CN112734559A - Enterprise credit risk evaluation method and device and electronic equipment - Google Patents
Enterprise credit risk evaluation method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides an enterprise credit risk evaluation method, an enterprise credit risk evaluation device and electronic equipment, and relates to the technical field of data processing, wherein when enterprise credit risk evaluation is carried out on a target enterprise, risk evaluation data of the target enterprise are obtained firstly; then determining target endogenous risk characteristics and target exogenous risk characteristics according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph; and determining a credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model. Compared with the existing evaluation mode based on rules, the evaluation mode based on mathematical statistics and machine learning is mainly based on the statistical rule of data, is less influenced by artificial subjective risk preference, and runs relatively stably, so that the effect is more stable, and the reliability of a risk evaluation result is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an enterprise credit risk evaluation method and device and electronic equipment.
Background
The credit risk of an enterprise is also called default risk, which means that the enterprise is unwilling or unable to fulfill contract conditions for various reasons to form default, so that the bank, investor or other transaction party suffers loss.
The current enterprise credit risk evaluation scheme is generally based on related rules formulated by experts, that is, the enterprise credit risk is evaluated according to manually set risk propagation rules. Because the scheme is greatly influenced by the subjectivity of experts, artificial deviation is easy to occur, the effect is unstable, and the reliability of the risk evaluation result is poor.
Disclosure of Invention
The invention aims to provide an enterprise credit risk evaluation method, an enterprise credit risk evaluation device and electronic equipment so as to improve the reliability of a risk evaluation result.
The embodiment of the invention provides an enterprise credit risk evaluation method, which comprises the following steps:
acquiring risk assessment data of a target enterprise;
determining target endogenous risk characteristics and target exogenous risk characteristics according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph;
and determining a credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model.
Further, the step of obtaining risk assessment data of the target enterprise comprises:
and purchasing risk evaluation data of the target enterprise through a preset data facilitator, wherein the risk evaluation data comprises business data, judicial data, stock market and debt trading data, announcement data, enterprise operation data, financial data, enterprise relationship data, enterprise guarantee data and rating data.
Further, the step of determining a target endogenous risk characteristic and a target exogenous risk characteristic according to the risk assessment data and a pre-constructed index system comprises:
processing and integrating the risk assessment data through a preset relational database to obtain a data report; the data report comprises an enterprise basic information table, a relation report, a financial report, an operation report, an announcement report, a public opinion report, a security report, a financial market transaction report, an external rating report, a judicial report and an enterprise credit report;
generating a target endogenous risk characteristic corresponding to the endogenous risk index and a target exogenous risk characteristic corresponding to the exogenous risk index according to the data report; the endogenous risk indexes comprise management structures, enterprise attributes, illegal information, credit records, profitability, asset structure evaluation, repayment capacity, cash flow, income quality, growth capacity and judicial complaints; the exogenous risk indicators include a structural indicator related to the number of internal members and the average degree of entrance and exit, a scale indicator related to the number of customers whose total assets, total liabilities, total incomes, total profits, and profits are negative, an attenuation factor related to the number of bad customers and the number of customers whose stock credit balances are negative, and an immune factor negatively related to the probability of default.
Further, the step of determining a credit risk evaluation result of the target enterprise according to the target endogenous risk feature, the target exogenous risk feature, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model includes:
inputting the target endogenous risk characteristics and the target exogenous risk characteristics into a trained endogenous risk evaluation model and a trained exogenous risk evaluation model respectively to obtain endogenous risk default probabilities output by the endogenous risk evaluation model and exogenous risk default probabilities output by the exogenous risk evaluation model;
and determining a credit risk evaluation result of the target enterprise according to the endogenous risk default probability and the exogenous risk default probability.
Further, the step of determining the credit risk evaluation result of the target enterprise according to the endogenous risk default probability and the exogenous risk default probability comprises:
comparing the endogenous risk default probability and the exogenous risk default probability with a preset probability threshold respectively to obtain a comparison result;
and determining a credit risk evaluation result of the target enterprise according to the comparison result.
