CN113888310A - Credit risk assessment method, credit risk assessment device and storage medium - Google Patents
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Abstract
The invention relates to the technical field of risk assessment, and discloses a credit risk assessment method, a credit risk assessment device and a credit risk assessment storage medium, wherein the credit risk assessment method comprises the steps of preprocessing relational data of a user to be assessed, wherein the relational data comprises user basic information and personal credit investigation information; acquiring target node data associated with a user to be evaluated from a graphic database; performing credit risk assessment on the preprocessed relational data to obtain an initial risk assessment result; obtaining a final risk evaluation result based on the attribute of the target node data and the initial risk evaluation result; the graph database records node data associated with each user and attributes of the node data, and the attribute representation of the node data shows that credit risk exists or credit risk does not exist. The credit risk assessment method, the credit risk assessment device and the credit risk assessment storage medium can improve the accuracy of credit risk assessment and reduce credit risk.
Description
Technical Field
The invention relates to the technical field of risk assessment, in particular to a credit risk assessment method, a credit risk assessment device and a credit risk assessment storage medium.
Background
Credit risk assessment is an important link in credit business, most of the traditional credit risk assessment is decision analysis by acquiring data in a relational database, such as personal basic information, personal credit investigation information and the like, while factors related to credit risk are various, assessment is performed only from data in the relational database, such as the personal basic information, the personal credit investigation information and the like, and the assessment is often incomplete and poor in accuracy, so that credit risk is increased.
Therefore, how to provide an effective scheme to improve the accuracy of credit risk assessment and reduce credit risk has become an urgent problem in the prior art.
Disclosure of Invention
In order to solve the problem of poor accuracy of credit risk assessment in the prior art, the invention aims to provide a credit risk assessment method, a credit risk assessment device and a credit risk assessment storage medium, so as to improve the accuracy of credit risk assessment and reduce credit risk.
In a first aspect, the present invention provides a credit risk assessment method, comprising:
preprocessing relational data of a user to be evaluated, wherein the relational data comprise user basic information and personal credit information;
acquiring target node data associated with the user to be evaluated from a graphic database;
performing credit risk assessment on the preprocessed relational data to obtain an initial risk assessment result;
obtaining a final risk evaluation result based on the attribute of the target node data and the initial risk evaluation result;
the graph database records node data associated with each user and attributes of the node data, and the attribute representation of the node data shows that credit risk exists or credit risk does not exist.
In one possible design, before preprocessing the relational data of the user to be evaluated, the method further includes:
acquiring the user basic information uploaded by the user to be evaluated;
and acquiring the personal credit information of the authorization query of the user to be evaluated from a third-party server based on the user basic information.
In one possible design, the preprocessing the relational data of the user to be evaluated includes:
calculating the mean, variance or coefficient of variation of numerical data in the relational data;
and classifying non-numerical data in the relational data.
In one possible design, the obtaining target node data associated with the user to be evaluated from a graph database includes:
and acquiring target node data associated with the user to be evaluated from a graphic database through a community discovery algorithm or a centrality algorithm.
In one possible design, the performing credit risk assessment on the preprocessed relational data includes:
performing credit risk assessment on the preprocessed relational data through predefined rules;
the predefined rules include at least one of conditional judgment, score cards, decision tables, and decision tree rules.
In one possible design, the graph database is Neo4j graph database.
In a second aspect, the present invention provides a credit risk assessment apparatus comprising:
the system comprises a preprocessing unit, a judging unit and a judging unit, wherein the preprocessing unit is used for preprocessing relational data of a user to be evaluated, and the relational data comprises user basic information and personal credit information;
the first acquisition unit is used for acquiring target node data associated with the user to be evaluated from a graph database;
the first evaluation unit is used for performing credit risk evaluation on the preprocessed relational data to obtain an initial risk evaluation result;
the second evaluation unit is used for obtaining a final risk evaluation result based on the attribute of the target node data and the initial risk evaluation result;
the graph database records node data associated with each user and attributes of the node data, and the attribute representation of the node data shows that credit risk exists or credit risk does not exist.
