Enterprise financing risk assessment method, device, equipment, medium and program product

Abstract

The invention provides an enterprise financing risk assessment method which can be applied to the technical field of artificial intelligence. The enterprise financing risk assessment method comprises the following steps: acquiring basic information of a target enterprise in response to an enterprise financing risk assessment instruction; generating a fund flow graph from the basic information of the target enterprise based on a knowledge graph technology, wherein the fund flow graph is used for representing fund flow information of an enterprise supply chain; calculating evaluation index data based on the fund flow graph; and inputting the assessment index data into a risk assessment model to output a risk assessment result, the risk assessment model being constructed based on an approximate ideal solution sorting method. The invention further provides an enterprise financing risk assessment device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the field of knowledge graph technology, and more specifically to a method, device, equipment, medium and program product for assessing corporate financing risks. Background Art

[0002] Since the basic information of bank financing customers is complex, involving asset status, financing status over the years, cash inflow status, external guarantee status, etc., it is difficult to rely on manual judgment to assess risks without data processing and integration. In addition, there are many core indicator dimensions for bank financing customer risk assessment, and the indicator weight distribution is unclear, making it difficult to form risk assessment operation standards. Especially when the number of bank financing customers is large, there is a lack of clear comparison and ranking methods for the quality of risk assessment results of target customers in the overall customer base.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, device, medium and program product for assessing corporate financing risks.

[0005] According to a first aspect of the present disclosure, there is provided a method for assessing corporate financing risk, comprising:

[0006] In response to the enterprise financing risk assessment instruction, collect the basic information of the target enterprise;

[0007] Generate a capital flow diagram based on the basic information of the target enterprise based on the knowledge graph technology, and the capital flow diagram is used to represent the capital flow information of the enterprise supply chain;

[0008] Calculating evaluation index data based on the fund flow diagram; and

[0009] The evaluation index data is input into a risk evaluation model to output a risk evaluation result, wherein the risk evaluation model is constructed based on an approximate ideal solution sorting method.

[0010] According to an embodiment of the present disclosure, generating a capital flow diagram from the basic information of the target enterprise based on the knowledge graph technology includes:

[0011] Identify entity information from the target enterprise basic information;

[0012] Identify relationships between entity information;

[0013] Generating triple data based on the recognized entity and relationship combinations; and

[0014] A knowledge graph is constructed based on the triple data.

[0015] According to an embodiment of the present disclosure, the entity information includes account information and media access control address information, and the relationship between the identification entity information includes:

[0016] Identify the flow of funds between accounts; and

[0017] Identify the relationship between the account and the media access control address information.

[0018] According to an embodiment of the present disclosure, the calculation of evaluation index data based on the capital flow diagram includes:

[0019] Determine the account fund interaction activity and transaction characteristics based on the fund flow diagram;

[0020] Determining the evaluation index weight of the account according to the account fund interaction activity and the transaction characteristics; and

[0021] The risk assessment index of the target enterprise is calculated according to the assessment index weights.

[0022] According to an embodiment of the present disclosure, inputting the evaluation indicator data into a risk assessment model to output a risk assessment result includes:

[0023] Construct a normalized initial matrix based on the evaluation indicator data;

[0024] Determining the optimal target and the worst target according to the normalized initial matrix;

[0025] Calculate the degree of correlation between the target enterprise and the best target and the worst target respectively;

[0026] Calculate how close the target enterprise is to the optimal target; and

[0027] The financing customer risk assessment results are ranked according to the degree of closeness.

[0028] According to an embodiment of the present disclosure, the optimal target is composed of the maximum value of each column element of the normalized initial matrix, and the worst target is composed of the minimum value of each column element of the normalized initial matrix.

[0029] A second aspect of the present disclosure provides a corporate financing risk assessment device, comprising:

[0030] A data collection module, used to respond to the enterprise financing risk assessment instruction and collect basic information of the target enterprise;

[0031] A knowledge graph generation module, used to generate a capital flow graph from the basic information of the target enterprise based on the knowledge graph technology, wherein the capital flow graph is used to characterize the capital flow information of the enterprise supply chain;

[0032] A calculation module, used to calculate evaluation index data based on the capital flow diagram; and

[0033] The risk assessment module is used to input the assessment index data into a risk assessment model to output a risk assessment result, wherein the risk assessment model is constructed based on an approximate ideal solution sorting method.

[0034] According to an embodiment of the present disclosure, the knowledge graph generation module includes: a first identification submodule, a second identification submodule, a generation submodule and a construction submodule.

