Small and micro enterprise risk control system and method based on multi-source data and financial intelligent model

Abstract

The invention discloses a small and micro enterprise risk control system and method based on multi-source data and a financial intelligent model, and belongs to the technical field of information. A small and micro enterprise risk control method based on multi-source data and an intelligent model comprises the following steps: S1, collecting small and micro enterprise data, storing the small and micro enterprise data as a source layer, unifying governance standards to obtain a standard layer, and performing fusion processing to obtain feature wide table data; s2, constructing a nine-dimensional index covering an enterprise; s3, on the basis of the feature wide table data and the nine-dimensional index system, index values are calculated to form an index table library; s4, fusing the extracted enterprise basic features in the index table, and forming a feature index table through feature screening; s5, training through an ensemble learning algorithm based on the feature index table, and finally obtaining a precision marketing model and a credit evaluation model; and S6, obtaining an association rule model by constructing a knowledge graph, and quantifying association risks by calculating association indexes.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and more specifically, to a risk control system and method for small and micro enterprises based on multi-source data and financial intelligence models. Background Art

[0002] As small and micro enterprises play an increasingly important role in the national economy, how to provide them with credit services efficiently and accurately has become a major challenge facing financial institutions. The traditional credit approval process often relies on manual review, which is time-consuming, inefficient, and difficult to comprehensively assess the credit risks of small and micro enterprises. Business personnel face problems in the loan process, such as difficulty in managing related relationships, difficulty in identifying risk information, and difficulty in sorting out due diligence materials.

[0003] The existing credit evaluation of small and micro enterprises mainly relies on information such as corporate financial statements and their historical debt performance records. However, this information may be incomplete or even incorrect, and cannot accurately reflect the credit status of small and micro enterprises. In addition, due to the large number of small and micro enterprises and large information differences, the efficiency of risk assessment is low. Taking a method for evaluating the credit of small and micro enterprises disclosed in Chinese invention patent documents (CN202410185819.3) as an example, this invention collects, reviews and organizes data on the operating conditions, credit records, financing conditions, industry risks, legal proceedings, etc. of small and micro enterprises, and obtains credit assessment scores through analysis and application, thereby improving the evaluation efficiency. However, this method can only be used as a screening for the credit evaluation of small and micro enterprises, and cannot be assigned to account managers based on the business characteristics of small and micro enterprises, which reduces the efficiency of credit business. At the same time, it is also impossible to determine the interest rate pricing method for different small and micro enterprises, which reduces the profitability and market competitiveness of high-credit business.

[0004] Therefore, we need a risk control system and method for small and micro enterprises based on multi-source data and financial intelligence models, which can integrate multi-source data and provide accurate customer allocation and full-process risk control for credit business. Summary of the invention

[0005] The purpose of the present invention is to provide a risk control method for small and micro enterprises based on multi-source data and intelligent models, which can integrate multi-source data and provide accurate customer allocation and full-process risk management for credit business, improve the efficiency and accuracy of credit approval, and reduce the operating costs and risks of financial institutions; another invention purpose of the present invention is to provide a risk control system for small and micro enterprises based on multi-source data and intelligent models, which can track and manage the entire process to achieve accurate risk assessment and customer allocation for small and micro enterprises, and improve the profitability and market competitiveness of credit business.

[0006] To achieve the above object, the present invention provides the following technical solutions: The risk control method for small and micro enterprises based on multi-source data and intelligent models of the present invention comprises the following steps: S1. Collect small and micro enterprise data from various government departments and financial business system channels and store them in the database as the source layer and unify the governance standards to obtain the standard layer, and obtain feature wide table data through fusion processing; S2. Construct a nine-dimensional indicator system covering enterprises, including enterprise entity, actual controller, legal representative, shareholders, senior executives, enterprise stable operation ability, enterprise social contribution ability, enterprise credit status and enterprise financial status; S3. Based on the feature wide table data and the nine-dimensional indicator system, write a JAVA script to calculate the indicator value and form an indicator table library; S4. Match the extracted sample labels with the indicator table, integrate the extracted basic enterprise features, and form a feature indicator table through feature screening; S5. Based on the feature index table as input, after training with the ensemble learning algorithm, the precision marketing model and credit assessment model are finally obtained; S6. Construct a graph by defining the attributes of customers, accounts, and transactions and their relationships, and use graph database technology to store and manage the knowledge graph. Use graph analysis technology to obtain the association rule model, and quantify the association risk by calculating the association indicators.

