CN115829716A - Client credit rating method, system, computer equipment and storage medium - Google Patents
Client credit rating method, system, computer equipment and storage medium Download PDFInfo
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- CN115829716A CN115829716A CN202211056919.3A CN202211056919A CN115829716A CN 115829716 A CN115829716 A CN 115829716A CN 202211056919 A CN202211056919 A CN 202211056919A CN 115829716 A CN115829716 A CN 115829716A
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Abstract
The application discloses a client credit rating method, a client credit rating system, computer equipment and a storage medium, which can be applied to the financial field and other fields. The method comprises the following steps: acquiring credit index data of a client; inputting the credit index data into a client credit rating model, and generating the credit rating of the client, wherein the client credit rating model is realized based on a LightGBM model. The method for grading the client credit under the mass client credit information is achieved by the LightGBM method, and accuracy of the client credit grading is improved.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method, system, computer device, and storage medium for rating credit of a bank customer.
Background
The customer credit rating is an evaluation of the total financial ability of a bank to pay the customer for the financial debt, is commonly used by depositors and investors to evaluate risk returns, optimize investment structures and avoid investment risks, and is also commonly used by commercial banks to widen financing channels, stabilize fund sources, reduce financing expenses and the like.
The existing client credit rating method is usually realized by adopting a BP neural network, a K-means clustering method and a support vector machine, but the accuracy of a client credit rating result is not high.
Disclosure of Invention
Based on the above problems, the present application provides a method, a system, a computer device and a storage medium for customer credit rating, which can perform joint control on all bank multi-level accounts.
The application discloses following technical scheme:
a first aspect of the present application provides a method for rating a client credit, comprising:
acquiring credit index data of a client;
inputting the credit index data into a client credit rating model, and generating the credit rating of the client, wherein the client credit rating model is realized based on a LightGBM model.
In one possible implementation, the training method of the customer credit rating model includes:
collecting initial credit indicator data for the rated customers;
carrying out Z-score standardization processing on the initial credit index data of the rated customers to obtain training set data;
and training the LightGBM model to be trained by utilizing the training set data.
In one possible implementation, the obtaining the credit indicator data of the client includes:
acquiring initial credit index data of a client;
and performing Z-score standardization processing on the initial credit index data to obtain credit index data for performing credit rating.
In one possible implementation, the credit indicator data includes:
financial data and business data.
A second aspect of the present application provides a customer credit rating system comprising:
the index data acquisition module is used for acquiring credit index data of the client;
and the rating module is used for inputting the credit index data into a client credit rating model and generating the credit rating of the client, wherein the client credit rating model is realized on the basis of a LightGBM model.
In one possible implementation, the system further includes: a training module of the customer credit rating model, the training module comprising:
a collecting module for collecting initial credit indicator data of the rated customers;
the training data acquisition module is used for carrying out Z-score standardization processing on the initial credit index data of the rated client to obtain training set data;
and the training submodule is used for training the LightGBM model to be trained by utilizing the training set data.
In one possible implementation manner, the index data obtaining module includes:
the first acquisition module is used for acquiring initial credit index data of a client;
and the second obtaining module is used for carrying out Z-score standardization processing on the initial credit index data to obtain credit index data for carrying out credit rating.
In one possible implementation, the credit indicator data includes:
financial data and business data.
A third aspect of the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when processing the computer program implementing the method of rating a customer credit as described in any of the first aspects of the present application.
A fourth aspect of the present application provides a computer-readable storage medium having stored therein instructions that, when run on a terminal device, cause the terminal device to process a customer credit rating method as defined in any one of the first aspects of the present application.
Compared with the prior art, the method has the following beneficial effects:
the application provides a bank customer credit rating method, which comprises the following steps: acquiring credit index data of a client; inputting the credit index data into a client credit rating model, and generating the credit rating of the client, wherein the client credit rating model is realized based on a LightGBM model. The method for grading the client credit under the condition of massive client credit information is achieved by the LightGBM method, and accuracy of the client credit grading is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a flowchart of a method for rating a client credit according to an embodiment of the present disclosure
FIG. 2 is a block diagram of a customer credit rating system provided by an embodiment of the present application;
fig. 3 is a block diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the drawings are described in detail below.
