Credit report-based method and system for proving whether enterprise has illegal and criminal records

Abstract

The invention discloses a credit report-based method and system for proving whether an enterprise has illegal and criminal records. The method comprises the following steps of S1, collecting and preprocessing data; s2, feature selection and model construction; s3, evaluating and optimizing the model; and S4, model deployment and application: by integrating multi-party data resources and applying a machine learning algorithm to construct a verification model, rapid and accurate verification of whether an enterprise has illegal and criminal records is realized, through an automatic and intelligent mode, the efficiency of judging whether the enterprise has illegal and criminal records is greatly improved, and through utilization of an advanced algorithm and model, the enterprise can be rapidly and accurately verified. The accuracy and reliability of judgment are improved, a convenient certification service is provided for an enterprise, and the tedious process of obtaining certification by the enterprise is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise credit management and risk prevention and control, and in particular to a method and system for proving whether an enterprise has a criminal record based on a credit report. Background Art

[0002] The system is designed to use information technology and big data analysis methods, combined with credit reports and relevant legal databases, to provide users with a convenient and accurate service for querying whether a company has a criminal record. In recent years, the integrity of corporate operations and compliance with laws and regulations have become the focus of attention from all walks of life. In business cooperation, bidding, financing and loans and other scenarios, the demand for verifying whether a company has a criminal record is increasing. However, traditional verification methods often rely on manual query and verification, which is inefficient and prone to errors. Therefore, the development of a method and system for proving whether a company has a criminal record based on a credit report has important practical significance and application value. Summary of the invention

[0003] In response to the problems in the related art, the present invention proposes a method and system for proving whether an enterprise has a criminal record based on a credit report, so as to overcome the above-mentioned technical problems existing in the existing related art.

[0004] To this end, the specific technical solution adopted by the present invention is as follows:

[0005] A method and system for proving whether an enterprise has a criminal record based on a credit report, comprising the following steps:

[0006] Step S1: data collection and preprocessing;

[0007] Step S2: feature selection and model building;

[0008] Step S3: model evaluation and optimization;

[0009] Step S4: Model deployment and application.

[0010] As a further solution of the present invention, the data collection and preprocessing steps in step S1 specifically include:

[0011] Step 1.1 Data Collection

[0012] Corporate credit report: First, collect a large number of corporate credit reports, which should contain the company's basic information, financial data, credit records, non-bank information (such as courts, social security, taxes, etc.) and possible illegal and irregular records.

[0013] Criminal records: At the same time, collect or access criminal record data provided by public security, judicial and other departments, which should be associated with the corporate entity.

[0014] Step 1.2 Data preprocessing

[0015] Cleaning data: removing duplicates, handling missing values, correcting erroneous data, etc.

[0016] Data integration: Match and integrate the information in the credit report with the criminal record to ensure the accuracy and consistency of the data.

[0017] Feature extraction: Extract features from credit reports that may be related to illegal or criminal behavior, such as financial anomalies, legal proceedings, and frequent changes of legal representatives.

[0018] As a further solution of the present invention, the steps of feature selection and model building in step S2 specifically include:

[0019] Step 2.1 Feature Selection

[0020] Correlation analysis: Use statistical methods (such as Pearson correlation coefficient, chi-square test, etc.) to analyze the correlation between each characteristic and illegal and criminal behavior.

[0021] Feature importance assessment: Use machine learning algorithms (such as random forests, gradient boosting trees, etc.) to evaluate the importance of each feature and select the most predictive features.

[0022] Step 2.2 Model construction

[0023] Choose the right algorithm: Choose the right machine learning algorithm based on the characteristics of the data and the complexity of the problem. Common algorithms include logistic regression, decision tree, support vector machine, neural network, etc.

[0024] Model training: Use the processed data set to train the model and improve the model's predictive performance by adjusting parameters and optimizing algorithms.

[0025] As a further solution of the present invention, the model evaluation and optimization steps in step S3 specifically include:

[0026] Step 3.1 Model evaluation

[0027] Performance evaluation: Use the test set to evaluate the model’s accuracy, recall, F1 score and other performance indicators.

[0028] Cross-validation: The cross-validation method is used to ensure the stability and reliability of the model on different data sets.

[0029] Step 3.2 Model optimization

[0030] Adjust parameters: Adjust the model parameters such as learning rate, regularization coefficient, etc. according to the evaluation results.

[0031] Feature Engineering: Further optimize the feature selection and processing process to improve the predictive ability of the model.

[0032] Ensemble learning: Consider using ensemble learning methods (such as random forests, gradient boosted trees, etc.) to improve the overall performance by combining the prediction results of multiple models.

[0033] As a further solution of the present invention, the steps of model deployment and application in step S4 specifically include:

[0034] Step 4.1 Model deployment

[0035] Deploy the trained model to actual application environments, such as corporate credit assessment systems, financial risk control platforms, etc.

[0036] Step 4.2 Real-time monitoring and early warning

[0037] The model is used to monitor corporate credit reports in real time, and an early warning is issued immediately if any signs of possible illegal or criminal behavior are found.

