CN115393034A - Method for carrying out risk identification on enterprise account based on natural language processing technology - Google Patents
Method for carrying out risk identification on enterprise account based on natural language processing technology Download PDFInfo
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Abstract
The disclosure relates to a method for carrying out risk identification on an enterprise account based on a natural language processing technology, which comprises the following steps: responding to a first operation of a user on a first page, and acquiring data to be identified; responding to a second operation of the user on the data to be identified, and processing the data to be identified to obtain target data; and screening the target data based on a target pre-training model, and identifying abnormal information in the target data. According to the method, the data to be recognized are processed firstly, the target data are obtained, the garbage data are screened out, useful data are reserved, unnecessary workload can be reduced, the working efficiency is improved, the target pre-training model can be used for intelligently recognizing the abnormal information, the structured data and the unstructured data can be recognized, the application range is wide, the manpower input for analyzing, processing and recognizing the abnormal information is reduced, the manual load is reduced, and therefore the utilization rate of human resources and the office efficiency are improved.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to a method for risk identification of an enterprise account based on a natural language processing technology.
Background
With the deep application of the internet to financial services, the internet finance gradually walks into the public vision, and the internet finance refers to the behaviors of carrying out the businesses such as fund financing, payment and related information services through or depending on internet technologies and tools, and provides a new information acquisition mode for finance by utilizing an internet platform, so that convenience is brought to people, and meanwhile, certain risks are brought.
In general, a money institution needs to master change information of an enterprise account in real time, needs to manage and monitor an enterprise bank account, needs to monitor and analyze the abnormality of opening, changing and canceling the enterprise bank account, and manages and maintains the enterprise account information, so as to identify risks in time.
However, in the existing management and maintenance, risk identification and other modes of enterprise accounts, a large amount of manpower is required to be invested to identify and analyze the accounts, so that the waste of human resources is caused, the labor load is large, and the office efficiency is low.
Disclosure of Invention
In order to solve the technical problem, the present disclosure provides a method for risk identification of an enterprise account based on a natural language processing technology, so as to reduce artificial load and improve the utilization rate of human resources and office efficiency.
In a first aspect, an embodiment of the present disclosure provides a method for risk identification of an enterprise account based on a natural language processing technology, including:
responding to a first operation of a user on a first page, and acquiring data to be identified;
responding to a second operation of the user on the data to be identified, and processing the data to be identified to obtain target data;
and screening the target data based on a target pre-training model, and identifying abnormal information in the target data.
In a second aspect, an embodiment of the present disclosure provides an apparatus for risk identification of an enterprise account based on a natural language processing technology, including:
the first acquisition module is used for responding to a first operation of a user on a first page and acquiring data to be identified;
the processing module is used for responding to a second operation of the user on the data to be identified and processing the data to be identified to obtain target data;
and the identification module is used for screening the target data based on a target pre-training model and identifying abnormal information in the target data.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of the first aspect.
In a fourth aspect, the present disclosure provides a computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method of the first aspect.
In a fifth aspect, the disclosed embodiments also provide a computer program product including a computer program or instructions, which when executed by a processor, implement the method for risk identification of an enterprise account based on natural language processing technology as described above.
According to the method for identifying risks of enterprise accounts based on the natural language processing technology, data to be identified are obtained through responding to a first operation of a user on a first page, processing is carried out on the data to be identified through responding to a second operation of the user on the data to be identified, target data are obtained, the target data are screened based on a target pre-training model, and abnormal information in the target data is identified. According to the method, the identification data are processed firstly, the target data are obtained, the garbage data are screened out, useful data are reserved, unnecessary workload can be reduced, the working efficiency is improved, the target pre-training model can be used for intelligently identifying abnormal information, the structured data and the unstructured data can be identified, the application range is wide, the manpower input for analysis, processing and identification of the abnormal information is reduced, the manual load is reduced, and therefore the utilization rate of human resources and the office efficiency are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the embodiments or technical solutions in the prior art description will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart of a method for risk identification of an enterprise account based on natural language processing technology according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of an application scenario provided in an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a user interface provided by an embodiment of the present disclosure;
FIG. 4 is a schematic view of a user interface provided by an embodiment of the present disclosure;
FIG. 5 is a flowchart of a method for risk identification of an enterprise account based on natural language processing technology according to another embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an apparatus for risk identification of an enterprise account based on natural language processing technology according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments. The specific embodiments described herein are merely illustrative of the invention and do not delimit the invention. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
With the deep application of the internet to financial services, the internet finance gradually walks into the public vision, and the internet finance refers to the behaviors of carrying out the businesses such as fund financing, payment and related information services through or depending on internet technologies and tools, and provides a new information acquisition mode for finance by utilizing an internet platform, so that convenience is brought to people, and meanwhile, certain risks are brought.