Further, the method further comprises:
acquiring a training set sample, wherein the training set sample comprises historical evaluation data of historical credit enterprises in a prediction window and actual credit default results of a prediction time point;
determining the sample endogenous risk characteristics and the sample exogenous risk characteristics according to the historical evaluation data and the index system;
training an initial endogenous risk evaluation model according to the sample endogenous risk characteristics and the actual credit default result to obtain a trained endogenous risk evaluation model;
and training the initial exogenous risk evaluation model according to the sample exogenous risk characteristics and the actual credit default result to obtain the trained exogenous risk evaluation model.
Further, according to the sample endogenous risk characteristics and the actual credit default result, training an initial endogenous risk evaluation model to obtain a trained endogenous risk evaluation model, wherein the training comprises the following steps:
training multiple preset initial machine learning models through K-fold cross validation and grid search algorithms according to the sample endogenous risk characteristics and the actual credit default result to obtain multiple trained machine learning models;
and determining the optimal model in the multiple machine learning models as a trained endogenous risk evaluation model.
The embodiment of the invention also provides an enterprise credit risk evaluation device, which comprises:
the data acquisition module is used for acquiring risk assessment data of the target enterprise;
the characteristic determining module is used for determining target endogenous risk characteristics and target exogenous risk characteristics according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph;
and the result determining module is used for determining a credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model.
The embodiment of the invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the computer program to realize the enterprise credit risk evaluation method.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the enterprise credit risk evaluation method is executed.
According to the enterprise credit risk evaluation method, the enterprise credit risk evaluation device and the electronic equipment, when enterprise credit risk evaluation is carried out on a target enterprise, risk evaluation data of the target enterprise are obtained firstly; then determining target endogenous risk characteristics and target exogenous risk characteristics according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph; and determining a credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model. Therefore, by using machine learning and knowledge graph theory tools, the credit risk evaluation result of the enterprise can be updated in a time-sharing frequency mode. Compared with the existing evaluation mode based on rules, the evaluation mode based on mathematical statistics and machine learning is mainly based on the statistical rule of data, is less influenced by artificial subjective risk preference, and runs relatively stably, so that the effect is more stable, and the reliability of a risk evaluation result is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of an enterprise credit risk evaluation method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of model training according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an implementation architecture of an enterprise credit risk assessment method according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a result of an enterprise credit risk assessment method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an enterprise credit risk evaluation device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Machine learning is the science of how to use computers to simulate or implement human learning activities, and the theory and method thereof have been widely applied to solve the complex problems in engineering applications and scientific fields, and are one of the most intelligent features in artificial intelligence and the most advanced research fields. Since the 80 s in the 20 th century, machine learning has attracted a great deal of interest in the artificial intelligence world as a way to implement artificial intelligence, and particularly, in recent decades, research work in the field of machine learning has been rapidly developing and has become one of the important issues of artificial intelligence. Machine learning has found wide application not only in knowledge-based systems, but also in many areas of natural language understanding, non-monotonic reasoning, machine vision, pattern recognition, and so on. Whether a system has learning capabilities has become an indicator of whether it has "intelligence". The study of machine learning is mainly divided into two categories of study directions: the first type is the research of traditional machine learning, which mainly researches the learning mechanism and focuses on exploring the learning mechanism of a dummy; the second type is the study of machine learning in big data environment, which mainly studies how to effectively utilize information, and focuses on obtaining hidden, effective and understandable knowledge from huge data.
Based on the above, the enterprise credit risk evaluation method, the device and the electronic equipment provided by the embodiment of the invention solve the problems of quantification and evaluation of internal risks and external risks of enterprises by means of machine learning and knowledge graph theory tools under the background of big data, and can improve the reliability of risk evaluation results.
In order to facilitate understanding of the embodiment, a detailed description is first given of an enterprise credit risk evaluation method disclosed in the embodiment of the present invention.
The embodiment of the invention provides an enterprise credit risk evaluation method, which can be executed by an electronic device with data processing capability, such as a desktop computer, a notebook computer, a tablet computer or a mobile phone. Referring to fig. 1, a schematic flow chart of an enterprise credit risk evaluation method is shown, which mainly includes the following steps S102 to S106:
and S102, acquiring risk assessment data of the target enterprise.
In some possible embodiments, the risk assessment data of the target enterprise, which may include business data, judicial data, stock market/debt trading data, public announcement data, enterprise business data, financial data, enterprise relationship data, enterprise guarantee data, rating data, and the like, may be purchased through a preset data facilitator of a third channel, such as smart, currency, elements, and the like.
