CN114707762A - Prediction method, device, equipment and medium of credit risk - Google Patents

Abstract

The present disclosure provides a method for predicting credit risk, which can be used in the field of big data technology. The method includes: acquiring personal information of n customers, wherein the personal information of each customer includes N information items; quantifying the personal information of the n customers to obtain a personal information matrix; for the personal information matrix, using spectral clustering The method calculates the graph Laplacian matrix corresponding to the personal information matrix; uses the local optimal block conjugate gradient method to reduce the dimension of the graph Laplacian matrix, and obtains the feature matrix corresponding to the graph Laplacian matrix; based on the feature A matrix is used to classify n customers by a clustering method; and according to the classified n customers, the credit risk of n customers is predicted. According to the prediction method in the embodiment of the present disclosure, the feature space of the graph Laplacian matrix can be quickly obtained by the locally optimal block conjugate gradient method, and the calculation speed is fast and the memory is small.

Description

Prediction method, device, equipment and medium of credit risk

technical field

The present disclosure relates to the technical field of big data, and in particular, to a method, device, device and medium for predicting credit risk.

Background technique

At present, the big data analysis of credit risk usually takes the information of each customer as a sample, uses machine learning methods to classify these customers, and obtains the credit risk level of each customer. But for banks with more than 10 million customer data, each time a new customer is added or a new piece of information is added, the machine needs to recalculate and classify all the data, which will result in a huge amount of calculation and time-consuming. and consume resources.

SUMMARY OF THE INVENTION

In view of the above problems, the present disclosure provides a method, apparatus, device, medium and program product for predicting credit risk.

According to a first aspect of the present disclosure, a method for predicting credit risk is provided, comprising the following steps:

Obtain customer authorization to obtain personal information;

Obtain the personal information of n customers under the condition of obtaining the authorization of the customer to obtain personal information, wherein the personal information of each customer includes N information items, and the N information items are all related to credit risk, and n is greater than An integer equal to 1, N is an integer greater than or equal to 2;

Quantifying the personal information of the n customers to obtain a personal information matrix, wherein the personal information matrix is a matrix of n rows and N columns, and each row of the personal information matrix represents the quantified personal information of a customer;

For the personal information matrix, a spectral clustering method is used to calculate a graph Laplacian matrix corresponding to the personal information matrix, wherein the graph Laplacian matrix is a matrix with n rows and n columns;

The local optimal block conjugate gradient method is used to reduce the dimension of the graph Laplacian matrix to obtain a feature matrix corresponding to the graph Laplacian matrix, wherein the feature matrix is a matrix with n rows and b columns , b is a positive integer and 1≤b<n;

classifying the n customers using a clustering method based on the feature matrix; and

According to the classified n customers, the credit risk of the n customers is predicted.

According to the prediction method in the embodiment of the present disclosure, by using the locally optimal block conjugate gradient method, the optimal gradient direction can be quickly searched, and the N information items of each customer can be dimensionally reduced, and then the graph Laplacian can be quickly obtained. The feature space of the matrix has fast calculation speed and small memory occupation, which greatly reduces the calculation amount and training speed of the neural network.

According to some exemplary embodiments, the dimensionality reduction of the graph Laplacian matrix by using the locally optimal block conjugate gradient method to obtain a feature matrix corresponding to the graph Laplacian matrix specifically includes:

Determine the search direction by an iterative method, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined; and

According to the determined search direction, a feature matrix corresponding to the graph Laplacian matrix is determined.

According to some exemplary embodiments, the iterative method is used to determine the search direction, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined, specifically including:

Based on the graph Laplacian matrix, an intermediate matrix is obtained, wherein the intermediate matrix is a matrix with n rows and b columns;

computing eigenvalues and eigenvectors of the intermediate matrix; and

According to the eigenvectors of the intermediate matrix, a first sub-matrix is generated, wherein the vector of each column in the first sub-matrix represents the search direction, and the search direction represented by the vector of each column in the first sub-matrix is different from the one to be The direction of the vector of each column in the determined feature matrix corresponds to the direction, and the first sub-matrix is a matrix with n rows and b columns.

According to some exemplary embodiments, the use of an iterative method to determine the search direction, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined, further specifically includes:

According to the eigenvalues and eigenvectors of the intermediate matrix and the first sub-matrix, a second sub-matrix is generated, and the second sub-matrix is a matrix with n rows and b columns;

Wherein, the vector of each column in the second sub-matrix represents the residual vector between the graph Laplacian matrix and the first sub-matrix.

According to some exemplary embodiments, the use of an iterative method to determine the search direction, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined, further specifically includes:

According to the eigenvectors of the intermediate matrix, the first sub-matrix and the second sub-matrix, generate a third sub-matrix, and the third sub-matrix is a matrix with n rows and b columns;

Wherein, the first sub-matrix, the second sub-matrix and the third sub-matrix constitute a search matrix representing a search sub-space.

According to some exemplary embodiments, the use of an iterative method to determine the search direction, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined, further specifically includes:

According to the eigenvectors of the intermediate matrix and the search matrix, the first sub-matrix is updated by an iterative method,

Wherein, in the iterative process, according to the eigenvector of the intermediate matrix in the previous iteration process and the search matrix in the previous iteration process, the first sub-matrix is updated to generate the first sub-matrix in the current iteration process. a submatrix.

According to some exemplary embodiments, the use of an iterative method to determine the search direction, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined, further specifically includes:

In the iterative process, according to the eigenvalues and eigenvectors of the intermediate matrix in the current iterative process and the first submatrix in the current iterative process, the second submatrix is updated to generate the current iterative process the second submatrix in .

According to some exemplary embodiments, the vector of each column in the third sub-matrix represents the difference between the bases of the search subspace in two adjacent iterations.

