CN113129120A - Financial institution data supervision method and device - Google Patents
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Abstract
The invention discloses a financial institution data supervision method and a financial institution data supervision device, which relate to the technical field of artificial intelligence, wherein the method comprises the following steps: acquiring the supervision data of a financial institution; based on the trained financial institution data supervision model, predicting to obtain retrieval keywords according to the supervision data of the financial institution; and obtaining the labels with the matching degree meeting the requirements from a label database according to the retrieval keywords. The method is based on basic model design results, and related results are automatically obtained by utilizing artificial intelligence modes such as machine learning, natural language processing and the like, so that the process of artificial complex search analysis is reduced.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a financial institution data supervision method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, the supervision requirements of the supervision institutions on various financial institutions are becoming stricter, and the requirements on the quality, accuracy and timeliness of data reported by the financial institutions are higher and higher. Meanwhile, the reporting requirements of different financial institutions are different, the definition of data items is also different, a general supervision data model is difficult to establish to realize the reporting of the supervision data of financial institutions of different industries, and the realization of supervision products and the branch management of the data models of different financial institution versions respond to the change of supervision policies in time, so that the method becomes a great pain point and difficulty.
Disclosure of Invention
The embodiment of the invention provides a financial institution data supervision method, which comprises the following steps:
acquiring the supervision data of a financial institution;
based on the trained financial institution data supervision model, predicting to obtain retrieval keywords according to the supervision data of the financial institution;
and obtaining the labels with the matching degree meeting the requirements from a label database according to the retrieval keywords.
In one embodiment, the financial institution data governance model is trained as follows:
and training to obtain a financial institution data supervision model by using the labels, the keywords, the financial industry related information and the financial industry related supervision information as a training data set and adopting a machine learning mode.
In one embodiment, the tag is determined as follows:
dividing the data into different industry themes according to the business range of the financial institution;
and generating corresponding label information according to the relevant supervision requirements of the financial industry and the business characteristics of the industry theme.
In one embodiment, further comprising:
and converting the label information and storing the label information through an ES.
In one embodiment, the converting the tag information and storing the converted tag information by an ES includes:
and converting the label information into a document form and storing the document form through an ES.
In one embodiment, further comprising:
and displaying the labels with the matching degrees meeting the requirements.
In one embodiment, further comprising:
receiving a selection result of a user selecting a label with a matching degree meeting the requirement;
and optimizing the trained financial institution data supervision model based on the selection result to obtain the optimized financial institution data supervision model.
In one embodiment, further comprising:
and exporting the trained financial institution data supervision model to developers for development.
In one embodiment, further comprising:
and according to the label with the matching degree meeting the requirement, labeling the supervision data of the financial institution.
The embodiment of the invention also provides a financial institution data supervision device, which comprises:
the data acquisition module is used for acquiring the supervision data of the financial institution;
the prediction module is used for predicting and obtaining retrieval keywords according to the supervision data of the financial institution based on the trained financial institution data supervision model;
and the matching search module is used for acquiring the tags with the matching degree meeting the requirements from the tag database according to the search keywords.
In one embodiment, further comprising:
the training module is used for training the financial institution data supervision model according to the following modes:
and training to obtain a financial institution data supervision model by using the labels, the keywords, the financial industry related information and the financial industry related supervision information as a training data set and adopting a machine learning mode.
In one embodiment, the tag determination module is configured to determine the tag as follows:
dividing the data into different industry themes according to the business range of the financial institution;
and generating corresponding label information according to the relevant supervision requirements of the financial industry and the business characteristics of the industry theme.
In one embodiment, further comprising: and the storage module is used for converting the label information and then storing the label information through an ES (electronic storage).
In one embodiment, the storage module is specifically configured to:
and converting the label information into a document form and storing the document form through an ES.
In one embodiment, further comprising:
and the display module is used for displaying the labels with the matching degrees meeting the requirements.
In one embodiment, further comprising:
the model optimization module is used for receiving a selection result of a user for selecting the label with the matching degree meeting the requirement; and optimizing the trained financial institution data supervision model based on the selection result to obtain the optimized financial institution data supervision model.
In one embodiment, further comprising:
and the export module is used for exporting the trained financial institution data supervision model to developers for the developers to develop.
In one embodiment, further comprising:
and the labeling module is used for labeling the supervision data of the financial institution according to the label with the matching degree meeting the requirement.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the financial institution data supervision method when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the financial institution data monitoring method.