[0035] A first identification submodule, used to identify entity information from the basic information of the target enterprise;

[0036] The second identification submodule is used to identify the relationship between entity information;

[0037] A generation submodule, for generating triple data according to the recognized entity and relationship combination; and

[0038] A construction submodule is used to construct a knowledge graph based on the triple data.

[0039] According to an embodiment of the present disclosure, the second identification submodule includes a first identification unit and a second identification unit.

[0040] A first identification unit, used to identify the fund flow relationship between accounts; and

[0041] The second identification unit is used to identify the relationship between the account and the media access control address information.

[0042] According to an embodiment of the present disclosure, the calculation module includes: a first determination submodule, a second determination submodule and a first calculation submodule.

[0043] A first determination submodule is used to determine the account fund interaction activity and transaction characteristics according to the fund flow diagram;

[0044] A second determination submodule is used to determine the evaluation index weight of the account according to the account fund interaction activity and the transaction characteristics; and

[0045] The first calculation submodule is used to calculate the risk assessment index of the target enterprise according to the assessment index weight.

[0046] According to an embodiment of the present disclosure, the risk assessment module includes: a matrix construction submodule, a third determination submodule, a second calculation submodule, a third calculation submodule and a sorting submodule.

[0047] The matrix construction submodule is used to construct a normalized initial matrix based on the evaluation index data;

[0048] A third determination submodule is used to determine the optimal target and the worst target according to the normalized initial matrix;

[0049] The second calculation submodule is used to calculate the correlation degree between the target enterprise and the optimal target and the worst target respectively;

[0050] The third calculation submodule is used to calculate the closeness between the target enterprise and the optimal target; and

[0051] The sorting submodule is used to sort the financing customer risk assessment results according to the degree of closeness.

[0052] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0053] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0054] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0055] The enterprise financing risk assessment method provided by the disclosed embodiment uses knowledge graph technology to intuitively analyze the capital association relationship of upstream and downstream enterprises in the supply chain, providing more accurate data support for risk assessment, and using the core indicator values ​​generated by the knowledge graph analysis results and the TOPSIS method to calculate the risk assessment score, making the assessment result more accurate. It provides data reference for banks to handle the financing business of upstream and downstream enterprises in the supply chain. By ranking the risk assessment scores, banks can give priority to enterprises with lower risks, thereby optimizing resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0057] Figure 1 A diagram schematically illustrates an application scenario of the enterprise financing risk assessment method, device, equipment, medium, and program product according to an embodiment of the present disclosure;

[0058] Figure 2 A flowchart of a method for assessing corporate financing risks according to an embodiment of the present disclosure is schematically shown;

[0059] Figure 3A A flowchart of a method for generating a capital flow diagram from the basic information of the target enterprise based on the knowledge graph technology according to an embodiment of the present disclosure is schematically shown;

[0060] Figure 3B A schematic diagram schematically shows a generated fund flow diagram according to an embodiment of the present disclosure;

[0061] Figure 4 A flowchart of calculating evaluation index data based on the capital flow diagram according to an embodiment of the present disclosure is schematically shown;

[0062] Figure 5 A flowchart schematically illustrates inputting the evaluation index data into a risk evaluation model to output a risk evaluation result according to an embodiment of the present disclosure;

[0063] Figure 6 A schematic diagram of a structure of a device for assessing corporate financing risks according to an embodiment of the present disclosure is shown; and

[0064] Figure 7 A block diagram of an electronic device suitable for implementing a method for assessing corporate financing risks according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0065] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0066] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0067] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0068] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0069] First, the terms that appear in the embodiments of the present disclosure are explained:

[0070] Knowledge graph: A structured semantic knowledge base that stores information related to real-world entities (such as people, places, organizations, etc.) and the relationships between entities. It organizes data in the form of a graph to facilitate complex queries and reasoning.

[0071] TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution, TOPSIS): Approximate ideal solution ranking method, a multi-criteria decision-making method used to evaluate and select the best options. It determines the relative advantage of each option by calculating the distance between each option and the ideal solution and the worst solution.

[0072] Normalization: Scale the data so that it falls into a small specific interval, such as [0,1]. In the TOPSIS method, normalization is used to eliminate the dimensional effects between different indicators.

[0073] Optimal target and worst target: In the TOPSIS method, the optimal target refers to the solution consisting of the best value of each evaluation indicator, while the worst target refers to the solution consisting of the worst value of each evaluation indicator.

