Enterprise credit assessment method and system based on artificial intelligence

Abstract

The invention relates to the technical field of enterprise credit assessment, and discloses an artificial intelligence-based enterprise credit assessment method and system, and the method comprises the steps: obtaining enterprise historical financial information and an industry development trend based on a multi-source data fusion technology; a data preprocessing step is adopted to eliminate deviations and abnormal values in the data. According to the artificial intelligence-based enterprise credit assessment method and system, by using a multi-source data fusion technology, the system can integrate wider information including financial statements, external economic indicators, industry research reports, macroeconomic environments and the like, so that more comprehensive and accurate enterprise credit analysis is provided; according to the comprehensive data analysis method, the economic condition and the credit risk of the enterprise can be more effectively identified, the enterprise credit rating classifier is constructed through the artificial intelligence technology, and the system can adjust the evaluation model in a personalized mode according to specific industry characteristics and enterprise operation modes.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise credit assessment, and in particular to an enterprise credit assessment method and system based on artificial intelligence. Background Art

[0002] Corporate credit assessment is a standard process used to determine a company's performance in financial management, debt repayment ability and overall operating conditions. This assessment is usually performed by financial institutions, credit rating agencies or other professional entities. The purpose is to assess the company's credit rating, that is, the company's ability and possibility to fulfill its financial obligations. The credit rating results will significantly affect whether the company can obtain loans, the loan conditions (such as interest rates and loan amounts), and other financial-related business decisions. However, traditional credit assessment methods often rely on limited data sources, resulting in incomplete or outdated assessment results. In previous systems, data preprocessing is often performed manually, which can easily cause inconsistent data processing or unstable quality, resulting in incorrect interpretation or analysis of data, which affects the final credit assessment results and leads to incorrect judgments on the company's credit status, which in turn affects major decisions such as loans and investments. At the same time, poor data quality or processing errors can easily conceal actual risk points, resulting in insufficient credit risk assessment or excessive trust in some seemingly well-performing companies. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] To achieve the above object, the present invention provides the following technical solutions: An enterprise credit assessment method and system based on artificial intelligence, the method steps include: Obtain historical financial information of enterprises and industry development trends based on multi-source data fusion technology; Data preprocessing steps were used to remove bias and outliers from the data; Obtain enterprise credit rating classifier by building artificial intelligence analysis model training; Based on the classifier, dynamic evaluation and risk warning of the target enterprise's credit are carried out.

[0005] As a further solution of the present invention: the acquisition of enterprise historical financial information and industry development trends based on multi-source data fusion technology includes: Integrate historical data of financial statements; Integrate externally disclosed enterprise-related economic indicators; Extract key indicators and development trends from industry research reports; Update information based on the macroeconomic environment and laws and regulations.

[0006] As a further solution of the present invention: the historical data of the integrated financial statements includes: Obtain detailed information on each part of the company's financial statements based on the accounting period, and normalize the financial ratios in the report information; When any financial ratio in the report reaches the preset standard ratio range of X% and X+%, it is considered a normal value, where X+ is the gain value of X; Use the profit distribution statement and asset impairment provision statement in the latest annual report to conduct further financial quality verification and confirm data consistency; Determine the weights between the indicators and calculate the overall financial health score S.

[0007] As a further solution of the present invention: when any financial ratio in the report reaches the preset standard ratio between X% and X+%, the normal value also includes: Calculate the correlation coefficient matrix of each item in the report to ensure that each coefficient Rij fluctuates within a certain range. , where Y is a given stability criterion; Based on the formula Calculate the comprehensive score S, where Wi is the weight corresponding to indicator i, and Ci is the standardized score corresponding to indicator i; Within the range of positive comprehensive scores, the enterprise is deemed to have a certain credit potential; The scoring results will be used to determine whether a more detailed analysis phase will be added later.

[0008] As a further solution of the present invention: the calculation of the correlation coefficient matrix of each item in the report and the verification further include: Screen out the relationship groups with high (R>Z) and low (R<=Z) correlation between financial indicators, where Z is a given correlation strength threshold; Using equations A linear regression equation between indicators is established to evaluate the prediction accuracy; where C refers to the coefficient vector matrix, E refers to the error matrix, and Rj represents the correlation vector between indicators; When Rj is consistent with the expected forecast (i.e., E≤D, where D is the predetermined error range), it is considered a signal of financial robustness and its corresponding assessment score T1 is adjusted; Increase the focus on relevant data with strong financial performance and reduce the proportion of irrelevant or weakly correlated assessments.

[0009] As a further solution of the present invention: the use of equations to establish a linear regression equation between indicators of prediction accuracy to evaluate the accuracy specifically includes: Use the standard variance method to determine the importance of each variable, and assign different adjustment factors according to the importance of variable V. , where K is a pre-set importance factor constant; Apply the variable importance assessment results to the equation and modify it to , where Rj is the corresponding correlation after importance calibration, and X represents the revised prediction; When the difference between X and the current actual observation value is small ( , which is regarded as good and in line with expectations; Correct the weight coefficients used in the evaluation to increase the contribution of highly correlated financial indicators in the forecast.