[0038] Based on the early warning results, take corresponding risk control measures, such as strengthening review, restricting credit, etc.

[0039] As a further solution of the present invention, a system for proving whether an enterprise has a criminal record based on a credit report includes a client, a network, a server, a data bus and a database, wherein the client is connected to the server via a network, the server has built-in information query module, analysis module, information verification module and report making module, and the information query module, analysis module, information verification module and report making module are connected via a data bus, and connected to the database by the data bus.

[0040] Beneficial effects of the present invention

[0041] By integrating data resources from multiple parties and using machine learning algorithms to build verification models, we have achieved rapid and accurate verification of whether a company has a criminal record. Through automation and intelligent methods, we have greatly improved the efficiency of determining whether a company has a criminal record. By utilizing advanced algorithms and models, we have improved the accuracy and reliability of judgments, provided companies with convenient certification services, and reduced the tedious process for companies to obtain certification. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 A flow chart of a method for proving whether an enterprise has a criminal record based on a credit report of the present invention; DETAILED DESCRIPTION

[0044] According to an embodiment of the present invention, a method and system for proving whether an enterprise has a criminal record based on a credit report is provided.

[0045] A method and system for proving whether an enterprise has a criminal record based on a credit report according to an embodiment of the present invention includes the following steps:

[0046] Step S1: data collection and preprocessing;

[0047] Step S2: feature selection and model building;

[0048] Step S3: model evaluation and optimization;

[0049] Step S4: Model deployment and application.

[0050] In an optional embodiment, the data collection and preprocessing steps in step S1 specifically include:

[0051] Step 1.1 Data Collection

[0052] Corporate credit report: First, collect a large number of corporate credit reports, which should contain the company's basic information, financial data, credit records, non-bank information (such as courts, social security, taxes, etc.) and possible illegal and irregular records.

[0053] Criminal records: At the same time, collect or access criminal record data provided by public security, judicial and other departments, which should be associated with the corporate entity.

[0054] Step 1.2 Data preprocessing

[0055] Cleaning data: removing duplicates, handling missing values, correcting erroneous data, etc.

[0056] Data integration: Match and integrate the information in the credit report with the criminal record to ensure the accuracy and consistency of the data.

[0057] Feature extraction: Extract features from credit reports that may be related to illegal or criminal behavior, such as financial anomalies, legal proceedings, and frequent changes of legal representatives.

[0058] In an optional embodiment, the steps of feature selection and model building in step S2 specifically include:

[0059] Step 2.1 Feature Selection

[0060] Correlation analysis: Use statistical methods (such as Pearson correlation coefficient, chi-square test, etc.) to analyze the correlation between each characteristic and illegal and criminal behavior.

[0061] Feature importance assessment: Use machine learning algorithms (such as random forests, gradient boosting trees, etc.) to evaluate the importance of each feature and select the most predictive features.

[0062] Step 2.2 Model construction

[0063] Choose the right algorithm: Choose the right machine learning algorithm based on the characteristics of the data and the complexity of the problem. Common algorithms include logistic regression, decision tree, support vector machine, neural network, etc.

[0064] Model training: Use the processed data set to train the model and improve the model's predictive performance by adjusting parameters and optimizing algorithms.

[0065] In an optional embodiment, the model evaluation and optimization steps in step S3 specifically include:

[0066] Step 3.1 Model evaluation

[0067] Performance evaluation: Use the test set to evaluate the model’s accuracy, recall, F1 score and other performance indicators.

[0068] Cross-validation: The cross-validation method is used to ensure the stability and reliability of the model on different data sets.

[0069] Step 3.2 Model optimization

[0070] Adjust parameters: Adjust the model parameters such as learning rate, regularization coefficient, etc. according to the evaluation results.

[0071] Feature Engineering: Further optimize the feature selection and processing process to improve the predictive ability of the model.

[0072] Ensemble learning: Consider using ensemble learning methods (such as random forests, gradient boosted trees, etc.) to improve the overall performance by combining the prediction results of multiple models.

[0073] In an optional embodiment, the steps of model deployment and application in step S4 specifically include:

[0074] Step 4.1 Model deployment

[0075] Deploy the trained model to actual application environments, such as corporate credit assessment systems, financial risk control platforms, etc.

[0076] Step 4.2 Real-time monitoring and early warning

[0077] The model is used to monitor corporate credit reports in real time, and an early warning is issued immediately if any signs of possible illegal or criminal behavior are found.

[0078] Based on the early warning results, take corresponding risk control measures, such as strengthening review, restricting credit, etc.

[0079] Precautions

[0080] Data privacy protection: During data processing and model training, relevant laws and regulations must be strictly observed to ensure data security and privacy.

[0081] Model interpretability: Try to build models that are interpretable so that you can understand the basis for the model’s predictions and potential biases.

[0082] Continuous iteration and optimization: As the amount of data increases and the model application deepens, the model needs to be continuously iterated and optimized to adapt to new changes and needs

[0083] In an optional embodiment, a system for proving whether an enterprise has a criminal record based on a credit report includes a client, a network, a server, a data bus and a database, wherein the client is connected to the server via a network, the server has an information query module, an analysis module, an information verification module and a report making module built in, and the information query module, analysis module, information verification module and report making module are connected via a data bus, and connected to the database by the data bus.

[0084] Working principle of the present invention:

[0085] First, obtain a credit report for the business. Obtain a credit report for the business, which will contain relevant information about the business. Second, analyze the credit report. Carefully analyze the business credit report to see if there are any records related to violations of law or crime. This may involve looking for information about any legal proceedings, fines, administrative penalties or other violations of the business or its principals. Then you need to verify the information. Ensure that the information extracted from the credit report is accurate to verify the accuracy of the record. Then generate supporting documents. Once it is confirmed that the business has no criminal record, you can generate the corresponding supporting documents. This may include a statement or certificate clearly stating that the business has not been convicted of a crime or fined or other legal sanctions within a certain period of time. Establish a system. Establish a system to handle requests for proof of criminal record of the business. This may include establishing an online platform or a standardized process for submitting requests to ensure timely processing and accuracy of requests. Protect data security. Ensure that relevant data protection laws and regulations are strictly followed when handling business credit reports and other sensitive information, and protect the privacy and data security of customers and businesses. Update regularly. Update the business credit report regularly and update the criminal record supporting documents as needed to ensure their accuracy and up-to-dateness.

[0086] 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 method for proving whether an enterprise has a criminal record based on a credit report, characterized in that: The following steps are involved: Step S1: data collection and preprocessing; Step S2: feature selection and model building; Step S3: model evaluation and optimization; Step S4: Model deployment and application.

2. The method for proving whether an enterprise has a criminal record based on a credit report according to claim 1, characterized in that: The data collection and preprocessing steps in step S1 specifically include: Step 1.1 Data Collection Corporate credit report: First, collect a large number of corporate credit reports, which should include the company's basic information, financial data, credit records, non-bank information (such as courts, social security, taxes, etc.) and possible illegal and irregular records. Criminal records: At the same time, collect or access criminal record data provided by public security, judicial and other departments. These data should be associated with corporate entities. Step 1.2 Data preprocessing Cleaning data: removing duplicates, handling missing values, correcting erroneous data, etc. Data integration: Match and integrate the information in the credit report with the criminal record to ensure the accuracy and consistency of the data. Feature extraction: Extract features from credit reports that may be related to illegal or criminal behavior, such as financial anomalies, legal proceedings, and frequent changes of legal representatives.

3. The method for proving whether an enterprise has a criminal record based on a credit report according to claim 1, characterized in that: The steps of feature selection and model building in step S2 specifically include: Step 2.1 Feature Selection Correlation analysis: Use statistical methods (such as Pearson correlation coefficient, chi-square test, etc.) to analyze the correlation between each characteristic and illegal and criminal behavior. Feature importance assessment: Use machine learning algorithms (such as random forests, gradient boosting trees, etc.) to evaluate the importance of each feature and select the most predictive features. Step 2.2 Model construction Choose the right algorithm: Choose the right machine learning algorithm based on the characteristics of the data and the complexity of the problem. Common algorithms include logistic regression, decision tree, support vector machine, neural network, etc. Model training: Use the processed data set to train the model and improve the model's predictive performance by adjusting parameters and optimizing algorithms.

4. The method for proving whether an enterprise has a criminal record based on a credit report according to claim 1, characterized in that: The steps of model evaluation and optimization in step S3 specifically include: Step 3.1 Model evaluation Performance evaluation: Use the test set to evaluate the model’s accuracy, recall, F1 score and other performance indicators. Cross-validation: The cross-validation method is used to ensure the stability and reliability of the model on different data sets. Step 3.2 Model optimization Adjust parameters: Adjust the model parameters such as learning rate, regularization coefficient, etc. according to the evaluation results. Feature Engineering: Further optimize the feature selection and processing process to improve the predictive ability of the model. Ensemble learning: Consider using ensemble learning methods (such as random forests, gradient boosted trees, etc.) to improve the overall performance by combining the prediction results of multiple models.

5. The method for proving whether an enterprise has a criminal record based on a credit report according to claim 1, characterized in that: The steps of model deployment and application in step S4 specifically include: Step 4.1 Model deployment Deploy the trained model to actual application environments, such as corporate credit assessment systems, financial risk control platforms, etc. Step 4.2 Real-time monitoring and early warning The model is used to monitor corporate credit reports in real time, and an early warning is issued immediately if any signs of possible illegal or criminal behavior are found. Based on the early warning results, take corresponding risk control measures, such as strengthening review, restricting credit, etc.

6. A system for proving whether an enterprise has a criminal record based on a credit report according to claims 1-5, characterized in that: It includes a client, a network, a server, a data bus and a database, wherein the client is connected to the server via a network, the server is equipped with an information query module, an analysis module, an information verification module and a report making module, the information query module, the analysis module, the information verification module and the report making module are connected via a data bus, and the data bus is connected to the database.

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