In one possible design, the credit risk assessment apparatus further includes:
the second acquisition unit is used for acquiring the user basic information uploaded by the user to be evaluated;
and the query unit is used for acquiring the personal credit information which is authorized and queried by the user to be evaluated from a third-party server based on the user basic information.
In a possible design, when the preprocessing unit is configured to preprocess the relational data of the user to be evaluated, the preprocessing unit is specifically configured to:
calculating the mean, variance or coefficient of variation of numerical data in the relational data;
and classifying non-numerical data in the relational data.
In a possible design, when the first obtaining unit is configured to obtain target node data associated with the user to be evaluated from a graph database, the first obtaining unit is specifically configured to:
and acquiring target node data associated with the user to be evaluated from a graphic database through a community discovery algorithm or a centrality algorithm.
In one possible design, the first evaluation unit, when being configured to perform the credit risk evaluation on the preprocessed relational data, is specifically configured to:
performing credit risk assessment on the preprocessed relational data through predefined rules;
the predefined rules include at least one of conditional judgment, score cards, decision tables, and decision tree rules.
In one possible design, the graph database is Neo4j graph database.
In a third aspect, the invention provides a credit risk assessment device, comprising a memory, a processor and a transceiver, which are sequentially connected in communication, wherein the memory is used for storing a computer program, the transceiver is used for transmitting and receiving messages, and the processor is used for reading the computer program and executing the credit risk assessment method according to any item above.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon instructions which, when run on a computer, perform the credit risk assessment method of the first aspect.
In a fifth aspect, the present invention provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the credit risk assessment method according to the first aspect.
At least one technical scheme adopted by one or more embodiments of the invention can achieve the following beneficial effects:
the method comprises the steps of obtaining target node data associated with a user to be assessed from a graph database, performing credit risk assessment on the preprocessed relational data to obtain an initial risk assessment result, and then obtaining a final risk assessment result based on the attribute of the target node data and the initial risk assessment result.
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.
FIG. 1 is a flow chart of a credit risk assessment method provided by the present invention.
FIG. 2 is a schematic diagram of the credit risk assessment device provided by the present invention.
FIG. 3 is a schematic diagram of another credit risk assessment device provided by the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It should be understood that specific details are provided in the following description to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.
Examples
In order to solve the problem of poor accuracy of credit risk assessment in the prior art, the embodiment of the application provides a credit risk assessment method, a credit risk assessment device and a storage medium.
The credit risk assessment method provided by the embodiment of the present application may be applied to a user terminal, which may be, but is not limited to, a smart phone, a tablet computer, a Personal Digital Assistant (PDA), and the like, and the credit risk assessment method provided by the embodiment of the present application will be described in detail below.
For convenience of description, the embodiments of the present application are described with reference to a user terminal as an implementation subject, unless otherwise specified.
It is to be understood that the described execution body does not constitute a limitation of the embodiments of the present application.
In a first aspect, an embodiment of the present application provides a credit risk assessment method. As shown in fig. 1, which is a flowchart of a credit risk assessment method according to an embodiment of the present application, the credit risk assessment method may include the following steps:
and step S101, preprocessing the relational data of the user to be evaluated.
The relational data includes, but is not limited to, user basic information, personal credit information, and the like.
In the embodiment of the application, before credit risk assessment, a user to be assessed may upload user basic information of the user to be assessed, and a user terminal obtains the user basic information uploaded by the user to be assessed, where the user basic information may be, but is not limited to, a name, a gender, an age, an identity card number, a mobile phone number, a work unit, a residence place, and the like of the user to be assessed, and is not specifically limited in the embodiment of the application.
The user to be evaluated can authorize and inquire the personal credit investigation information of the user to be evaluated, and after the user basic information uploaded by the user to be evaluated is obtained, the user terminal can obtain the personal credit investigation information authorized and inquired by the user to be evaluated from the third-party server based on the user basic information. The third-party server is a server for recording personal credit investigation information of the user, such as a server for carrying out credit investigation or credit investigation.
When preprocessing the relational data of the user to be evaluated, for the numerical data (for example, age) in the relational data, a mean, a variance, a variation coefficient, or the like of the numerical data in the relational data can be calculated. Non-numerical data (e.g., gender, residence, etc.) in the relational data may be categorized.
