CN117593100A - Method, equipment and medium for identifying relationship between staff and enterprise of financial institution - Google Patents
Method, equipment and medium for identifying relationship between staff and enterprise of financial institution Download PDFInfo
- Publication number
- CN117593100A CN117593100A CN202311586354.4A CN202311586354A CN117593100A CN 117593100 A CN117593100 A CN 117593100A CN 202311586354 A CN202311586354 A CN 202311586354A CN 117593100 A CN117593100 A CN 117593100A
- Authority
- CN
- China
- Prior art keywords
- enterprise
- financial institution
- staff
- information
- enterprises
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000007418 data mining Methods 0.000 claims abstract description 36
- 238000007781 pre-processing Methods 0.000 claims abstract description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 21
- 238000003860 storage Methods 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 238000007621 cluster analysis Methods 0.000 claims description 7
- 238000003066 decision tree Methods 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 7
- 230000009286 beneficial effect Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 239000000284 extract Substances 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010835 comparative analysis Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012954 risk control Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Business, Economics & Management (AREA)
- Technology Law (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Accounting & Taxation (AREA)
- General Business, Economics & Management (AREA)
- Finance (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The application discloses a method, equipment and medium for identifying relationship between financial institution staff and enterprises, which are used for solving the problem that the conventional identification mode has risks of misjudgment and missed judgment due to human factors. The method comprises the following steps: extracting enterprise information with business relation with a financial institution based on a pre-constructed data mining model, and preprocessing the extracted enterprise information through the data mining model; determining enterprises and associated enterprises corresponding to the preprocessed enterprise information, and comparing the enterprises and the associated enterprises with staff information of financial institutions respectively; determining whether an association relationship exists between the financial institution staff and the enterprise and the association enterprise according to the comparison result, and determining the association degree between the financial institution staff and the enterprise and the association enterprise under the condition that the association relationship exists; and under the condition that the association degree is larger than a preset threshold value, determining that financial risk exists in the staff of the financial institution, and sending the financial risk to the corresponding enterprises and the associated enterprises for early warning.
Description
Technical Field
The present disclosure relates to the field of relationship identification technologies, and in particular, to a method, an apparatus, and a medium for identifying a relationship between a financial institution employee and an enterprise.
Background
Currently, financial institutions are at great risk in everyday operations, one of which is the potential benefit relationship that exists between employees and customers. The traditional identification method mainly comprises the steps of comparing, examining and judging manually, so that the time cost in the identification process is increased, the subjective judgment is easily influenced by subjective consciousness and experience of an individual, and the risk of increasing the possibility of misjudgment and missed judgment exists. Moreover, the data source is limited only by relying on the employee list and the target unit information, so that the complex relationship between the employee and the client is difficult to comprehensively know.
Disclosure of Invention
The embodiment of the application provides a method, equipment and medium for identifying the relationship between financial institution staff and enterprises, which are used for solving the technical problem that the existing identification mode depends on staff lists and target unit information and the risk of misjudgment and missed judgment exists due to human factors.
In one aspect, an embodiment of the present application provides a method for identifying a relationship between a financial institution employee and an enterprise, including:
based on a pre-constructed data mining model, extracting enterprise information with business relation with a financial institution, and preprocessing the extracted enterprise information through the data mining model;
determining enterprises and associated enterprises corresponding to the preprocessed enterprise information, and comparing the enterprises and associated enterprises with staff information of the financial institutions respectively;
determining whether an association relationship exists between a financial institution employee and an enterprise and between the financial institution employee and the enterprise and the association enterprise according to a comparison result, and determining the association degree between the financial institution employee and the enterprise and between the financial institution employee and the association enterprise under the condition that the association relationship exists;
and under the condition that the association degree is larger than a preset threshold value, determining that financial risk exists in the staff of the financial institution, and sending the financial risk to a corresponding enterprise and an associated enterprise for early warning.
In one implementation manner of the present application, the extracting enterprise information having a business relationship with a financial institution based on a pre-constructed data mining model specifically includes:
constructing a corresponding data mining model based on a preset algorithm; the preset algorithm at least comprises a clustering algorithm and a decision tree algorithm;
performing data mining in an internal and external data source of an enterprise through a plurality of preset algorithms in the data mining model so as to extract enterprise information with business relation with a financial institution;
the enterprise internal and external data sources at least comprise: an enterprise internal database, enterprise external public data and third party data, wherein the enterprise information at least comprises: enterprise personnel information, associated enterprise information, and associated enterprise personnel information.
