CN112686679A - Intelligent analysis system and method for customer incidence relation - Google Patents
Intelligent analysis system and method for customer incidence relation Download PDFInfo
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Abstract
The invention relates to the technical field of bank big data processing, and discloses a system and a method for intelligently analyzing a customer incidence relation, wherein the system comprises a basic information database, a data access layer, a data operation layer and a view layer which are sequentially connected, wherein the basic information database comprises an internal data module and an external data module; the data access layer comprises a data classification module, a data analysis module and a data standardization storage module; the data operation layer comprises a knowledge graph operation analysis module and a risk analysis operation module; the view layer comprises a map display module, an early warning module and a retrieval module; the system can monitor the change condition of external risk indexes inside and outside a customer and the future risk default prediction result of the customer in time, provides a novel management means for bank personnel to carry out post-loan management, finds risks as early as possible and solves the risks as early as possible, and improves the business decision efficiency and effectiveness.
Description
Technical Field
The invention relates to the technical field of bank big data processing, in particular to a system and a method for intelligently analyzing a customer association relation.
Background
Nowadays, data sharing is increasingly frequent, aiming at national big data strategy, in order to prevent systematic financial risk, big data is applied more and more in banking industry, in the prior art, potential customers are mined aiming at bank products by using big data, but now collective operation brings various novel credit risks to enterprises, so that the difficulty of credit review and the risk after credit increase in banking industry, the group relationship and the customer relationship are complicated, the judgment on the risk of a group or a customer cannot be well made by simply depending on the existing retrieval or query, and hysteresis exists.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a system and a method for intelligently analyzing a customer association relation.
In order to achieve the above purpose, the invention provides the following technical scheme:
an intelligent analysis system for a client association relation comprises a basic information database, a data access layer, a data operation layer and a view layer which are sequentially connected, wherein the basic information database comprises an internal data module and an external data module; the data access layer comprises a data classification module, a data analysis module and a data standardization storage module; the data operation layer comprises a knowledge graph operation analysis module and a risk analysis operation module; the map layer comprises a map display module, an early warning module and a retrieval module.
In the present invention, preferably, the internal data module stores basic information data of a customer, loan detail data, account information data, and risk record data, and the external data module stores banking data, personal credit data, business data, court data, Chinese debt data, and public opinion data.
In the present invention, preferably, the data classification module includes a structured data storage module and an unstructured data storage module, which are respectively used for storing structured data and unstructured data extracted from the basic information database.
In the present invention, preferably, the data analysis module is connected to the unstructured data storage module and the data standardized storage module, and the data analysis module includes an NLP framework therein, and is configured to perform text recognition and analysis on data in the unstructured data module, and send the processed data to the data standardized storage module for storage.
In the present invention, preferably, the knowledge graph operation and analysis module includes a group relationship analysis module and a customer relationship analysis module, and the risk analysis and operation module is mainly used for performing operation and analysis on group risks and group-associated risks.
An intelligent analysis method for customer association relationship comprises the following steps:
s1: extracting and storing group and customer related data from each unit system and internal system connected with the intelligent analysis system;
s2: extracting structured data for standardized processing and storage, and performing identification analysis and extraction on non-structural data by a data analysis module;
s3: the knowledge graph operation and analysis module performs operation based on data to obtain a group relation analysis view and a customer relation analysis view, and the risk analysis and operation module performs operation according to the group relation analysis view and the customer relation analysis view to obtain risk early warning;
s4: and displaying the corresponding group relation analysis view, the client relation analysis view and the risk early warning ranking.
In the present invention, it is preferable that the data extracted by each unit system is stored in the external data block of the basic information database and the internal data extracted by the internal system is stored in the internal data block of the basic information database in step S1.
In the present invention, it is preferable that the step S2 further includes the steps of:
s201: the data classification module exchanges and stores the data in the internal data module into the structured data storage module, and the data in the external data module is stored into the unstructured data storage module;
s202: the data analysis module is used for cleaning, integrating and screening data in the non-structural data storage module and then storing the data into the data standardized storage module.