Under normal conditions, a money institution needs to master change information of an enterprise account in real time, needs to manage and monitor an enterprise bank account, needs to monitor and analyze the opening, change and cancellation abnormalities of the enterprise bank account, and manages and maintains the enterprise account information, so that risks are identified in time.
However, in the existing management and maintenance, risk identification and other modes of enterprise accounts, a large amount of manpower is required to be invested to identify and analyze the accounts, so that the waste of human resources is caused, the labor load is large, and the office efficiency is low. In view of this problem, the embodiments of the present disclosure provide a method for risk identification of an enterprise account based on a natural language processing technology, and the method is described below with reference to specific embodiments.
Fig. 1 is a flowchart of a method for risk identification of an enterprise account based on natural language processing technology according to an embodiment of the present disclosure. The method can be applied to the application scenario shown in fig. 2, where the application scenario includes a server 21 and a terminal 22, and the terminal 22 may specifically be a terminal, for example, a smart phone, a palm computer, a tablet computer, a wearable device with a display screen, a desktop computer, a notebook computer, an all-in-one machine, an intelligent home device, and the like. It can be understood that the method for risk identification of an enterprise account based on natural language processing technology provided by the embodiment of the disclosure can also be applied in other scenarios.
In the following, with reference to the application scenario shown in fig. 2, a method for identifying risks of an enterprise account based on natural language processing technology shown in fig. 1 is described, where the method includes the following specific steps:
s101, responding to a first operation of a user on a first page, and acquiring data to be identified.
For example, in response to the user's obtaining operation on the first page, the terminal 22 shown in fig. 2 obtains data to be identified from the server 21, where the data to be identified may be structured data or unstructured data.
Optionally, the data to be identified may be external data from industry and commerce, tax, and the like, or may be data captured from a crawler, and the like.
And S102, responding to a second operation of the user on the data to be identified, and processing the data to be identified to obtain target data.
As shown in fig. 3, the user performs a preprocessing operation on the selected data to be recognized, and the terminal 22 processes the data to be recognized in response to the preprocessing operation on the data to be recognized by the user, screens out some garbage data and retains useful data to obtain target data, thereby reducing unnecessary workload and improving work efficiency.
S103, screening the target data based on a target pre-training model, and identifying abnormal information in the target data.
After the target data are obtained, the terminal screens the target data based on a target pre-training model, and abnormal information in the target data is identified. The number of the pre-training models is multiple, and a user can select the most suitable pre-training model as a target pre-training model according to needs.
According to the method and the device for identifying the abnormal information in the target data, the data to be identified are obtained in response to a first operation of a user on a first page, the data to be identified are processed in response to a second operation of the user on the data to be identified, the target data are obtained, the target data are screened based on a target pre-training model, and the abnormal information in the target data is identified. According to the method, the identification data are processed firstly, the target data are obtained, the garbage data are screened out, useful data are reserved, unnecessary workload can be reduced, the working efficiency is improved, the target pre-training model can be used for intelligently identifying abnormal information, the structured data and the unstructured data can be identified, the application range is wide, the manpower input for analysis, processing and identification of the abnormal information is reduced, the manual load is reduced, and therefore the utilization rate of human resources and the office efficiency are improved.
On the basis of the foregoing embodiment, before the screening the target data based on the target pre-training model, the method further includes: responding to a third operation of the user on the first page, and displaying a pre-training model list, wherein the pre-training model list comprises identification information corresponding to one or more pre-training models respectively; and determining the target pre-training model in response to a fourth operation of the user on the identification information of the target pre-training model in the one or more pre-training models.