[0007] As a further improvement of the present invention, step S1 collects data on small and micro enterprises by obtaining data from industrial and commercial registration information, court announcements, comprehensive financial data and external channels, while integrating internal data on operations, assets, credit and transaction flows recorded by banks.

[0008] As a further improvement of the present invention, the unified governance standard in step S1 is to use ETL technology to clean and standardize the data to ensure the accuracy and consistency of the data.

[0009] As a further improvement of the present invention, the fusion processing described in step S1 is based on the unified data governance standard, and then the various standard layers are fused to form a feature wide table through feature derivation; for one-to-many relationships, they are stored in a dictionary manner.

[0010] As a further improvement of the present invention, the feature derivation includes but is not limited to polynomial feature derivation, time series derivation and cross combination.

[0011] As a further improvement of the present invention, a timed task is set between the standard layer and the feature wide table in step S1 to synchronize and update the feature wide table data in a timely manner.

[0012] As a further improvement of the present invention, the indicator calculation described in step S3 is based on the multi-source data fusion module and the three-level indicator business logic, and a JAVA script is written to calculate the indicator value and form an indicator table library.

[0013] As a further improvement of the present invention, the processing means for feature screening in step S4 include but are not limited to random forests and decision trees; the evaluation values ​​for feature screening include PSI, IV, WOE, and VIF.

[0014] As a further improvement of the present invention, the index value is dynamically calculated to ensure the timeliness and accuracy of the index.

[0015] The risk control system for small and micro enterprises based on multi-source data and intelligent models is obtained through a risk control method for small and micro enterprises based on multi-source data and intelligent models, and includes an information query module, a customer management module and a credit auxiliary function module.

[0016] The information query module is used to evaluate the economic strength and repayment ability of the enterprise.

[0017] The customer management module is based on the model building module, is trained with an integrated learning algorithm, screens potential customers, and pushes business opportunities to account managers.

[0018] The credit assistance module is based on the model building module and generates loan reports, risk factors and interest rate pricing sub-functions through the credit assessment model and small and micro enterprise loan application information.

[0019] As a further improvement of the present invention, the information query module includes an industrial and commercial view, a risk view and an association view; the industrial and commercial view is used to provide comprehensive data on the basic information, shareholder structure and operating conditions of the enterprise, so that the account manager can quickly understand the basic situation and market position of the enterprise; the risk view provides a comprehensive risk assessment for the bank by integrating more dimensional information such as judicial risk, operating risk and industrial and commercial changes; the association view is used to reveal the complex relationship between the enterprise and its related parties, including but not limited to equity relationship, actual controller relationship and guarantee relationship.

[0020] As a further improvement of the present invention, the customer management module includes a precise push system and a customer allocation system; the precise push system intelligently screens potential customers based on the customer's establishment time, credit status and operating conditions, and pushes business opportunities to account managers; the customer allocation system automatically allocates customers to suitable account managers based on the account manager's jurisdiction and service capability factors.

[0021] As a further improvement of the present invention, the loan report can record the customer's basic situation, credit status, loan purpose and repayment ability information, providing a strong basis for the bank to approve the loan and saving the account manager's time for traditional manual filling; the risk factor is used to dig out the key factors affecting the customer's credit status and risk level, and promptly remind the account manager; interest rate pricing can automatically calculate and recommend a reasonable interest rate level based on the customer's credit assessment results, loan amount and loan term factors.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: by using the risk control method for small and micro enterprises based on multi-source data and intelligent models of the present invention, accurate risk assessment of small and micro enterprises is achieved through big data technology, the efficiency and accuracy of credit approval are improved, and accurate marketing and credit assessment models are obtained in combination with the basic characteristics of the enterprise, thereby reducing the operating costs and risks of financial institutions; by collecting, cleaning and integrating data of small and micro enterprises, outliers and missing values ​​in the data can be processed to improve data quality; by dynamically calculating the indicator values ​​in the JAVA script written in the nine-dimensional indicator system to form an indicator table and store it in the database, the timeliness and accuracy of the indicators are ensured; by combining the operating data of small and micro enterprises with the jurisdiction and service capabilities of the account manager, potential customers can be screened and customers can be intelligently assigned to suitable account managers, thereby not only improving the credit efficiency and service quality, but also improving the work efficiency of the account manager; by combining the loan application information and credit assessment of small and micro enterprises, a reasonable interest rate level can be automatically calculated and recommended, thereby helping banks to improve the profitability and market competitiveness of their credit business. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the risk control method for small and micro enterprises based on multi-source data and intelligent models of the present invention; Figure 2 This is a schematic diagram of the module structure of the risk control system for small and micro enterprises based on multi-source data and intelligent models of the present invention. DETAILED DESCRIPTION

[0024] Specific embodiment 1: Please refer to Figure 1 The risk control method for small and micro enterprises based on multi-source data and intelligent models includes the following steps: S1. Collect small and micro enterprise data from various government departments and financial business system channels and store them in the database as the source layer and unify the governance standards to obtain the standard layer, and obtain characteristic wide table data through fusion processing.

[0025] Specifically, step S1 collects data on small and micro enterprises by obtaining data from industrial and commercial registration information, court announcements, comprehensive financial data and external channels, while integrating internal data on operations, assets, credit and transaction flows recorded by banks.

[0026] Specifically, the unified governance standard described in step S1 is to use ETL technology to clean and standardize data. By formulating unified data governance standards, multi-source heterogeneous low-quality data is governed into standardized data to ensure data accuracy and consistency.

[0027] Specifically, the fusion processing described in step S1 is based on the unified data governance standard, and then the various standard layers are fused to form a feature wide table through feature derivation; for one-to-many relationships, they are stored in a dictionary manner.

[0028] Specifically, the feature derivation includes but is not limited to polynomial feature derivation, time series derivation and cross combination.

[0029] Specifically, in step S1, a timed task is set between the standard layer and the feature wide table to synchronize and update the feature wide table data in a timely manner.

[0030] S2. Construct a nine-dimensional indicator system covering enterprises, including corporate entities, actual controllers of enterprises, legal representatives, shareholders, senior executives, stable operating capabilities of enterprises, social contribution capabilities of enterprises, credit status of enterprises and financial status of enterprises.

[0031] S3. Based on the feature wide table data and the nine-dimensional indicator system, write a JAVA script to calculate the indicator value and form an indicator table database.

[0032] Specifically, the indicator calculation described in step S3 is based on the multi-source data fusion module and the three-level indicator business logic, and a JAVA script is written to calculate the indicator value and form an indicator table.

[0033] Specifically, the indicator value is dynamically calculated to ensure the timeliness and accuracy of the indicator.

[0034] S4. Match the extracted sample labels with the indicator table, integrate the extracted basic enterprise features, and form a feature indicator table through feature screening.

[0035] Specifically, the processing means for feature screening in step S4 include but are not limited to random forests and decision trees, which are used to process outliers and missing values ​​in the data of the feature wide table; the evaluation values ​​of feature screening include PSI, IV, WOE, and VIF.

[0036] Specifically, the processing of outliers and missing values ​​requires value distribution evaluation, which uses statistical charts such as histograms, box plots, pie charts or bar charts to describe the overall sample situation and test the normal hypothesis to provide a basis for subsequent data preprocessing and feature screening; outliers in features are processed based on isolation forests, and the data space is segmented by randomly selecting a feature and a feature value until all data points are isolated, thereby improving data quality; missing values ​​in features are processed based on random forests. Since random forests can handle complex data patterns and nonlinear relationships between features, they can provide more accurate and reliable missing value predictions.