[0074] Based on the above technical problems, an embodiment of the present disclosure provides a method for assessing corporate financing risks, the method comprising: in response to an enterprise financing risk assessment instruction, collecting basic information of a target enterprise; generating a cash flow diagram from the basic information of the target enterprise based on knowledge graph technology, the cash flow diagram being used to characterize the cash flow information of the enterprise supply chain; calculating assessment index data based on the cash flow diagram; and inputting the assessment index data into a risk assessment model to output a risk assessment result, wherein the risk assessment model is constructed based on a method of approximating an ideal solution sorting method.

[0075] Figure 1 The application scenario diagram of the enterprise financing risk assessment method, device, equipment, medium and program product according to the embodiment of the present disclosure is schematically shown.

[0076] like Figure 1As shown, the application scenario 100 according to this embodiment may include a large-scale server installation scenario. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0077] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0078] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0079] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0080] It should be noted that the enterprise financing risk assessment method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the enterprise financing risk assessment device provided in the embodiment of the present disclosure can generally be set in the server 105. The enterprise financing risk assessment method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the enterprise financing risk assessment device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0081] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements. Figure 1The application scenarios shown are only examples of application scenarios and system architectures to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios. It should be noted that the enterprise financing risk assessment method and device determined in the present disclosure can be used in the financial field, and can also be used in any field other than the financial field. The application field of the method and device for enterprise financing risk assessment determined in the present disclosure is not limited.

[0082] The following will be based on Figure 1 The scene described by Figure 2~Figure 6 The enterprise financing risk assessment method of the disclosed embodiment is described in detail.

[0083] Figure 2 The flowchart of the enterprise financing risk assessment method according to the embodiment of the present disclosure is schematically shown.

[0084] like Figure 2 As shown, the enterprise financing risk assessment method of this embodiment includes operations S210 to S240, and the transaction processing method can be executed by a server or other computing devices.

[0085] In operation S210, in response to an enterprise financing risk assessment instruction, basic information of the target enterprise is collected.

[0086] In one example, the basic information of financing customers is collected, including the company's assets, financing status over the years, cash inflows and outflows, external guarantees, etc. as shown in the following table:

[0087]

[0088] In operation S220, a capital flow diagram is generated based on the knowledge graph technology using the basic information of the target enterprise.

[0089] According to an embodiment of the present disclosure, a capital flow diagram is used to characterize capital flow information of an enterprise supply chain.

[0090] In one example, a capital flow graph is generated based on the data collected in operation S210 based on the knowledge graph technology. For the specific process, see Figure 3A and Figure 3B The operation shown. In the fund flow graph, the main parts include nodes and directed edges. Each node represents an account or mac address. The red node represents the account, the blue node represents the mac address, the directed edge between the red node and the red node represents the flow of funds between the two accounts, and the directed edge between the blue node and the red node represents that the account has a transfer transaction at the mac address.

[0091] In operation S230, evaluation index data is calculated based on the capital flow diagram.

[0092] In an example, the evaluation index data is calculated based on the cash flow diagram. The evaluation indicators include the verifiable sales financing ratio, the taxable sales financing ratio, the high-quality asset financing ratio and the sales external guarantee ratio. The verifiable sales financing ratio = the total financing of the associated entity in the most recent period / the verifiable sales of the associated entity in the previous year; the taxable sales financing ratio = the total financing of the associated entity in the most recent period / the taxable sales of the associated entity in the previous year; the high-quality asset financing ratio: the total high-quality assets of the associated entity / the total financing of the associated entity in the most recent period; the sales external guarantee ratio: the total amount of external guarantees of the associated entity / the sales of the associated entity in the previous 12 months.

[0093] In operation S240, the evaluation index data is input into a risk evaluation model to output a risk evaluation result.

[0094] According to an embodiment of the present disclosure, the risk assessment model is constructed based on an approximate ideal solution sorting method.

[0095] In one example, the TOPSIS method is used to calculate the correlation between the risk assessment results of financing customers and the optimal and worst targets in all samples, and the risk assessment results of financing customers are ranked. The higher the ranking, the closer the assessment object is to the optimal target.

[0096] The enterprise financing risk assessment method provided by the disclosed embodiment uses knowledge graph technology to intuitively analyze the capital association relationship of upstream and downstream enterprises in the supply chain, providing more accurate data support for risk assessment, and using the core indicator values ​​generated by the knowledge graph analysis results and the TOPSIS method to calculate the risk assessment score, making the assessment result more accurate. It provides data reference for banks to handle the financing business of upstream and downstream enterprises in the supply chain. By ranking the risk assessment scores, banks can give priority to enterprises with lower risks, thereby optimizing resource allocation.

