CN107993143A - A kind of Credit Risk Assessment method and system - Google Patents
A kind of Credit Risk Assessment method and system Download PDFInfo
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- CN107993143A CN107993143A CN201711186546.0A CN201711186546A CN107993143A CN 107993143 A CN107993143 A CN 107993143A CN 201711186546 A CN201711186546 A CN 201711186546A CN 107993143 A CN107993143 A CN 107993143A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The present invention is suitable for information technology field, there is provided a kind of Credit Risk Assessment method and system, the described method includes：The multi-dimensional data of enterprise is obtained by cloud computing ERP system, and the multi-dimensional data is analyzed, obtains analysis result；The classification of enterprise is determined according to the analysis result, and adjusts the risk evaluation model weight of different classes of enterprise, to calculate the credit score of enterprise；Credit score based on enterprise, judges whether the credit applications that enterprise is submitted pass through；If by being monitored in real time after credit is provided by cloud computing ERP system.Credit Risk Assessment method provided by the invention can improve the accuracy and efficiency of Credit Risk Assessment, there is higher practicality.
Description
Technical Field
The invention belongs to the technical field of information, and particularly relates to a credit risk assessment method and system.
Background
At present, as the whole social credit system including the medium and small enterprises and credit investigation management are not formed in China, the medium and small enterprises have no other publicly accessible credit information, and the credit risk assessment of the medium and small enterprises has no unified assessment system. However, the internal management of most of the small and medium-sized enterprises in China is not standard, the regulation and the regulation are not perfect, the information disclosure is asymmetric, and the data such as financial statements and the like cannot reflect the real situation.
In fact, the traditional finance and lending institution mainly collects scattered data such as bank flow, financial statements, order contracts and the like through manual collection under lines, namely, the data submitted by customers is the main, the authenticity of the data is difficult to distinguish, the reliable information is few, and the collection and analysis process is seriously lagged. Enterprises in the internet field mainly acquire data through channels such as e-commerce platforms or social networks, but the information is not directly related to enterprise operation management and credit conditions, the data correlation and predictability are not large, and the statistical standards of the data among different platforms are different, so that the data cannot be mutually supplemented with certificates, and cannot be comprehensively utilized, so that the enterprises in the internet field are difficult to analyze in combination with various aspects of actual operation of the enterprises. The method has the problems of incomplete information, unreliable data, unreachable data and the like, and brings great difficulty to credit risk assessment of small and medium enterprises. The models and methods of credit risk assessment are largely data-limited. Limited by the data obtained, the evaluation method still depends on subjective judgment, so that the evaluation result of the prior art is unreliable, and the collection and analysis process is seriously lagged, so that the risk evaluation is not time-efficient.
Disclosure of Invention
In view of this, embodiments of the present invention provide a credit risk assessment method and system to improve accuracy and efficiency of credit risk assessment.
A first aspect of an embodiment of the present invention provides a credit risk assessment method, including:
obtaining multidimensional data of an enterprise through a cloud computing ERP system, and analyzing the multidimensional data to obtain an analysis result;
determining the category of the enterprise according to the analysis result, and adjusting the weight of risk assessment models of different categories of enterprises to calculate the credit score of the enterprise;
judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise;
and if the credit is passed, real-time monitoring is carried out through the cloud computing ERP system after credit is provided.
A second aspect of an embodiment of the present invention provides a credit risk assessment system, including:
the data module is used for acquiring multidimensional data of an enterprise through a cloud computing ERP system, analyzing the multidimensional data and acquiring an analysis result;
the processing module is used for determining the category of the enterprise according to the analysis result and adjusting the risk assessment model weight of different categories of enterprises to calculate the credit score of the enterprise;
the control module is used for judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise;
and the data module is also used for carrying out real-time monitoring through the cloud computing ERP system after credit extension if the data module passes the credit extension.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method as described above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the embodiment of the invention provides a credit risk assessment method, which comprises the steps of obtaining multi-dimensional data of an enterprise through a cloud computing ERP system, analyzing the multi-dimensional data and obtaining an analysis result; determining the category of the enterprise according to the analysis result, and adjusting the weight of risk assessment models of different categories of enterprises to calculate the credit score of the enterprise; judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise; and if the credit passes, real-time monitoring is carried out through the cloud computing ERP system after the credit is provided. The embodiment of the invention collects and analyzes the multidimensional data of the enterprise through the cloud computing ERP system, carries out credit risk assessment, improves assessment accuracy and assessment efficiency, calls and analyzes the data in real time after credit is provided for monitoring, is efficient and accurate, can carry out risk early warning, and has high practicability.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flow chart illustrating an implementation of a credit risk assessment method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating an implementation of a credit risk assessment method according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a credit risk assessment system according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal device according to a fourth embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of an implementation of a credit risk assessment method according to an embodiment of the present invention, as shown in the figure, the method may include the following steps:
in step S101, multidimensional data of an enterprise is obtained through the cloud computing ERP system, and the multidimensional data is analyzed to obtain an analysis result.