As previously mentioned, the customer credit rating is an evaluation of the overall financial ability of a bank to pay off a customer's financial debt, and is commonly used by depositors and investors to evaluate risk returns, optimize investment structure, avoid investment risk, and also commonly used by commercial banks to widen financing channels, stabilize funding sources, reduce financing costs, and the like.
The existing customer credit rating method is usually realized by adopting a BP neural network, a K-means clustering method and a support vector machine, but the method has the following defects:
the BP neural network cannot observe the internal learning process, the output result is difficult to interpret, and the reliability and the acceptable degree of the result are influenced; more importantly, the learning time is too long, and the learning purpose can not be achieved.
The K-means clustering method has many problems in practice, for example, it is very difficult to select K clustering centers selected in the algorithm; the artificial initial division has strong subjectivity and has great influence on the accuracy of the result.
The Support Vector Machine (SVM) solves the support vector by means of quadratic programming, the solving of the quadratic programming involves the calculation of an m-order matrix (m is the number of samples), when the number of the samples is large, the storage and calculation of the matrix consume a large amount of machine memory and operation time, and in addition, the method is sensitive to missing data and is not suitable for being used in projects of mass data, and a universal solution scheme is not provided for non-linear problems.
The single decision tree of the random forest has poor prediction effect, and for data with different levels and attributes, attributes with more level divisions have great influence on the random forest, and the accuracy cannot be guaranteed.
In view of this, the embodiment of the present application provides a method and a system for rating a client credit.
Referring to fig. 1, fig. 1 is a flowchart of a method for rating a client credit according to an embodiment of the present application. As shown in fig. 1, the method includes:
s110, acquiring credit index data of a client;
in some embodiments, said obtaining credit metric data for the customer comprises:
acquiring initial credit index data of a client;
and performing Z-score standardization processing on the initial credit index data to obtain credit index data for credit rating.
The Z-score normalization process formula is as follows:
wherein x ij A j index value representing an i sample,
mean value representing j index, σ: and the standard deviation of the jth index is represented, the mean value of all processed index data is 0, and the standard deviation is 1, so that the influence of the index dimension on the model is eliminated.
In some embodiments, the credit indicator data comprises:
financial data and business data. Such as customer business capacity, profitability, repayment capacity, developmental capacity, and customer qualifications and credit status.
And S120, inputting the credit index data into a client credit rating model to generate the credit rating of the client, wherein the client credit rating model is realized based on a LightGBM model.
In some embodiments, the training method of the customer credit rating model comprises:
collecting initial credit indicator data for the rated customers;
establishing an index system of the credit rating of the customer from the aspects of the operation capacity, the profitability, the repayment capacity, the development capacity, the quality and the credit condition of the customer, and collecting the index data of the credit rating of different customers in the modes of credit investigation, customer evaluation information input and the like.
Carrying out Z-score standardization processing on the initial credit index data of the rated customers to obtain training set data;
and training the LightGBM model to be trained by utilizing the training set data.
The LightGBM adopts a histogram algorithm to carry out 'barreling' on the characteristic values, disperses continuous floating point characteristic values into k integers, effectively solves the problem of excessive split points, adopts a unilateral gradient sampling algorithm and a mutual exclusion characteristic binding algorithm, can solve the problem of excessive sample quantity and characteristic quantity, and can effectively classify clients by training a training set, thereby constructing a client credit rating model. When a new customer applies for financing, the bank can judge the credit level of the customer accurately and efficiently by using the corresponding index data of the customer, and the risk is controlled more efficiently.
According to the embodiment of the application, the LightGBM method is used for grading the credit of the client under the condition of mass credit information of the client, and the accuracy of the credit grading of the client is improved.
Referring to fig. 2, fig. 2 is a block diagram of a customer credit rating system according to an embodiment of the present disclosure. As shown in fig. 2, a bank customer credit rating system includes:
an index data obtaining module 210, configured to obtain credit index data of a client;
a rating module 220, configured to input the credit indicator data into a client credit rating model, and generate a credit rating of the client, where the client credit rating model is implemented based on a LightGBM model.