[0097] Figure 3A The flowchart of the method for generating a capital flow diagram from the basic information of the target enterprise based on the knowledge graph technology according to an embodiment of the present disclosure is schematically shown. Figure 3B A schematic diagram of a cash flow diagram generated according to an embodiment of the present disclosure is schematically shown.

[0098] like Figure 3A As shown, operation S220 includes operations S221 to S224.

[0099] In operation S221, entity information is identified from the target enterprise basic information.

[0100] In operation S222, a relationship between entity information is identified.

[0101] According to an embodiment of the present disclosure, the entity information includes account information and media access control address information. The identifying relationship between entity information includes: identifying the fund flow relationship between accounts; and identifying the relationship between accounts and media access control address information.

[0102] In operation S223, triple data is generated according to the recognized entity and relationship combinations.

[0103] In operation S224, a knowledge graph is constructed based on the triple data.

[0104] In one example, after collecting the basic information of the target enterprise, the entity is identified from the collected data, including account information and MAC address information. The account can be a corporate account, a personal account, etc. The MAC address represents the device identifier of the account where the transaction occurs.

[0105] In one example, the relationship between entities, that is, the relationship between capital flows, is determined. Specifically, the capital flow relationship between accounts is identified to form directed edges, indicating that funds flow from one account to another; the relationship between accounts and MAC addresses is identified, indicating that the account has had a transfer transaction at a specific MAC address. The identified entities and relationships are combined into triples, such as (account A, capital flow, account B) and (account A, transaction location, MAC address). Use a graph database or a specialized knowledge graph construction tool to import the triple data and construct a knowledge graph. In the knowledge graph, accounts and MAC addresses are used as nodes, and capital flows are used as edges to construct a capital flow graph such as Figure 3B shown.

[0106] Figure 4 A flowchart for calculating evaluation index data based on the capital flow diagram according to an embodiment of the present disclosure is schematically shown.

[0107] like Figure 4 As shown, operation S230 includes operation S231 to operation S233.

[0108] In operation S231, the account fund interaction activity and transaction characteristics are determined according to the fund flow diagram.

[0109] In operation S232, the evaluation index weight of the account is determined according to the account fund interaction activity and the transaction characteristics.

[0110] In operation S233, a risk assessment index of the target enterprise is calculated according to the assessment index weight.

[0111] In one example, the fund flow graph is analyzed to identify the transaction characteristics of transfers between accounts. The account fund interaction activity and transaction characteristics are determined to identify accounts that use multiple MAC addresses for transfers, which may be high-quality assets. Weights are assigned to accounts based on the account fund interaction activity and transaction characteristics. Accounts with high weights will receive higher scores in subsequent risk assessments. As new fund flow data is generated, the knowledge graph is updated regularly to maintain its timeliness and accuracy. Using the results of the knowledge graph analysis, core indicator data are calculated, such as the ratio of verifiable sales financing, the ratio of taxable sales financing, the ratio of high-quality asset financing, and the ratio of sales to external guarantees.

[0112] Figure 5 A flowchart is schematically shown of inputting the evaluation indicator data into a risk evaluation model to output a risk evaluation result according to an embodiment of the present disclosure.

[0113] like Figure 5 As shown, operation S240 includes operations S241 to S245.

[0114] In operation S241 , a normalized initial matrix is ​​constructed based on the evaluation index data.

[0115] In operation S242, an optimal target and a worst target are determined according to the normalized initial matrix.

[0116] According to an embodiment of the present disclosure, the optimal target is composed of the maximum value of each column element of the normalized initial matrix, and the worst target is composed of the minimum value of each column element of the normalized initial matrix.

[0117] In operation S243, the correlation degree between the target enterprise and the best target and the worst target is calculated respectively.

[0118] In operation S244, the degree of closeness between the target enterprise and the optimal target is calculated.

[0119] In operation S245, the financing client risk assessment results are sorted according to the degree of closeness.

[0120] In an example, construct a normalized initial matrix:

[0121] Same direction processing:

[0122] Data normalization processing:

[0123] Construct a normalized matrix after normalization :

[0124]

[0125] Among them, each row element of the matrix Z represents the target enterprise to be evaluated, and each column element represents each evaluation indicator data.