In the embodiment of the invention, the monitoring data of the financial institution is obtained; based on the trained financial institution data supervision model, predicting to obtain retrieval keywords according to the supervision data of the financial institution; and obtaining the labels with the matching degree meeting the requirements from a label database according to the retrieval keywords. The method is based on basic model design results, and related results are automatically obtained by utilizing artificial intelligence modes such as machine learning, natural language processing and the like, so that the process of artificial complex search analysis is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a method for supervising financial institution data according to an embodiment of the present invention;
FIG. 2 is a flow chart of a financial institution data monitoring method according to an embodiment of the invention;
FIG. 3 is a flow chart of a method for supervising financial institution data according to an embodiment of the present invention;
FIG. 4 is a flowchart of a financial institution data monitoring method according to an embodiment of the invention;
FIG. 5 is a flow chart of a financial institution data monitoring method according to an embodiment of the invention;
FIG. 6 is a block diagram of a financial institution data monitoring apparatus according to an embodiment of the present invention;
FIG. 7 is a block diagram of a financial institution data monitoring apparatus according to an embodiment of the present invention;
FIG. 8 is a block diagram of the data monitoring device of the financial institution in accordance with the embodiment of the present invention;
FIG. 9 is a block diagram of the data monitoring device of the financial institution in accordance with the embodiment of the present invention;
FIG. 10 is a block diagram of the data monitoring device of the financial institution in accordance with an embodiment of the present invention;
FIG. 11 is a block diagram of a financial institution data monitoring apparatus according to an embodiment of the present invention;
FIG. 12 is a block diagram of a financial institution data monitoring apparatus according to an embodiment of the present invention;
fig. 13 is a schematic block diagram of a system configuration of an electronic apparatus 600 according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Technical term interpretation:
and (3) data model: the data feature abstraction describes the static features, dynamic behaviors and constraints of the system from the abstraction level, and provides an abstract framework for information representation and operation of the database system.
Artificial intelligence: the multi-disciplinary cross speciality covers probability theory knowledge, statistical knowledge, approximate theoretical knowledge and complex algorithm knowledge, uses a computer as a tool and is dedicated to simulating a human learning mode in real time, and knowledge structure division is carried out on the existing content to effectively improve the learning efficiency.
ES: elastic search, a distributed, highly extended, highly real-time search and data analysis engine.
The invention provides a financial institution data supervision method, which mainly adopts a theme method and a label method, and then intelligently analyzes changes caused by changes of related supervision requirements by combining with the current mature artificial intelligence processing modes such as machine learning, natural language processing and the like.
Example 1
Fig. 1 is a flowchart (one) of a financial institution data monitoring method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step 101: acquiring the supervision data of a financial institution;
step 102: based on the trained financial institution data supervision model, predicting to obtain retrieval keywords according to the supervision data of the financial institution;
step 103: and obtaining the labels with the matching degree meeting the requirements from a label database according to the retrieval keywords.
In the embodiment of the present invention, the financial institution supervision data mentioned in step 101 refers to real-time changing financial institution supervision requirements.
In an embodiment of the invention, the financial institution data supervision model is trained as follows:
and training to obtain a financial institution data supervision model by using the labels, the keywords, the financial industry related information and the financial industry related supervision information as a training data set and adopting a machine learning mode.
Specifically, the tag, the keyword, the financial industry related information, and the financial industry related supervision information may be divided into two parts, where the tag, the keyword, the financial industry related information, and the financial industry related supervision information in one part are used as a training data set, the tag, the keyword, the financial industry related information, and the financial industry related supervision information in the other part are used as a verification data set, and after the training data set is used for model training to obtain the financial institution data supervision model, the verification data set is used for verifying the financial institution data supervision model.
Machine Learning (ML) is a multi-domain cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer.
It is the core of artificial intelligence, and is a fundamental way for computer to possess intelligence, and its application is extensive in every field of artificial intelligence, and it mainly uses induction, synthesis, rather than deduction.
In the embodiment of the present invention, after acquiring the supervision data of the financial institution in step 101, the supervision data of the financial institution may be preprocessed. Data preprocessing (also known as data cleansing, data grooming, or data processing) refers to the process of performing various checks and audits on data to correct missing values, misspellings, normalize/standardize values for comparability, transform data (e.g., log transform), and the like.
