CN114049189A

CN114049189A - Method for processing financial data

Info

Publication number: CN114049189A
Application number: CN202111348556.6A
Authority: CN
Inventors: 李璐莎; 汪能博
Original assignee: Shengdoushi Shanghai Technology Development Co Ltd
Current assignee: Shengdoushi Shanghai Science and Technology Development Co Ltd
Priority date: 2021-11-15
Filing date: 2021-11-15
Publication date: 2022-02-15

Abstract

The present disclosure relates to a method for processing financial data, comprising: determining subjects with budget abnormal use and expense detail data corresponding to the subjects based on financial data; extracting detail key words from the expense detail data; and fusing the detail keywords based on the relevance of the detail keywords to generate an abnormal reason prompt of the subject. By means of the method, subjects with budget abnormal use can be obtained, and abnormal reason prompts of the subjects can be generated, so that the user can visually know the subjects with budget abnormal use and the abnormal reasons causing the subjects to exceed the budget, the user who is not in professional financial affairs can visually know the financial condition of departments conveniently, and the user can make relevant decisions.

Description

Method for processing financial data

Technical Field

The present disclosure relates to the field of financial management and data processing technology, and in particular, to a method for processing financial data.

Background

For enterprises, financial data is important reference data for their business development. The related financial systems will generally only output financial data in the form of financial statements. However, such an output method has a problem that it is not intuitive, and particularly for a user who is not specialized in finance, the financial status cannot be intuitively known from the financial report, for example, the leader of an enterprise cannot intuitively see whether the responsible department is over-budget and the main reason of over-budget from the financial report.

Disclosure of Invention

According to an embodiment of the present disclosure, a method for processing financial data is proposed, one object of which is to solve at least one of the problems set forth above, presenting financial conditions to a user in an intuitive manner.

According to an aspect of the present disclosure, a method for processing financial data is presented, the method comprising:

determining subjects with budget abnormal use and expense detail data corresponding to the subjects based on financial data;

extracting detail key words from the expense detail data; and

and fusing the detail keywords based on the relevance of the detail keywords to generate an abnormal reason prompt of the subject.

Optionally, the extracting of the detail keyword from the expense detail data includes:

detail keywords are extracted from the expense description in the expense detail data based on the amount data in the expense detail data.

Optionally, extracting the detail keyword from the expense description in the expense detail data based on the amount data in the expense detail data comprises:

preprocessing the expense description by combining the expense descriptions with similar meanings and removing special symbols in the expense description, and creating a project customized dictionary according to the preprocessed expense description;

performing word segmentation on the expense description according to the project customized dictionary;

weighting words obtained after the words are cut based on the amount data in the expense detail data; and

and selecting detailed keywords according to the weighted sum of each word.

Optionally, the cost description in the cost detail data is tokenized using a natural language processing model.

Optionally, fusing the detail keywords based on the relevance of the detail keywords to generate the abnormal reason cue of the subject includes:

determining the relevance of the detail keywords by using a relevance algorithm, and forming detail keyword groups by the mutually related detail keywords; and

and fusing the detail keywords in the detail keyword group into the abnormal reason prompt words.

Optionally, determining the relevance of the detail keyword using a relevance algorithm comprises: and judging whether the times of the plurality of detail keywords appearing in the same expense description exceed a preset time threshold, if so, considering that the plurality of detail keywords are mutually associated, and if not, considering that the plurality of detail keywords are not mutually associated.

Optionally, if all detail keywords are not correlated with each other, each individual detail keyword is used as an abnormal cause prompt.

Optionally, determining that there is a subject with budget use anomaly based on the financial data further comprises:

determining, based on the user permissions, that there is a subject with budget use anomaly, wherein,

determining a subject with abnormal budget use in a department to which the user authority belongs under the condition that the category of the user authority is a department authority;

determining a department with abnormal budget use in departments related to the functional authority and further determining a subject with abnormal budget use in the department with abnormal budget use under the condition that the category of the user authority is the functional authority;

in the case that the category of the user permission is a market permission, determining a market with abnormal budget use related to the market permission and further determining subjects with abnormal budget use in the market with abnormal budget use; and

in the case that the category of the user right is high management right, determining an object with abnormal budget use in the objects related to the high management right, and further determining the subject with abnormal budget use in the object with abnormal budget use, wherein the object comprises one or more of department, function, market and brand.