In the present invention, it is preferable that the step S3 further includes the steps of:
s301: the knowledge map operation analysis module firstly extracts seven relation categories corresponding to a group or a client from the data standardized storage module, namely seven types of stock right relation, guarantee relation, control relation, transaction relation, interpersonal relation, address relation and negative event relation;
s302: then filtering, de-duplication and combination are carried out on the data of different sources under each relation category to obtain the integrated relation;
s303: finally, according to the integrated relationship analysis and settlement, obtaining and storing a group relationship analysis view or a customer relationship analysis view;
s304: and the risk analysis operation module extracts risk data from the data standardization storage module, and generates risk early warning data for the group or the client exceeding the set threshold value by combining the group relation analysis view or the client relation analysis view operation.
In the present invention, preferably, in step S4, the graph display module displays the corresponding group relationship analysis view, the client relationship analysis view and the risk early warning ranking.
Compared with the prior art, the invention has the beneficial effects that:
the system integrates multiple unit data and bank internal data by building a basic information database and a data access layer, and then establishes a group or customer incidence relation view based on seven major relations through a knowledge map operation analysis module and a risk analysis operation module in the data operation layer, and monitors the change condition of internal and external risk indexes of a system customer and the future risk default prediction result of the customer in time, thereby providing a novel management means for bank personnel to carry out post-credit management, finding risks as early as possible and solving the risks as early as possible, and improving the business decision efficiency and effectiveness.
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Fig. 1 is a block diagram of a structure of an intelligent analysis system for customer association according to the present invention.
Fig. 2 is a schematic flow chart of an intelligent analysis method for customer association according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a preferred embodiment of the present invention provides an intelligent analysis system for customer association relationship, which mainly uses big data, a knowledge graph, and a new data mining technology to construct a customer association relationship family spectrum analysis model and a default early warning model, so as to implement a customer association relationship view, a group association relationship view, and a risk prompt function, so as to comprehensively deal with various novel credit risks brought to enterprises by current group management, help banks quickly sort customer association relationships, and find potential association risks in advance, so that bank risk control and post-loan management can be better performed, the system includes a basic information database, a data access layer, a data operation layer, and a view layer, which are sequentially connected, wherein the basic information database includes an internal data module and an external data module; the data access layer comprises a data classification module, a data analysis module and a data standardization storage module; the data operation layer comprises a knowledge graph operation analysis module and a risk analysis operation module; the map layer comprises a map display module, an early warning module and a retrieval module.
Specifically, the basic information database is used for storing data which are stored in each large unit system and the system and are in communication connection with the system, and the basic information database is divided into an internal data module and an external data module according to stored data sources, wherein the internal data module stores basic information data of customers, loan detail data, account information data and risk record data, and the external data module stores banking supervision data, human behavior credit data, industrial and commercial data, court data, Chinese debt data and public opinion data; the data access layer is connected between the basic information database and the data operation layer and mainly used for carrying out basic classification processing and related data extraction on initial data in the basic information database, wherein the data classification module is used for classifying and storing structured data and unstructured data, and the data classification module is used for sorting and extracting unstructured data in the data classification module, converting the unstructured data into standard data and storing the standard data in the data standardization storage module so as to be convenient for the application of the subsequent data operation layer; the data operation layer is mainly used for analyzing and processing data in the data standardization storage module to obtain a group or customer incidence relation, the knowledge map operation analysis module is mainly used for extracting, analyzing and calculating the group or customer relation to obtain a group or customer incidence relation network, and the knowledge map operation analysis module is used for carrying out comparison operation on the data of the data standardization storage module and the group or customer incidence relation network together to obtain a group or customer risk list exceeding a safety threshold; and finally, the visual layer displays the association relationship network and the risk list of the group or the client, the early warning module gives an early warning when the group or the client needs the early warning, the retrieval module is provided for workers to independently and purposefully retrieve, the whole system can timely monitor the change condition of external risk indexes inside and outside the client and the future risk default prediction result of the client, a novel management means and an intelligent decision support are provided for the bank personnel to carry out post-loan management, and the business decision efficiency and effectiveness are improved.