Step S104, determining target endogenous risk characteristics and target exogenous risk characteristics according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph.
In some possible embodiments, the risk assessment data may be processed and integrated through a preset relational database to obtain a data report; the data report includes enterprise basic information, relation report, finance report, management report, announcement report, public opinion report, guarantee report, financial market transaction report, external rating report, judicial report, enterprise credit report, etc. Then generating a target endogenous risk characteristic corresponding to the endogenous risk index and a target exogenous risk characteristic corresponding to the exogenous risk index according to the data report; the endogenous risk indexes comprise management structures, enterprise attributes, illegal information, credit records, profitability, asset structure evaluation, repayment capacity, cash flow, income quality, growth capacity and judicial complaints; the exogenous risk indexes include a structural index, a scale index, a decay factor and an immune factor, wherein the structural index is related to the number of internal members and the average income degree, the scale index is related to the number of customers with negative total assets, total liabilities, total income, total profits and profits, the decay factor is related to the number of bad customers and the number of customers with negative inventory credit balance, and the immune factor is negatively related to the default probability (generally, the higher the default probability is, the lower the immune factor is).
The exogenous risk indicator is constructed based on a knowledge graph, which is also called a historical enterprise graph network, and is formed by investigating data, documents, and the like. The enterprise exogenous risk can be propagated to the enterprise through the relationship in the historical enterprise graph network, namely, the related risk propagation. The associated risk propagation is related to attenuation factors and immune factors, wherein the attenuation factors refer to attenuation degree coefficients of the exogenous risks of the enterprises which are transmitted to the enterprises through the relationship in the maps, the larger the attenuation degree is generally, the smaller the path and influence of the propagation of the external risks are, the attenuation factors (also called attenuation coefficients) are mainly related to the steady-state bad client rate in the historical enterprise map network, the steady-state bad client rates of the historical enterprise map networks with different sizes are different, and the attenuation factors are also different; the immune factor (also referred to as immune coefficient) refers to the tolerance of an enterprise to exogenous risks, and generally, the higher the immune factor is, the greater the tolerance of the enterprise to exogenous risks is, and the immune factor is 1-default probability.
In concrete implementation, after purchasing and accessing the risk assessment data, the risk assessment data can be stored in a preset relational database, data processing and integration are carried out, and the data report is formed after the integration is finished. Data cleaning and processing can then be performed: and finishing the processing of the index system according to the definition of each index (endogenous risk index and exogenous risk index) in the constructed index system, and uniformly filling zero values aiming at abnormal values and vacancy values to obtain a target endogenous risk characteristic corresponding to the endogenous risk index and a target exogenous risk characteristic corresponding to the exogenous risk index.
And S106, determining a credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model.
In some possible embodiments, the target endogenous risk feature and the target exogenous risk feature may be input into the trained endogenous risk evaluation model and the trained exogenous risk evaluation model, respectively, to obtain an endogenous risk default probability output by the endogenous risk evaluation model and an exogenous risk default probability output by the exogenous risk evaluation model; and then determining a credit risk evaluation result of the target enterprise according to the endogenous risk default probability and the exogenous risk default probability.
Further, the step of determining the credit risk evaluation result of the target enterprise according to the endogenous risk default probability and the exogenous risk default probability may be implemented by the following processes: comparing the endogenous risk default probability and the exogenous risk default probability with a preset probability threshold respectively to obtain comparison results; and determining a credit risk evaluation result of the target enterprise according to the comparison result.
Further, the credit risk evaluation result may include an enterprise endogenous risk evaluation corresponding to the endogenous risk default probability, an enterprise exogenous risk evaluation corresponding to the exogenous risk default probability, and an enterprise comprehensive risk evaluation corresponding to the comprehensive default probability; the enterprise endogenous risk evaluation refers to the quantification and evaluation of risks generated in the production and operation activities of the enterprise; the enterprise exogenous risk evaluation refers to quantification and evaluation of external risks of the enterprise, and the external risks refer to influences of other enterprises in the map relationship network where the enterprise is located; the enterprise comprehensive risk evaluation refers to comprehensive quantification and evaluation of the endogenous risk and the exogenous risk of an enterprise, and the comprehensive default probability is the sum of the default probability of the endogenous risk and the default probability of the exogenous risk.