Optionally, determining that there is a subject with budget use anomaly based on the financial data further comprises determining categories of budget use anomalies, the categories comprising: not budgeted results in an over-budget, and/or budgeted and over-budgeted.

Optionally, determining that there is a subject with budget use anomaly based on the financial data comprises:

selecting, as subjects having budget use anomalies, subjects that satisfy at least one of the following conditions based on the financial data:

the difference of the actual cost exceeding the budget cost exceeds a difference threshold;

the extent to which the actual cost exceeds the budget cost exceeds an extent threshold; and

the difference or degree to which the actual cost exceeds the budget cost ranks top among all subjects.

Optionally, the method further comprises presenting the user with a subject with budget use abnormality and an abnormality reason prompt corresponding to the subject.

According to another aspect of the present disclosure, a computer-readable storage medium is proposed, on which a computer program is stored, the computer program comprising executable instructions which, when executed by a processor, implement the method as described above.

According to yet another aspect of the present disclosure, an electronic device is presented, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the executable instructions to implement the method as described above.

The method for processing financial data proposed by the present disclosure has at least the following advantages:

by the method, subjects with budget abnormal use can be obtained, and abnormal reason prompts of the subjects can be generated, so that a user can visually know the subjects with budget abnormal use and the abnormal reasons causing the subjects to exceed the budget, the user who is not in professional financial affairs can visually know the financial condition of departments, and the user can make relevant decisions.

Drawings

Further details and advantages of the present disclosure will become apparent from the detailed description provided hereinafter. It is to be understood that the following drawings are merely illustrative and not drawn to scale and are not to be considered limiting of the disclosure, the detailed description of which is set forth below with reference to the accompanying drawings, in which:

FIG. 1 shows a schematic flow diagram of a method for processing financial data according to one embodiment of the present disclosure.

2A-2D illustrate schematic flow diagrams of methods for processing financial data according to another embodiment of the present disclosure.

Fig. 3 is a schematic block diagram of an electronic device according to yet another embodiment of the present disclosure.

Detailed Description

Exemplary embodiments will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. In the drawings, the size of some of the elements may be exaggerated or distorted for clarity. The same reference numerals denote the same or similar structures in the drawings, and thus detailed descriptions thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, etc. In other instances, well-known structures, methods, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.

An exemplary method for processing financial data according to an embodiment of the present disclosure is described below in conjunction with FIG. 1.

First, in step S110, a subject having an abnormal budget use and charge detail data corresponding to the subject are determined based on financial data.

The associated responsible person of the enterprise typically pays attention to the overhead of various financial subjects (e.g., travel fees, conference fees, consulting fees, communication fees, etc.) in the department and wishes to know the specifics of the over-budget subject. The method can determine the subjects with budget abnormal use based on the user authority, and the authority of the user can be related to the positions of the user in the enterprise. For example, when the category of the user right is a department right, the subject with abnormal budget use in the department to which the user right belongs is determined. When the category of the user authority is the functional authority, determining a department with abnormal budget use in the departments related to the functional authority, and further determining a subject with abnormal budget use in the department with abnormal budget use. When the category of the user right is a market right (for example, the user is a market manager or a regional vice president), a market with abnormal budget use related to the market right (for example, a market with abnormal budget use) is determined, and further a subject with abnormal budget use in the market with abnormal budget use is determined. When the category of the user right is a high administration (CEO) right, then a department, function, market, and/or brand with abnormal budget use in the department, function, market, and/or brand (e.g., KFC brand) associated with the high administration right is determined and further a subject with abnormal budget use in the department, function, market, and/or brand with abnormal budget use is determined.