In the embodiment of the present invention, the cloud-computing ERP system is an Enterprise Resource Planning system (ERP) based on a cloud, and the cloud-computing ERP system may be an autonomously developed system or a third-party system, which is not limited herein. The multidimensional data comprises application system data, behavior data and extensible data which are acquired through a cloud computing ERP system.
The multidimensional data may be collected via a database. Optionally, the database may be a cloud computing ERP system. The cloud computing ERP system is a management platform which is established on the basis of information technology and provides decision means for enterprise employees and a decision layer by using a systematized management idea, and is also a database which reflects the external operation condition and the internal management performance of an enterprise.
The following specifically describes an application of the cloud computing ERP system by taking a preferred implementation of the embodiment of the present invention as an example.
Illustratively, the cloud computing ERP system may include a plurality of application systems, such as a task system, a Customer Relationship Management (CRM) system, a purchase, sale and inventory system, an attendance system, an approval system, a personnel salary system, a performance assessment system, a cost analysis system, behavior data, and an extensible data interface, and may collect application system data, behavior data, and extensible data.
The task system arranges all levels of employee work tasks based on an enterprise organization framework, all the work is released in a task form, the work process and the result are recorded, the basic data such as the task duration, the task quantity, the completion quantity, the overdue quantity and the like can be recorded, the grading rating of the task by the superior is recorded, and the task data is formed.
The attendance System is intelligently connected with the approval System, supports double card punching of a Global Positioning System (GPS) and WiFi, supports different personnel to set different attendance schemes, and can record data of late arrival, early departure, absenteeism, leave, going out, overtime and the like of the personnel to form attendance data.
The CRM system manages customers, records sales processes and results such as sales calls, visit records and the like, counts business activity data and sales data in real time by a data report, and records the number of customers, visit times, call times, newly added customer number, sales group scores and the like to form CRM data.
The approval system supports complete self-definition, each approval has a customized flow to process and record in the system, and can record data such as personnel, duration, amount and the like in preset or self-defined templates for asking for leave, reimbursement, going out and the like, the reflection speed of related personnel in the approval flow and the like to form approval data.
The purchase, sales and inventory system tracks and manages the whole process of the purchase, shipment, wholesale, sale and the like in the enterprise production and operation, provides detailed and accurate data in each step, and can record data such as the number of suppliers, inventory records, purchase cost and the like to form purchase, sales and inventory data.
The personnel salary system can collect various basic information of the staff before and after the staff enters the work, can set different salary templates, automatically associates the performance assessment result, quotes attendance checking or approval data and forms personnel salary data.
The performance assessment system comprises periodic performance and real-time performance. The periodic performance can record effective output, execution capacity, input degree, occupational literacy evaluation and other data of the employee in a corresponding working period, and the periodic performance score of the employee is calculated according to the weight proportion of each index set by a user; and automatically recording the effective output, innovation score, idle time, working time and other data of the employee from the current time in the month by the real-time performance, and finally obtaining the real-time performance score. The periodic performance and the real-time performance of the performance assessment system form performance assessment data.
The cost analysis system calculates the cost of each employee based on various activities by integrating employee salaries, working hours, task scores, approval reimbursements, production costs and the like, is accurate to each cost factor of an enterprise, including but not limited to task costs, team costs, customer costs, supplier costs and the like, and forms cost analysis data.
