repeated measurement data at the population layer. The intercept or slope of each individual does not change with the estimation. Assuming that the intercept and slope do vary from individual to individual, the predicted values ​​of the intercept and slope can be determined. To be more specific, Figure 3 is a diagram of the relationship between individuals and groups provided according to an embodiment of the present application, such as Figure 3As shown. The dependent variable y is predicted by the random slope and random intercept of x at the individual level. The random intercept and random slope are also the result variables at the group level, and can also be predicted by the predicted quantity at the group level. Therefore, the observed value of the total credit card consumption amount Y can be predicted from the random intercept and random slope of the independent variables X2 and X3. In this embodiment, for customers whose independent variables X2 and X3 are more than the average, their total credit card consumption amount Y is also relatively large (using X2 and X3 to predict the random intercept); or for customers whose independent variables X2 and X3 are less than the average, whether their total credit card consumption amount Y changes less (the random slope is relatively flat). Since the average independent variables X2 and X3 do not change, they are inter-individual variables. Figure 3 Explains the relationship between them.

[0117] It should be noted that, in this embodiment, the random intercept can represent the minimum value of the customer's total credit card consumption, and the random slope can represent the extent to which the growth of each customer's total credit card consumption is affected by the independent variables X1, X2, X3, X4, X5, and X6.

[0118] Assuming there are 100 employees, we can observe whether the total credit card spending of customers whose X2 and X3 are higher than the average is also higher.

[0119] For customers whose independent variables X2 and X3 are less than the average, does the change in their total credit card spending Y also occur less (the random slope is relatively flat).

[0120] The fixed intercept of the total credit card consumption amount Y and the fixed slope of the total credit card consumption amount Y can both be predicted by the predictors (X2, X3, X4, X5, X6) at the group level.

[0121] In this embodiment, the individual layer and group layer of the record date can be used to predict the total credit card consumption amount Y using the random intercept and slope, and the average number of consumption times X2 and X3 can be used to predict the random intercept and slope.

[0122] If, the random parameter random intercept γ for each customer N0 and the random slope γ N2 and γ N3 , that is, the third data set, as shown in Table 1.

[0123] Table 1

[0124]

[0125]

[0126] γ i0 As the x-axis, and yi as the y-axis, we can establish a coordinate system. Figure 4 ,in, Figure 4is a schematic diagram of a first coordinate system provided according to an embodiment of the present application;

[0127] γ i2 As the x-axis, and yi as the y-axis, we can establish a coordinate system. Figure 5 ,in, Figure 5 It is a schematic diagram of a second coordinate system provided according to an embodiment of the present application.

[0128] from Figure 4 and Figure 5 , it can be determined that if the customer sample is large enough, the random intercept (stranger's work unit) and random slope (stranger's position level) can not only be calculated by the least squares method, but also the random intercept can be predicted based on the dependent variable total credit card consumption Y, and the random slope can be predicted based on the independent variables X2, X3 and the dependent variable total credit card consumption Y. (independent variables X1, X2, X3, X, X5, X6, dependent variable Y), the second prediction model obtained is:

[0129] γ i0 =c0+c1y i +ε 0i

[0130] γ i2 =d0+d1y i +ε 2i ,i=1,2,...,N,

[0131] γ i3 =f0+f1y i +ε 3i

[0132] Among them, c0, c1, d0, d1, f0, f1 are the estimated parameters of the second prediction model. In the second prediction model, yi is the independent variable and the random intercept γ i0 and the random slope γ i2 and γ i3 As the dependent variable, the random intercept of total credit card consumption Y and the fixed slope of total credit card consumption Y relative to X1, X2, X3, X4, X5 and X6 can be predicted by the group-level predictors (average independent variables X1, X2, X3, X4, X5 and X6).

[0133] In this embodiment, a third prediction model can be constructed based on the random slope and random intercept of each of the N mixed models. The third prediction model can be used to predict the random intercept and random slope of the mixed model associated with a certain object when the total transaction amount of the target financial account of the object in the target time period is known. The random intercept can represent the minimum value of the total consumption of the target financial account of the object, and the random slope can represent the extent to which the growth of the total consumption of the target financial account of the object is affected by the independent variables X2 and X3 of the random effects.