In one implementation manner of the present application, the preprocessing, by the data mining model, the extracted enterprise information specifically includes:
determining whether redundant information exists in the extracted enterprise information based on the data mining model, and if yes, deleting the redundant information in the enterprise information;
continuously determining whether abnormal information exists in the enterprise information after redundancy removal, if so, deleting the abnormal information in the enterprise information after redundancy removal to finish data cleaning of the enterprise information;
and extracting features from the enterprise information after data cleaning to obtain corresponding enterprise features, and carrying out cluster analysis on the corresponding enterprise features to mine out association rules of enterprises according to the result of the cluster analysis.
In one implementation manner of the present application, the comparing the employee information of the enterprise and the associated enterprise with the employee information of the financial institution respectively specifically includes:
determining the names and contact ways of staff of a financial institution based on staff information of the financial institution, and determining the names and contact ways of staff of management layers corresponding to enterprises and associated enterprises according to the enterprises corresponding to the enterprise information and the associated enterprises;
comparing the names of the staff of each financial institution with the names of the staff of the management layer of the enterprise, and comparing the contact ways of the staff of each financial institution with the contact ways of the staff of the management layer of the enterprise, so as to obtain a comparison result between the staff of the financial institution and the enterprise;
and comparing the name and the contact information of each financial institution employee with the name and the contact information of the management layer personnel corresponding to the associated enterprise of the enterprise, and obtaining a comparison result between the financial institution employee and the associated enterprise corresponding to the enterprise.
In one implementation manner of the present application, the determining, according to the comparison result, whether an association relationship exists between the staff of the financial institution and the enterprise specifically includes:
based on a comparison result between the financial institution staff and the enterprise, under the condition that the names of the financial institution staff and the names of the management layer staff of the enterprise are determined to be the same, whether the contact ways of the corresponding financial institution staff and the management layer staff are the same is continuously judged;
if yes, determining that an association relationship exists between the financial institution staff and the enterprise, and if not, determining that the financial institution staff does not coincide with management layer staff of the enterprise;
determining whether the names and the contact ways of the financial institution staff are the same as those of the management layer staff of the enterprise corresponding to the enterprise based on a comparison result between the financial institution staff and the enterprise corresponding to the enterprise under the condition that the names and the contact ways of the financial institution staff are not consistent with those of the management layer staff of the enterprise or the contact ways of the financial institution staff are different from those of the management layer staff of the enterprise;
if yes, determining that an association relationship exists between the financial institution staff and the associated enterprise corresponding to the enterprise, and if not, determining that the financial institution staff does not coincide with management layer staff of the enterprise corresponding to the enterprise.
In one implementation manner of the present application, the determining the association degree between the financial institution employee and the enterprise and the association enterprise in the case that the association relationship exists specifically includes:
under the condition that an association relationship exists between the financial institution staff and the enterprise, determining the role type of the financial institution staff in the enterprise based on management staff in the enterprise, which has the same name and contact way with the financial institution staff; the job types include at least: dong Jiangao, legal or stakeholder;
and acquiring the association information between the financial institution staff and the enterprise, and determining the association degree between the financial institution staff and the enterprise based on a preset weight coefficient corresponding to the job type according to the duration of the association relation between the financial institution staff and the enterprise in the association information.
In one implementation manner of the present application, the determining the association degree between the financial institution employee and the enterprise and the association enterprise in the case that the association relationship exists specifically includes:
continuously determining whether an external investment relation exists between the associated enterprise corresponding to the enterprise and the enterprise under the condition that the associated relation exists between the financial institution staff and the associated enterprise corresponding to the enterprise;
if yes, determining a role type of the financial institution staff in the associated enterprises corresponding to the enterprises based on management staff with the same name and contact manner as those of the financial institution staff in the associated enterprises of the enterprises, and determining the association degree between the financial institution staff and the associated enterprises corresponding to the enterprises based on the role type and a preset weight coefficient corresponding to the role type.