[0037] Specifically, IV value screening is to eliminate variables with low IV values ​​based on the IV value results of the training set to ensure that the retained features have higher information content and predictive ability; WOE screening is to statistically check whether the WOE trends of the training set and the test set are consistent and monotonic at the same binning point, and eliminate variables with inconsistent trends and non-monotonic variables to ensure the stability and reliability of the features; PSI screening is to calculate the PSI value of the variable, and eliminate variables with PSI values ​​exceeding the threshold to ensure the stability of the features in different time periods; VIF screening is to calculate the VIF value of the variable, and eliminate variables with serious collinearity to avoid model overfitting and decreased prediction performance.

[0038] S5. Based on the feature index table as input, after training with the integrated learning algorithm, we finally get the precision marketing model and credit assessment model.

[0039] Specifically, the precision marketing model includes business expansion, corporate credit needs and legal person capital needs, and is used as the basis for assigning account managers; the credit assessment model is calculated by scoring the various information of the enterprise in the characteristic indicator table, and the comprehensive credit score is obtained by accumulating the scores of various information, and comparing the different credit thresholds corresponding to different loan amounts to evaluate and grade the enterprises, thereby reducing the risk of loans.

[0040] S6. Construct a graph by defining the attributes of customers, accounts, and transactions and their relationships, and use graph database technology to store and manage the knowledge graph. Use graph analysis technology to obtain the association rule model, and quantify the association risk by calculating the association indicators.

[0041] Specifically, the association rule model is based on the path of the Bayesian network and is used to reveal the spread of risks; the association rule model includes explicit associations, implicit associations and equity penetration; the quantification of the strength of the association relationship is to use the strength of the association relationship between customers, such as the shareholding ratio to quantify the probability distribution of each node under a given risk state, thereby evaluating the risk of risk transmission to connected nodes; the prediction of future risk status is through the mutual propagation of associated nodes, using the reasoning ability of the Bayesian network to measure possible future risk states and provide forward-looking guidance for risk management.

[0042] See also Figure 2 The risk control system for small and micro enterprises based on multi-source data and intelligent models is obtained through the risk control method for small and micro enterprises based on multi-source data and intelligent models, including information query module, customer management module and credit auxiliary function module.

[0043] The information query module is used to evaluate the economic strength and repayment ability of the enterprise.

[0044] The customer management module is based on the model building module, is trained with an integrated learning algorithm, screens potential customers, and pushes business opportunities to account managers.

[0045] The credit assistance module is based on the model building module and generates loan reports, risk factors and interest rate pricing sub-functions through the credit assessment model and small and micro enterprise loan application information.

[0046] Specifically, the information query module includes the industrial and commercial view, risk view and association view; the industrial and commercial view is used to provide comprehensive data on the company's basic information, shareholder structure, and operating conditions, so that account managers can quickly understand the company's basic situation and market position; the risk view provides banks with comprehensive risk assessment by integrating more dimensional information on judicial risks, operating risks, and industrial and commercial changes; the association view is used to reveal the complex relationships between an enterprise and its related parties, including but not limited to equity relationships, actual controller relationships, and guarantee relationships.

[0047] Specifically, the customer management module includes a precision push system and a customer allocation system; the precision push system intelligently screens potential customers based on the customer's establishment time, credit status and operating conditions, and pushes business opportunities to account managers; the customer allocation system automatically allocates customers to suitable account managers based on the account manager's jurisdiction and service capabilities.

[0048] Specifically, the loan report can record the customer's basic information, credit status, loan purpose and repayment ability, providing a strong basis for banks to approve loans and saving the account manager's time for traditional manual filling out. Risk factors use big data analysis technology to dig out the key factors that affect the customer's credit status and risk level in real time, and promptly remind the account manager to ensure the timeliness and effectiveness of risk management. Interest rate pricing can automatically calculate and recommend a reasonable interest rate level based on the customer's credit assessment results, loan amount and loan term.

[0049] The data involved in this application have been obtained with full consent and authorization, and the collection, use and processing of relevant information must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0050] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A risk control method for small and micro enterprises based on multi-source data and intelligent models, characterized by: The following steps are involved: S1. Collect small and micro enterprise data from various government departments and financial business system channels and store them in the database as the source layer and unify the governance standards to obtain the standard layer, and obtain feature wide table data through fusion processing; S2. Construct a nine-dimensional indicator system covering enterprises, including enterprise entity, actual controller, legal representative, shareholders, senior executives, enterprise stable operation ability, enterprise social contribution ability, enterprise credit status and enterprise financial status; S3. Based on the feature wide table data and the nine-dimensional indicator system, write a JAVA script to calculate the indicator value and form an indicator table library; S4. Match the extracted sample labels with the indicator table, integrate the extracted basic enterprise features, and form a feature indicator table through feature screening; S5. Based on the feature index table as input, after training with the ensemble learning algorithm, the precision marketing and credit assessment model is finally obtained; S6. Construct a knowledge graph by defining the attributes and relationships of customers, accounts, and transactions, use graph analysis technology to obtain an association rule model, and quantify association risks by calculating association indicators.

2. The risk control method for small and micro enterprises based on multi-source data and intelligent models according to claim 1 is characterized by: Step S1 collects data on small and micro enterprises by obtaining data from industrial and commercial registration information, court announcements, comprehensive financial data and external channels, while integrating internal data on operations, assets, credit and transaction flows recorded by banks.

3. The risk control method for small and micro enterprises based on multi-source data and intelligent models according to claim 1 is characterized by: The unified governance standard described in step S1 is to use ETL technology to clean and standardize the data to ensure the accuracy and consistency of the data.

4. The risk control method for small and micro enterprises based on multi-source data and intelligent models according to claim 1 is characterized by: The fusion processing described in step S1 is based on the unified data governance standard, and then the various standard layers are fused to form a feature wide table through feature derivation.

5. The risk control method for small and micro enterprises based on multi-source data and intelligent models according to claim 1 is characterized by: The indicator calculation described in step S3 is based on the multi-source data fusion module and the three-level indicator business logic, and a JAVA script is written to calculate the indicator value and form an indicator table.

6. The risk control method for small and micro enterprises based on multi-source data and intelligent models according to claim 1 is characterized by: The processing means for feature screening in step S4 include but are not limited to random forest and decision tree; the evaluation values ​​of feature screening include PSI, IV, WOE, and VIF.

7. The risk control system for small and micro enterprises based on multi-source data and intelligent models is characterized by: The system uses the risk control method for small and micro enterprises based on multi-source data and intelligent models as described in any one of claims 1 to 6, including an information query module, a customer management module and a credit auxiliary function module; The information query module is used to evaluate the economic strength and repayment ability of the enterprise; The customer management module is used to screen potential customers and push business opportunities to account managers; The credit assistance module is based on the model building module and generates loan reports, risk factors and interest rate pricing sub-functions through the credit assessment model and small and micro enterprise loan application information.

8. The risk control system for small and micro enterprises based on multi-source data and intelligent models according to claim 7 is characterized by: The information query module includes an industrial and commercial view, a risk view, and an associated view; the industrial and commercial view is used to provide comprehensive data on the basic information, shareholder structure, and operating conditions of the enterprise; The risk view provides banks with a comprehensive risk assessment by integrating multi-dimensional information on judicial risk, operational risk and industrial and commercial changes; the association view is used to reveal the complex relationships between an enterprise and its related parties, including but not limited to equity relationships, actual controller relationships and guarantee relationships.

9. The risk control system for small and micro enterprises based on multi-source data and intelligent models according to claim 7 is characterized by: The customer management module includes a precision push system and a customer allocation system; the precision push system intelligently screens potential customers based on the customer's establishment time, credit status and operating conditions, and pushes business opportunities to account managers; the customer allocation system automatically allocates customers to suitable account managers based on the account manager's jurisdiction and service capability factors.

10. The risk control system for small and micro enterprises based on multi-source data and intelligent models according to claim 7 is characterized by: The loan report can record the customer's basic information, credit status, loan purpose and repayment ability; the risk factor is used to dig out the key factors that affect the customer's credit status and risk level, and promptly remind the account manager; the interest rate pricing can automatically calculate and recommend a reasonable interest rate level based on the customer's credit assessment results, loan amount and loan term factors.

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