In this embodiment, the data classifying module includes a structured data storage module and an unstructured data storage module, which are respectively used for storing structured data and unstructured data extracted from the basic information database, wherein the structured data stored in the internal data module is integrated, and the external data module is unstructured data due to storing data from different sources, and is used for subsequent data extraction operation through classified storage.
In this embodiment, the data analysis module is connected to the unstructured data storage module and the data standardized storage module, and the data analysis module mainly integrates unstructured data to form subsequently available standard data, and the data analysis module includes an NLP frame for performing text recognition and analysis on data in the unstructured data module, and sending the processed data to the data standardized storage module for storage, and the data standardized storage module still stores the data in groups of units, so as to facilitate subsequent targeted data calling.
In this embodiment, the knowledge graph operation and analysis module includes a group relationship analysis module and a customer relationship analysis module, and the risk analysis and operation module is mainly used for performing operation and analysis on group risks and group-associated risks.
Referring to fig. 2, another preferred embodiment of the present invention provides a method for intelligently analyzing a client association relationship, including the following steps:
s1: extracting and storing group and customer related data from each unit system and internal system connected with the intelligent analysis system;
s2: extracting structured data for standardized processing and storage, and performing identification analysis and extraction on non-structural data by a data analysis module;
s3: the knowledge graph operation and analysis module performs operation based on data to obtain a group relation analysis view and a customer relation analysis view, and the risk analysis and operation module performs operation according to the group relation analysis view and the customer relation analysis view to obtain risk early warning;
s4: and displaying the corresponding group relation analysis view, the client relation analysis view and the risk early warning ranking.
Specifically, in step S1, the data extracted by each unit system is grouped by each unit and stored in the external data module of the basic information database, and the internal data extracted by the internal system is stored in the internal data module of the basic information database.
Specifically, in step S2, the method further includes the following steps:
s201: the data classification module exchanges and stores the data in the internal data module into the structured data storage module, and the data in the external data module is stored into the unstructured data storage module;
s202: the data analysis module is used for cleaning, integrating and screening data in the non-structural data storage module and then storing the data into the data standardized storage module.
Specifically, in step S3, the method further includes the following steps:
s301: the knowledge map operation analysis module firstly extracts seven kinds of relation category data corresponding to a group or a client from the data standardization storage module, namely seven kinds of stock right relation, guarantee relation, control relation, transaction relation, interpersonal relation, address relation and negative event relation;
s302: then filtering, removing duplication and combining the data of different sources under each relation category to obtain the integrated relation;
s303: finally, according to the integrated relationship analysis and settlement, obtaining and storing a group relationship analysis view or a customer relationship analysis view;
s304: and the risk analysis operation module extracts operation risk data from the data standardization storage module, and generates risk early warning data for the group or the client exceeding the set threshold value by combining the group relation analysis view or the client relation analysis view operation.
Further, the risk analysis operation module comprises a risk conduction early warning model, the risk conduction early warning model adopts a deep learning priority algorithm, through early data training, risk data such as bad loan data and overdue data extracted from the data standardized storage module are input into the risk conduction early warning model, meanwhile, a group relation analysis view or a customer relation analysis view is referred, a set threshold value is calculated and compared through the deep learning priority algorithm, and finally, a group or a customer main body corresponding to a risk event, a risk impact value and a conducting path are output, so that risk early warning data are generated, the problem of external risk quantification of an enterprise is solved, the enterprise is quantified through a negative impact value of external associated enterprise risk or occurrence of macroscopic industry or regional emergency events through an association relationship, and a risk early warning ranking is generated for a person to check.