In the embodiment of the present invention, as shown in fig. 2, the tag is determined as follows:
step 201: dividing the data into different industry themes according to the business range of the financial institution;
step 202: and generating corresponding label information according to the relevant supervision requirements of the financial industry and the business characteristics of the industry theme.
Specifically, the financial institutions include commercial banks, insurance companies, investment banks, security companies, trust companies, leasing companies, investment funds, financial companies, credit unions, and policy financial institutions.
(1) Commercial banking scope: liability, asset, intermediate, and off-meter services.
Liability service: deposit payment, borrowing liabilities, liabilities in settlement.
Asset service: cash, loans, securities, disputes, fixed assets, etc.
Intermediate service: settlement class, proxy class, information consultation class, other intermediate services.
An off-table service: loan commitment, standing letter of credit, loan sale, etc.
(2) Insurance company business scope:
(21) personal insurance services, including life insurance, health insurance, accidental injury insurance and other insurance services;
(22) property insurance services, including insurance services such as property loss insurance, responsibility insurance, credit insurance, guarantee insurance and the like;
(23) other services related to insurance approved by an insurance supervision authority.
(3) Investment banking scope: securities underwriting, securities trading, project financing, corporate mergers and acquisitions, fund management, inauguration investments, financial consultants, asset securitization, and financial derivative tool services.
(4) Securities company business scope: securities brokering, securities investment consulting, financial consultants related to securities trading, securities investment activities, securities underwriting and sponsoring, securities self-service, securities asset management, and other securities business.
(5) Business scope of trust company: (51) a capital trust; (52) real estate trusts; (53) the mobile property trusts; (54) securities trust; (55) other property or property title trusts; (56) engaging in an investment fund business as an initiator of an investment fund or fund management company; (57) business such as enterprise asset reorganization, purchase, project financing, company financing, financial consultant and the like is operated; (58) the securities underwriting business approved by the entrusted operation related department; (59) transacting services such as intermediate, consultation, credit investigation and the like; (510) a substitute storage and a safe deposit box service; (511) legal regulations or other business approved by banking regulatory agencies.
(6) Leasing company business scope: (61) a financing lease service; (62) absorbing the regular deposit of the stockholders for more than 1 year (inclusive); (63) receiving a lease guarantee fund of a lessee; (64) the commercial bank transfers the lease fees to be collected; (65) approved for issuance of financial bonds; (66) dismantling and borrowing the same industry; (67) borrowing money from a financial institution; (68) borrowing foreign exchange; (69) residual value of the leased goods is changed and sold and the business is processed; (610) economic consultation; (611) other businesses approved by banking regulatory agencies.
(7) Investment fund business scope:
(71) investment funds may be classified into growth funds, income-type funds, growth-income-type funds, active growth funds, balance funds, specialty funds, and the like, depending on the target of investment.
(72) The investment fund may be classified into a money market fund, a bond certificate fund, a general stock fund, a gold fund, and the like according to the investment target.
(73) The investment fund is classified into domestic fund and international fund according to the investment country.
(8) Financial company business scope: (81) absorbing the periodic deposit of the member unit for more than 3 months; (82) issuing financial company bonds; (83) dismantling and borrowing the same industry; (84) transacting loan financing and financing lease to member units; (85) transacting consumption credit, buyer credit and financing lease of group member unit products; (86) handling acceptance and cash-on of member unit commercial draft; (87) transacting the entrusted loan and entrusted investment of the member unit; (88) the equity of the securities financial institution and the equity investment of the member units; (89) transacting financial consultants, credit certificates and other consulting agency services for member units; (810) providing a guarantee to the member unit; (811) borrowing foreign exchange; (812) other services approved by a bank prison.
(9) Credit cooperation business scope: collective deposits, loans, individual savings, settlement, and brokerage services (e.g., brokerage of wages, water (electricity) fees, brokerage insurance company services, etc.) and other approved services.
(10) Business scope of policy financial institution: the loan of the RMB (medium and long term basic construction loan, technical improvement loan, short term turnover equipment reserve loan) the foreign exchange loan (the fixed property loan of the foreign exchange, the mobile capital loan of the foreign exchange), the issuance of financial bonds, the guarantee and the letter of credit, consultation and financial consultants, the investment and management of industry funds, and the underwriting of enterprise bonds.
In an embodiment of the present invention, as shown in fig. 2, the financial institution data monitoring method further includes:
step 203: and converting the label information and storing the label information through an ES.
ES is an open source distributed search engine based on RESTful web interface and built on top of Apache Lucene.