As shown in fig. 3, the user clicks the "model management" icon on the first page, and the terminal displays a pre-training model list as shown in fig. 4 in response to the user clicking the "model management" icon on the first page, where the pre-training model list includes identification information corresponding to one or more pre-training models respectively. The identification information includes, but is not limited to, a name, an icon. The present disclosure is given by name example, and different names are used as identification information corresponding to the one or more pre-training models, specifically, the pre-training model list includes a plurality of pre-training models such as a pre-training model a, a pre-training model B, a pre-training model C, and a pre-training model D. Then, the user clicks the identification information of the target pre-training model, the terminal responds to the operation of clicking the identification information of the target pre-training model by the user to determine the target pre-training models, the number of the pre-training models is multiple, and the user can select the most suitable pre-training model as the target pre-training model according to the needs, so that an ideal recognition result is obtained.
Optionally, screening the target data based on the target pre-training model, and identifying abnormal information in the target data includes: and inputting the target data into the target pre-training model, screening the target data through the target pre-training model, and identifying abnormal information in the target data.
In this step, the terminal inputs the obtained target data into a target pre-training model, screens the target data through the target pre-training model, and identifies abnormal information in the target data. Specifically, target content in the text can be marked through the target pre-training model, similarity between the texts can be calculated, and the like, so that abnormal information is captured, and the abnormal information is visually and clearly identified.
Optionally, the target data includes a plurality of texts, and a manner of processing the target data by the target pre-training model includes at least one of: marking target content in the text, calculating the similarity of the text, classifying the text and analyzing the emotion polarity of the text.
For example, the target content in the annotation text is to perform annotation identification on entities with special meanings in the text, such as names of people, places, organizations, proper nouns and the like; calculating the similarity of text is to give a higher similarity for semantically similar phrases but with different characters, for example: the similarity of the 'railway station' and the 'passenger station' is only 33% in characters, but the characters are very close to each other semantically, and abnormal information does not need to be identified, so that the accuracy of identifying the abnormal information is improved; the classification of the text is automatically classified according to the text content, for example, news articles can be classified as: entertainment news, scientific news, sports news, and the like; analyzing the emotion polarity of the text refers to determining the emotion polarity type (positive, negative, neutral) of the text containing subjective opinion information and giving confidence (performing type labeling).
The data to be identified is acquired by responding to the first operation of a user on the first page, and the data to be identified is processed by responding to the second operation of the user on the data to be identified, so that the target data is acquired. Further, responding to a third operation of the user on the first page, displaying a pre-training model list, wherein the pre-training model list comprises identification information corresponding to one or more pre-training models respectively, and responding to a fourth operation of the user on the identification information of a target pre-training model in the one or more pre-training models, and determining the target pre-training model. And then inputting the target data into the target pre-training model, screening the target data through the target pre-training model, and identifying abnormal information in the target data. Because there are a plurality of pre-training models, the user can select the most suitable pre-training model as the target pre-training model according to the needs, so that an ideal recognition result can be obtained. Target content in the text can be marked through the target pre-training model, similarity between the texts can be calculated, and the like, so that abnormal information is captured, the abnormal information is visually and clearly identified, the artificial load is further reduced, and the office efficiency is improved.
Fig. 5 is a flowchart of a method for risk identification of an enterprise account based on natural language processing technology according to another embodiment of the present disclosure, as shown in fig. 5, the method includes the following steps:
s501, responding to a first operation of a user on a first page, and acquiring data to be identified.
Specifically, the implementation process and principle of S501 and S101 are consistent, and are not described herein again.
S502, responding to a second operation of the user on the data to be identified, and processing the data to be identified to obtain target data.
Specifically, the implementation process and principle of S502 and S102 are the same, and are not described herein again.
S503, screening the target data based on a target pre-training model, and identifying abnormal information in the target data.
Specifically, the implementation process and principle of S503 and S103 are the same, and are not described herein again.
S504, acquiring a preset identification result of the abnormal information in the target data.
For example, the terminal obtains a preset recognition result of the abnormal information in the target data, and the preset recognition result may be manually recognized.
And S505, comparing the abnormal information in the target data with the preset identification result to obtain the identification condition of the target pre-training model on the abnormal information in the target data.
In this step, the terminal compares the abnormal information in the identified target data with the obtained preset identification result to obtain the identification condition of the target pre-training model on the abnormal information in the target data. The identification condition comprises identification accuracy, precision and identification speed, wherein the identification accuracy is the identification accuracy of the structured data, the precision is the identification accuracy of the unstructured data, and the identification speed is the speed of identification.
S506, determining whether the target pre-training model meets a preset condition or not based on the recognition condition, if so, executing S507, and otherwise, executing S508.