[0010] An enterprise credit assessment system based on artificial intelligence, wherein the system modules include: a data acquisition module, a data processing module, an artificial intelligence analysis model module, a credit assessment and risk warning module and a user interface module.

[0011] As a further solution of the present invention: the data acquisition module includes a financial data interface, an industry data interface, a macroeconomic data interface and a legal and regulatory update data interface; the data processing module includes a data cleaning submodule, an outlier detection submodule and a data normalization submodule.

[0012] As a further solution of the present invention: the output end of the data acquisition module is communicatively connected to the input end of the data processing module, the output end of the data processing module is communicatively connected to the input end of the artificial intelligence analysis model module, the output end of the artificial intelligence analysis model module is communicatively connected to the input end of the credit assessment and risk warning module, and the output end of the credit assessment and risk warning module is communicatively connected to the input end of the user interface module.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By utilizing multi-source data fusion technology, the system can integrate a wider range of information, including financial statements, external economic indicators, industry research reports and macroeconomic environment, so as to provide a more comprehensive and accurate corporate credit analysis. This comprehensive data analysis method can more effectively identify the economic status and credit risk of the enterprise. By building a corporate credit rating classifier through artificial intelligence technology, the system can personalize the evaluation model according to specific industry characteristics and corporate operating modes, improve the model's adaptability and prediction accuracy, and combine outlier detection and robustness verification. It not only enhances the accuracy of data processing, but also can identify and reduce potential deviations in model analysis, thereby optimizing the overall risk management framework. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a system connection diagram of an enterprise credit assessment system based on artificial intelligence. DETAILED DESCRIPTION

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0017] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0018] Example See also Figure 1 , is an embodiment of the present invention, which provides an enterprise credit assessment method and system based on artificial intelligence, including the following steps: Step 1: Data Acquisition Module Financial data interface: Automatically collect annual and quarterly reports of enterprises from financial information providers such as Bloomberg, Reuters, domestic and foreign stock exchanges, etc. This includes balance sheets, income statements, and cash flow statements; Industry data interface: Capture the development trends of specific industries, market share of major companies and technological innovation information from industry association websites, market research reports and news reports; Macroeconomic data interface: Obtain macroeconomic indicators such as GDP growth rate, inflation rate and unemployment rate from the World Bank, the International Monetary Fund or national economic research institutions; Legal and regulatory update data interface: connect to the government legal announcement system to collect the latest laws and regulations on corporate taxation, financial reporting requirements and industry compliance standards in a timely manner; Step 2: Data processing module Data cleaning submodule: Cleans the raw data using techniques such as outlier marking, removing duplicate records, and correcting typos or format misalignments; Outlier detection submodule: uses box plots and 3σ principle statistical methods to identify and process outliers in the data to ensure the accuracy of subsequent analysis; Data normalization submodule: Use MinMax or Z-score method to normalize the data to meet the model requirements; Step 3: Artificial Intelligence Analysis Model Module Model building: Combine machine learning algorithms (decision tree, support vector machine and neural network) to build the initial credit rating model; Feature selection: Based on industry expert opinions and automated feature selection technology, we select the key factors that most influence credit assessment; Model training and optimization: Use past credit events and financial data to train the model and adjust model parameters through cross-validation to improve accuracy and generalization ability; Step 4: Credit Assessment and Risk Warning Module Real-time evaluation execution: When a request is made to evaluate a business, the module activates real-time data collection and completes preprocessing, and then calculates its credit score through the trained AI model; Risk warning settings: Set credit score thresholds. If the company's credit drops to a certain dangerous level, the system will trigger an early warning and notify relevant personnel via email or SMS; Step 5: User Interface Module Query function: Users can search for the credit rating of a specific company through the web interface or mobile application; Results display: Displays the enterprise credit score, score history and change trend chart, and provides a brief industry comparative analysis; Customized report generation: Users can request a credit assessment report for a specific date range for audit or internal review purposes.

[0019] In summary, by utilizing multi-source data fusion technology, the system can integrate a wider range of information, including financial statements, external economic indicators, industry research reports and macroeconomic environment, so as to provide a more comprehensive and accurate corporate credit analysis. This comprehensive data analysis method can more effectively identify the economic status and credit risk of the enterprise. By building a corporate credit rating classifier through artificial intelligence technology, the system can personalize the evaluation model according to specific industry characteristics and corporate operating modes, improve the adaptability and prediction accuracy of the model, and combine outlier detection and robustness verification. It not only enhances the accuracy of data processing, but also can identify and reduce potential deviations in model analysis, thereby optimizing the overall risk management framework.

[0020] Importantly, it should be noted that the construction and arrangement of the present application shown in a number of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible (e.g., the size, scale, structure, shape and proportion of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), mounting arrangements, use of materials, color, changes in orientation, etc.) without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, an element shown as integrally formed may be composed of multiple parts or elements, the position of the element may be inverted or otherwise changed, and the nature or number or position of the discrete element may be altered or changed. Therefore, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method steps may be changed or reordered according to an alternative embodiment. In the claims, any "device plus function" clause is intended to cover the structure described herein that performs the function, and is not only structurally equivalent but also equivalent structure. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0021] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention).

[0022] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0023] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An enterprise credit assessment method based on artificial intelligence, characterized by: The method steps include: Obtain historical financial information of enterprises and industry development trends based on multi-source data fusion technology; Data preprocessing steps were used to remove bias and outliers from the data; Obtain enterprise credit rating classifier by building artificial intelligence analysis model training; Based on the classifier, dynamic evaluation and risk warning of the target enterprise's credit are carried out.

2. The enterprise credit assessment method based on artificial intelligence according to claim 1 is characterized by: The acquisition of enterprise historical financial information and industry development trends based on multi-source data fusion technology includes: Integrate historical data of financial statements; Integrate externally disclosed enterprise-related economic indicators; Extract key indicators and development trends from industry research reports; Update information based on the macroeconomic environment and laws and regulations.

3. The enterprise credit assessment method based on artificial intelligence according to claim 2 is characterized by: The historical data of the integrated financial statements include: Obtain detailed information on each part of the company's financial statements based on the accounting period, and normalize the financial ratios in the report information; When any financial ratio in the report reaches the preset standard ratio range of X% and X+%, it is considered a normal value, where X+ is the gain value of X; Use the profit distribution statement and asset impairment provision statement in the latest annual report to conduct further financial quality verification and confirm data consistency; Determine the weights between the indicators and calculate the overall financial health score S.

4. The enterprise credit assessment method based on artificial intelligence according to claim 3 is characterized by: When any financial ratio in the report reaches the preset standard ratio between X% and X+%, the normal value also includes: Calculate the correlation coefficient matrix of each item in the report to ensure that each coefficient Rij fluctuates within a certain range. , where Y is a given stability criterion; Based on the formula Calculate the comprehensive score S, where Wi is the weight corresponding to indicator i, and Ci is the standardized score corresponding to indicator i; Within the range of positive comprehensive scores, the enterprise is deemed to have a certain credit potential; The scoring results will be used to determine whether a more detailed analysis phase will be added later.

5. The enterprise credit assessment method based on artificial intelligence according to claim 4 is characterized by: The calculation and verification of the correlation coefficient matrix of each item in the report also includes: Screen out the relationship groups with high (R>Z) and low (R<=Z) correlation between financial indicators, where Z is a given correlation strength threshold; Using equations A linear regression equation between indicators is established to evaluate the prediction accuracy; where C refers to the coefficient vector matrix, E refers to the error matrix, and Rj represents the correlation vector between indicators; When Rj is consistent with the expected forecast (i.e., E≤D, where D is the predetermined error range), it is considered a signal of financial robustness and its corresponding assessment score T1 is adjusted; Increase the focus on relevant data with strong financial performance and reduce the proportion of irrelevant or weakly correlated assessments.

6. The enterprise credit assessment method based on artificial intelligence according to claim 1 is characterized by: The method of using an equation to establish a linear regression equation between indicators of prediction accuracy to evaluate the accuracy specifically includes: Use the standard variance method to determine the importance of each variable, and assign different adjustment factors according to the importance of variable V. , where K is a pre-set importance factor constant; Apply the variable importance assessment results to the equation and modify it to , where Rj is the corresponding correlation after importance calibration, and X represents the revised prediction; When the difference between X and the current actual observation value is small ( , which is regarded as good and in line with expectations; Correct the weight coefficients used in the evaluation to increase the contribution of highly correlated financial indicators in the forecast.

7. An enterprise credit evaluation system based on artificial intelligence, comprising an enterprise credit evaluation method based on artificial intelligence according to any one of claims 1 to 6, characterized in that: The system modules include: a data acquisition module, a data processing module, an artificial intelligence analysis model module, a credit assessment and risk warning module and a user interface module.

8. The enterprise credit assessment method and system based on artificial intelligence according to claim 7, characterized in that: The data acquisition module internally includes a financial data interface, an industry data interface, a macroeconomic data interface, and a legal and regulatory update data interface; the data processing module internally includes a data cleaning submodule, an outlier detection submodule, and a data normalization submodule.

9. The enterprise credit assessment method and system based on artificial intelligence according to claim 7, characterized in that: The output end of the data acquisition module is communicatively connected to the input end of the data processing module, the output end of the data processing module is communicatively connected to the input end of the artificial intelligence analysis model module, the output end of the artificial intelligence analysis model module is communicatively connected to the input end of the credit assessment and risk warning module, and the output end of the credit assessment and risk warning module is communicatively connected to the input end of the user interface module.

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