[0134] Optionally, in the financial data processing method provided in the embodiment of the present application, the financial data of the target financial account includes: the total transaction amount of the target financial account in the target time period. After building the second prediction model based on the third data set, it also includes: obtaining the financial data of the target financial account of the third object in the financial institution to obtain second financial data; inputting the second financial data into the second prediction model, and outputting a target slope and a target intercept, wherein the target slope is used to determine the degree to which the second financial data is affected by the attribute data of the third object or the purchase of financial products by the third object, and the target intercept is used to determine the minimum total transaction amount of the third object at multiple time points in the target time period.

[0135] In this embodiment, when the total transaction amount (for example, total consumption amount) of a target financial account of a certain object (corresponding to the third object) in a target time period is known, the total transaction amount of the object in the target time period can be input into the third prediction model, and the random intercept and random slope of the associated mixed model of the object are output through the third prediction model, and the random intercept can represent the minimum value of the total consumption amount of the target financial account of the object, and the random slope can represent the influence of the random effect independent variables X2 and X3 on the growth of the total consumption amount of the target financial account of the object, and the content corresponding to the random effect of the object can also be inferred based on the random slope. For example, if the random effects associated with the mixed model corresponding to the random slope are working city A and occupation B, then the working city A and occupation B of the object can be exited, thereby achieving the purpose of predicting customer information based on the total consumption amount.

[0136] Optionally, in the financial data processing method provided in the embodiment of the present application, after inputting the first data set into the first prediction model and outputting the first financial data, it also includes: obtaining a preset mapping rule, matching the first financial data and the preset mapping rule to obtain a second matching result, wherein the preset mapping rule includes: a mapping relationship between the financial data of the target financial account and T types of financial products, where T is a positive integer; based on the second matching result, determining the target financial product, wherein the target financial product includes: a financial product to be recommended to the first object.

[0137] In an optional example, the preset mapping rules may also include: mapping rules between the financial data of the target financial account and T types of financial products, for example, mapping rules between the total transaction amount of the target financial account and T types of financial products, and specifically, the total transaction amount within different preset amount ranges may have a mapping relationship with different financial products.

[0138] In this embodiment, the total transaction amount (total consumption data) involved in the first financial data can be matched with the preset mapping rules, and the financial product associated with the total transaction amount (i.e., the target financial product) can be determined according to the preset amount range to which the total transaction amount belongs. Then, the target financial product can be recommended to the first object. Based on the total transaction amount of the first object, financial products are recommended to the object, thereby avoiding the situation where a financial product that the object does not have the purchasing power is recommended to the first object, thereby achieving the technical effect of improving the accuracy of financial product recommendations.

[0139] It should be noted that the random intercept slope model is used: i =X i β i +Z i γ i +ε i , i = 1, 2, ..., N, fit the data set, estimate the parameters, and obtain the estimated parameters β and γ. When Y is a 0, 1 variable, or a multinomial fixed variable (0, 1, 2, 3, 4, 5), the random intercept slope model can also be written as a generalized linear random intercept slope logistic model (logistic regression model):

[0140]

[0141]

[0142] More generally, y i =X i β i +Z i γ i +ε i , i=1,2,...,N, can also be written as:

[0143] y i =f(X1,X2,...,X p )+Z i γ i +ε i ,i=1,2,...,N,

[0144] Among them, f(X1,X2,...,X p ) is used to explain the fixed effect part, which can be linear or nonlinear. i γi is the random utility part. The dependent variable observer Y is not only a normally distributed continuous variable fitted by a linear model, but also a binomial distributed variable fitted by a logistic model, or can be an exponential distribution, a Gamma distribution, an inverse Gaussian distribution, or a Poisson distribution. This modeling formula greatly improves the adaptability and flexibility of the model. The modeling formulas of various distributions are shown in Table 2.