Determining whether the target pre-training model meets a preset condition or not based on the recognition condition, and if the target pre-training model meets the preset condition, executing S507 and the steps after S507; and if the target pre-training model does not meet the preset condition, executing the steps after the step S508 and the step S508.
And S507, identifying abnormal information by using the target pre-training model.
And if the target pre-training model meets the preset conditions, putting the target pre-training model into use, and identifying abnormal information by using the target pre-training model.
And S508, optimizing the target pre-training model.
And if the target pre-training model does not meet the preset conditions, optimizing the target pre-training model.
And S509, ending.
According to the method and the device for identifying the abnormal information in the target data, the data to be identified are obtained in response to a first operation of a user on a first page, the data to be identified are processed in response to a second operation of the user on the data to be identified, the target data are obtained, the target data are screened based on a target pre-training model, and the abnormal information in the target data is identified. Further, a preset identification result of abnormal information in the target data is obtained, the abnormal information in the target data is compared with the preset identification result to obtain the identification condition of the target pre-training model on the abnormal information in the target data, and whether the target pre-training model meets a preset condition is determined based on the identification condition. And if the target pre-training model meets the preset condition, identifying abnormal information by using the target pre-training model. According to the method and the device, the abnormal information in the target data is compared with the preset identification result, the identification condition of the target pre-training model on the abnormal information in the target data is obtained, the identification condition comprises identification accuracy, precision and identification speed, whether the target pre-training model meets the preset condition is determined based on the identification condition, if the identification condition is met, the target pre-training model is put into use, and if the identification condition is not met, model optimization processing is carried out, so that a good identification effect is achieved, manual work is replaced better, the identification accuracy is improved, the manual load is reduced, and the utilization rate of human resources and the office efficiency are improved.
Fig. 6 is a schematic structural diagram of an apparatus for risk identification of an enterprise account based on natural language processing technology according to an embodiment of the present disclosure. The device for risk identification of the enterprise account based on the natural language processing technology may be the terminal as described in the above embodiment, or the device for risk identification of the enterprise account based on the natural language processing technology may be a component or assembly in the terminal. The device for risk identification of an enterprise account based on a natural language processing technology according to the embodiment of the present disclosure may execute the processing procedure provided in the method for risk identification of an enterprise account based on a natural language processing technology, as shown in fig. 6, the device 60 for risk identification of an enterprise account based on a natural language processing technology includes: a first acquisition module 61, a processing module 62 and an identification module 63; the first obtaining module 61 is configured to obtain data to be identified in response to a first operation of a user on a first page; the processing module 62 is configured to process the data to be identified to obtain target data in response to a second operation performed on the data to be identified by the user; the identification module 63 is configured to screen the target data based on a target pre-training model, and identify abnormal information in the target data.
Optionally, the apparatus further comprises: a display module 64, a determination module 65; the display module 64 is configured to display a pre-training model list in response to a third operation of the user on the first page, where the pre-training model list includes identification information corresponding to one or more pre-training models respectively; the determining module 65 is configured to determine a target pre-training model of the one or more pre-training models in response to a fourth operation performed by the user on the target pre-training model.
Optionally, when the identification module 63 screens the target data based on a target pre-training model and identifies abnormal information in the target data, the identification module is specifically configured to: and inputting the target data into the target pre-training model, screening the target data through the target pre-training model, and identifying abnormal information in the target data.
Optionally, the target data includes a plurality of texts, and a manner of processing the target data by the target pre-training model includes at least one of: marking target content in the text, calculating the similarity of the text, classifying the text and analyzing the emotion polarity of the text.
Optionally, the apparatus 60 for risk identification of an enterprise account based on natural language processing technology further includes: a second obtaining module 66, a comparing module 67 and a using module 68; the second obtaining module 66 is configured to obtain a preset identification result of the abnormal information in the target data; the comparison module 67 is configured to compare the abnormal information in the target data with the preset identification result, obtain an identification condition of the target pre-training model for the abnormal information in the target data, and determine whether the target pre-training model meets a preset condition based on the identification condition; the using module 68 is configured to identify abnormal information by using the target pre-training model when the target pre-training model satisfies a preset condition.