Specifically, in step S4, the graph display module displays the corresponding group relationship analysis view, the client relationship analysis view, and the risk early warning ranking.
The above description is intended to describe in detail the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the claims of the present invention, and all equivalent changes and modifications made within the technical spirit of the present invention should fall within the scope of the claims of the present invention.
Claims (10)
1. An intelligent analysis system for the association relationship of clients is characterized by comprising a basic information database, a data access layer, a data operation layer and a view layer which are sequentially connected,
the basic information database comprises a row internal data module and an external data module;
the data access layer comprises a data classification module, a data analysis module and a data standardization storage module;
the data operation layer comprises a knowledge graph operation analysis module and a risk analysis operation module;
the map layer comprises a map display module, an early warning module and a retrieval module.
2. The system for intelligently analyzing the association relationship between customers as claimed in claim 1, wherein the internal data module stores basic information data, loan detail data, account information data and risk record data of customers, and the external data module stores banking data, human credit data, industrial and commercial data, court data, Chinese and debt data and public opinion data.
3. The system of claim 2, wherein the data classification module comprises a structured data storage module and an unstructured data storage module, which are respectively used for storing structured data and unstructured data extracted from the basic information database.
4. The system according to claim 3, wherein the data analysis module is connected to the unstructured data storage module and the data standardized storage module, and the data analysis module comprises an NLP framework for performing text recognition and analysis on data in the unstructured data module, and sending the processed data to the data standardized storage module for storage.
5. The system of claim 4, wherein the intellectual graph analysis module comprises a group relationship analysis module and a customer relationship analysis module, and the risk analysis module is mainly used for performing operational analysis on group risks and group-associated risks.
6. A customer association relation intelligent analysis method, a customer association relation intelligent analysis system according to claim 5, characterized by comprising the following steps:
s1: extracting and storing group and customer related data from each unit system and internal system connected with the intelligent analysis system;
s2: extracting structured data for standardized processing and storage, and performing identification analysis and extraction on non-structural data by a data analysis module;
s3: the knowledge graph operation and analysis module performs operation based on data to obtain a group relation analysis view and a customer relation analysis view, and the risk analysis and operation module performs operation according to the group relation analysis view and the customer relation analysis view to obtain risk early warning;
s4: and displaying the corresponding group relation analysis view, the client relation analysis view and the risk early warning ranking.
7. The intelligent analysis method for client relationship according to claim 6, wherein in step S1, the data extracted by each unit system is stored in the external data module of the basic information database, and the internal data extracted by the internal system is stored in the internal data module of the basic information database.
8. The intelligent analysis method for client association relationship as claimed in claim 7, wherein in step S2, the method further comprises the following steps:
s201: the data classification module exchanges and stores the data in the internal data module into the structured data storage module, and the data in the external data module is stored into the unstructured data storage module;
s202: the data analysis module is used for cleaning, integrating and screening data in the non-structural data storage module and then storing the data into the data standardized storage module.
9. The intelligent analysis system method for client association relationship as claimed in claim 8, wherein in step S3, the method further comprises the following steps:
s301: the knowledge map operation analysis module firstly extracts seven relation categories corresponding to a group or a client from the data standardized storage module, namely seven types of stock right relation, guarantee relation, control relation, transaction relation, interpersonal relation, address relation and negative event relation;
s302: then filtering, de-duplication and combination are carried out on the data of different sources under each relation category to obtain the integrated relation;
s303: finally, according to the integrated relationship analysis and settlement, obtaining and storing a group relationship analysis view or a customer relationship analysis view;
s304: and the risk analysis operation module extracts risk data from the data standardization storage module, and generates risk early warning data for the group or the client exceeding the set threshold value by combining the group relation analysis view or the client relation analysis view operation.
10. The intelligent analysis method for client association relationship as claimed in claim 9, wherein in step S4, the graph display module displays the corresponding group relationship analysis view, client relationship analysis view and risk pre-warning ranking.
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