ES is also a distributed document database in which each field can be indexed and the data in each field can be searched, expanding laterally to hundreds of servers storing and processing PB-level data.
Specifically, step 203 converts the tag information into a document format and stores the document format in the ES. The tag information needs to be converted into a technical language to be stored in a document form through the ES.
A large amount of data can be stored, searched, and analyzed in an extremely short time. Typically as a core engine with complex search scenarios.
ES is grown for high availability and scalability. On one hand, the system extension can be completed by upgrading hardware, and is called Vertical Scale/Scaling Up.
On the other hand, more servers are added to complete the system expansion, called Horizontal expansion or Scaling Out. Although the ES can utilize more powerful hardware, vertical expansion is, after all, at its limits. The real expandability comes from horizontal expansion, and the load is shared by adding more nodes into the cluster, so that the reliability is increased. The ES is inherently distributed and it knows how to manage multiple nodes to accomplish the expansion and achieve high availability. Meaning that the application does not require any modification.
In an embodiment of the present invention, the financial institution data monitoring method further includes:
and displaying the labels with the matching degrees meeting the requirements.
The financial institution data supervision method can be realized through a background server and then displayed through a front-end page.
In an embodiment of the present invention, as shown in fig. 3, the financial institution data monitoring method further includes:
step 301: receiving a selection result of a user selecting a label with a matching degree meeting the requirement;
step 302: and optimizing the trained financial institution data supervision model based on the selection result to obtain the optimized financial institution data supervision model.
Specifically, after the tags with the matching degrees meeting the requirements are displayed on the front-end page, the user can see the tags, and then the user can select the tags with the matching degrees meeting the requirements, so that the manual quality inspection is realized. And (5) using the result of manual selection as an optimized training data set to optimize the financial institution data supervision model.
In an embodiment of the present invention, as shown in fig. 4, the financial institution data monitoring method further includes:
step 401: and exporting the trained financial institution data supervision model to developers for development.
In the embodiment of the present invention, as shown in fig. 5, the method further includes:
step 501: and according to the label with the matching degree meeting the requirement, labeling the supervision data of the financial institution.
The management method provided by the invention mainly comprises a theme method and a label method, and then changes caused by changes of related supervision requirements are intelligently analyzed by combining with the current relatively mature artificial intelligence processing modes such as machine learning, natural language processing and the like. First, according to the business scope of the financial institution, different topics are divided, such as loan, deposit, financing, foreign exchange … … and the like, and for each topic, information related to the industry is required. And meanwhile, generating related label information according to the supervision requirement and the service characteristics of each theme, wherein each label information is also associated with the industry information. After the information is sorted, the related results are converted into technical language and stored through the ES, so that query and retrieval are facilitated. Meanwhile, the combed label results, the industry information, the keywords and the related supervision texts and specifications are used as a training set for machine learning, and a stable training model is obtained by combining natural language processing. When new reporting requirements are monitored, inputting the new reporting requirements into the model, automatically extracting related keywords through a machine learning method, searching whether the same or similar labels exist in the ES through the keywords, automatically selecting the related labels according to the matching degree, and outputting the related labels to a user for selection. And after the user selects, returning a related selection result to the model, optimizing a related training model, cutting a related model design result out for the user to export, and submitting the design result to development of a developer.
The data model management method mainly classifies the data and labels, and solves the dilemma of data model management through the commonality of the labels.
The embodiment of the invention also provides a financial institution data supervision device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to the financial institution data supervision method, the implementation of the device can refer to the implementation of the financial institution data supervision method, and repeated parts are not described again.
Example 2
Fig. 6 is a block diagram (one) of the structure of the financial institution data monitoring apparatus according to the embodiment of the present invention, as shown in fig. 6, including:
the data acquisition module 02 is used for acquiring the supervision data of the financial institution;
the prediction module 04 is used for predicting and obtaining retrieval keywords according to the trained financial institution data supervision model and the supervision data of the financial institution;
and the matching search module 06 is used for acquiring the tags with matching degrees meeting the requirements from the tag database according to the search keywords.
In the embodiment of the present invention, as shown in fig. 7, the method further includes:
a training module 08 for training the financial institution data supervision model as follows:
and training to obtain a financial institution data supervision model by using the labels, the keywords, the financial industry related information and the financial industry related supervision information as a training data set and adopting a machine learning mode.
In the embodiment of the present invention, as shown in fig. 8, the tag determining module 10 is configured to determine the tag according to the following manner:
dividing the data into different industry themes according to the business range of the financial institution;
and generating corresponding label information according to the relevant supervision requirements of the financial industry and the business characteristics of the industry theme.
In the embodiment of the present invention, as shown in fig. 9, the method further includes: and the storage module 12 is configured to convert the tag information and store the converted tag information in the ES.
In the embodiment of the present invention, the storage module 12 is specifically configured to:
and converting the label information into a document form and storing the document form through an ES.
In the embodiment of the present invention, the method further includes:
and the display module is used for displaying the labels with the matching degrees meeting the requirements.
In the embodiment of the present invention, as shown in fig. 10, the method further includes:
the model optimization module 14 is configured to receive a selection result obtained by selecting a tag with a matching degree meeting requirements by a user; and optimizing the trained financial institution data supervision model based on the selection result to obtain the optimized financial institution data supervision model.
In the embodiment of the present invention, as shown in fig. 11, the method further includes:
and the export module 16 is used for exporting the trained financial institution data supervision model to developers for the developers to develop.
In the embodiment of the present invention, as shown in fig. 12, the method further includes:
and the labeling module 18 is used for labeling the supervision data of the financial institution according to the label with the matching degree meeting the requirement.
The data supervision devices of financial institutions in different financial industries can be divided into an application interface layer, a summary layer, a detail derivation layer, a detail layer and a basic layer from the bottom layer technology. The application interface layer corresponds to the supervision requirement one by one, and is an instantiation of the supervision requirement. The summary layer is mainly used for summarizing the detailed data and providing supervision data related to indexes. The detail layer and the detail derivative layer are used for organizing basic data and labeling the data with regulatory requirements.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the financial institution data supervision method when executing the computer program.
Example 3
Embodiment 3 provides a computer device, which may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the computer device may refer to the implementation of the method in embodiment 1 and the apparatus in embodiment 2, and the contents thereof are incorporated herein, and repeated descriptions are omitted.
Fig. 13 is a schematic block diagram of a system configuration of a computer apparatus 600 according to an embodiment of the present invention. As shown in fig. 13, the computer apparatus 600 may include a central processor 100 and a memory 140; the memory 140 is coupled to the central processor 100. Notably, this diagram is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the financial institution data administration functions may be integrated into the central processor 100. The central processor 100 may be configured to control as follows:
acquiring the supervision data of a financial institution;
based on the trained financial institution data supervision model, predicting to obtain retrieval keywords according to the supervision data of the financial institution;
and obtaining the labels with the matching degree meeting the requirements from a label database according to the retrieval keywords.
The financial institution data supervision model is trained according to the following modes:
and training to obtain a financial institution data supervision model by using the labels, the keywords, the financial industry related information and the financial industry related supervision information as a training data set and adopting a machine learning mode.
Wherein the tag is determined as follows:
dividing the data into different industry themes according to the business range of the financial institution;
and generating corresponding label information according to the relevant supervision requirements of the financial industry and the business characteristics of the industry theme.
Wherein, still include:
and converting the label information and storing the label information through an ES.
Wherein, the storage through ES after the label information conversion, including:
converting the label information into a document form and storing the document form through an ES (electronic storage)
Wherein, still include:
and displaying the labels with the matching degrees meeting the requirements.
Wherein, still include:
receiving a selection result of a user selecting a label with a matching degree meeting the requirement;
and optimizing the trained financial institution data supervision model based on the selection result to obtain the optimized financial institution data supervision model.
Wherein, still include:
and exporting the trained financial institution data supervision model to developers for development.
Wherein, still include:
and according to the label with the matching degree meeting the requirement, labeling the supervision data of the financial institution.
In another embodiment, the financial institution data monitoring apparatus may be configured separately from the central processor 100, for example, the financial institution data monitoring apparatus may be configured as a chip connected to the central processor 100, and the financial institution data monitoring function is realized by the control of the central processor.
As shown in fig. 13, the computer apparatus 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the computer device 600 does not necessarily include all of the components shown in FIG. 13; furthermore, the computer device 600 may also comprise components not shown in fig. 13, as can be seen in the prior art.
As shown in fig. 13, the central processor 100, sometimes referred to as a controller or operational control, may comprise a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the computer apparatus 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. A program for executing the relevant information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the computer device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the computer apparatus 600 by the central processing unit 100.
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same computer device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the financial institution data monitoring method.
In the embodiment of the invention, the monitoring data of the financial institution is obtained; based on the trained financial institution data supervision model, predicting to obtain retrieval keywords according to the supervision data of the financial institution; and obtaining the labels with the matching degree meeting the requirements from a label database according to the retrieval keywords. The method is based on basic model design results, and related results are automatically obtained by utilizing artificial intelligence modes such as machine learning, natural language processing and the like, so that the process of artificial complex search analysis is reduced.
The following advantages are also included:
the dependence on professional analysis designers is reduced, only the manual judgment of the accuracy added in the final result is needed, the burden of personnel is greatly reduced, and the repeated work and the error probability of manual analysis are reduced.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (20)
1. A financial institution data administration method, comprising:
acquiring the supervision data of a financial institution;
based on the trained financial institution data supervision model, predicting to obtain retrieval keywords according to the supervision data of the financial institution;
and obtaining the labels with the matching degree meeting the requirements from a label database according to the retrieval keywords.
2. The financial institution data oversight method of claim 1, wherein the financial institution data oversight model is trained as follows:
and training to obtain a financial institution data supervision model by using the labels, the keywords, the financial industry related information and the financial industry related supervision information as a training data set and adopting a machine learning mode.
3. The financial institution data administration method of claim 1, wherein the tag is determined as follows:
dividing the data into different industry themes according to the business range of the financial institution;
and generating corresponding label information according to the relevant supervision requirements of the financial industry and the business characteristics of the industry theme.
4. The financial institution data administration method of claim 3, further comprising:
and converting the label information and storing the label information through an ES.
5. The financial institution data administration method as claimed in claim 4, wherein the tag information is converted and stored through an ES, comprising:
and converting the label information into a document form and storing the document form through an ES.
6. The financial institution data administration method of claim 1, further comprising:
and displaying the labels with the matching degrees meeting the requirements.
7. The financial institution data administration method of claim 6, further comprising:
receiving a selection result of a user selecting a label with a matching degree meeting the requirement;
and optimizing the trained financial institution data supervision model based on the selection result to obtain the optimized financial institution data supervision model.
8. The financial institution data administration method of claim 1, further comprising:
and exporting the trained financial institution data supervision model to developers for development.
9. The financial institution data administration method of claim 1, further comprising:
and according to the label with the matching degree meeting the requirement, labeling the supervision data of the financial institution.
10. A financial institution data monitoring apparatus, comprising:
the data acquisition module is used for acquiring the supervision data of the financial institution;
the prediction module is used for predicting and obtaining retrieval keywords according to the supervision data of the financial institution based on the trained financial institution data supervision model;
and the matching search module is used for acquiring the tags with the matching degree meeting the requirements from the tag database according to the search keywords.
11. The financial institution data administration apparatus of claim 10, further comprising:
the training module is used for training the financial institution data supervision model according to the following modes:
and training to obtain a financial institution data supervision model by using the labels, the keywords, the financial industry related information and the financial industry related supervision information as a training data set and adopting a machine learning mode.
12. The financial institution data administration apparatus of claim 10, wherein the tag determination module is to determine the tag as follows:
dividing the data into different industry themes according to the business range of the financial institution;
and generating corresponding label information according to the relevant supervision requirements of the financial industry and the business characteristics of the industry theme.
13. The financial institution data administration apparatus of claim 12, further comprising: and the storage module is used for converting the label information and then storing the label information through an ES (electronic storage).
14. The financial institution data administration apparatus of claim 13, wherein the storage module is specifically configured to:
and converting the label information into a document form and storing the document form through an ES.
15. The financial institution data administration apparatus of claim 10, further comprising:
and the display module is used for displaying the labels with the matching degrees meeting the requirements.
16. The financial institution data administration apparatus of claim 10, further comprising:
the model optimization module is used for receiving a selection result of a user for selecting the label with the matching degree meeting the requirement; and optimizing the trained financial institution data supervision model based on the selection result to obtain the optimized financial institution data supervision model.
17. The financial institution data administration apparatus of claim 10, further comprising:
and the export module is used for exporting the trained financial institution data supervision model to developers for the developers to develop.
18. The financial institution data administration apparatus of claim 10, further comprising:
and the labeling module is used for labeling the supervision data of the financial institution according to the label with the matching degree meeting the requirement.
19. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the financial institution data monitoring method of any of claims 1 to 9.
20. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the financial institution data monitoring method as claimed in any of claims 1 to 9.
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