The application system data refers to data collected by a plurality of application systems in the cloud computing ERP system, and includes, but is not limited to, approval data formed by the approval systems, purchase-sale-stock data formed by the purchase-sale-stock systems, personnel salary data formed by the personnel salary systems, performance assessment data formed by the performance assessment systems, cost analysis data formed by the cost analysis systems, and the like.
In the embodiment of the present invention, the multidimensional data may include behavior data, where the behavior data refers to data describing behavior activities of an enterprise and its employees, and includes, but is not limited to, login time and usage frequency, task execution time, idle time, approval reaction speed, comment quantity, and the like in a cloud-computing ERP system.
The cloud computing ERP system is deployed at the cloud end, can be used on terminal equipment such as a PC (personal computer) end and a mobile end, is not limited by a machine room and a server, is low in cost, flexible, convenient and fast, and is suitable for small and medium-sized enterprises and employees thereof. Meanwhile, the cloud computing ERP system stores the behavior data records of small and medium-sized enterprises and employees thereof in the cloud, and converts a plurality of unstructured data into structured data in a cloud computing mode by utilizing system functions such as performance management, cost analysis and the like. For example, the cloud computing ERP system records attendance records of sales personnel such as outgoing visits, check-in and check-out, and also records sales volume, expense reimbursement and other data of the sales personnel, and performs combined analysis to obtain structured data with economic significance such as order cost, per-person cost, production value and the like. The structured number refers to data that has statistical criteria and meaning and can be stored in a database for logical representation of an implementation in a two-dimensional table structure. The unstructured data refers to data which have no statistical standard and significance and are inconvenient to express by a database two-dimensional logic table, and the data comprise office documents, texts, pictures, XML, HTML, various reports, images, audio frequency, video information and the like in all formats.
In addition, the multidimensional data may also include expandable data, and the expandable data may include other openness information data acquired by the cloud computing ERP system through an expandable data interface, for example, the business registration information of the national enterprise credit information public system, the national court executed person information, the chinese decision papernetwork information, and the like; the extensible data can also comprise purchasing and selling data based on a third-party transaction platform, or data based on enterprise internal network databases, external network industry information and the like.
The cloud computing ERP system can be opened for all enterprises, is high in function extensibility and completeness, software does not need to be deployed, only needs to be downloaded by a mobile phone, is extremely high in reproducibility, and can meet requirements of middle and small enterprises in various industries, so that a large amount of effective data of the middle and small enterprises can be accumulated, and the cloud computing ERP system can be used for comparing and analyzing levels and dynamic changes of specific enterprises in different types of enterprises.
Furthermore, the external management condition and the internal management performance of the enterprise can be analyzed and recorded through the logic library, and the logic library can be a data logic system which can analyze and record a large amount of data collected by the cloud computing ERP system.
For example, the logic library can take a risk assessment model as a main body, mainly considers indexes in multiple aspects such as enterprise scale, financial performance, employee competitiveness, enterprise competitiveness, customer competitiveness and the like, and analyzes and records multidimensional data collected by the cloud computing ERP system through assessment logic so as to be used for credit risk assessment of small and medium enterprises.
Exemplary multidimensional data evaluation metrics in embodiments of the present invention include, but are not limited to, the following: comprehensively measuring the scale of the enterprise according to the number of employees, the sales volume of orders, the quantity of stocks and the like; comprehensively evaluating financial performance according to sales income, production cost, purchasing cost, salary cost, management cost and the like; examining the competitiveness of the staff according to the indexes such as staff flow rate, average task number, average working time, performance salary matching degree and the like; the competitiveness of an enterprise is inspected in the aspects of average task cost, customer cost, supplier cost, average labor cost, total working time, average performance of a company and the like; and examining the client competitiveness in the aspects of client quantity, client scale, order concentration, account receivable rate and the like.
Specifically, the following describes an implementation of analyzing the multidimensional data to obtain an analysis result through a plurality of examples, but it should be understood that the described examples are only a part of examples of the present invention, and not all examples. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive step, are within the scope of the present invention.