Determining that there is a budget use anomaly based on the financial data may further include determining a category of budget use anomalies, which may be defined according to the actual needs of the user. For example, in one alternative embodiment, the category of budget use exceptions may include one of: not budgeted results in an over-budget, and budgeted and over-budgeted.

Determining that there is a budget use anomaly based on the financial data may include: selecting, as subjects having budget use anomalies, subjects that satisfy at least one of the following conditions based on the financial data:

1. the difference in the actual cost over the budget cost exceeds the difference threshold. The specific value of the difference threshold may be user-specified. For example, if the difference threshold is set to be any amount of 10 ten thousand yuan to 100 ten thousand yuan, the subject whose difference between the actual cost and the budget cost exceeds the amount is regarded as the subject whose budget use is abnormal.

2. The extent to which the actual cost exceeds the budget cost exceeds the extent threshold. The specific value of the degree threshold may be user-specified. For example, if the degree threshold value is set to any value of 10% to 50%, the subject whose actual cost exceeds the budget cost by more than the value may be regarded as the subject whose budget use is abnormal.

3. The difference or degree to which the actual cost exceeds the budget cost ranks top among all subjects. That is, the subjects whose difference or degree exceeding the budget fees is ranked in the top N number may be used as the subjects whose budget use is abnormal, where N is a positive integer. The value of N can be selected according to the actual needs of the user. For example, the subjects whose difference or degree exceeding the budget fees is ranked in the top three may be taken as the subjects whose budget use is abnormal.

In step S120, a detail keyword is extracted from the charge detail data.

The detail key may be extracted from the expense description in the expense detail data based on the amount data in the expense detail data.

In particular, the cost description in the cost detail data may first be preprocessed. Specifically, the cost description can be preprocessed, for example, by combining cost descriptions with similar meanings and removing special symbols in the cost description, so that the data amount is reduced and the operation pressure is relieved. Then, a project customization dictionary is created from the preprocessed expense description. Thereafter, words may be cut from the expense description in the expense data according to the project customization dictionary, and words obtained after the words are cut may be weighted based on the amount data in the expense data. And finally, selecting detailed keywords according to the weighted sum of each word after weighting. For example, the words may be sorted according to the weighted amount, N words with the highest weighted amount may be used as detail keywords, and the specific value of N may be specified by the user, and may be any integer from 3 to 10, for example. Alternatively, the weighted amount may be compared with a preset amount threshold, and a word whose weighted amount is greater than the preset amount threshold is used as the detail keyword, where the preset amount threshold may be self-defined by the user, and may be any amount from 10 ten thousand yuan to 100 ten thousand yuan, for example. Any suitable natural language processing model may be used to tokenize the cost description in the cost specification data, for example, the NLP-jieba model may be used.

The manner of extracting the detail keyword from the expense detail data is explained below in a specific example, in which the expense description in the expense detail data and the amount data corresponding to the expense description are shown in table 1:

TABLE 1

After the words are cut into the expense description, the following five words are obtained: "city a", "city B", "city C", "business trip" and "meeting". The correspondence of each word to the weighted amount is shown in table 2:

word	Weighted sum (Yuan)
		City A	100
City B	900
		City C	200
Business trip	500
		Conference	700

TABLE 2

If the first three words with the highest weighted sum are taken as detail keywords, the obtained detail keywords are respectively as follows: "city B", "business trip", and "meeting".

In step S130, the detail keywords are fused based on their relevance to generate an abnormality cause cue for the subject.

Relevance of the detail keywords can be determined by using a relevance algorithm, the detail keywords which are mutually related form corresponding detail keyword groups, and the detail keywords in the detail keyword groups are fused into an abnormal reason prompt. Specifically, in the process of fusion, detail keywords with high relevance are selected and fused in a splicing mode. Any suitable association algorithm may be used to determine the association of the detail keywords, for example, Apriori algorithm may be used to determine the association of the detail keywords. Specifically, it may be determined whether the number of times that the plurality of detail keywords appear in the same expense description exceeds a preset number threshold, and if the determination result is yes, the plurality of detail keywords are considered to be associated with each other, and if the determination result is no, the plurality of detail keywords are considered not to be associated with each other. The specific value of the time threshold may be user-defined. For example, the threshold number of times may be set to any number of times from 10 to 50 times, and if the number of times that a plurality of detail keywords appear in the same expense description exceeds the number of times, the plurality of detail keywords are considered to be associated with each other, and the plurality of detail keywords should be grouped into the same detail keyword group and merged into the same prompt language for the abnormality cause. In addition, if all detail keywords are not correlated with each other, each individual detail keyword is used as an abnormal reason prompt.