In one implementation manner of the present application, when the association degree is greater than a preset threshold, determining that the financial institution employee has a financial risk, and sending the financial risk to a corresponding enterprise and an associated enterprise for early warning specifically includes:
comparing the association degree with a preset threshold value to determine that financial risk exists for the financial institution staff under the condition that the association degree is larger than the preset threshold value, and determining risk early warning information corresponding to the financial risk according to the corresponding job types of the financial institution staff in the enterprise or the association enterprise and the duration of the association relationship between the financial institution staff and the enterprise; the risk early warning information at least comprises: risk level, risk type, and risk source;
the risk early warning information is sent to a corresponding enterprise or an associated enterprise, and visual display is carried out in the enterprise or the associated enterprise through a preset display form; the preset display form at least comprises the following steps: charts and dashboards.
In another aspect, an embodiment of the present application further provides a device for identifying a relationship between a staff of a financial institution and an enterprise, where the device includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of identifying a relationship of a financial institution employee to an enterprise as described above.
On the other hand, the embodiment of the application also provides a non-volatile computer storage medium, which stores computer executable instructions, wherein the computer realizes the relationship identification method of the financial institution staff and the enterprise when executing the executable instructions.
The embodiment of the application provides a method, equipment and medium for identifying the relationship between financial institution staff and enterprises, which at least comprise the following beneficial effects:
the enterprise information with business relation with the financial institutions can be accurately extracted through the data mining model, the object of interest is ensured to be accurate, the data mining model carries out preprocessing on the extracted enterprise information, the data quality is improved, errors in the subsequent comparison process are reduced, and the analysis is more reliable and efficient; by comparing the identified enterprises and related enterprises with the staff information of the financial institutions, the comprehensiveness of relationship analysis can be ensured, the enterprises are considered, potential related parties are also included, the related relationships among the staff of the financial institutions, the enterprises and the related enterprises are accurately determined through comparison results, errors caused by human factors are eliminated, and the accuracy of the related relationships is improved; under the condition that the association degree is determined to be greater than a preset threshold value, financial risk of financial institution staff is found in time, and early warning information is sent to corresponding enterprises and associated enterprises, so that necessary measures can be taken quickly, potential risk influence is reduced, an automatic examination flow is integrated, manual intervention requirements are reduced, automatic management of financial risk is achieved, and risk identification efficiency and comprehensiveness are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of a method for identifying relationships between financial institution personnel and enterprises according to an embodiment of the present application;
fig. 2 is a schematic diagram of an internal structure of a relationship identifying device for a financial institution employee and an enterprise according to an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application provides a method, equipment and medium for identifying the relationship between a financial institution employee and an enterprise, which can accurately extract enterprise information with business relationship with the financial institution through a data mining model, ensure that an object of interest is accurate, and the data mining model preprocesses the extracted enterprise information, so that the data quality is improved, the error in the subsequent comparison process is reduced, and the analysis is more reliable and efficient; by comparing the identified enterprises and related enterprises with the staff information of the financial institutions, the comprehensiveness of relationship analysis can be ensured, the enterprises are considered, potential related parties are also included, the related relationships among the staff of the financial institutions, the enterprises and the related enterprises are accurately determined through comparison results, errors caused by human factors are eliminated, and the accuracy of the related relationships is improved; under the condition that the association degree is determined to be greater than a preset threshold value, financial risk of financial institution staff is found in time, and early warning information is sent to corresponding enterprises and associated enterprises, so that necessary measures can be taken quickly, potential risk influence is reduced, an automatic examination flow is integrated, manual intervention requirements are reduced, automatic management of financial risk is achieved, and risk identification efficiency and comprehensiveness are improved. The method solves the technical problem that the existing identification mode depends on the employee list and target unit information, and the risk of misjudgment and missed judgment exists due to human factors.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a flow chart of a method for identifying relationships between staff and enterprises in a financial institution according to an embodiment of the present application.
The implementation of the analysis method according to the embodiment of the present application may be a terminal device or a server, which is not particularly limited in this application. For ease of understanding and description, the following embodiments are described in detail with reference to a server.
It should be noted that the server may be a single device, or may be a system formed by a plurality of devices, that is, a distributed server, which is not specifically limited in this application.
As shown in fig. 1, a method for identifying a relationship between a staff of a financial institution and an enterprise according to an embodiment of the present application includes:
101. based on a pre-constructed data mining model, extracting enterprise information with business relation with a financial institution, and preprocessing the extracted enterprise information through the data mining model.