The preset probability threshold may be set according to actual requirements, and is not limited herein. For example, the preset probability threshold is 0.5, if the comparison result is that the default probability of the endogenous risk is greater than 0.5, the credit risk evaluation result of the target enterprise is that the endogenous risk of the enterprise is high; if the comparison result shows that the default probability of the exogenous risk is greater than 0.5, the credit risk evaluation result of the target enterprise shows that the exogenous risk of the enterprise is higher; if the comparison result is that the sum of the default probability of the endogenous risk and the default probability of the exogenous risk is greater than 0.5, the credit risk evaluation result of the target enterprise is that the comprehensive risk of the enterprise is high; and if the comparison result is that the sum of the default probability of the endogenous risk and the default probability of the exogenous risk is less than or equal to 0.5, the credit risk evaluation result of the target enterprise is that the comprehensive risk of the enterprise is low.
The enterprise credit risk evaluation method provided by the embodiment of the invention utilizes machine learning and a graph network theory to construct a quantitative evaluation result which can update the internal and external risks of the enterprise at a time-sharing frequency. Compared with the existing evaluation mode based on rules, the evaluation mode based on mathematical statistics and machine learning is mainly based on the statistical rule of data, is less influenced by artificial subjective risk preference, and runs relatively stably, so that the effect is more stable, and the reliability of a risk evaluation result is improved.
The embodiment of the present invention further provides a training process of the endogenous risk evaluation model and the exogenous risk evaluation model, referring to a schematic flow diagram of model training shown in fig. 2, the training process of the endogenous risk evaluation model and the exogenous risk evaluation model includes the following steps:
step S202, obtaining a training set sample, wherein the training set sample comprises historical evaluation data of historical credit enterprises in a prediction window and actual credit default results of a prediction time point.
When obtaining the samples, firstly selecting a prediction time point and a prediction window, wherein the prediction window can be selected for 6 months or 1 year, obtaining a characteristic width table according to the prediction time point, selecting historical credit enterprises as samples according to the prediction window, and randomly dividing the samples into training set samples and testing set samples according to a certain proportion. For example, a feature width table is obtained according to the predicted time point 2018, 06, 30, a sample is constructed by 1-year historical credit enterprises, and the historical overdue enterprises are taken as target samples according to the following steps of 7: 3, randomly dividing the training set sample and the test set sample. The actual credit default results can be subdivided into five categories, default caused by endogenous risk, default caused by exogenous risk, default caused by comprehensive risk and non-default.
And step S204, determining the sample endogenous risk characteristics and the sample exogenous risk characteristics according to the historical evaluation data and the index system.
The sample endogenous risk feature and the sample exogenous risk feature are both formed by cross-sectional data of the prediction time point, and the specific process of step S204 may refer to the corresponding content of step S104, which is not described herein again.
And S206, training the initial endogenous risk evaluation model according to the sample endogenous risk characteristics and the actual credit default result to obtain the trained endogenous risk evaluation model.
In some possible embodiments, multiple preset initial machine learning models can be trained through K-fold cross validation and grid search algorithms according to the sample endogenous risk features and the actual credit default results to obtain multiple trained machine learning models; and determining the optimal model in the multiple machine learning models as the trained endogenous risk evaluation model.
The initial machine learning model can be selected according to actual requirements, for example, four models of logistic regression, random forest, Xgboost and Adaboost are selected. The optimal values of the main parameters of the model can be found through K-fold cross validation and a grid search algorithm, and then the optimal model is selected according to the service angle comprehensive evaluation and the model accuracy and recall rate balance. For example, model training is performed by using the four mainstream machine learning models and the unbalanced sample processing method, and a final endogenous risk evaluation model is preferentially obtained by using an F1 value as a standard.
And S208, training the initial exogenous risk evaluation model according to the sample exogenous risk characteristics and the actual credit default result to obtain the trained exogenous risk evaluation model.
The specific process of step S208 may refer to the corresponding content of step S206, which is not described herein again.
In order to facilitate understanding, the embodiment of the present invention further provides an implementation process of the enterprise credit risk assessment method, which is as follows:
the first step is as follows: preparing, performing data research, literature research, expert research in the industry and the like, constructing a risk characteristic index library, constructing an Oscar database and index system based on big data, completing preparation of basic work such as development environment, basic codes, model codes and the like, and establishing a feasible technical scheme implementation route.