[0145] Table 2

[0146]

[0147] In the case of using customer information, and the customer knows and authorizes the collection and use, in this embodiment, the accuracy is higher than that of the general regression analysis model. The random intercept slope model is used, and the independent variables X1, X2, X3, X4, X5, and X6 of N customers are used. Each customer has ni observations of the total credit card consumption Y and the independent variables X2 and X3. N regression models containing fixed effects and random effects are established for N customers. The fixed effect parameters of the regression model are the same, and the characteristics of the customer group are mined. The parameters of the random effects are different, and the characteristics of the individual are mined. The general regression model only establishes a regression model with only fixed effects for the customer group, and can only discover the characteristics of the customer group; or N different regression models are established for N customers, and the parameters of the regression model are calculated N times, and only the characteristics of the individual customers can be discovered. The general regression model cannot simultaneously mine the characteristics of the customer group and the individual. The relative time series model makes more efficient use of the historical data of credit cards. The random intercept random slope model is used, and N customers, each customer has ni observations, and these ni different observations are recorded at different times t. The number of observations for each customer may not be the same, the observation intervals may not be the same, and the number of observations may not be large. Compared to the time series model which requires a large number of observations at equal intervals, the model used in this embodiment can effectively utilize data with unequal observation times.

[0148] In this embodiment, the linear random intercept random slope model can be extended to the generalized linear random intercept slope logistic model, and then to the general form y i =f(X1,X2,...,X p )+Z i γ i +ε i , in the formula f(X1,X2,...,X p ) is used to explain the fixed effect part, which can be linear or nonlinear. i γ iis the random utility part. The dependent variable observer Y is not only a normally distributed continuous variable fitted by a linear model, but also a binomial distributed variable fitted by a logistic model, or can be an exponential distribution, Gamma distribution, inverse Gaussian distribution, Poisson distribution. This greatly improves the adaptability and flexibility of the model.

[0149] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0150] Embodiment 2

[0151] The embodiment of the present application also provides a financial data processing device. It should be noted that the financial data processing device of the embodiment of the present application can be used to execute the financial data processing method provided in the embodiment of the present application. The financial data processing device provided in the embodiment of the present application is introduced below.

[0152] According to an embodiment of the present application, a device for implementing the above-mentioned financial data processing method is also provided. Figure 6 As shown, the device includes: a first acquisition unit 61, a screening unit 62 and a first processing unit 63.

[0153] The first acquisition unit 61 is used to acquire a first data set of a first object, wherein the first data set includes: attribute data of the first object and M types of financial data generated by the first object purchasing financial products in a historical time period, where M is a positive integer;

[0154] A screening unit 62 is used to screen out a first prediction model from N hybrid models based on the first data set, wherein the model types of the N hybrid models include: a random slope intercept model, the random slope intercept model includes: a model constructed based on a random slope and a random intercept, and N is a positive integer;

[0155] The first processing unit 63 is used to input the first data set into the first prediction model and output first financial data, wherein the first financial data includes: financial data related to the target financial account of the first object in the financial institution, and the financial data includes: consumption data.

[0156] The financial data processing device provided in the embodiment of the present application comprises a first acquisition unit 61 for acquiring a first data set of a first object, wherein the first data set comprises: attribute data of the first object and M types of financial data generated by the first object purchasing financial products in a historical time period, where M is a positive integer; a screening unit 62 for screening out a first prediction model from N hybrid models based on the first data set, wherein the model types of the N hybrid models comprise: a random slope intercept model, and the random slope intercept model comprises: a model constructed based on a random slope and a random intercept, and N is a positive integer; a first processing unit 63 for inputting the first data set into the first prediction model and outputting first financial data, wherein the first financial data comprises: financial data involved in the target financial account of the first object in a financial institution, and the financial data comprises: consumption data. Thus, the technical problem of low accuracy in the related art of using a single regression equation to output consumption data of all customers' financial accounts is solved.

[0157] In this embodiment, based on the data set of the first object, the first prediction model associated with the first object is screened out from N mixed models, and the consumption data of the target financial account of the first object is output using the first prediction model, thereby avoiding the situation in the related technology where a single regression equation is used to output the consumption data of the target financial accounts of all customers and individual differences cannot be distinguished, thereby achieving the technical effect of accurately determining the consumption data of a single customer's financial account.

[0158] Furthermore, in the financial data processing device provided in the embodiment of the present application, the screening unit includes: an acquisition subunit, used to acquire N second data sets, wherein the N second data sets include: attribute data of each second object among the N second objects and M types of financial data generated by the second object purchasing financial products; a matching subunit, used to match the first data set with the N second data sets to obtain a first matching result, wherein the first matching result is used to indicate a second data set among the N second data sets that successfully matches the first data set; and a first determination subunit, used to determine the hybrid model associated with the second data set indicated by the matching result as the first prediction model.

[0159] Furthermore, in the financial data processing device provided in the embodiment of the present application, N mixed models are obtained through the following sub-units: a second determination sub-unit, used to determine S linear models, wherein the model types of the S linear models include at least one of the following: a random slope model, a random intercept model, and a random slope intercept model, and S is a positive integer; a fitting sub-unit, used to perform data fitting on N second data sets using each linear model to obtain a fitting result of each linear model, and determine N mixed models based on the fitting results, wherein the data fitting is used to determine the parameter values ​​of unknown parameters in the linear model.

[0160] Furthermore, in the financial data processing device provided in the embodiment of the present application, the fitting subunit includes: a verification module, which is used to verify each fitting result based on a target verification strategy to obtain a verification result of each linear model, wherein the target verification strategy includes at least one of the following: significance test, regression coefficient test and goodness of fit test; a determination module, which is used to determine N mixed models based on the fitting results of the random slope intercept model when the verification results of the S linear models all meet the preset conditions.

[0161] Furthermore, in the financial data processing device provided in the embodiment of the present application, the financial data processing device also includes: an extraction unit, which is used to extract the random slope and random intercept of each of the N mixed models after determining N mixed models based on the fitting results of the random slope-intercept model, so as to obtain a third data set; a construction subunit, which is used to construct a second prediction model based on the third data set, wherein the second prediction model includes: a model with the random slope and random intercept as dependent variables and the financial data generated by the target financial account as independent variables.

[0162] Furthermore, in the financial data processing device provided in the embodiment of the present application, the financial data of the target financial account includes: the total transaction amount of the target financial account in the target time period, and the financial data processing device also includes: a second acquisition unit, which is used to acquire the financial data of the target financial account of the third object in the financial institution after building the second prediction model based on the third data set, so as to obtain second financial data; a second processing unit, which is used to input the second financial data into the second prediction model, and output a target slope and a target intercept, wherein the target slope is used to determine the degree to which the second financial data is affected by the attribute data of the third object or the purchase of financial products by the third object, and the target intercept is used to determine the minimum total transaction amount of the third object at multiple time points in the target time period.

[0163] Furthermore, in the financial data processing device provided in the embodiment of the present application, the financial data processing device also includes: a third acquisition unit, which is used to obtain a preset mapping rule after inputting the first data set into the first prediction model and outputting the first financial data, and match the first financial data with the preset mapping rule to obtain a second matching result, wherein the preset mapping rule includes: a mapping relationship between the financial data of the target financial account and T types of financial products, where T is a positive integer; a determination unit, which is used to determine the target financial product based on the second matching result, wherein the target financial product includes: a financial product to be recommended to the first object.

[0164] It should be noted that the first acquisition unit 61, the screening unit 62 and the first processing unit 63 described above correspond to steps S201 to S203 in the first embodiment, and the examples and application scenarios implemented by the two modules and the corresponding steps are the same, but are not limited to the contents disclosed in the first embodiment described above. It should be noted that the above modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above modules may also be part of a device and may be run in the computer terminal 10 provided in the first embodiment.

[0165] Embodiment 3

[0166] An embodiment of the present application may provide an electronic device, Figure 7 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 7 As shown, the electronic device may include: one or more ( Figure 7 (only one is shown) processor 702, memory 704, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0167] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0168] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain a first data set of the first object, wherein the first data set includes: attribute data of the first object and M types of financial data generated by the first object purchasing financial products in a historical time period, M is a positive integer; based on the first data set, screen out a first prediction model from N hybrid models, wherein the model types of the N hybrid models include: random slope intercept model, the random slope intercept model includes: a model constructed based on a random slope and a random intercept, and N is a positive integer; input the first data set into the first prediction model, and output first financial data, wherein the first financial data includes: financial data involved in a target financial account of the first object in a financial institution, and the financial data includes: consumption data.

[0169] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: based on the first data set, screen out the first prediction model from N hybrid models, including: obtaining N second data sets, wherein the N second data sets include: attribute data of each second object in the N second objects and M types of financial data generated by the second object purchasing financial products; matching the first data set with the N second data sets to obtain a first matching result, wherein the first matching result is used to indicate a second data set in the N second data sets that successfully matches the first data set; and determining the hybrid model associated with the second data set indicated by the matching result as the first prediction model.

[0170] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: N mixed models are obtained in the following manner: S linear models are determined, wherein the model types of the S linear models include at least one of the following: a random slope model, a random intercept model, and a random slope intercept model, and S is a positive integer; N second data sets are fitted using each linear model to obtain fitting results for each linear model, and based on the fitting results, N mixed models are determined, wherein the data fitting is used to determine the parameter values ​​of unknown parameters in the linear model.

[0171] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: based on the fitting results, determine N mixed models, including: verifying each fitting result based on the target verification strategy to obtain the verification result of each linear model, wherein the target verification strategy includes at least one of the following: significance test, regression coefficient test and goodness of fit test; when the verification results of the S linear models all meet the preset conditions, determine N mixed models based on the fitting results of the random slope intercept model.

[0172] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: after determining N hybrid models based on the fitting results of the random slope-intercept model, it also includes: extracting the random slope and random intercept of each of the N hybrid models to obtain a third data set; based on the third data set, constructing a second prediction model, wherein the second prediction model includes: a model with random slope and random intercept as dependent variables and financial data generated by the target financial account as independent variables.

[0173] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: the financial data of the target financial account includes: the total transaction amount of the target financial account in the target time period. After building the second prediction model based on the third data set, it also includes: obtaining the financial data of the target financial account of the third object in the financial institution to obtain second financial data; inputting the second financial data into the second prediction model, and outputting the target slope and target intercept, wherein the target slope is used to determine the degree to which the second financial data is affected by the attribute data of the third object or the purchase of financial products by the third object, and the target intercept is used to determine the minimum total transaction amount of the third object at multiple time points in the target time period.

[0174] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: after inputting the first data set into the first prediction model and outputting the first financial data, it also includes: obtaining a preset mapping rule, matching the first financial data and the preset mapping rule, and obtaining a second matching result, wherein the preset mapping rule includes: a mapping relationship between the financial data of the target financial account and T types of financial products, T is a positive integer; based on the second matching result, determining the target financial product, wherein the target financial product includes: a financial product to be recommended to the first object.

[0175] By using the embodiment of the present application, a solution for processing financial data is provided. Based on the data set of the first object, a first prediction model associated with the first object is screened out from N mixed models, and the consumption data of the target financial account of the first object is output using the first prediction model, thereby avoiding the situation in which a single regression equation is used in the related art to output the consumption data of the target financial accounts of all customers and individual differences cannot be distinguished, thereby achieving the technical effect of accurately determining the consumption data of the financial account of a single customer. This solves the technical problem of low accuracy in the related art of using a single regression equation to output the consumption data of the financial accounts of all customers.

[0176] It can be understood by those skilled in the art that Figure 7 The structure shown is for illustration only, and the electronic device may also be a smart phone, a tablet computer, a PDA, a mobile Internet device (MID), a PAD or other terminal device. Figure 7 The structure of the electronic device is not limited. Figure 7 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 7 Different configurations shown.

[0177] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0178] Embodiment 4

[0179] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the financial data processing method provided in the first embodiment.

[0180] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0181] The present application also provides a computer program product which, when executed on a data processing device, is suitable for executing the program steps of the method for processing financial data.

[0182] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0183] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0188] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for processing financial data, characterized in that: include: Acquire a first data set of a first object, wherein the first data set includes: attribute data of the first object and M types of financial data generated by the first object when purchasing financial products in a historical time period, where M is a positive integer; Based on the first data set, a first prediction model is screened out from N hybrid models, wherein the model types of the N hybrid models include: a random slope intercept model, the random slope intercept model includes: a model constructed based on a random slope and a random intercept, and N is a positive integer; The first data set is input into the first prediction model, and first financial data is output, wherein the first financial data includes: financial data related to the target financial account of the first object in a financial institution, and the financial data includes: consumption data.

2. The processing method according to claim 1, characterized in that: Based on the first data set, a first prediction model is selected from N hybrid models, including: Acquire N second data sets, wherein the N second data sets include: attribute data of each second object in the N second objects and M types of financial data generated by the second object purchasing a financial product; Matching the first data set with the N second data sets to obtain a first matching result, wherein the first matching result is used to indicate a second data set among the N second data sets that successfully matches the first data set; The hybrid model associated with the second data set indicated by the matching result is determined as the first prediction model.

3. The processing method according to claim 2, characterized in that: The N mixed models are obtained in the following way: Determine S linear models, wherein the model types of the S linear models include at least one of the following: a random slope model, a random intercept model, and a random slope-intercept model, and S is a positive integer; Each of the linear models is used to perform data fitting on the N second data sets to obtain fitting results of each of the linear models, and based on the fitting results, N mixed models are determined, wherein the data fitting is used to determine parameter values ​​of unknown parameters in the linear model.

4. The processing method according to claim 3, characterized in that: Based on the fitting results, determining N of the mixed models includes: Verifying each of the fitting results based on a target verification strategy to obtain a verification result of each of the linear models, wherein the target verification strategy includes at least one of the following: significance test, regression coefficient test and goodness of fit test; When the verification results of the S linear models all meet the preset conditions, the N mixed models are determined based on the fitting results of the random slope intercept model.

5. The processing method according to claim 4, characterized in that: After determining N mixed models based on the fitting results of the random slope intercept model, the method further includes: extracting the random slope and the random intercept of each of the N mixed models to obtain a third data set; Based on the third data set, a second prediction model is constructed, wherein the second prediction model includes: a model with a random slope and a random intercept as dependent variables and financial data generated by a target financial account as an independent variable.

6. The processing method according to claim 5, characterized in that: The financial data of the target financial account includes: the total transaction amount of the target financial account in the target time period, and after building the second prediction model based on the third data set, further includes: Acquire financial data of a target financial account of a third object in the financial institution to obtain second financial data; The second financial data is input into the second prediction model, and a target slope and a target intercept are output, wherein the target slope is used to determine the extent to which the second financial data is affected by the attribute data of the third object or the purchase of financial products by the third object, and the target intercept is used to determine the minimum total transaction amount of the third object at multiple time points in the target time period.

7. The processing method according to claim 1, characterized in that: After inputting the first data set into the first prediction model and outputting the first financial data, the method further includes: Obtaining a preset mapping rule, matching the first financial data with the preset mapping rule, and obtaining a second matching result, wherein the preset mapping rule includes: a mapping relationship between the financial data of the target financial account and T types of financial products, where T is a positive integer; Based on the second matching result, a target financial product is determined, wherein the target financial product includes: a financial product to be recommended to the first object.

8. A financial data processing device, characterized in that: include: A first acquisition unit is configured to acquire a first data set of a first object, wherein the first data set includes: attribute data of the first object and M types of financial data generated by the first object when purchasing financial products in a historical period, where M is a positive integer; A screening unit, configured to screen out a first prediction model from N hybrid models based on the first data set, wherein the model types of the N hybrid models include: a random slope intercept model, the random slope intercept model includes: a model constructed based on a random slope and a random intercept, and N is a positive integer; The first processing unit is used to input the first data set into the first prediction model and output first financial data, wherein the first financial data includes: financial data related to the target financial account of the first object in a financial institution, and the financial data includes: consumption data.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for processing financial data according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the method for processing financial data as described in any one of claims 1 to 7.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method for processing financial data described in any one of claims 1 to 7 are implemented.

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