The server performs data mining based on a pre-built data mining model and using various algorithm modules to extract relevant data information from internal and external data sources of the enterprise.
Specifically, in one embodiment of the present application, to extract enterprise information that has a business relationship with a financial institution, we will construct a data mining model based on a preset algorithm, and this model will use a K-means clustering algorithm, apriori algorithm, decision tree algorithm, etc. in order to more comprehensively analyze the internal and external data sources of the enterprise. It should be noted that, the clustering algorithm is used to classify similar enterprises into a class so as to find potential business relationship groups, and the decision tree algorithm is used to extract rules from data so as to better understand relationships between enterprise information. In the enterprise internal and external data sources, the server integrates an enterprise internal database, enterprise external public data and third party data, wherein the enterprise internal database comprises enterprise information such as finance, personnel and operation data, the enterprise external public data covers publicly available enterprise information such as annual reports, financial reports and the like, and the third party data utilizes information of a third party data provider to acquire more enterprise associated information.
The server extracts enterprise personnel information, associated enterprise information and associated enterprise personnel information through a data mining model, wherein the enterprise personnel information comprises roles, seniorities, identification numbers, contact ways, addresses and other key information of personnel in an enterprise, the associated enterprise information is an enterprise which is found out to have business relations with a financial institution through a clustering algorithm and a decision tree algorithm, and the associated enterprise personnel information is used for determining enterprise personnel related to the business relations of the financial institution and knowing the background and responsibility of the enterprise personnel information. Through the steps, the server establishes an effective data mining model, can extract key enterprise information with business relation with the financial institutions from the internal and external data sources of the enterprises, is beneficial to the financial institutions to better know business partners, reduces risks and improves accuracy of business decisions.
Specifically, in one embodiment of the present application, the server first performs redundant information detection on the extracted enterprise information based on the constructed data mining model, and identifies and deletes the redundant information by comparing correlation and repeatability between different features, so as to ensure compactness and accuracy of data. After the redundant information is cleaned, the server further detects and deletes abnormal data in the enterprise information, which may include abnormal values, outliers or other data which do not conform to business logic, and by adopting a statistical method or a machine learning algorithm, the abnormal information can be identified and removed, so that the data quality is improved. After the redundant information and the abnormal information are cleaned, the tidied enterprise information can be obtained, and the accuracy and the reliability of the enterprise information are ensured.
The server performs feature extraction on the basis of the cleaned enterprise information, and by selecting key enterprise attributes and indexes, a more compact feature set with rich information can be established, so that the effect of subsequent cluster analysis is improved. The extracted enterprise features are used for cluster analysis, similar enterprises can be classified into a group through a cluster algorithm, potential association rules can be identified, and common features and relations among the enterprises are found through analysis of the cluster results, so that deeper insight is provided for financial institutions. Through the steps, the enterprise information can be comprehensively cleaned and the characteristics of the enterprise can be extracted, and the association rule of the enterprise is mined through cluster analysis, so that a financial institution can more accurately understand the business relationship, the data error is reduced, and the credibility and accuracy of business decision are improved.
102. And determining enterprises and associated enterprises corresponding to the preprocessed enterprise information, and comparing the enterprises and associated enterprises with staff information of financial institutions respectively.
Specifically, in one embodiment of the present application, the server can first determine names and contact ways of staff of a financial institution based on staff information of the financial institution, and obtain names and contact ways of management layer staff of the enterprise and related enterprises, including information of staff inside the enterprise and key management staff of related enterprises, through a constructed data mining model.
The server then compares the name of each financial institution employee with the names of the business and the management layer personnel of the associated business to determine if there is a match, and compares the contact of each financial institution employee with the contact of the business and the management layer personnel of the associated business to verify if there is a consistency of the contact. The server can obtain the relation between the staff and the enterprise by summarizing the comparison result of the names and the contact ways of the staff, is helpful for knowing the contact situation of the staff in the financial institution and the enterprise management layer, and similarly, compares the names and the contact ways of the staff in the financial institution with the management layer staff of the associated enterprise so as to reveal the contact situation between the staff in the financial institution and the enterprise associated enterprise. Through the comparative analysis of the staff and the enterprise information of the financial institutions, the relationship between the financial institutions and the enterprises and the relationship between the financial institutions and the enterprises can be revealed, the cooperative understanding of the financial institutions and the enterprises can be enhanced, the knowledge of business partners can be improved, and more effective business decision-making and management can be supported.