As shown in fig. 3, the data layer covers 14 levels of industry, judicial works, management, financial reports, ratings, credit investigation, transactions, markets, announcements, public opinions, groups, guarantees, industries, regions, and the like, the risk index layer includes nearly 400 indexes of endogenous risk indexes and exogenous risk indexes, the endogenous risk indexes include management structures, enterprise attributes, illegal information, credit records, profitability, asset structure evaluation, repayment capacity, cash flow, income quality, growth capacity, judicial complaints, and the like, and the exogenous risk indexes include (1) structural indexes: the number of internal members and the average degree of entry and exit; (2) scale index: total assets, total liabilities, total revenue, total profits, and number of customers whose profits are negative; (3) attenuation factor: the number of bad customers and the number of customers whose stock amounts are credited with balance; (4) immune factors: probability of breach.
The second step is that: and integrating each data source, cleaning and processing the data to form index factor characteristics. The data sources may include up to 100 financial websites and the related data may include business data, judicial data, stock market/debt trading data, public announcement data, business management data, financial data, warranty data, rating data, investment relationship data, and the like.
The third step: dividing the index factor characteristics into an endogenous risk characteristic and an exogenous risk characteristic, constructing a sample by using a 1-year historical credit client, and using a historical overdue client as a target sample.
As shown in fig. 3, endogenous risk identification and exogenous risk propagation are performed at the risk occurrence layer.
The fourth step: as shown in fig. 3, in the risk evaluation model layer, the four machine learning models are used to train a model, the trained endogenous risk evaluation model can output an endogenous risk default probability D of a single enterprise, and the endogenous risk default probability is used as an endogenous risk evaluation of the enterprise.
The fifth step: attenuation factors and immune factors were calculated.
And a sixth step: as shown in fig. 3, in the risk evaluation model layer, an exogenous risk evaluation model is constructed by the structural index, the scale index, the attenuation factor and the immune factor: exogenous risk default probability ═ F (structural index, scale index, attenuation factor, and immune factor).
The seventh step: and outputting the credit risk evaluation result of the enterprise.
As shown in fig. 3, in the evaluation result output layer, the output contents are as follows: the default probability of the endogenous risk is more than 0.5, and the endogenous risk of the output enterprise is higher; the default probability of the exogenous risk is more than 0.5, and the exogenous risk of the output enterprise is higher; the default probability of endogenous risk and the default probability of exogenous risk are more than 0.5, and the comprehensive risk of an output enterprise is higher; the default probability of endogenous risk + the default probability of exogenous risk is less than or equal to 0.5, and the comprehensive risk of the output enterprise is low.
Eighth step: after the endogenous risk evaluation model and the exogenous risk evaluation model are constructed, the risk characteristics can be input in real time, and the endogenous risk, the exogenous risk and the comprehensive risk of the enterprise can be evaluated in real time.
The embodiment of the invention also provides a result display example of the enterprise credit risk evaluation method, which is shown in a result display schematic diagram of the enterprise credit risk evaluation method shown in fig. 4, and is a client risk early warning monitoring platform of an XX bank company, and mainly comprises a model monitoring area and an early warning list area. The model monitoring area comprises two sub-areas, and one sub-area is used for displaying the following contents: the system comprises a head office early warning client statistics and an earlier change, a branch office early warning client statistics and an earlier change, an industry early warning client statistics and an earlier change, and a regional early warning client statistics and an earlier change; the other sub-region is used to present the following: precision, recall, KS (Kolmogorov-Smirnov), AUC (area Under cut), effective pre-alarm rate, false alarm rate and false alarm rate. The early warning list area mainly shows the following contents: customer number, customer name, affiliation group, affiliation branch, industry, area, early warning reason, early warning level and early warning feedback.
Corresponding to the above-mentioned enterprise credit risk evaluation method, an embodiment of the present invention further provides an enterprise credit risk evaluation apparatus, referring to a schematic structural diagram of the enterprise credit risk evaluation apparatus shown in fig. 5, the apparatus includes:
a data obtaining module 52, configured to obtain risk assessment data of a target enterprise;
a characteristic determination module 54, configured to determine a target endogenous risk characteristic and a target exogenous risk characteristic according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph;
and the result determining module 56 is used for determining the credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model.