The device for risk identification of an enterprise account based on natural language processing technology in the embodiment shown in fig. 6 can be used for executing the technical solution of the above method embodiment, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. The electronic device may be a terminal as described in the above embodiments. The electronic device provided in the embodiment of the present disclosure may execute the processing flow provided in the embodiment of the method for risk identification of an enterprise account based on a natural language processing technology, as shown in fig. 7, the electronic device 70 includes: memory 71, processor 72, computer programs, and communications interface 73; wherein a computer program is stored in the memory 71 and configured to execute the method for risk identification of an enterprise account based on natural language processing techniques as described above by the processor 72.
In addition, the embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for risk identification of an enterprise account based on natural language processing technology described in the foregoing embodiment.
Furthermore, the embodiments of the present disclosure also provide a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the method for risk identification of an enterprise account based on natural language processing technology as described above is implemented.
It should be noted that the computer readable medium of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer 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 of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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. In the present disclosure, a computer 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. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
responding to a first operation of a user on a first page, and acquiring data to be identified;
responding to a second operation of the user on the data to be identified, and processing the data to be identified to obtain target data;
and screening the target data based on a target pre-training model, and identifying abnormal information in the target data.
In addition, the electronic device may also perform other steps in the method for risk identification of an enterprise account based on natural language processing technology as described above.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C + +, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is noted that, in this document, relational terms such as "first" and "second," and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for risk identification of an enterprise account based on natural language processing technology, the method comprising:
responding to a first operation of a user on a first page, and acquiring data to be identified;
responding to a second operation of the user on the data to be identified, and processing the data to be identified to obtain target data;
and screening the target data based on a target pre-training model, and identifying abnormal information in the target data.
2. The method of claim 1, wherein prior to the screening the target data based on the target pre-trained model, further comprising:
responding to a third operation of the user on the first page, and displaying a pre-training model list, wherein the pre-training model list comprises identification information corresponding to one or more pre-training models respectively;
determining a target pre-training model of the one or more pre-training models in response to a fourth operation of the user on identification information of the target pre-training model.
3. The method according to claim 1 or 2, wherein the screening the target data based on a target pre-training model, identifying abnormal information in the target data, comprises:
and inputting the target data into the target pre-training model, screening the target data through the target pre-training model, and identifying abnormal information in the target data.
4. The method of claim 3, wherein the target data comprises a plurality of texts, and wherein the target pre-training model processes the target data in a manner that includes at least one of:
marking target content in the text, calculating the similarity of the text, classifying the text and analyzing the emotion polarity of the text.
5. The method of claim 1, wherein after identifying anomalous information in the target data, the method further comprises:
acquiring a preset identification result of abnormal information in the target data;
comparing the abnormal information in the target data with the preset identification result to obtain the identification condition of the target pre-training model on the abnormal information in the target data, and determining whether the target pre-training model meets the preset condition based on the identification condition;
and if the target pre-training model meets the preset condition, identifying abnormal information by using the target pre-training model.
6. An apparatus for risk identification of an enterprise account based on natural language processing techniques, comprising:
the first acquisition module is used for responding to a first operation of a user on a first page and acquiring data to be identified;
the processing module is used for responding to a second operation of the user on the data to be identified and processing the data to be identified to obtain target data;
and the identification module is used for screening the target data based on a target pre-training model and identifying abnormal information in the target data.
7. The apparatus of claim 6, further comprising:
the display module is used for responding to a third operation of a user on the first page, and displaying a pre-training model list, wherein the pre-training model list comprises identification information corresponding to one or more pre-training models respectively;
the determining module is used for responding to a fourth operation of the user on identification information of a target pre-training model in the one or more pre-training models, and determining the target pre-training model.
8. The apparatus according to claim 6 or 7, wherein the recognition module, when screening the target data based on a target pre-training model and recognizing abnormal information in the target data, is specifically configured to:
and inputting the target data into the target pre-training model, screening the target data through the target pre-training model, and identifying abnormal information in the target data.
9. An electronic device, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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| CN202211001296.XA CN115393034A (en) | 2022-08-19 | 2022-08-19 | Method for carrying out risk identification on enterprise account based on natural language processing technology |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116129440A (en) * | 2023-04-13 | 2023-05-16 | 新兴际华集团财务有限公司 | Abnormal user side alarm method, device, electronic equipment and medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116129440A (en) * | 2023-04-13 | 2023-05-16 | 新兴际华集团财务有限公司 | Abnormal user side alarm method, device, electronic equipment and medium |
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