103. And determining whether an association relationship exists between the financial institution staff and the enterprise and the association enterprise according to the comparison result, and determining the association degree between the financial institution staff and the enterprise and the association enterprise under the condition that the association relationship exists.
It should be noted that, in the embodiment of the present application, the determination of the association relationship may be based on factors such as a common service, a stock-holding situation, and the like.
Specifically, in one embodiment of the present application, the server further determines whether the contact manner is consistent when determining that the name of the staff of the financial institution is the same as the name of the staff of the management layer of the enterprise according to the comparison result, if the contact manner of the staff of the financial institution is the same as the name of the staff of the management layer of the enterprise, it is determined that the staff of the financial institution has a direct association relationship with the enterprise, and if the contact manner is different, it is determined that the staff of the financial institution does not coincide with the staff of the management layer of the enterprise.
And the server determines whether the names and the contact ways of the financial institution staff and the management layer staff of the enterprise corresponding to the enterprise are the same or not based on the comparison result of the financial institution staff and the enterprise corresponding to the enterprise under the condition that the names and the contact ways of the financial institution staff and the enterprise management layer staff are not consistent or different according to the comparison result. If the names and the contact ways of the financial institution staff and the management layer staff of the associated enterprises are the same, the association relation between the financial institution staff and the corresponding associated enterprises of the enterprises can be determined, and if the names or the contact ways of the financial institution staff and the management layer staff of the associated enterprises are not consistent, the non-conforming of the financial institution staff and the management layer staff of the corresponding associated enterprises of the enterprises is confirmed. Through the comparative analysis, the relationship between the staff and the enterprise and the relationship between the staff and the enterprise can be accurately judged, the financial institution is helped to know the enterprise network related to the business more deeply, the cognition of the business partner is improved, and therefore more accurate business decision and risk management are supported.
In one embodiment, the server determines the type of job the financial institution employee plays in the enterprise based on the management staff in the enterprise who has the same name and contact with the financial institution employee in the case that the financial institution employee has an association with the enterprise. It should be noted that, in the embodiment of the present application, the job types at least include: dong Jiangao, corporate or stakeholder, dong Jiangao includes directors, prison, high-level manager, dong Jiangao for confirming whether a financial institution employee belongs to a high-level management layer of an enterprise, corporate for confirming whether a financial institution employee is acting as a legal representative or legal job in an enterprise, and stakeholder for confirming whether a financial institution employee is one of the stakeholders of an enterprise. Then, the server acquires association information between the financial institution staff and the enterprise, including relations so as to calculate association degree between the financial institution staff and the enterprise by utilizing a preset weight coefficient corresponding to the duration and the job type in the association information.
In one embodiment, the server further needs to continuously determine whether an external investment relationship exists between the associated enterprises corresponding to the enterprises under the condition that the associated enterprises corresponding to the enterprises exist between the financial institution employees and the associated enterprises, if so, determines that the financial institution employees and the enterprises have indirect associated relationships based on management layer personnel with the same names and contact modes as those of the financial institution employees in the associated enterprises of the enterprises, and needs to determine the association degree between the financial institution employees and the enterprises according to the job types corresponding to the financial institution employees in the associated enterprises, preset weight coefficients and investment conditions between the enterprises and the corresponding associated enterprises. Through the analysis, the quantitative result of the association degree between the staff and the enterprise of the financial institution can be obtained, the relationship between the staff and the enterprise can be more comprehensively known by the financial institution, and potential business risks and opportunities can be evaluated through the association degree evaluation.
104. And under the condition that the association degree is larger than a preset threshold value, determining that financial risk exists in the staff of the financial institution, and sending the financial risk to the corresponding enterprises and the associated enterprises for early warning.
Specifically, in one embodiment of the present application, the server compares the association degree with a preset threshold value to determine whether the association degree is greater than the preset threshold value, and determines that the financial institution staff has financial risk if the association degree is greater than the preset threshold value, so as to determine the corresponding job type of the financial institution staff in the enterprise or the association enterprise and the duration of the association relationship between the two, so as to generate the duration to determine risk early warning information corresponding to the financial institution staff. It should be noted that, the risk early warning information in the embodiment of the present application at least includes: risk class, risk type, and risk source.