[0126] Determine the best and worst targets

[0127] Optimal Target Depend on The maximum value of each column element in is:

[0128]

[0129] Worst Target Depend on The minimum value of each column element in is:

[0130]

[0131] Calculate the degree of correlation between each evaluation object and the optimal target.

[0132]

[0133] Calculate the degree of association between each evaluation object and the worst target.

[0134]

[0135] Calculate the degree of closeness between each evaluation object and the optimal target :

[0136]

[0137] , The closer to 1, the better the evaluation object. The risk assessment results of financing customers are sorted according to the size of the risk assessment results.

[0138] Based on the above-mentioned enterprise financing risk assessment method, the present disclosure also provides an enterprise financing risk assessment device. Figure 6 The device is described in detail.

[0139] Figure 6 The structural block diagram of the enterprise financing risk assessment device according to an embodiment of the present disclosure is schematically shown.

[0140] like Figure 6 As shown, the enterprise financing risk assessment device 600 of this embodiment includes a data collection module 610, a knowledge graph generation module 620, a calculation module 630 and a risk assessment module 640.

[0141] The data collection module 610 is used to collect basic information of the target enterprise in response to the enterprise financing risk assessment instruction. In one embodiment, the data collection module 610 can be used to perform the operation S210 described above, which will not be described in detail here.

[0142] The knowledge graph generation module 620 is used to generate a capital flow diagram based on the knowledge graph technology from the basic information of the target enterprise, and the capital flow diagram is used to characterize the capital flow information of the enterprise supply chain. In one embodiment, the knowledge graph generation module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0143] The calculation module 630 is used to calculate the evaluation index data based on the capital flow diagram. In one embodiment, the calculation module 630 can be used to perform the operation S230 described above, which will not be described in detail here.

[0144] The risk assessment module 640 is used to input the assessment index data into a risk assessment model to output a risk assessment result, wherein the risk assessment model is constructed based on the approximate ideal solution sorting method. In one embodiment, the risk assessment module 640 can be used to perform the operation S240 described above, which will not be repeated here.

[0145] According to an embodiment of the present disclosure, the knowledge graph generation module includes: a first identification submodule, a second identification submodule, a generation submodule and a construction submodule.

[0146] The first identification submodule is used to identify entity information from the target enterprise basic information. In one embodiment, the first identification submodule can be used to perform the operation S221 described above, which will not be described in detail here.

[0147] The second identification submodule is used to identify the relationship between entity information. In one embodiment, the second identification submodule can be used to perform the operation S222 described above, which will not be repeated here.

[0148] The generation submodule is used to generate triple data according to the recognized entity and relationship combination; and in one embodiment, the generation submodule can be used to perform the operation S223 described above, which will not be repeated here.

[0149] The construction submodule is used to construct a knowledge graph according to the triple data. In one embodiment, the construction submodule can be used to perform the operation S224 described above, which will not be described in detail here.

[0150] According to an embodiment of the present disclosure, the second identification submodule includes a first identification unit and a second identification unit.

[0151] The first identification unit is used to identify the fund flow relationship between accounts. In one embodiment, the first identification unit can be used to perform the operation S222 described above, which will not be repeated here.

[0152] The second identification unit is used to identify the relationship between the account and the media access control address information. In one embodiment, the second identification unit can be used to perform the operation S222 described above, which will not be described in detail here.

[0153] According to an embodiment of the present disclosure, the calculation module includes: a first determination submodule, a second determination submodule and a first calculation submodule.

[0154] The first determination submodule is used to determine the account fund interaction activity and transaction characteristics according to the fund flow diagram; in one embodiment, the first determination submodule can be used to execute the operation S231 described above, which will not be repeated here.

[0155] The second determination submodule is used to determine the evaluation index weight of the account according to the account fund interaction activity and the transaction characteristics; in one embodiment, the second determination submodule can be used to perform the operation S232 described above, which will not be repeated here.

[0156] The first calculation submodule is used to calculate the risk assessment index of the target enterprise according to the assessment index weight. In one embodiment, the first calculation submodule can be used to perform the operation S233 described above, which will not be described in detail here.

[0157] According to an embodiment of the present disclosure, the risk assessment module includes: a matrix construction submodule, a third determination submodule, a second calculation submodule, a third calculation submodule and a sorting submodule.

[0158] The matrix construction submodule is used to construct a normalized initial matrix based on the evaluation index data; in one embodiment, the matrix construction submodule can be used to perform the operation S241 described above, which will not be repeated here.