Continuing with the specific example in step S120, after obtaining the three detail keywords "city B", "business trip", and "meeting", it is determined by the association algorithm that "city B" is correlated with "business trip", and "city B" is correlated with "meeting", thereby obtaining two detail keyword groups: "city B" is compared to "business trip", and "city B" is compared to "meeting". And finally, fusing the obtained abnormal reason prompts of 'urban B business trip' and 'urban B meeting'.

After the abnormal reason prompting words of the budget use abnormal subjects are obtained, subjects with budget use abnormality and abnormal reason prompting words corresponding to the subjects can be presented to the user. The output may be via any suitable output device, such as a display screen of a desktop computer, laptop computer, or smartphone.

In the above manner, a user (e.g., a relevant responsible person of an enterprise) can intuitively know the subject with abnormal budget use of the responsible department and the abnormal reason causing the budget exceeding of the subject, so that the user can conveniently make relevant decisions.

A more specific schematic flow chart of a method for processing financial data is disclosed in fig. 2A-2D, according to the principles of the method for processing financial data in fig. 1.

Fig. 2A shows a case where the user right is a department right. For example, the department to which the department authority belongs is a retail department. Then in the method shown in fig. 2A, it is first determined whether the retail establishment as a whole is over-budgeted, i.e., whether the retail establishment is not over-budgeted, is not budgeted, results in over-budgeted, or is budgeted and over-budgeted. Then, the method according to the disclosure finds out the subject with the budget abnormal use in the retail department and generates an abnormal reason prompting language of the subject. One exemplary output result is as follows: in month 1 of 2021, the retail department exceeded the budget. In subjects, Supplies (Supplies) are over-budget, with important attention: insect control in an office, stationery reservation in an xx office and purchase of printing paper; external service (outside-service) over-budget, important concerns: xx service fee, xx consultation fee and xx fee.

Fig. 2B shows a case where the user right is a function right. For example, the role authority corresponds to an IT role. Then in the method shown in fig. 2A, IT is first determined whether the IT function is over-budgeted, i.e., whether IT function is under-budgeted, under-budgeted results in over-budgeted, or over-budgeted and over-budgeted. Then, the department with abnormal budget use in the departments related to the IT function is determined, and then the subjects with abnormal budget use in the departments with abnormal budget use are found out according to the method of the disclosure and the abnormal cause prompt words of the subjects are generated. One exemplary output result is as follows: in 1 month 2021, the IT function is not over-budget. Among them, the Data department over-budgets because it does not make budgets, and in subjects, depreciation over-budgets needs to pay attention: xx chips, xx items, xx processors; the travel over budget needs to pay attention to the following points: city B business trip, xx conference fare, city a ticket fare. The over-budget of the Digital department is consulted in the subject, and important attention is needed: xx company expert consulting fee, xx meeting fee. In the subjects, the Advertisement (Advertisement) department needs to pay more attention because the budget is not made, and the training needs to pay more attention because the budget is not made: xx live broadcast and xx conference.

The case where the user right is a market right is shown in fig. 2C. For example, the market right corresponds to a regional vice president. Then in the method shown in fig. 2C, it is first determined whether the market for each relevant city is over-budgeted, i.e., whether the market for each relevant city is not over-budgeted, is not budgeted resulting in over-budgeted, or is budgeted and over-budgeted. Then, according to the method disclosed by the invention, the subject with abnormal budget use in the market of each relevant city is found out, and an abnormal reason prompt of the subject is generated. One exemplary output result is as follows: the city A market exceeds the budget due to no budget, and in subjects, the travel exceeds the budget, so that important attention needs to be paid: xx conference ticket fees; the urban B market over-budget needs to be paid key attention to training over-budget in subjects: xx purchase fees.

The case where the user right is a high-management right is shown in fig. 2D. In the method shown in FIG. 2D, it is first determined whether the department, function, market, and/or brand associated with the high administration privilege is over-budget, i.e., whether the department, function, market, and/or brand associated with the high administration privilege is under-budget, under-budget results in over-budget, or is over-budget and over-budget. Then, finding out the department, function, market and/or brand with the budget abnormal use according to the method of the disclosure, and generating an abnormal reason prompt of the department. One exemplary output result is as follows: please focus on the following in the function: xx (job principal) urban B market over-budget, wherein xx subjects are over-budgeted due to not being budgeted, and in subjects, traveling over-budget, important attention is paid: xx conference ticket fees. The brand focuses on: city a market over-budget, where xx subjects over-budget, requires significant attention: a4 paper and stationery.

In an exemplary embodiment of the disclosure, there is also provided a computer readable storage medium, on which a computer program is stored, the program comprising executable instructions which, when executed by, for example, a processor, may implement the steps of the method for processing financial data in any of the above-described embodiments. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the method for processing financial data of the present description, when the program product is run on the terminal device.

A program product for implementing the above method according to an embodiment of the present disclosure may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

In an exemplary embodiment of the present disclosure, there is also provided an electronic device, which may include a processor, and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the method for processing financial data in any of the above embodiments via execution of the executable instructions.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 200 according to this embodiment of the present disclosure is described below with reference to fig. 3. The electronic device 200 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 3, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.

Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform steps according to various exemplary embodiments of the present disclosure described in the methods for processing financial data of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 1.

The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.

The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 230 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which may be a personal computer, a server, or a network device, etc.) execute the method for processing financial data according to the embodiments of the present disclosure.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method for processing financial data, comprising:

extracting detail key words from the expense detail data; and

2. The method of claim 1, wherein extracting detail keywords from the expense detail data comprises:

3. The method of claim 2, wherein extracting the detail key from the cost description in the cost detail data based on the amount data in the cost detail data comprises:

and selecting detailed keywords according to the weighted sum of each word.

4. The method of claim 3, wherein the cost description in the cost detail data is tokenized using a natural language processing model.

5. The method according to any one of claims 1 to 4, wherein fusing the detail keywords based on their relevance to generate the abnormal cause cue for the subject comprises:

determining the relevance of the detail keywords by using a relevance algorithm, and forming corresponding detail keyword groups by the mutually related detail keywords; and

6. The method of claim 5, wherein determining the relevance of the detail keywords using a relevance algorithm comprises:

and judging whether the times of the plurality of detail keywords appearing in the same expense description exceed a preset time threshold, if so, considering that the plurality of detail keywords are mutually associated, and if not, considering that the plurality of detail keywords are not mutually associated.

7. The method of claim 5, wherein if all detail keywords are not correlated with each other, then each detail keyword alone is used as an anomaly cause prompt.

8. The method of claim 1, wherein determining that there is a subject with budget use anomaly based on financial data further comprises:

determining subject matter with budget abnormal usage based on user permissions, wherein

9. The method of claim 1, wherein determining that a subject has budget use anomalies based on financial data further comprises determining categories of budget use anomalies, the categories comprising: not budgeted results in an over-budget, and/or budgeted and over-budgeted.

10. The method of claim 1, wherein determining that a subject with budget use anomalies exists based on financial data comprises:

11. The method of claim 1, further comprising presenting to a user a subject for which there is a budget use anomaly and the anomaly cause prompt corresponding to the subject.

12. A computer-readable storage medium, on which a computer program is stored, the computer program comprising executable instructions that, when executed by a processor, carry out the method according to any one of claims 1 to 11.

13. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to execute the executable instructions to implement the method of any of claims 1 to 11.