Then, the server sends the risk early warning information to a corresponding enterprise or an associated enterprise, and performs visual display, reminding, early warning and reporting on risks with potential benefit relationships through a preset display form, so that the enterprise can take corresponding risk control measures in time, and meanwhile, a corresponding risk report can be generated, and reference and decision basis are provided for the enterprise. It should be noted that, in the embodiment of the present application, the preset display form at least includes: charts and dashboards.
According to the method and the device, a large amount of data can be automatically processed and analyzed through the data mining and analysis technology, and the accuracy and the efficiency of investigation are improved. In addition, according to comparison of preset benefit relation indexes, risks with potential benefit relation can be accurately identified, reminding and early warning can be timely carried out, and powerful support is provided for enterprises to timely take corresponding risk control measures.
The foregoing is a method embodiment presented herein. Based on the same inventive concept, the embodiment of the application also provides a relationship identifying device for staff and enterprises of a financial institution, and the structure of the relationship identifying device is shown in fig. 2.
Fig. 2 is a schematic diagram of an internal structure of a relationship identifying device for a financial institution employee and an enterprise according to an embodiment of the present application. As shown in fig. 2, the apparatus includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
based on a pre-constructed data mining model, extracting enterprise information with business relation with a financial institution, and preprocessing the extracted enterprise information through the data mining model;
determining enterprises and associated enterprises corresponding to the preprocessed enterprise information, and comparing the enterprises and the associated enterprises with staff information of financial institutions respectively;
determining whether an association relationship exists between the financial institution staff and the enterprise and the association enterprise according to the comparison result, and determining the association degree between the financial institution staff and the enterprise and the association enterprise under the condition that the association relationship exists;
and under the condition that the association degree is larger than a preset threshold value, determining that financial risk exists in the staff of the financial institution, and sending the financial risk to the corresponding enterprises and the associated enterprises for early warning.
The embodiments of the present application also provide a non-volatile computer storage medium, storing computer executable instructions, where the computer is capable of, when executing the executable instructions:
based on a pre-constructed data mining model, extracting enterprise information with business relation with a financial institution, and preprocessing the extracted enterprise information through the data mining model;
determining enterprises and associated enterprises corresponding to the preprocessed enterprise information, and comparing the enterprises and the associated enterprises with staff information of financial institutions respectively;
determining whether an association relationship exists between the financial institution staff and the enterprise and the association enterprise according to the comparison result, and determining the association degree between the financial institution staff and the enterprise and the association enterprise under the condition that the association relationship exists;
and under the condition that the association degree is larger than a preset threshold value, determining that financial risk exists in the staff of the financial institution, and sending the financial risk to the corresponding enterprises and the associated enterprises for early warning.
All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for the apparatus and medium embodiments, the description is relatively simple, as it is substantially similar to the method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The devices and media provided in the embodiments of the present application are in one-to-one correspondence with the methods, so that the devices and media also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media are not described in detail herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method for identifying a relationship between a financial institution employee and an enterprise, the method comprising:
based on a pre-constructed data mining model, extracting enterprise information with business relation with a financial institution, and preprocessing the extracted enterprise information through the data mining model;
determining enterprises and associated enterprises corresponding to the preprocessed enterprise information, and comparing the enterprises and associated enterprises with staff information of the financial institutions respectively;
determining whether an association relationship exists between a financial institution employee and an enterprise and between the financial institution employee and the enterprise and the association enterprise according to a comparison result, and determining the association degree between the financial institution employee and the enterprise and between the financial institution employee and the association enterprise under the condition that the association relationship exists;
and under the condition that the association degree is larger than a preset threshold value, determining that financial risk exists in the staff of the financial institution, and sending the financial risk to a corresponding enterprise and an associated enterprise for early warning.
2. The method for identifying a relationship between a financial institution employee and an enterprise according to claim 1, wherein the extracting enterprise information having a business relationship with the financial institution based on a pre-constructed data mining model specifically comprises:
constructing a corresponding data mining model based on a preset algorithm; the preset algorithm at least comprises a clustering algorithm and a decision tree algorithm;
performing data mining in an internal and external data source of an enterprise through a plurality of preset algorithms in the data mining model so as to extract enterprise information with business relation with a financial institution;
the enterprise internal and external data sources at least comprise: an enterprise internal database, enterprise external public data and third party data, wherein the enterprise information at least comprises: enterprise personnel information, associated enterprise information, and associated enterprise personnel information.