Step S102, target node data associated with the user to be evaluated is obtained from the graph database.
The graphic database is a type of NoSQL database, which stores relationship information between entities using graphic theory, and the graphic database is a non-relational database, which stores relationship information between entities using graphic theory. The graph database may be, but is not limited to, a Neo4j graph database, an OrientDB graph database, or the like.
In the embodiment of the application, the graph database is a Neo4j graph database, node data associated with each user and attributes of the node data are recorded in the graph database, and the attribute representation of the node data has a credit risk or does not have a credit risk.
Specifically, the data in the graph database includes node data, edges and attributes, where the node data records basic information (such as province certificates, telephones, etc.) of users, and the edges are used for connecting node data of users or node data of other associated users. For example, if the id number of the user a is a1, the phone number is a2, the node data recording the id number a1 and the node data recording the phone number a2 can be connected in the graph database by the edge, the user a and the user B are in a husband-wife relationship, and the id number of the user B is B1, the node data recording the id number a1 and the node data recording the id number B1 can be connected in the graph database by the edge, so that the node data recording the phone number a2 and the node data recording the id number B1 can be respectively associated with the node data recording the id number a1 by the edge, that is, the node data recording the phone number a2 and the node data recording the id number B1 in the graph database are both the associated data recording the id number a 1. The attribute of the node data is recorded in the graph database, the attribute of the node data represents that credit risk exists or credit risk does not exist, the attribute of the corresponding node data is set to have the credit risk for users losing credit or users fraud, and the attribute of the corresponding node data is set to have no credit risk for users with good credit.
After obtaining the relational data of the user, target node data associated with the relational data, namely the target node data associated with the user to be evaluated, can be searched from the graph database according to the relational data of the user. When the target node data associated with the user to be evaluated is obtained from the graph database, the target node data associated with the user to be evaluated can be obtained from the graph database through a community discovery algorithm or a centrality algorithm.
And step S103, performing credit risk assessment on the preprocessed relational data to obtain an initial risk assessment result.
Specifically, the credit risk assessment may be performed on the preprocessed relational data through a predefined rule, where the predefined rule includes at least one of a condition judgment (IF/ELSE), a score card, a decision table, and a decision tree rule, and is not specifically limited in the embodiment of the present application.
For example, in one or more embodiments, if the relational data includes age data, the evaluation may be performed by first determining whether the preprocessed age data exceeds a preset range through a conditional judgment, and if the preprocessed age data exceeds the preset range, directly determining that a credit risk exists. And if the credit risk does not exceed the preset range, performing credit risk assessment through at least one of the scoring card, the decision table and the decision tree rule to obtain an initial risk assessment result.
In the embodiment of the present application, Rules such as condition judgment, score card, decision table, and decision tree may be developed based on a Drools (JBoss Rules, having an open business Rules engine that is easy to access enterprise policies, easy to adjust, and easy to manage) rule engine. The Drools rule is run on a Java application, and the order of steps it performs is determined by the code. To accomplish this, the Drools rules engine converts the business rules into an execution tree, each rule condition divided into tiles, connected and reused in the tree structure. Each time data is added to the rule engine, it will evaluate in a tree similar thereto and arrive at an action node where they will be marked as data ready to execute a particular rule.
The initial risk assessment result may be whether a credit risk exists or a coefficient of the credit risk exists, and is not specifically limited in the embodiment of the present application. For example, in one or more embodiments, the initial risk assessment results derived by the scoring card rules may be a coefficient of credit risk, with larger coefficients indicating a higher likelihood of credit risk being present, and the initial risk assessment results derived by the decision tree rules may be credit risk present or credit risk not present.
And step S104, obtaining a final risk evaluation result based on the attribute of the target node data and the initial risk evaluation result.
In the embodiment of the present application, after the initial risk assessment result is obtained, a final risk assessment result may be obtained based on comprehensive consideration of the attribute of the target node data and the initial risk assessment result, where the final risk assessment result may be whether a credit risk exists or a coefficient of the credit risk, and is not specifically limited in the embodiment of the present application.
In summary, the credit risk assessment method provided in the embodiment of the application obtains the target node data associated with the user to be assessed from the graph database, performs credit risk assessment on the preprocessed relational data to obtain an initial risk assessment result, and then obtains a final risk assessment result based on the attribute of the target node data and the initial risk assessment result, so that during credit risk assessment, assessment analysis can be performed according to the relational data of the user to be assessed, and assessment analysis can be performed according to the non-relational node data associated with the user to be assessed, so that the user to be assessed can be comprehensively assessed in various aspects, accuracy of credit risk assessment is improved, credit risk is reduced, and decision-making errors in a credit process are reduced. Meanwhile, the credit risk assessment method provided by the embodiment of the application can plan the credit risk assessment rule by relying on a computer program, and the credit risk assessment efficiency is improved.
In a second aspect, an embodiment of the present application provides a credit risk assessment apparatus, please refer to fig. 2, the credit risk assessment apparatus includes:
the system comprises a preprocessing unit, a judging unit and a judging unit, wherein the preprocessing unit is used for preprocessing relational data of a user to be evaluated, and the relational data comprises user basic information and personal credit information;
the first acquisition unit is used for acquiring target node data associated with the user to be evaluated from a graph database;
the first evaluation unit is used for performing credit risk evaluation on the preprocessed relational data to obtain an initial risk evaluation result;
the second evaluation unit is used for obtaining a final risk evaluation result based on the attribute of the target node data and the initial risk evaluation result;
the graph database records node data associated with each user and attributes of the node data, and the attribute representation of the node data shows that credit risk exists or credit risk does not exist.
In one possible design, the credit risk assessment apparatus further includes:
the second acquisition unit is used for acquiring the user basic information uploaded by the user to be evaluated;
and the query unit is used for acquiring the personal credit information which is authorized and queried by the user to be evaluated from a third-party server based on the user basic information.
In a possible design, when the preprocessing unit is configured to preprocess the relational data of the user to be evaluated, the preprocessing unit is specifically configured to:
calculating the mean, variance or coefficient of variation of numerical data in the relational data;
and classifying non-numerical data in the relational data.
In a possible design, when the first obtaining unit is configured to obtain target node data associated with the user to be evaluated from a graph database, the first obtaining unit is specifically configured to:
and acquiring target node data associated with the user to be evaluated from a graphic database through a community discovery algorithm or a centrality algorithm.
In one possible design, the first evaluation unit, when being configured to perform the credit risk evaluation on the preprocessed relational data, is specifically configured to:
performing credit risk assessment on the preprocessed relational data through predefined rules;
the predefined rules include at least one of conditional judgment, score cards, decision tables, and decision tree rules.
In one possible design, the graph database is Neo4j graph database.
For the working process, the working details, and the technical effects of the apparatus provided in the second aspect of this embodiment, reference may be made to the first aspect of this embodiment, which is not described herein again.
As shown in fig. 3, a third aspect of the embodiments of the present application provides a credit risk assessment apparatus, including a memory, a processor and a transceiver, which are sequentially and communicatively connected, wherein the memory is used for storing a computer program, the transceiver is used for sending and receiving messages, and the processor is used for reading the computer program and executing the credit risk assessment method according to the first aspect of the embodiments.
By way of specific example, the Memory may include, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Flash Memory (Flash Memory), a first-in-first-out Memory (FIFO), a first-in-last-out Memory (FILO), and/or the like; the processor may not be limited to a processor adopting an architecture processor such as a model STM32F105 series microprocessor, an arm (advanced RISC machines), an X86, or a processor of an integrated NPU (neutral-network processing unit); the transceiver may be, but is not limited to, a WiFi (wireless fidelity) wireless transceiver, a bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee protocol (ieee 802.15.4 standard-based low power local area network protocol), a 3G transceiver, a 4G transceiver, and/or a 5G transceiver, etc.
For the working process, the working details, and the technical effects of the apparatus provided in the third aspect of this embodiment, reference may be made to the first aspect of the embodiment, which is not described herein again.