The enterprise credit risk evaluation device provided by the embodiment of the invention utilizes machine learning and a map network theory to construct a quantitative evaluation result which can update the internal and external risks of the enterprise at a time-sharing frequency. Compared with the existing evaluation mode based on rules, the evaluation mode based on mathematical statistics and machine learning is mainly based on the statistical rule of data, is less influenced by artificial subjective risk preference, and runs relatively stably, so that the effect is more stable, and the reliability of a risk evaluation result is improved.
Further, the data obtaining module 52 is specifically configured to: and purchasing risk evaluation data of the target enterprise through a preset data facilitator, wherein the risk evaluation data comprises business data, judicial data, stock market and debt trading data, announcement data, enterprise operation data, financial data, enterprise relationship data, enterprise guarantee data and rating data.
Further, the characteristic determining module 54 is specifically configured to: processing and integrating the risk evaluation data through a preset relational database to obtain a data report; the data report comprises an enterprise basic information table, a relation report, a financial report, an operation report, an announcement report, a public opinion report, a security report, a financial market transaction report, an external rating report, a judicial report and an enterprise credit report; generating a target endogenous risk characteristic corresponding to the endogenous risk index and a target exogenous risk characteristic corresponding to the exogenous risk index according to the data report; the endogenous risk indexes comprise management structures, enterprise attributes, illegal information, credit records, profitability, asset structure evaluation, repayment capacity, cash flow, income quality, growth capacity and judicial complaints; the exogenous risk indicators include structural indicators related to internal membership and average income, scale indicators related to total assets, total liabilities, total income, total profits and profit-negative customer numbers, attenuation factors related to bad customer numbers and inventory credit balance customer numbers, and immune factors negatively related to default probability.
Further, the result determining module 56 is specifically configured to: respectively inputting the target endogenous risk characteristics and the target exogenous risk characteristics into the trained endogenous risk evaluation model and the trained exogenous risk evaluation model to obtain endogenous risk default probability output by the endogenous risk evaluation model and exogenous risk default probability output by the exogenous risk evaluation model; and determining a credit risk evaluation result of the target enterprise according to the endogenous risk default probability and the exogenous risk default probability.
Further, the result determination module 56 is further configured to: comparing the endogenous risk default probability and the exogenous risk default probability with a preset probability threshold respectively to obtain comparison results; and determining a credit risk evaluation result of the target enterprise according to the comparison result.
Further, the apparatus further comprises a training module connected to the result determining module 56, for:
acquiring a training set sample, wherein the training set sample comprises historical evaluation data of historical credit enterprises in a prediction window and actual credit default results of a prediction time point;
determining the endogenous risk characteristics and the exogenous risk characteristics of the sample according to historical evaluation data and an index system;
training the initial endogenous risk evaluation model according to the sample endogenous risk characteristics and the actual credit default result to obtain a trained endogenous risk evaluation model;
and training the initial exogenous risk evaluation model according to the exogenous risk characteristics of the sample and the actual credit default result to obtain the trained exogenous risk evaluation model.
Further, the training module is specifically configured to: training multiple preset initial machine learning models through K-fold cross validation and a grid search algorithm according to the sample endogenous risk characteristics and the actual credit default result to obtain multiple trained machine learning models; and determining the optimal model in the multiple machine learning models as the trained endogenous risk evaluation model.
The device provided by the embodiment has the same implementation principle and technical effect as the method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the method embodiments without reference to the device embodiments.
Referring to fig. 6, an embodiment of the present invention further provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The Memory 61 may include a Random Access Memory (RAM) or a non-volatile Memory (NVM), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 62 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
The memory 61 is configured to store a program, and the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the enterprise credit risk assessment method described in the foregoing method embodiment. The computer-readable storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk.
In all examples shown and described herein, any particular value should be construed as merely exemplary, and not as a limitation, and thus other examples of example embodiments may have different values.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block 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 will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An enterprise credit risk evaluation method is characterized by comprising the following steps:
acquiring risk assessment data of a target enterprise;
determining target endogenous risk characteristics and target exogenous risk characteristics according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph;
and determining a credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model.
2. The enterprise credit risk assessment method of claim 1, wherein the step of obtaining risk assessment data for the target enterprise comprises:
and purchasing risk evaluation data of the target enterprise through a preset data facilitator, wherein the risk evaluation data comprises business data, judicial data, stock market and debt trading data, announcement data, enterprise operation data, financial data, enterprise relationship data, enterprise guarantee data and rating data.
3. The enterprise credit risk assessment method according to claim 2, wherein the step of determining a target endogenous risk profile and a target exogenous risk profile according to the risk assessment data and a pre-constructed index system comprises:
processing and integrating the risk assessment data through a preset relational database to obtain a data report; the data report comprises an enterprise basic information table, a relation report, a financial report, an operation report, an announcement report, a public opinion report, a security report, a financial market transaction report, an external rating report, a judicial report and an enterprise credit report;
generating a target endogenous risk characteristic corresponding to the endogenous risk index and a target exogenous risk characteristic corresponding to the exogenous risk index according to the data report; the endogenous risk indexes comprise management structures, enterprise attributes, illegal information, credit records, profitability, asset structure evaluation, repayment capacity, cash flow, income quality, growth capacity and judicial complaints; the exogenous risk indicators include a structural indicator related to the number of internal members and the average degree of entrance and exit, a scale indicator related to the number of customers whose total assets, total liabilities, total incomes, total profits, and profits are negative, an attenuation factor related to the number of bad customers and the number of customers whose stock credit balances are negative, and an immune factor negatively related to the probability of default.
4. The method according to claim 1, wherein the step of determining the credit risk assessment result of the target enterprise according to the target endogenous risk feature, the target exogenous risk feature, the trained endogenous risk assessment model and the trained exogenous risk assessment model comprises:
inputting the target endogenous risk characteristics and the target exogenous risk characteristics into a trained endogenous risk evaluation model and a trained exogenous risk evaluation model respectively to obtain endogenous risk default probabilities output by the endogenous risk evaluation model and exogenous risk default probabilities output by the exogenous risk evaluation model;
and determining a credit risk evaluation result of the target enterprise according to the endogenous risk default probability and the exogenous risk default probability.
5. The enterprise credit risk assessment method according to claim 4, wherein the step of determining the credit risk assessment result of the target enterprise according to the endogenous risk default probability and the exogenous risk default probability comprises:
comparing the endogenous risk default probability and the exogenous risk default probability with a preset probability threshold respectively to obtain a comparison result;
and determining a credit risk evaluation result of the target enterprise according to the comparison result.
6. The enterprise credit risk assessment method of claim 1, further comprising:
acquiring a training set sample, wherein the training set sample comprises historical evaluation data of historical credit enterprises in a prediction window and actual credit default results of a prediction time point;
determining the sample endogenous risk characteristics and the sample exogenous risk characteristics according to the historical evaluation data and the index system;
training an initial endogenous risk evaluation model according to the sample endogenous risk characteristics and the actual credit default result to obtain a trained endogenous risk evaluation model;
and training the initial exogenous risk evaluation model according to the sample exogenous risk characteristics and the actual credit default result to obtain the trained exogenous risk evaluation model.
7. The enterprise credit risk assessment method of claim 6, wherein the step of training an initial endogenous risk assessment model to obtain a trained endogenous risk assessment model according to the sample endogenous risk characteristics and the actual credit default results comprises:
training multiple preset initial machine learning models through K-fold cross validation and grid search algorithms according to the sample endogenous risk characteristics and the actual credit default result to obtain multiple trained machine learning models;
and determining the optimal model in the multiple machine learning models as a trained endogenous risk evaluation model.
8. An enterprise credit risk assessment device, comprising:
the data acquisition module is used for acquiring risk assessment data of the target enterprise;
the characteristic determining module is used for determining target endogenous risk characteristics and target exogenous risk characteristics according to the risk assessment data and a pre-constructed index system; the index system comprises an endogenous risk index and an exogenous risk index obtained based on a knowledge graph;
and the result determining module is used for determining a credit risk evaluation result of the target enterprise according to the target endogenous risk characteristics, the target exogenous risk characteristics, the trained endogenous risk evaluation model and the trained exogenous risk evaluation model.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any one of claims 1-7 when executing the computer program.
10. A computer-readable storage medium, having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, is adapted to carry out the method of any one of claims 1-7.
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