In one possible implementation, the system further includes: a training module of the customer credit rating model, the training module comprising:
a collection module for collecting initial credit indicator data of rated customers;
the training data acquisition module is used for carrying out Z-score standardization processing on the initial credit index data of the rated client to obtain training set data;
and the training submodule is used for training the LightGBM model to be trained by utilizing the training set data.
In some embodiments, the metric data acquisition module includes:
the first acquisition module is used for acquiring initial credit index data of a client;
and the second obtaining module is used for carrying out Z-score standardization processing on the initial credit index data to obtain credit index data for carrying out credit rating.
In some embodiments, the credit indicator data comprises:
financial data and business data.
In practice, the computer-readable storage medium may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction processing system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction processing system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may be processed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
As shown in fig. 3, a schematic structural diagram of a computer device according to an embodiment of the present application is provided. Fig. 3 shows computer device 12, and computer device 12 is only an example and should not bring any limitations to the function and scope of the application.
As shown in FIG. 3, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3 and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to process the functionality of embodiments of the present application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 42 generally handle the functions and/or methodologies of the embodiments described herein.
The processor unit 16 executes various functional applications and data processing, such as implementing the methods provided by the embodiments of the present application, by executing programs stored in the system memory 28.
The client credit rating method, the client credit rating system, the computer equipment and the storage medium can be used in the financial field or other fields, for example, can be used in the application scene of client credit rating in the financial field. The other fields are arbitrary fields other than the financial field, for example, the data processing field. The foregoing is merely an example and is not intended to limit the application of the present invention to a method, system, computer device and storage medium for rating customer credit.
It is noted that, as used herein, the term "include" and its variants are intended to be inclusive, i.e., "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It is noted that, although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other combinations of features described above or equivalents thereof without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A method for rating a credit of a customer, comprising:
acquiring credit index data of a client;
inputting the credit index data into a client credit rating model, and generating the credit rating of the client, wherein the client credit rating model is realized based on a LightGBM model.
2. The method of claim 1, wherein the training of the customer credit rating model comprises:
collecting initial credit indicator data for the rated customers;
carrying out Z-score standardization processing on the initial credit index data of the rated customers to obtain training set data;
and training the LightGBM model to be trained by utilizing the training set data.
3. The method of claim 1, wherein obtaining credit indicator data for the customer comprises:
acquiring initial credit index data of a client;
and performing Z-score standardization processing on the initial credit index data to obtain credit index data for performing credit rating.
4. The method of claim 1, wherein the credit indicator data comprises:
financial data and business data.
5. A customer credit rating system, comprising:
the index data acquisition module is used for acquiring credit index data of the client;
and the rating module is used for inputting the credit index data into a client credit rating model and generating the credit rating of the client, wherein the client credit rating model is realized on the basis of a LightGBM model.
6. The system of claim 5, further comprising: a training module of the customer credit rating model, the training module comprising:
a collection module for collecting initial credit indicator data of rated customers;
the training data acquisition module is used for carrying out Z-score standardization processing on the initial credit index data of the rated client to obtain training set data;
and the training submodule is used for training the LightGBM model to be trained by utilizing the training set data.
7. The system of claim 5, wherein the metric data acquisition module comprises:
the first acquisition module is used for acquiring initial credit index data of a client;
and the second obtaining module is used for carrying out Z-score standardization processing on the initial credit index data to obtain credit index data for carrying out credit rating.
8. The system of claim 5, wherein the credit metric data comprises:
financial data and business data.
9. A computer device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when processing the computer program implementing the customer credit rating method of any of claims 1-4.
10. A computer-readable storage medium having stored therein instructions which, when run on a terminal device, cause the terminal device to process the customer credit rating method of any of claims 1-4.
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| CN202211056919.3A CN115829716A (en) | 2022-08-31 | 2022-08-31 | Client credit rating method, system, computer equipment and storage medium |
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| CN202211056919.3A CN115829716A (en) | 2022-08-31 | 2022-08-31 | Client credit rating method, system, computer equipment and storage medium |
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