A fourth aspect of the present embodiment provides a computer-readable storage medium storing instructions that include the credit risk assessment method according to the first aspect of the present embodiment, where the computer-readable storage medium has instructions stored thereon, and when the instructions are executed on a computer, the credit risk assessment method according to the first aspect is performed. The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, flash disks and/or Memory sticks (Memory sticks), etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
For a working process, working details, and technical effects of the computer-readable storage medium provided in the fourth aspect of this embodiment, reference may be made to the first aspect of the embodiment, which is not described herein again.
A fifth aspect of the present embodiments provides a computer program product comprising instructions which, when run on a computer, which may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus, cause the computer to perform the credit risk assessment method of the first aspect of the embodiments.
The embodiments described above are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a repository code combining means to execute the methods according to the embodiments or parts of the embodiments.
The invention is not limited to the above alternative embodiments, and any other various forms of products can be obtained by anyone in the light of the present invention, but any changes in shape or structure thereof, which fall within the scope of the present invention as defined in the claims, fall within the scope of the present invention.
Claims (10)
1. A credit risk assessment method, comprising:
preprocessing relational data of a user to be evaluated, wherein the relational data comprise user basic information and personal credit information;
acquiring target node data associated with the user to be evaluated from a graphic database;
performing credit risk assessment on the preprocessed relational data to obtain an initial risk assessment result;
obtaining a final risk evaluation result based on the attribute of the target node data and the initial risk evaluation result;
the graph database records node data associated with each user and attributes of the node data, and the attribute representation of the node data shows that credit risk exists or credit risk does not exist.
2. The method of claim 1, wherein prior to preprocessing the relational data of the user to be evaluated, the method further comprises:
acquiring the user basic information uploaded by the user to be evaluated;
and acquiring the personal credit information of the authorization query of the user to be evaluated from a third-party server based on the user basic information.
3. The method of claim 1, wherein preprocessing the relational data of the user to be evaluated comprises:
calculating the mean, variance or coefficient of variation of numerical data in the relational data;
and classifying non-numerical data in the relational data.
4. The method of claim 1, wherein the obtaining target node data associated with the user to be evaluated from a graph database comprises:
and acquiring target node data associated with the user to be evaluated from a graphic database through a community discovery algorithm or a centrality algorithm.
5. The method according to claim 1, wherein said performing credit risk assessment on the preprocessed relational data comprises:
performing credit risk assessment on the preprocessed relational data through predefined rules;
the predefined rules include at least one of conditional judgment, score cards, decision tables, and decision tree rules.
6. The method of claim 1, wherein the graph database is Neo4j graph database.
7. A credit risk assessment apparatus, comprising:
the system comprises a preprocessing unit, a judging unit and a judging unit, wherein the preprocessing unit is used for preprocessing relational data of a user to be evaluated, and the relational data comprises user basic information and personal credit information;
the first acquisition unit is used for acquiring target node data associated with the user to be evaluated from a graph database;
the first evaluation unit is used for performing credit risk evaluation on the preprocessed relational data to obtain an initial risk evaluation result;
the second evaluation unit is used for obtaining a final risk evaluation result based on the attribute of the target node data and the initial risk evaluation result;
the graph database records node data associated with each user and attributes of the node data, and the attribute representation of the node data shows that credit risk exists or credit risk does not exist.
8. The credit risk assessment device of claim 7, further comprising:
the second acquisition unit is used for acquiring the user basic information uploaded by the user to be evaluated;
and the query unit is used for acquiring the personal credit information which is authorized and queried by the user to be evaluated from a third-party server based on the user basic information.
9. A credit risk assessment apparatus, characterized by: the credit risk assessment method comprises a memory, a processor and a transceiver which are sequentially connected in communication, wherein the memory is used for storing a computer program, the transceiver is used for transmitting and receiving messages, and the processor is used for reading the computer program and executing the credit risk assessment method according to any one of claims 1-6.
10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored thereon instructions that, when executed on a computer, perform the credit risk assessment method according to any one of claims 1 to 6.
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| CN116503163A (en) * | 2023-06-21 | 2023-07-28 | 建信金融科技有限责任公司 | Service risk identification method, device, computer equipment and storage medium |
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| CN116503163A (en) * | 2023-06-21 | 2023-07-28 | 建信金融科技有限责任公司 | Service risk identification method, device, computer equipment and storage medium |
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