According to some exemplary embodiments, the use of an iterative method to determine the search direction, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined, further specifically includes:

In the iterative process, the third sub-matrix is updated according to the eigenvector of the intermediate matrix in the previous iterative process and the first sub-matrix and the second sub-matrix in the previous iterative process to generate the current iterative process the third submatrix in .

According to some exemplary embodiments, the determining a feature matrix corresponding to the graph Laplacian matrix according to the determined search direction specifically includes:

In the iterative process, when the updated vector of the i-th column in the second sub-matrix satisfies the first prescribed condition, the updated vector of the i-th column in the first sub-matrix is determined as the feature matrix A column of , where i is a positive integer and 1≤i<b.

According to some exemplary embodiments, the updated vector of the i-th column in the second sub-matrix satisfies the first prescribed condition including:

The norm of the vector in the i-th column of the updated second sub-matrix is smaller than a predetermined threshold.

According to some exemplary embodiments, the obtaining an intermediate matrix based on the graph Laplacian matrix specifically includes:

The intermediate matrix is generated based on the graph Laplacian matrix, the search matrix, and a transpose matrix of the search matrix.

According to some exemplary embodiments, the first sub-matrix used for the first time in the iterative process is a random matrix with n rows and b columns.

According to some exemplary embodiments, the method further comprises: updating the feature matrix when at least one information item of at least one customer among the n customers changes; and/or,

After acquiring the personal information of the n+1th customer, update the feature matrix.

According to some exemplary embodiments, in the process of updating the feature matrix, the first sub-matrix used for the first time in the iterative process is the feature matrix before updating.

A second aspect of the present disclosure provides a device for predicting credit risk, including:

The customer authorization acquisition module is used to obtain the authorization of the customer to obtain personal information;

The personal information acquisition module is used to: obtain the personal information of n customers under the condition of obtaining the authorization of the customer to obtain personal information, wherein the personal information of each customer includes N information items, and the N information items are Related to credit risk, n is an integer greater than or equal to 1, and N is an integer greater than or equal to 2;

A personal information matrix acquisition module, configured to: quantify the personal information of the n customers to obtain a personal information matrix, wherein the personal information matrix is a matrix with n rows and N columns, and each row of the personal information matrix represents Quantified personal information about a customer;

A graph Laplacian matrix calculation module, configured to: for the personal information matrix, use a spectral clustering method to calculate a graph Laplacian matrix corresponding to the personal information matrix, wherein the graph Laplacian matrix is a matrix with n rows and n columns;

A feature matrix acquisition module, used for: reducing the dimension of the graph Laplacian matrix by using the local optimal block conjugate gradient method to obtain a feature matrix corresponding to the graph Laplacian matrix, wherein the feature The matrix is a matrix with n rows and b columns, where b is a positive integer and 1≤b<n;

a classification module for: classifying the n customers by using a clustering method based on the feature matrix; and

The credit risk prediction module is used for: predicting the credit risk of the n customers according to the classified n customers.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more programs When executed by the processor, one or more processors are caused to execute the above method.

A fourth aspect of the present disclosure also provides a computer-readable storage medium having executable instructions stored thereon, the instructions, when executed by a processor, cause the processor to perform the above method.

A fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

Description of drawings

The foregoing and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically shows an application scenario diagram of a method for predicting credit risk according to an embodiment of the present disclosure;

FIG. 2 schematically shows a flowchart of a method for predicting credit risk according to an embodiment of the present disclosure;

FIG. 3 schematically shows a flowchart of reducing the dimension of the graph Laplacian matrix by using the locally optimal block conjugate gradient method according to an embodiment of the present disclosure;

FIG. 4 schematically shows a structural block diagram of an apparatus for predicting credit risk according to an embodiment of the present disclosure; and

FIG. 5 schematically shows a block diagram of an electronic device suitable for implementing a credit risk prediction method according to an embodiment of the present disclosure.

Detailed ways

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that these descriptions are exemplary only, and are not intended to limit the scope of the present disclosure. In the following detailed description, for convenience of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It will be apparent, however, that one or more embodiments may be practiced without these specific details. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the present disclosure. The terms "comprising", "comprising" and the like as used herein indicate the presence of stated features, steps, operations and/or components, but do not preclude the presence or addition of one or more other features, steps, operations or components.

All terms (including technical and scientific terms) used herein have the meaning as commonly understood by one of ordinary skill in the art, unless otherwise defined. It should be noted that terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly rigid manner.

Where expressions like "at least one of A, B, and C, etc.," are used, they should generally be interpreted in accordance with the meaning of the expression as commonly understood by those skilled in the art (eg, "has A, B, and C") At least one of the "systems" shall include, but not be limited to, systems with A alone, B alone, C alone, A and B, A and C, B and C, and/or A, B, C, etc. ).

At present, major banks usually use manual judgment or machine prediction to predict the credit risk of customers. Manual classification has a large influence of subjective factors, and it is difficult to form an objective evaluation standard. For classification and prediction by applying machine learning, it is usually the The information of each customer is used as a sample, and then algorithms such as SVM (Support Vector Machine), neural network or decision tree are used to obtain the credit risk level of each customer.

However, for banks with more than 10 million customer data, each time a new customer is added or a new information item is added, the machine needs to recalculate and classify all the data, which will generate a huge amount of calculation and consume a lot of money. Time-consuming and resource-intensive, and may not be able to respond to changing data in a timely manner.

Since human judgment is relatively subjective, the prediction method provided in this application is improved on machine learning. Among the multiple information items of the customer, there are a lot of irrelevant information or unimportant information, which interferes with the assessment result of credit risk and increases unnecessary computational burden. This application is to effectively avoid such information to quickly find useful information. information items, and then classify the credit of financial customers or enterprises. Different from the dimensionality reduction in the prior art, the present application uses the local optimal block conjugate gradient method to reduce the dimension of the graph Laplacian matrix, which can quickly approximate the feature space of the graph Laplacian matrix. The prediction method has the advantages of less memory consumption and fast calculation.

In order to facilitate the understanding of the technical solutions of the present application, the following will introduce the technical terms involved in the present application.

Spectral clustering algorithm: an unsupervised machine learning method. The spectral clustering algorithm is based on the spectral graph theory in graph theory. Compared with the class algorithm, it has the advantage of being able to cluster in any shape of the sample space and converging to the global optimal solution.

Dimensionality reduction: It is a key step in the spectral clustering algorithm. In this application, the input data can be reduced, thereby reducing the amount of calculation. For example, the amount of data is reduced from n×n to n×b, where b must be less than n.

Eigenvector and eigenvalue: The eigenvector of a matrix is one of the important concepts in matrix theory. The eigenvector of a linear transformation is a non-degenerate vector whose direction does not change under this transformation, and the vector is scaled under this transformation. The scale becomes its eigenvalue. Mathematically, if the vector v and the transformation A satisfy Av=λv, then the vector v is an eigenvector of the transformation A, and λ is the corresponding eigenvalue.

Feature space: The space where the feature vector is located, and each feature corresponds to a unique coordinate in the feature space.

Embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood, however, that these descriptions are exemplary only, and are not intended to limit the scope of the disclosure of the present application. In the following detailed description, for convenience of explanation, numerous specific details are set forth and to provide a thorough explanation of the embodiments of the present application. However, one or more embodiments may be practiced without these specific details. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessary confusion.

It should be noted that, in the technical solution of this application, the acquisition, storage and application of the customer's personal information involved are in compliance with the relevant laws and regulations, and necessary confidentiality measures have been taken, and do not violate public order and good customs.

FIG. 1 schematically shows an application scenario diagram of a method for predicting credit risk according to an embodiment of the present disclosure.

As shown in FIG. 1 , an application scenario 100 according to this embodiment may include terminal devices 101 , 102 , 103 , a network 104 , and a server 105 . The network 104 is a medium used to provide a communication link between the terminal devices 101 , 102 , 103 and the server 105 . The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like. Various communication client applications may be installed on the terminal devices 101 , 102 and 103 , such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

The server 105 may be a server that provides various services, such as a background management server (just an example) that provides support for websites browsed by users using the terminal devices 101 , 102 , and 103 . The background management server can analyze and process the received user requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

It should be noted that, the method for predicting credit risk provided by the embodiment of the present disclosure may generally be executed by the server 105 . Correspondingly, the apparatus for predicting credit risk provided by the embodiment of the present disclosure may generally be set in the server 105 . The credit risk prediction method provided by the embodiment of the present disclosure may also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101 , 102 , 103 and/or the server 105 . Correspondingly, the credit risk prediction apparatus provided by the embodiments of the present disclosure may also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101 , 102 , 103 and/or the server 105 .

It should be understood that the numbers of terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.

FIG. 2 schematically shows a flowchart of a method for predicting credit risk according to an embodiment of the present disclosure.

As shown in FIG. 2 , the prediction method of this embodiment includes operations S110 to S170.

In operation S110, the authorization of the customer to obtain personal information is obtained.

In the embodiments of the present disclosure, before acquiring the information of the customer, the consent or authorization of the customer needs to be obtained. For example, prior to operation S120, the client may be notified, and a request to obtain his/her personal information and other associated information may be issued to the client. In the case that the customer agrees or authorizes that the personal information can be obtained, operation S120 is performed.

In operation S120, the personal information of n customers is obtained under the condition of obtaining the authorization from the customer to obtain the personal information, wherein the personal information of each customer includes N information items, and the N information items are related to credit risk, and n is an integer greater than or equal to 1, and N is an integer greater than or equal to 2.

Before making credit risk prediction for a customer, it is necessary to obtain personal information related to the customer, which is commonly known as customer data. From the customer information, we can usually learn the customer's customer management ability, profitability, debt repayment ability, development ability, customer quality and credit status, etc., and judge the customer's credit according to these relevant information.

The channels for obtaining customers' personal information can be diverse, for example, in a specific example, the following fields can be included: "A company, established in 2015, turnover, earnings per share, net profit growth rate, contract performance, ... ". From the above-mentioned data and information, we can learn the relevant information about the property and operation of the expropriation agency. In some instances, the above-mentioned data and information can be further expanded. For example, the information of the institution may include: supplementary information on enterprise registration, information on changes in enterprise registration, shareholder registration information, information on unit participation in insurance, enterprise legal person, enterprise financial data, Enterprise tax information, enterprise tax registration information, enterprise provident fund payment information, etc.; other information such as: institution name, enterprise operating income, enterprise basic information, etc.

After obtaining the personal information of the customer, the personal information is organized into N information items, that is, each customer corresponds to N information items. However, among the N information items, there may be some information items that are related. For example, if the company or individual has high income, the tax payment will also increase accordingly. The two information items of income information and tax amount must exist in direct proportion. Therefore, Related information items can be combined to achieve the purpose of dimensionality reduction. This application involves dimension reduction, so in this operation, the acquired information item N is an integer greater than or equal to 2.

It can be understood that there is a negative ratio between multiple information items, and it can also be merged and reduced in dimension.

In operation S130, personal information of n customers is quantified to obtain a personal information matrix, wherein the personal information matrix is a matrix of n rows and N columns, and each row of the personal information matrix represents the quantified personal information of one customer.

Before dimensionality reduction, mathematical information processing is performed on the personal information of n customers.

The specific processing method can be adjusted according to the specific content in the information item. For example, the profitability (net profit, gross profit rate, etc.) is represented by simple numbers. Expressed as a percentage of the total number of credits.

The quantized customer data can be represented by the element X _ij , where i represents the ith customer, and j represents the jth information item of the ith customer. Therefore both i and j can be represented as nodes in an undirected graph.

In operation S140, for the personal information matrix, a spectral clustering method is used to calculate a graph Laplacian matrix corresponding to the personal information matrix, wherein the graph Laplacian matrix is a matrix with n rows and n columns.

When calculating the graph Laplacian, it is necessary to use the quantized personal information to generate the adjacency matrix W first. The adjacency matrix is the matrix representation of the graph. With the help of it, the structure of the graph can be conveniently stored, and the problem of the graph can be studied by the method of linear algebra. In this application, it is the solution for n customers, so the adjacency matrix W is a matrix of n*n, that is, the adjacency matrix W is a matrix of n*n dimensions, wherein, the matrix elements are the X _ij elements obtained in operation S130, Expressed as the weight of the edge (i, j), if there is no edge connection between two nodes, the corresponding element in the adjacency matrix is 0. Specifically, through the k-proximity method, the KNN algorithm is used to traverse all the sample points, and only the k points closest to each sample are reserved as the nearest neighbors, that is, only the k points closest to the sample are W _ij > 0, and the rest are set to Set to 0.

Calculated as follows:

In the above formula, X _i is the data of the i-th row, that is, the personal information of the i-th customer, and X _j is the data of the j-th row, that is, the personal information of the j-th customer.

It can be understood that formula (1) represents that as long as X _i is in the K adjacent set of X _j , W _ij is retained; formula (2) represents that the two points of X _i and X _j must be in each other's k adjacent set of each other, in order to preserve Wi _ij .

For the calculation of _Wij , Euclidean distance can be used to measure the distance between any two points (X _i and X _j ). In the process of preset function, polynomial kernel function, Gaussian kernel function and Sigmoid kernel function are commonly used.

As a specific embodiment of the present application, the most commonly used Gaussian kernel function is used for calculation.

After the adjacency matrix W is obtained by the above method, the diagonal matrix D, that is, the degree matrix, is further calculated.

Since this application belongs to an undirected graph, for an undirected graph, the weighted degree of a node is the sum of the weights of all edges related to the node. The weighted degree of the node i of the adjacency matrix W of the undirected graph is the sum of the elements in the ith row of the adjacency matrix.

The corresponding diagonal matrix D is calculated according to the adjacent matrix W, and the formula is:

Finally, the diagonal matrix D is obtained as:

According to n nodes (n clients) in an undirected graph, the adjacency matrix is W and the diagonal matrix is D, the graph Laplacian matrix can be defined on the basis of the adjacency matrix W and the diagonal matrix D, that is, the graph The Laplacian matrix A is defined as the difference between the diagonal matrix D and the adjacency matrix W:

A=D-W

In operation S150, the local optimal block conjugate gradient method is used to reduce the dimension of the graph Laplacian matrix to obtain a feature matrix corresponding to the graph Laplacian matrix, wherein the feature matrix is a matrix with n rows and b columns, b is a positive integer and 1≤b<n.

The obtained graph Laplacian matrix A is an n*n-dimensional matrix. In this operation, the local optimal block conjugate gradient method is used to reduce the dimension of the graph Laplacian matrix, that is, from n*n dimensions The graph Laplacian matrix of , becomes the feature matrix of n*b dimension.

The locally optimal block conjugate gradient method can explore the optimal gradient direction, so that the feature space of graph Laplacian A can be quickly approximated during machine training and use.

In operation S160, based on the feature matrix, a clustering method is adopted to classify the n customers.

For the n*b-dimensional feature matrix Q, use the data of each row as a sample, and use K-means to classify customers. It needs to be clear that the feature matrix Q is a matrix with n rows and b columns. This operation is to use the dimension-reduced personal information b corresponding to each customer in customer n as a sample for classification.

In the embodiment of the present disclosure, after using the above-mentioned dimensionality reduction method to obtain an n*b-dimensional feature matrix, the data of each row is used as a sample to classify customers. The embodiment of the present disclosure is not limited to the above K -means clustering method, you can also use training neural network or decision tree (including classical decision tree method and derivative methods such as random forest) to classify all customers.

It should be noted that in practical applications, according to experience, the number of the final b column should be the same as the number of categories after classifying customers, otherwise the error will become larger. That is, if customers are to be divided into 7 categories, there should be n*7 data in the feature matrix Q, that is, to find the 7 feature vectors of the graph Laplacian A, the feature matrix Q after dimension reduction is n rows of 7 A matrix of columns.

In operation S170, the credit risks of n customers are predicted according to the classified n customers.

Each category is set to a different credit level, that is, n customers can be divided into b categories, corresponding to b credit risks.

In a specific embodiment, after the customer classification is completed in operation S160, the contract performance status of all customers is retrieved, and the contract performance status of the customers in each group is added in the classified group, and then sorted according to the obtained value. . The highest score, that is, the best performance in a group, can be set to the first level, the second highest is set to the second level, and so on.

According to the prediction method in the embodiment of the present disclosure, by using the locally optimal block conjugate gradient method, the optimal gradient direction can be quickly searched, and the N information items of each customer can be dimensionally reduced, and then the graph Laplacian can be quickly obtained. The feature space of the matrix has fast calculation speed and small memory occupation, which greatly reduces the calculation amount and training speed of the neural network.

FIG. 3 schematically shows a flowchart of reducing the dimension of the graph Laplacian matrix by using the locally optimal block conjugate gradient method according to an embodiment of the present disclosure.

As shown in FIG. 3 , the dimensionality reduction process in this embodiment includes operations S210 to S220.

In operation S210, an iterative method is used to determine the search direction, so that the search direction is gradually consistent with the direction of the vector of each column in the feature matrix to be determined.

For operation S210, first, an intermediate matrix is obtained based on the graph Laplacian matrix, where the intermediate matrix is a matrix with n rows and b columns.

It can be understood that the dimension of Laplacian matrix A changes from n*n to the dimension of n*b in the intermediate matrix B, that is, dimensionality reduction is realized.

In the machine learning process in this step, the problem of solving partial differential equations will be involved. In practical applications, the finite element method can simplify the solution process and obtain approximate solutions of calculus equations.

Then, the eigenvalues and eigenvectors of the intermediate matrix are calculated.

Finally, a first sub-matrix is generated according to the eigenvectors of the intermediate matrix, wherein the vector of each column in the first sub-matrix represents the search direction, and the search direction represented by the vector of each column in the first sub-matrix is different from the eigenmatrix to be determined. The direction of the vector in each column corresponds to, and the first sub-matrix is a matrix with n rows and b columns.

The algorithm used in the present application for the above solution process is the Rayleigh-Ritz method, which is a process of directly triggering from a functional function to find a function that can minimize it.

Exemplarily, the application of the Rayleigh-Ritz algorithm may be the following process:

Input: A∈R ^n*n , matrix;

S∈R ^n*b , matrix, (0≤b<n);

Output: θ: diagonal matrix of order b

Y: matrix of order b

RR:

Calculation matrix B=S ^T AS

Find all eigenvectors y ₁ , y ₂ , ..., y _b of B and all eigenvalues θ ₁ , θ ₂ , ..., θ _b

Let Y = [y ₁ y ₂ ... y _b ],

Among them, the matrix B is the intermediate matrix, the input matrix A is the graph Laplacian matrix, S is the search matrix, the output matrix θ is the eigenvalue matrix, the output Y is the eigenvector, and RR is the abbreviation of Rayleigh-Ritz. Represents the Rayleigh-Ritz algorithm.

The above algorithm can be interpreted as calculating an orthogonal base in S ∈ ^{R n*b} , approximating the feature space corresponding to b feature vectors (b is the desired number of categories, which is explained in operations S160 and S170), Calculate the intermediate matrix B, and solve the eigenvector Y and eigenvalue θ of the intermediate matrix B.

When calculating the intermediate matrix B, based on the graph Laplacian matrix, the search matrix and the transpose matrix of the search matrix, the intermediate matrix is generated, ie, B=S ^T AS.

The following will use the eigenvalue θ and eigenvector Y obtained by the Rayleigh-Ritz algorithm to reduce the dimension of the graph Laplacian matrix.

According to the eigenvalues and eigenvectors of the intermediate matrix and the first sub-matrix, a second sub-matrix is generated, and the second sub-matrix is a matrix with n rows and b columns; wherein, the vector of each column in the second sub-matrix represents the graph Laplacian Residual vector between the matrix and the first submatrix.

The second sub-matrix R ₀ is solved according to the intermediate matrix B, the eigenvector Y and the eigenvalue θ, and the residual vector characterizes the desired accuracy.

During this process, Rayleigh-Ritz can be used again, using

RR(Y, θ)=RR(A, X ₀ )

Among them, X ₀ is a random matrix, and X ₀ is a matrix with n rows and b columns, that is, X ₀ ∈R ^n*b , and then by

R ₀ =AX ₀ -X ₀ θ ₀

The second sub-matrix R ₀ is obtained, and the second sub-matrix R ₀ can be understood as the search direction of the subspace residual.

It should be noted that the first sub-matrix used for the first time in the iterative process is a random matrix with n rows and b columns, that is, the first sub-matrix is X ₀ .

According to the eigenvectors of the intermediate matrix, the first sub-matrix and the second sub-matrix, a third sub-matrix is generated, and the third sub-matrix is a matrix with n rows and b columns; wherein, the first sub-matrix, the second sub-matrix and the third sub-matrix The matrix constitutes a search matrix representing the search subspace.

The third sub-matrix P ₀ is solved according to the eigenvector Y of the intermediate matrix, the first sub-matrix X ₀ and the second sub-matrix R ₀ , and finally the first sub-matrix X ₀ , the second sub-matrix R ₀ and the third sub-matrix P ₀ A search matrix S ₀ representing the search subspace is formed, that is, it can be expressed as

S ₀ =[X ₀ , R ₀ , P ₀ ]

According to the eigenvectors of the intermediate matrix and the search matrix, the first sub-matrix is updated by an iterative method, wherein, in the iterative process, according to the eigenvectors of the intermediate matrix in the previous iteration process and the search matrix in the previous iteration process, the first sub-matrix is updated. a submatrix to generate the first submatrix in the current iteration.

In the iterative process, according to the eigenvalues and eigenvectors of the intermediate matrix in the current iteration process and the first submatrix in the current iteration process, update the second submatrix to generate the second submatrix in the current iteration process .

In the iteration process, the third sub-matrix is updated according to the eigenvectors of the intermediate matrix in the previous iteration process and the first sub-matrix and the second sub-matrix in the previous iteration process to generate the third sub-matrix in the current iteration process submatrix.

In the computer field, k can be used to represent the number of iterations, usually indicated in the lower corner. In the iterative process, after each iteration process, the eigenvector of the intermediate matrix, the first sub-matrix, the second sub-matrix value and the third sub-matrix are +1 on the basis of the original subscript, based on the eigenvector of the intermediate matrix, the first sub-matrix The relationship between a sub-matrix, the second sub-matrix value and the third sub-matrix, the iterative process can be expressed as:

X _k+1 =S _k Y _k

P _k+1 =[0, R _k , P _k ]Y _k

R _k+1 =AX _k+1 -X _k+1 θ _k+1

k=k+1

Further, wherein, the vector of each column in the third sub-matrix represents the difference between the bases of the search subspace in two adjacent iterative processes.

In operation S220, a feature matrix corresponding to the graph Laplacian matrix is determined according to the determined search direction.

For operation S220, in the iterative process, when the vector of the i-th column in the updated second sub-matrix satisfies the first prescribed condition, the vector of the i-th column in the updated first sub-matrix is determined as a column of the feature matrix , where i is a positive integer and 1≤i<b.

Further, that the vector of the i-th column in the updated second sub-matrix satisfies the first predetermined condition includes: the norm of the vector of the i-th column in the updated second sub-matrix is smaller than a predetermined threshold.

For example, in an embodiment of the present disclosure, the feature matrix Q may be determined according to the following iterative process.

(1) Generate a random matrix X ₀ ∈R ^n*b , (0≤b<n);

(2) Calculate the eigenvectors and eigenvalues using the Rayleigh-Ritz algorithm:

(3) Give the initial value of iteration: R ₀ :=AX ₀ -X ₀ θ ₀ , k:=0, Q:=[], P ₀ :=[];

(4) When the number of columns of the feature matrix Q is less than b, perform the following iterative process:

Let Q be normalized to R _k norm;

Let S _k :=[X _k , R _k , P _k ],

X _k+1 :=S _k Y _k ;

P _k+1 :=[0, R _k , P _k ]Y _k ;

R _k+1 :=AX _k+1 -X _k+1 θk ₊₁ ;

k:=k+1;

If the norm of some columns of matrix R _k+1 is less than the specified threshold ε, put the corresponding columns in X _k+1 into the feature matrix Q; set the corresponding columns in X _k+1 as random vectors, then: X ₀ :=X _k+1 , k:=0, and the above iterative process is performed until the number of columns of the characteristic matrix Q is equal to b.

According to an embodiment of the present application, the prediction method further includes: when at least one information item of at least one customer among the n customers changes, updating the feature matrix; and/or, after acquiring the personal information of the n+1th customer Then, update the feature matrix.

When a new information item is added, the field value corresponding to one of the customer's information items is changed, and a new customer is added, the graph Laplacian matrix must be updated, and the feature matrix will also be updated.

In one embodiment, in the process of updating the feature matrix, the first sub-matrix used for the first time in the iterative process is the feature matrix before updating.

In the prior art, when a new information item is added, a field value corresponding to one of the customer's information items is changed, and a new customer is added, all data needs to be recalculated.

Considering that even if a new information item or a new customer is added, the various eigenvalues do not change much, and the newly calculated feature space must be close to the original feature space, so a new feature space can be obtained quickly. Through the above concept, the prediction method of the present application is calculated on the original feature matrix. When the customer's information changes, a new graph Laplacian can be established on the feature space of the existing graph Laplacian matrix. The eigenspace of the Si matrix, that is, X ₀ =0 in operation S150 is directly replaced by letting X ₀ =Q.

In a single trial, the second calculation can be hundreds to thousands of times faster than the first. It can be concluded that the prediction method of the present application can reduce the amount of machine computation, thereby saving resources and reducing computation time.

Based on the above-mentioned method for predicting credit risk, the present disclosure also provides a device for predicting credit risk. The device will be described in detail below with reference to FIG. 4 . FIG. 4 schematically shows a structural block diagram of a prediction apparatus according to an embodiment of the present disclosure.

As shown in FIG. 4, the prediction apparatus 800 of this embodiment includes a customer authorization acquisition module 810, a personal information acquisition module 820, a personal information matrix acquisition module 830, a graph Laplace matrix calculation module 840, a feature matrix acquisition module 850, a classification module 860 and credit risk prediction module 870.

The client authorization acquisition module 810 is used to acquire the authorization of the client to acquire personal information. In one embodiment, the client authorization acquisition module 810 may be configured to perform the operation S110 described above, which will not be repeated here.

The personal information acquisition module 820 is used to: obtain the personal information of n customers under the condition of obtaining the authorization of the customer to obtain personal information, wherein the personal information of each customer includes N information items, and the N information items are related to the credit. Risk correlation, n is an integer greater than or equal to 1, and N is an integer greater than or equal to 2. In one embodiment, the personal information obtaining module 820 may be configured to perform the operation S120 described above, which will not be repeated here.

The personal information matrix obtaining module 830 is configured to: quantify the personal information of n customers to obtain a personal information matrix, wherein the personal information matrix is a matrix with n rows and N columns, and each row of the personal information matrix represents the quantified data of one customer. Personal information. In one embodiment, the personal information matrix acquisition module may be configured to perform the operation S130 described above, which will not be repeated here.

The graph Laplacian matrix calculation module 840 is configured to: for the personal information matrix, use the spectral clustering method to calculate the graph Laplacian matrix corresponding to the personal information matrix, wherein the graph Laplacian matrix has n rows and n columns matrix. In one embodiment, the graph Laplacian matrix calculation module may be used to perform the operation S140 described above, which will not be repeated here.

The feature matrix obtaining module 850 is used for: using the local optimal block conjugate gradient method to reduce the dimension of the graph Laplacian matrix to obtain a feature matrix corresponding to the graph Laplacian matrix, wherein the feature matrix is n rows and b columns , where b is a positive integer and 1≤b<n. In one embodiment, the feature matrix obtaining module may be configured to perform the operation S150 described above, which will not be repeated here.

The classification module 860 is configured to: classify the n customers by adopting a clustering method based on the feature matrix. In one embodiment, the classification module may be configured to perform the operation S160 described above, which will not be repeated here.

The credit risk prediction module 870 is used for: predicting the credit risk of n customers according to the classified n customers. In one embodiment, the credit risk prediction module 830 may be configured to perform the operation S170 described above, which will not be repeated here.

According to the prediction device in the embodiment of the present disclosure, the above-mentioned prediction method can be implemented, and the optimal gradient direction can be quickly searched by using the local optimal block conjugate gradient method, and the dimension of each customer's N information items can be reduced, and then The feature space of the graph Laplacian matrix is quickly obtained, the calculation speed is fast, and the memory is small, which greatly reduces the calculation amount and training speed of the neural network.

According to the embodiment of the present disclosure, the customer authorization acquisition module 810, the personal information acquisition module 820, the personal information matrix acquisition module 830, the graph Laplace matrix calculation module 840, the feature matrix acquisition module 850, the classification module 860 and the credit risk prediction module Any number of modules in 870 may be combined into one module for implementation, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to the embodiment of the present disclosure, the customer authorization acquisition module 810, the personal information acquisition module 820, the personal information matrix acquisition module 830, the graph Laplace matrix calculation module 840, the feature matrix acquisition module 850, the classification module 860 and the credit risk prediction module At least one of 870 may be implemented, at least in part, as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit ( ASIC), or can be realized by hardware or firmware such as any other reasonable way of integrating or encapsulating the circuit, or in any one of the three implementation modes of software, hardware and firmware or in any appropriate combination of any of them to fulfill. Or, at least one of the customer authorization acquisition module 810 , the personal information acquisition module 820 , the personal information matrix acquisition module 830 , the graph Laplace matrix calculation module 840 , the feature matrix acquisition module 850 , the classification module 860 and the credit risk prediction module 870 Can be implemented at least in part as computer program modules which, when executed, can perform corresponding functions.

FIG. 5 schematically shows a block diagram of an electronic device suitable for implementing a credit risk prediction method according to an embodiment of the present disclosure.

As shown in FIG. 5 , an electronic device 900 according to an embodiment of the present disclosure includes a processor 901 that can be loaded into a random access memory (RAM) 903 according to a program stored in a read only memory (ROM) 902 or from a storage portion 908 program to perform various appropriate actions and processes. The processor 901 may include, for example, a general-purpose microprocessor (eg, a CPU), an instruction set processor and/or a related chipset, and/or a special-purpose microprocessor (eg, an application-specific integrated circuit (ASIC)), and the like. The processor 901 may also include on-board memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiments of the present disclosure.

In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are stored. The processor 901 , the ROM 902 and the RAM 903 are connected to each other through a bus 904 . The processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and/or the RAM 903 . Note that the program may also be stored in one or more memories other than the ROM 902 and the RAM 903 . The processor 901 may also perform various operations of the method flow according to the embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the electronic device 900 may also include an input/output (I/O) interface 905 which is also connected to the bus 904 . Electronic device 900 may also include one or more of the following components connected to I/O interface 905: input portion 906 including keyboard, mouse, etc.; including components such as cathode ray tube (CRT), liquid crystal display (LCD), etc., and An output section 907 of speakers and the like; a storage section 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, and the like. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 910 as needed so that a computer program read therefrom is installed into the storage section 908 as needed.

The present disclosure also provides a computer-readable storage medium. The computer-readable storage medium may be included in the device/apparatus/system described in the above embodiments; it may also exist alone without being assembled into the device/system. device/system. The above-mentioned computer-readable storage medium carries one or more programs, and when the above-mentioned one or more programs are executed, implement the method according to the embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM) , erasable programmable read only memory (EPROM or flash memory), portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include one or more memories other than ROM 902 and/or RAM 903 and/or ROM 902 and RAM 903 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flowchart. When the computer program product runs in the computer system, the program code is used to make the computer system implement the credit risk prediction method provided by the embodiments of the present disclosure.

When the computer program is executed by the processor 901, the above-described functions defined in the system/apparatus of the embodiment of the present disclosure are performed. According to embodiments of the present disclosure, the systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules.

In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal over a network medium, and downloaded and installed through the communication section 909, and/or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909, and/or installed from the removable medium 911. When the computer program is executed by the processor 901, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to embodiments of the present disclosure, the above-described systems, apparatuses, apparatuses, modules, units, etc. can be implemented by computer program modules.

According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure may be written in any combination of one or more programming languages, and specifically, high-level procedures and/or object-oriented programming may be used. programming language, and/or assembly/machine language to implement these computational programs. Programming languages include, but are not limited to, languages such as Java, C++, python, "C" or similar programming languages. The program code may execute entirely on the client computing device, partly on the client device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the client computing device through any kind of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, can be implemented in special purpose hardware-based systems that perform the specified functions or operations, or can be implemented using A combination of dedicated hardware and computer instructions is implemented.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in various embodiments and/or claims of the present disclosure are possible, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments of the present disclosure and/or in the claims may be made without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of this disclosure.

Embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only, and are not intended to limit the scope of the present disclosure. Although the various embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should all fall within the scope of the present disclosure.

Claims (19)

1. A method for predicting credit risk, comprising the steps of:

obtaining the authorization of a client to obtain personal information;

under the condition that the authorization of the client for obtaining the personal information is obtained, obtaining the personal information of N clients, wherein the personal information of each client comprises N information items, the N information items are all related to credit risk, N is an integer greater than or equal to 1, and N is an integer greater than or equal to 2;

quantizing the personal information of the N clients to obtain a personal information matrix, wherein the personal information matrix is a matrix with N rows and N columns, and each row of the personal information matrix represents quantized personal information of one client;

aiming at the personal information matrix, calculating a graph Laplacian matrix corresponding to the personal information matrix by using a spectral clustering method, wherein the graph Laplacian matrix is a matrix with n rows and n columns;

reducing the dimension of the graph Laplace matrix by adopting a local optimal block conjugate gradient method to obtain a characteristic matrix corresponding to the graph Laplace matrix, wherein the characteristic matrix is a matrix with n rows and b columns, b is a positive integer and b is more than or equal to 1 and less than n;

classifying the n customers by adopting a clustering method based on the characteristic matrix; and

and predicting credit risks of the n clients according to the classified n clients.

2. The method according to claim 1, wherein the dimensionality reduction of the graph laplacian matrix by using a locally optimal block conjugate gradient method to obtain an eigen matrix corresponding to the graph laplacian matrix specifically includes:

determining a search direction by adopting an iteration method, wherein the search direction is gradually consistent with the direction of a vector of each column in a characteristic matrix to be determined; and

and determining a characteristic matrix corresponding to the graph Laplacian matrix according to the determined search direction.

3. The method according to claim 2, wherein the determining the search direction using an iterative method such that the search direction gradually coincides with a direction of a vector of each column in the feature matrix to be determined comprises:

obtaining an intermediate matrix based on the graph Laplace matrix, wherein the intermediate matrix is a matrix with n rows and b columns;

calculating eigenvalue and eigenvector of the intermediate matrix; and

and generating a first sub-matrix according to the feature vector of the intermediate matrix, wherein the vector of each column in the first sub-matrix represents a search direction, the search direction represented by the vector of each column in the first sub-matrix corresponds to the direction of the vector of each column in the feature matrix to be determined, and the first sub-matrix is a matrix with n rows and b columns.

4. The method according to claim 3, wherein the determining the search direction by using the iterative method such that the search direction gradually coincides with a direction of a vector of each column in the feature matrix to be determined, further comprises:

generating a second sub-matrix according to the eigenvalue and the eigenvector of the intermediate matrix and the first sub-matrix, wherein the second sub-matrix is a matrix with n rows and b columns;

wherein the vector of each column in the second sub-matrix represents a residual vector between the graph laplacian matrix and the first sub-matrix.

5. The method according to claim 4, wherein the determining the search direction by using the iterative method such that the search direction gradually coincides with a direction of a vector of each column in the feature matrix to be determined, further comprises:

generating a third sub-matrix according to the feature vector of the intermediate matrix, the first sub-matrix and the second sub-matrix, wherein the third sub-matrix is a matrix with n rows and b columns;

wherein the first, second, and third sub-matrices constitute a search matrix representing a search subspace.

6. The method according to any one of claims 2 to 5, wherein the determining the search direction using an iterative method such that the search direction gradually coincides with a direction of a vector of each column in the feature matrix to be determined, further comprises:

updating the first sub-matrix using an iterative method based on the feature vectors of the intermediate matrix and the search matrix,

and in the iteration process, updating the first sub-matrix according to the feature vector of the intermediate matrix in the previous iteration process and the search matrix in the previous iteration process to generate the first sub-matrix in the current iteration process.

7. The method according to claim 6, wherein the determining the search direction by using the iterative method such that the search direction gradually coincides with a direction of a vector of each column in the feature matrix to be determined, further comprises:

in the iteration process, updating the second sub-matrix according to the eigenvalue and the eigenvector of the intermediate matrix in the current iteration process and the first sub-matrix in the current iteration process to generate the second sub-matrix in the current iteration process.

8. The method of claim 7, wherein the vector of each column in the third sub-matrix represents a difference between bases of the search subspace in two adjacent iterations.

9. The method according to claim 8, wherein the determining the search direction by using the iterative method such that the search direction gradually coincides with a direction of a vector of each column in the feature matrix to be determined, further comprises:

in the iteration process, updating the third sub-matrix according to the feature vector of the intermediate matrix in the previous iteration process and the first sub-matrix and the second sub-matrix in the previous iteration process so as to generate the third sub-matrix in the current iteration process.

10. The method according to claim 9, wherein the determining a feature matrix corresponding to the graph laplacian matrix according to the determined search direction specifically includes:

in the iteration process, when the vector of the ith column in the updated second sub-matrix meets a first specified condition, determining the vector of the ith column in the updated first sub-matrix as one column of the feature matrix, wherein i is a positive integer and is more than or equal to 1 and less than b.

11. The method of claim 10, wherein the updated vector of the ith column in the second sub-matrix satisfying a first specified condition comprises:

and the norm of the vector of the ith column in the updated second sub-matrix is smaller than a specified threshold value.

12. The method according to claim 3, wherein the obtaining an intermediate matrix based on the graph laplacian matrix specifically includes:

generating the intermediate matrix based on the graph laplacian matrix, the search matrix, and a transpose of the search matrix.

13. The method according to claim 6, wherein the first sub-matrix used for the first time in the iterative process is a random matrix with n rows and b columns.

14. The method of claim 13, further comprising: updating the feature matrix when at least one information item of at least one of the n customers changes; and/or the presence of a gas in the gas,

and after the personal information of the (n + 1) th customer is acquired, updating the feature matrix.

15. The method of claim 14, wherein in the updating of the feature matrix, the first sub-matrix used for the first time in the iterative process is the feature matrix before updating.

16. An apparatus for predicting a credit risk, comprising:

the client authorization acquisition module is used for acquiring the authorization of the client for acquiring the personal information;

a personal information acquisition module to: under the condition that the authorization of the client for obtaining the personal information is obtained, obtaining the personal information of N clients, wherein the personal information of each client comprises N information items, the N information items are all related to credit risk, N is an integer greater than or equal to 1, and N is an integer greater than or equal to 2;

a personal information matrix acquisition module to: quantizing the personal information of the N customers to obtain a personal information matrix, wherein the personal information matrix is a matrix with N rows and N columns, and each row of the personal information matrix represents the quantized personal information of one customer;

a graph laplacian matrix calculation module to: aiming at the personal information matrix, calculating a graph Laplacian matrix corresponding to the personal information matrix by using a spectral clustering method, wherein the graph Laplacian matrix is a matrix with n rows and n columns;

a feature matrix acquisition module to: reducing the dimension of the graph Laplace matrix by adopting a local optimal block conjugate gradient method to obtain a characteristic matrix corresponding to the graph Laplace matrix, wherein the characteristic matrix is a matrix with n rows and b columns, b is a positive integer and b is more than or equal to 1 and less than n;

a classification module to: classifying the n customers by adopting a clustering method based on the characteristic matrix; and

a credit risk prediction module to: and predicting credit risks of the n clients according to the classified n clients.

17. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-15.

18. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 15.

19. A computer program product comprising a computer program which, when executed by a processor, carries out the method according to any one of claims 1 to 15.

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