3. The method for identifying relationship between staff and enterprises according to claim 1, wherein the preprocessing of the extracted enterprise information by the data mining model specifically comprises:
determining whether redundant information exists in the extracted enterprise information based on the data mining model, and if yes, deleting the redundant information in the enterprise information;
continuously determining whether abnormal information exists in the enterprise information after redundancy removal, if so, deleting the abnormal information in the enterprise information after redundancy removal to finish data cleaning of the enterprise information;
and extracting features from the enterprise information after data cleaning to obtain corresponding enterprise features, and carrying out cluster analysis on the corresponding enterprise features to mine out association rules of enterprises according to the result of the cluster analysis.
4. The method for identifying a relationship between a financial institution employee and an enterprise according to claim 1, wherein the comparing the enterprise and the associated enterprise with employee information of the financial institution respectively comprises:
determining the names and contact ways of staff of a financial institution based on staff information of the financial institution, and determining the names and contact ways of staff of management layers corresponding to enterprises and associated enterprises according to the enterprises corresponding to the enterprise information and the associated enterprises;
comparing the names of the staff of each financial institution with the names of the staff of the management layer of the enterprise, and comparing the contact ways of the staff of each financial institution with the contact ways of the staff of the management layer of the enterprise, so as to obtain a comparison result between the staff of the financial institution and the enterprise;
and comparing the name and the contact information of each financial institution employee with the name and the contact information of the management layer personnel corresponding to the associated enterprise of the enterprise, and obtaining a comparison result between the financial institution employee and the associated enterprise corresponding to the enterprise.
5. The method for identifying a relationship between a financial institution employee and an enterprise according to claim 4, wherein determining whether an association exists between the financial institution employee and the enterprise according to the comparison result comprises:
based on a comparison result between the financial institution staff and the enterprise, under the condition that the names of the financial institution staff and the names of the management layer staff of the enterprise are determined to be the same, whether the contact ways of the corresponding financial institution staff and the management layer staff are the same is continuously judged;
if yes, determining that an association relationship exists between the financial institution staff and the enterprise, and if not, determining that the financial institution staff does not coincide with management layer staff of the enterprise;
determining whether the names and the contact ways of the financial institution staff are the same as those of the management layer staff of the enterprise corresponding to the enterprise based on a comparison result between the financial institution staff and the enterprise corresponding to the enterprise under the condition that the names and the contact ways of the financial institution staff are not consistent with those of the management layer staff of the enterprise or the contact ways of the financial institution staff are different from those of the management layer staff of the enterprise;
if yes, determining that an association relationship exists between the financial institution staff and the associated enterprise corresponding to the enterprise, and if not, determining that the financial institution staff does not coincide with management layer staff of the enterprise corresponding to the enterprise.
6. The method for identifying a relationship between a financial institution employee and an enterprise according to claim 5, wherein determining the association degree between the financial institution employee and the enterprise and the associated enterprise in the case that the association relationship exists specifically comprises:
under the condition that an association relationship exists between the financial institution staff and the enterprise, determining the role type of the financial institution staff in the enterprise based on management staff in the enterprise, which has the same name and contact way with the financial institution staff; the job types include at least: dong Jiangao, legal or stakeholder;
and acquiring the association information between the financial institution staff and the enterprise, and determining the association degree between the financial institution staff and the enterprise based on a preset weight coefficient corresponding to the job type according to the duration of the association relation between the financial institution staff and the enterprise in the association information.
7. The method for identifying a relationship between a financial institution employee and an enterprise according to claim 5, wherein determining the association degree between the financial institution employee and the enterprise and the associated enterprise in the case that the association relationship exists specifically comprises:
continuously determining whether an external investment relation exists between the associated enterprise corresponding to the enterprise and the enterprise under the condition that the associated relation exists between the financial institution staff and the associated enterprise corresponding to the enterprise;
if yes, determining a role type of the financial institution staff in the associated enterprises corresponding to the enterprises based on management staff with the same name and contact manner as those of the financial institution staff in the associated enterprises of the enterprises, and determining the association degree between the financial institution staff and the associated enterprises corresponding to the enterprises based on the role type and a preset weight coefficient corresponding to the role type.