[0159] The third determination submodule is used to determine the optimal target and the worst target according to the normalized initial matrix. In one embodiment, the third determination submodule can be used to perform the operation S242 described above, which will not be repeated here.

[0160] The second calculation submodule is used to calculate the degree of association between the target enterprise and the optimal target and the worst target respectively. In one embodiment, the second calculation submodule can be used to perform the operation S243 described above, which will not be repeated here.

[0161] The third calculation submodule is used to calculate the degree of closeness between the target enterprise and the optimal target. In one embodiment, the third calculation submodule can be used to perform the operation S244 described above, which will not be repeated here.

[0162] The sorting submodule is used to sort the risk assessment results of the financing customers according to the closeness. In one embodiment, the sorting submodule can be used to perform the operation S245 described above, which will not be repeated here.

[0163] According to an embodiment of the present disclosure, any multiple modules of the data acquisition module 610, the knowledge graph generation module 620, the calculation module 630 and the risk assessment module 640 can be combined in one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the data acquisition module 610, the knowledge graph generation module 620, the calculation module 630 and the risk assessment module 640 can be at least partially implemented 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 implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the data collection module 610, the knowledge graph generation module 620, the calculation module 630 and the risk assessment module 640 can be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function can be performed.

[0164] Figure 7 A block diagram of an electronic device suitable for implementing a method for assessing corporate financing risks according to an embodiment of the present disclosure is schematically shown.

[0165] like Figure 7 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 to a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard 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 an embodiment of the present disclosure.

[0166] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0167] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (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 installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 908 as needed.

[0168] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the enterprise financing risk assessment method according to the embodiment of the present disclosure is implemented.

[0169] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0170] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the enterprise financing risk assessment method provided by the embodiment of the present disclosure.

[0171] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 901. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0172] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0173] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0174] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0175] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0176] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0177] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for assessing corporate financing risk, characterized in that: The method comprises: In response to the enterprise financing risk assessment instruction, collect the basic information of the target enterprise; Generate a capital flow diagram based on the basic information of the target enterprise based on the knowledge graph technology, and the capital flow diagram is used to represent the capital flow information of the enterprise supply chain; Calculating evaluation index data based on the fund flow diagram; and The evaluation index data is input into a risk evaluation model to output a risk evaluation result, wherein the risk evaluation model is constructed based on an approximate ideal solution sorting method.

2. The method according to claim 1, characterized in that The generating of a capital flow diagram from the basic information of the target enterprise based on the knowledge graph technology includes: Identify entity information from the target enterprise basic information; Identify relationships between entity information; Generating triple data based on the recognized entity and relationship combinations; and A knowledge graph is constructed based on the triple data.

3. The method according to claim 2, characterized in that The entity information includes account information and media access control address information, and the relationship between the identification entity information includes: Identify the flow of funds between accounts; and Identify the relationship between the account and the media access control address information.

4. The method according to claim 1, characterized in that: The calculation of evaluation index data based on the capital flow diagram includes: Determine the account fund interaction activity and transaction characteristics based on the fund flow diagram; Determining the evaluation index weight of the account according to the account fund interaction activity and the transaction characteristics; and The risk assessment index of the target enterprise is calculated according to the assessment index weights.

5. The method according to claim 1, characterized in that The step of inputting the assessment indicator data into a risk assessment model to output a risk assessment result comprises: Construct a normalized initial matrix based on the evaluation indicator data; Determining the optimal target and the worst target according to the normalized initial matrix; Calculate the degree of correlation between the target enterprise and the best target and the worst target respectively; Calculate how close the target enterprise is to the optimal target; and The financing customer risk assessment results are ranked according to the degree of closeness.

6. The method according to claim 5, characterized in that The optimal target is formed by the maximum value of each column element of the normalized initial matrix, and the worst target is formed by the minimum value of each column element of the normalized initial matrix.

7. An enterprise financing risk assessment device, characterized in that: The device comprises: A data collection module, used to respond to the enterprise financing risk assessment instruction and collect basic information of the target enterprise; A knowledge graph generation module, used to generate a capital flow graph from the basic information of the target enterprise based on the knowledge graph technology, wherein the capital flow graph is used to characterize the capital flow information of the enterprise supply chain; A calculation module, used to calculate evaluation index data based on the capital flow diagram; and The risk assessment module is used to input the assessment index data into a risk assessment model to output a risk assessment result, wherein the risk assessment model is constructed based on an approximate ideal solution sorting method.

8. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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