Taking the enterprise scale as an example, different types of enterprises have different standards for measuring the enterprise scale, and the stability needs to be considered, which is better to be the condition that no obvious decline or stable growth exists. For example, if the initial value of the index such as the number of employees, the sales amount of orders, or the stock amount is high and the increase or decrease is small, the enterprise is large in scale and stable, and the risk of credit for the enterprise is small.
Taking the financial performance aspect as an example, overall, the higher the sales revenue, the lower the cost costs, the better the financial performance, and the lower the business credit risk. In particular, liability conditions, repayment capacity, profitability and operational capacity also need to be considered. For example, the number of turnaround days for inventory, accounts receivable, accounts payable may account for the cash cycle period, with shorter turnaround days reflecting higher efficiency of the operating asset.
Taking the employee competitiveness aspect as an example, the higher the average working time, the task number and the performance salary matching degree of the employee is, the lower the personnel flow rate is, the stronger the employee competitiveness is, and the smaller the enterprise credit risk is.
Taking enterprise competitiveness as an example, the lower the average task cost and the average labor cost of an enterprise, the higher the total working time and the average performance of a company are, the stronger the enterprise competitiveness is, and the smaller the enterprise credit risk is.
Taking customer competitiveness as an example, not only the number of customers but also various aspects such as customer order concentration, account recovery condition, customer relationship maintenance condition and the like need to be considered comprehensively, and the higher the customer competitiveness is, the smaller the credit risk is. Customer competitiveness refers to the ability of an enterprise to obtain, maintain, and develop customer value. For example, the larger the number of customer resources, the higher the proportion of customers who have achieved cooperation, the smaller the number of accounts receivable turnaround days, and the higher the rate of completion of the customer follow-up plan, the stronger the customer competitiveness.
In step S102, the category of the enterprise is determined according to the analysis result, and the risk assessment model weights of different categories of enterprises are adjusted to calculate the credit score of the enterprise.
Optionally, in the embodiment of the present invention, a certain number of sample enterprises may be decimated based on the multidimensional data of the enterprises, and the sample enterprises may be classified into different categories through cluster analysis. And analyzing the characteristics of the categories according to evaluation characteristic values such as the average value, the standard deviation and the like of the evaluation index data of each category of enterprises, and judging the risk level and the main risk points of the category. And then, combining actual credit records in various types of enterprises in the system, adjusting the risk assessment model based on the characteristics of the types of enterprises, and determining the weight proportion of the risk assessment system indexes of the types of enterprises. The cluster analysis is an analysis process for grouping a set of physical or abstract objects into a plurality of classes composed of similar objects, and actually measures the similarity between different data sources and classifies the data sources into different clusters. The cluster analysis is an exploratory analysis, and in the classification process, people do not need to give a classification standard in advance, and the cluster analysis can automatically classify from sample data. From a practical application perspective, cluster analysis is one of the main tasks of data mining. And clustering can be used as an independent tool to obtain the distribution condition of data, observe the characteristics of each cluster of data and intensively analyze a specific cluster set for further analysis. Clustering analysis can also be used as a pre-processing step for other algorithms such as classification and qualitative induction algorithms.
In addition, the embodiment of the invention can also divide the categories according to the scale of the industry and the enterprise, determine the risk assessment system indexes and weights of the categories by combining the credit records of the sample enterprise, and check the credit score of the enterprise.
Optionally, in the embodiment of the present invention, the data may be further updated periodically to perform category classification and model adjustment, for example, every three months or half a year is taken as a period, so as to further improve the accuracy of the evaluation.
Furthermore, the embodiment of the invention can also calculate the credit score of the credit application enterprise, and calculate the credit line according to the credit score of the credit application enterprise. Optionally, the operation of calculating the credit score of the credit application enterprise can be triggered by the enterprise submitting a credit application in the cloud computing ERP system, and can also be triggered by the credit auditor submitting the application in the background. And after triggering, extracting the data of the enterprise from the database, checking the credit score of the enterprise according to the risk assessment model of the category of the enterprise and the weight of the risk assessment model, and judging the risk level and the main risk point of the enterprise.
In step S103, it is determined whether the credit application submitted by the business passes based on the credit score of the business.
Optionally, in the embodiment of the present invention, the risk level and the main risk point of the enterprise may be determined according to the credit score of the credit application enterprise, the credit limit may be checked, and it may be determined whether the credit application submitted by the enterprise passes the risk assessment review. If the enterprise early warning parameters pass the auditing, the enterprise early warning parameters are set according to the evaluated risk points, and the enterprise early warning parameters include but are not limited to enterprise credit score early warning and specific index early warning such as customer number, average customer acquisition cost, per capita output value and the like.
In step S104, if the credit is passed, real-time monitoring is performed by the cloud computing ERP system after the credit is extended.
Optionally, in the embodiment of the present invention, if the credit application of the enterprise passes the audit, the information of the enterprise after the credit is provided may be collected and analyzed in real time by the cloud computing ERP system, for example, real-time monitoring after the credit automatically enters into the credit, and data may be directly obtained from the cloud computing ERP system in real time for analysis; and when the credit score of the enterprise is lower than a threshold value or the fluctuation of a certain index exceeds a preset range, giving an alarm to remind follow-up to know the condition.
As can be seen from the embodiment of fig. 1, in the embodiment of the present invention, multidimensional data of an enterprise is obtained through a cloud computing ERP system, and the multidimensional data is analyzed to obtain an analysis result; determining the category of the enterprise according to the analysis result, and adjusting the weight of risk assessment models of different categories of enterprises to calculate the credit score of the enterprise; judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise; and if the credit passes, real-time monitoring is carried out through the cloud computing ERP system after the credit is provided. The embodiment of the invention collects and analyzes the multidimensional data of the enterprise through the cloud computing ERP system, carries out credit risk assessment, improves assessment accuracy and assessment efficiency, calls and analyzes the data in real time after credit is provided for monitoring, is efficient and accurate, can carry out risk early warning, and has high practicability.
Fig. 2 is a schematic flow chart of an implementation of a credit risk assessment method according to a second embodiment of the present invention, as shown in the figure, the method may include the following steps:
in step S201, multidimensional data of an enterprise is obtained through the cloud computing ERP system, and the multidimensional data is analyzed to obtain an analysis result.
Step S201 of this embodiment is the same as step S101, and reference may be specifically made to the description related to step S101, which is not repeated herein.
In step S202, sample enterprises are selected, and the sample enterprises are classified into different categories by cluster analysis.
Optionally, in the embodiment of the present invention, N sample enterprises may be randomly decimated from all enterprises in the database, and the sample enterprises are classified into m categories by a k-means clustering algorithm based on all index data in a risk assessment system of the sample enterprises, where N is greater than or equal to m.
In step S203, risk levels and major risk points of different types of enterprises are analyzed according to evaluation feature values of the types of enterprises, where the evaluation feature values include an evaluation index data average value and a standard deviation.
Optionally, in the embodiment of the present invention, characteristics of each category of enterprises may be analyzed according to evaluation characteristic values such as an average value and a standard deviation of evaluation index data of each category of enterprises, a degree of influence of specific indexes on the operation of each category of enterprises is judged, and a main risk point is identified. M for each class i I =1,2, … m. For example, m 1 The category enterprises mainly sell, and index data such as the number of customers and orders in the CRM system have large influence on enterprise operation; m is 2 The category enterprises mainly produce, and index data such as inventory quantity, purchase cost, production cost and the like in the purchase-sale-stock system have great influence.
In step S204, the risk assessment model is adjusted according to the actual credit records, risk levels and main risk points in the enterprises of each category, and the weight proportions of the enterprises of different categories are determined to calculate the credit scores of the enterprises.
Optionally, in the embodiment of the present invention, the risk assessment model may be adjusted based on characteristics of each category of enterprise, and a weight ratio of the risk assessment model of each category of enterprise is determined. The credit risk assessment system based on the cloud computing ERP system multi-dimensional data at least comprises 5 primary indexes of enterprise scale, financial performance, employee competitiveness, enterprise competitiveness, customer competitiveness and the like, and each primary index comprises a plurality of secondary indexes. E.g. m 1 And m 2 The weights of corresponding indexes in the two types of risk assessment systems are different, and the corresponding indexes are increased or decreased in respective models, so that the weight of important indexes is increased, and the weight of useless indexes is reduced.
Furthermore, the embodiment of the invention can also judge the overall risk level of each category by combining the actual credit records of enterprises of each category, and divide the credit score of each enterprise into specific levels and credit limit ranges according to the score intervals in each category. For example, the overall risk level of a category is divided into five levels (A > B > C > D > E) from high to low, and the business credit score in each category is further divided into 5 levels and credit limit ranges.
Optionally, the embodiment of the present invention may also update data periodically to perform category classification and model adjustment.
In step S205, it is determined whether the credit application submitted by the business passes based on the credit score of the business.
Optionally, according to the preferred implementation manner in the above steps S202, S203, and S204, in the embodiment of the present invention, for the enterprise submitting the credit application, the category to which the enterprise belongs may be automatically matched, and the credit score (credit) of the enterprise is calculated according to the risk assessment model of the category to which the enterprise belongs. The formula for calculating the credit score of a business is:
wherein the content of the first and second substances,i＝1,2,…K,K≥5，j＝1,2,…k i ，k i ≥ 1，X i is a preset i-th primary index, W i Is the said X i Weight, x, corresponding to the primary index ij Is the jth secondary index under the preset ith primary index, w ij Is said x ij The corresponding weight.
The following describes a preferred parameter setting method for calculating the credit score of a business according to an embodiment of the present invention by using a table.
Optionally, the embodiment of the present invention may further determine the risk level and the main risk point of the enterprise according to the credit score of the credit application enterprise on the basis of the overall risk level of the category to which the credit application enterprise belongs, approve the credit line, confirm whether the credit application submitted by the enterprise passes risk assessment and audit, and automatically set the enterprise early warning parameters. E.g. m 1 All the category enterprises have no bad loan records and belong to the A level; a business meridianSystem match is m 1 Class, calculating the credit score of the enterprise in the 2 nd interval of the class, namely A 2 Corresponding credit limits can be checked out, and important indexes such as the number of customers and orders in the CRM system are set as important parameters.
In step S206, if the credit is passed, real-time monitoring is performed by the cloud-computing ERP system after the credit is extended.
Optionally, the embodiment of the invention can automatically perform real-time monitoring after the enterprise obtains the credit loan, and multi-dimensional data of small and medium-sized enterprises in daily operation management can be called and analyzed in real time through the cloud computing ERP system. And if the credit score of the enterprise is reduced to an enterprise early warning threshold value or the fluctuation of a specific index reaches a risk early warning threshold value, giving an alarm. E.g. m 1 Multiple data changes for category enterprises result in credit scores falling below A 3 Or A 4 The level, or the customer and order quantity drop by more than 30%, will trigger an early warning alarm in real time.
As can be seen from the embodiment of fig. 2, in the embodiment of the present invention, multidimensional data of an enterprise is obtained through a cloud computing ERP system, and the multidimensional data is analyzed to obtain an analysis result; sampling enterprises are selected, and the sampling enterprises are divided into different categories through clustering analysis; analyzing risk levels and main risk points of enterprises of different categories according to evaluation characteristic values of the enterprises of the categories, wherein the evaluation characteristic values comprise an evaluation index data average value and a standard deviation; adjusting a risk evaluation model according to the actual credit records, the risk grades and the main risk points in the enterprises of all classes, determining the weight proportion of the enterprises of different classes, and calculating the credit score of the enterprises; judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise; and if the credit is passed, real-time monitoring is carried out through the cloud computing ERP system after credit is provided. The embodiment of the invention further improves the evaluation accuracy by acquiring and analyzing the multidimensional data of the enterprise through the cloud computing ERP system and by methods of sampling classification, category analysis, weight determination, credit classification, periodic updating and the like, reduces the cost of heavy offline monitoring and manual monitoring, shortens the auditing time, ensures that the monitoring is in place in real time after loan, can perform risk early warning according to the real-time data change condition, and has high practicability.
Fig. 3 is a schematic diagram of a credit risk assessment system according to a third embodiment of the present invention, and only the parts related to the third embodiment of the present invention are shown for convenience of illustration.
The terminal device includes:
the data module 31 is used for acquiring multidimensional data of an enterprise through a cloud computing ERP system, analyzing the multidimensional data and acquiring an analysis result;
the processing module 32 is used for determining the category of the enterprise according to the analysis result and adjusting the risk assessment model weight of different categories of enterprises to calculate the credit score of the enterprise;
the control module 33 is used for judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise;
the data module 31 is also used for real-time monitoring through the cloud computing ERP system after credit is provided if the credit passes.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40, when executing the computer program 42, implements the steps in the various method embodiments described above, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 31 to 33 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the terminal device 4. For example, the computer program 42 may be divided into a data module, a processing module, and a control module, and the specific functions of each module are as follows:
the data module is used for acquiring multidimensional data of an enterprise through a cloud computing ERP system, analyzing the multidimensional data and acquiring an analysis result;
the processing module is used for determining the category of the enterprise according to the analysis result and adjusting the risk assessment model weight of different categories of enterprises to calculate the credit score of the enterprise;
the control module is used for judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise;
and the data module is also used for carrying out real-time monitoring through the cloud computing ERP system after the credit is provided if the credit passes.
The terminal device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 4 and does not constitute a limitation of terminal device 4 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be 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 through some interfaces, devices or units, 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. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, and software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A credit risk assessment method, comprising:
the method comprises the steps that multidimensional data of an enterprise are obtained through a cloud computing ERP system, and the multidimensional data are analyzed to obtain an analysis result;
determining the category of the enterprise according to the analysis result, and adjusting the weight of risk assessment models of different categories of enterprises to calculate the credit score of the enterprise;
judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise;
and if the credit passes, real-time monitoring is carried out through the cloud computing ERP system after the credit is provided.
2. The credit risk assessment method of claim 1, wherein the multi-dimensional data includes application system data, behavioral data, and extensible data.
3. The credit risk assessment method of claim 1 wherein determining the category of business based on the analysis results and adjusting the risk assessment model weights for different categories of businesses comprises:
selecting sample enterprises, and classifying the sample enterprises into different categories through clustering analysis;
analyzing risk levels and main risk points of enterprises of different categories according to evaluation characteristic values of the enterprises of the categories, wherein the evaluation characteristic values comprise an evaluation index data average value and a standard deviation;
and adjusting the risk evaluation model according to the actual credit records, the risk levels and the main risk points in the enterprises of all classes, and determining the weight proportion of the enterprises of different classes.
4. The credit risk assessment method of claim 1, wherein the formula for calculating the credit score of a business is:
wherein the content of the first and second substances,i＝1，2，...K，K≥5，j＝1，2，...k i ，k i ≥1，X i is a preset i-th primary index, W i Is that it isX i Weight, x, corresponding to the primary index ij Is the jth secondary index under the preset ith primary index, w ij Is said x ij The corresponding weight.
5. The credit risk assessment method of claim 1, wherein said real-time monitoring by a cloud computing ERP system after credit extension comprises:
and acquiring and analyzing the information of the enterprise after credit is provided in real time, and giving an alarm when the credit score of the enterprise is lower than a threshold value or the fluctuation of some index exceeds a preset range.
6. A credit risk assessment system, comprising:
the data module is used for acquiring multidimensional data of an enterprise through a cloud computing ERP system, analyzing the multidimensional data and acquiring an analysis result;
the processing module is used for determining the category of the enterprise according to the analysis result and adjusting the risk assessment model weight of different categories of enterprises to calculate the credit score of the enterprise;
the control module is used for judging whether the credit application submitted by the enterprise passes or not based on the credit score of the enterprise;
and the data module is also used for carrying out real-time monitoring through the cloud computing ERP system after the credit is provided if the credit passes.
7. The credit risk assessment system of claim 6, wherein said data module comprises a database and a logic library:
the database comprises a cloud computing ERP system and is used for acquiring multi-dimensional data of an enterprise;
the logic library is used for analyzing the multi-dimensional data to obtain an analysis result.
8. The credit risk assessment system of claim 6, wherein said data module is further configured to collect and analyze information about a business that has extended credit in real time, and to issue an alert if the credit score of the business falls below a threshold or if the fluctuation of an indicator exceeds a predetermined range.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor realizes the steps of the method according to any of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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