8. The method for identifying a relationship between a financial institution employee and an enterprise according to claim 1, wherein when the association degree is greater than a preset threshold, determining that the financial institution employee has a financial risk, and sending the financial risk to a corresponding enterprise and an associated enterprise for early warning, specifically comprising:
comparing the association degree with a preset threshold value to determine that financial risk exists for the financial institution staff under the condition that the association degree is larger than the preset threshold value, and determining risk early warning information corresponding to the financial risk according to the corresponding job types of the financial institution staff in the enterprise or the association enterprise and the duration of the association relationship between the financial institution staff and the enterprise; the risk early warning information at least comprises: risk level, risk type, and risk source;
the risk early warning information is sent to a corresponding enterprise or an associated enterprise, and visual display is carried out in the enterprise or the associated enterprise through a preset display form; the preset display form at least comprises the following steps: charts and dashboards.
9. A financial institution employee-to-business relationship identification apparatus, the apparatus comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of identifying a relationship of a financial institution employee to an enterprise as claimed in any of claims 1 to 8.
10. A non-transitory computer storage medium storing computer executable instructions which when executed implement a method of identifying a relationship of a financial institution employee to an enterprise as claimed in any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311586354.4A CN117593100A (en) | 2023-11-24 | 2023-11-24 | Method, equipment and medium for identifying relationship between staff and enterprise of financial institution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311586354.4A CN117593100A (en) | 2023-11-24 | 2023-11-24 | Method, equipment and medium for identifying relationship between staff and enterprise of financial institution |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117593100A true CN117593100A (en) | 2024-02-23 |
Family
ID=89917888
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311586354.4A Pending CN117593100A (en) | 2023-11-24 | 2023-11-24 | Method, equipment and medium for identifying relationship between staff and enterprise of financial institution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117593100A (en) |
-
2023
- 2023-11-24 CN CN202311586354.4A patent/CN117593100A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US12056588B2 (en) | System and method for incremental training of machine learning models in artificial intelligence systems, including incremental training using analysis of network identity graphs | |
| CN108876600B (en) | Early warning information pushing method, device, computer equipment and medium | |
| US11062026B2 (en) | Counter-fraud operation management | |
| US9661010B2 (en) | Security log mining devices, methods, and systems | |
| US7930242B2 (en) | Methods and systems for multi-credit reporting agency data modeling | |
| CN106164896B (en) | Multi-dimensional recursion method and system for discovering counterparty relationship | |
| US11122073B1 (en) | Systems and methods for cybersecurity risk mitigation and management | |
| US10671926B2 (en) | Method and system for generating predictive models for scoring and prioritizing opportunities | |
| US11429899B2 (en) | Data model processing in machine learning using a reduced set of features | |
| CN112669039B (en) | Knowledge graph-based customer risk management and control system and method | |
| US20220374401A1 (en) | Determining domain and matching algorithms for data systems | |
| CN117670359A (en) | Abnormal transaction data identification method and device, storage medium and electronic equipment | |
| CA3046225A1 (en) | Correcting bias in supervised machine learning data | |
| CN117593100A (en) | Method, equipment and medium for identifying relationship between staff and enterprise of financial institution | |
| CN112966024A (en) | Financial wind control data analysis system based on big data | |
| Svabova et al. | The impact of Data structure on classification ability of financial failure prediction model | |
| Sharma et al. | Customer Relationship Management (CRM) Automation Using Machine Learning Algorithm | |
| CN117194436A (en) | Data processing method, system, device and storage medium | |
| CN118535369A (en) | System fault processing method and device, storage medium and electronic equipment | |
| CN117649078A (en) | Evaluation method and device of technical scheme and electronic equipment | |
| CN117670445A (en) | Credit evaluation method, apparatus, device and storage medium | |
| CN113723710A (en) | Customer loss prediction method, system, storage medium and electronic equipment | |
| CN118505230A (en) | Training method and device for detection model, computer equipment and storage medium | |
| CN117033360A (en) | Bank data restoration method, device, equipment and medium based on blood margin analysis | |
| CN118211832A (en) | Financial tax data risk monitoring method, system, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |