Financial credit intelligent analysis and risk management method based on artificial intelligence
By applying artificial intelligence-based financial credit intelligent analysis and risk management methods in the field of financial credit, establishing correlation analysis models and risk management models, the problem of traditional methods being difficult to deal with massive data and accurately identify complex credit risks is solved, and more efficient risk assessment and management is achieved.
Patent Information
- Application Number
- CN202411839987.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-13
- Publication Date
- 2025-05-02
AI Technical Summary
Traditional financial credit analysis and risk management methods are difficult to efficiently process massive data, and they cannot accurately identify complex and diversified credit risks, resulting in increased difficulty in risk prediction.
Using the financial credit intelligent analysis and risk management method based on artificial intelligence, a financial credit correlation analysis model is established, and the risk assessment results are obtained through feature engineering, correlation analysis and historical scoring decay processing of credit data, and risk behavior analysis, processing restrictions and result feedback are carried out through the risk management model.
It realizes efficient processing and risk assessment of credit data, can more accurately identify potential risks, improve the risk management level and decision-making efficiency of financial institutions, and dynamically adjust and continuously optimize risk management processes.
Smart Images
Figure CN119919223A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of credit analysis, and in particular to a financial credit intelligent analysis and risk management method based on artificial intelligence. Background Art
[0002] With the continuous development of the financial industry and the rapid advancement of science and technology, the application of artificial intelligence in the financial field is becoming more and more extensive. In the field of financial credit, traditional analysis and risk management methods face many challenges. On the one hand, a large amount of data is constantly generated, and traditional manual analysis methods are difficult to efficiently process and mine the value of it. On the other hand, the complexity and diversity of credit risks have increased, and more accurate analysis methods are needed to identify potential risks. At the same time, competition in the financial market has also prompted financial institutions to seek more efficient and intelligent solutions.
[0003] In addition, the uncertainty of the global economic situation has also put forward higher requirements for financial credit risk management. Economic fluctuations, industry changes and other factors make credit risks more difficult to predict. Artificial intelligence technology can quickly adapt to market changes through real-time analysis of massive data, and provide financial institutions with more timely and accurate risk assessment and decision-making support. With its powerful data analysis and pattern recognition capabilities, artificial intelligence technology provides new opportunities for intelligent analysis and risk management of financial credit, and can more accurately assess borrowers' credit, predict default risks, and improve the risk management level and decision-making efficiency of financial institutions. Summary of the invention
[0004] In view of the above technical problems, the present invention provides a financial credit intelligent analysis and risk management method based on artificial intelligence, and establishes a financial credit association analysis model to obtain risk assessment results based on credit data, and its expression is:
[0005] R=α×f(E1)+β×g(E2)+γ×h(E3)+δ,
[0006] Wherein, R represents the risk assessment result, E1 represents the credit data entry vector, E2 represents the credit data associated entry vector, E3 represents the historical credit score vector, α, β, γ represent the weight coefficients corresponding to each vector respectively, and δ represents the bias term; the f(E1) is the function representation after feature engineering processing of the credit data entry; the g(E2) is the function representation after feature extraction and conversion of the associated credit data associated entries; the h(E3) is the feature representation after the historical score is attenuated according to the time series; the credit data entry at least includes basic loan information, borrower basic information, borrower financial information, loan purpose information, and credit record related information; the credit data associated entries at least include associated enterprise information, guarantee information, and industry-related data information; a risk management model is established, and the results of the financial credit association analysis model are risk assessed through the risk management model.
[0007] Furthermore, the steps of establishing a financial credit correlation analysis model include:
[0008] Step S101, data collection and preprocessing, obtaining internal and external credit data, cleaning, converting and integrating the credit data; using Z-score to standardize the data.
[0009] Step S102, feature selection is performed on the credit data according to statistical methods, dimensionality reduction is performed on high-dimensional data, and feature representation is performed on text data using natural language processing technology.
[0010] Step S103, model training and parameter optimization, establish a model according to the association analysis model expression, train the model, continuously adjust the model parameters, regularly use the validation set to evaluate the model performance, and finally obtain a model for association analysis.
[0011] Furthermore, the risk management model includes risk behavior analysis, processing restrictions and result feedback. The risk behavior analysis credit data entries, the results of the credit analysis model, the processing restrictions and the preset risk management parameters determine how to continue credit risk management. The risk behavior analysis results include credit approval, credit rejection, and associated authorization; the processing restrictions are used to check whether the borrower's information is up to date, whether the borrower is on the abnormal blacklist, whether the number of consecutive borrowings exceeds the limit, and whether the user is a new user.
[0012] Furthermore, the step of performing risk management through a risk management model includes:
[0013] Step S201, obtain and confirm the unique information of the lending subject, perform processing restrictions based on the unique information, and obtain the processing restriction result, which is a status list and weighted result of the inspection content. The status list is the existence of the processing restriction inspection content, which is set to 1 if it exists and 0 if it does not exist.
[0014] Step S202, checking whether the status list and the weighted results meet the risk management conditions according to the preset risk management parameters, obtaining a first risk management result, performing risk behavior analysis on the first risk management result, and obtaining a second risk management result; the first risk management result is the result of whether the risk management conditions of the preset risk management parameters are met, and the second risk management result is the final result of the loan risk management.
[0015] Step S203: Feedback the second risk management result and update the borrower information with the second risk management result.
[0016] Further, the step of obtaining the processing restriction result includes:
[0017] Step S301, obtain the inspection items of the processing restrictions, generate a status list according to the entry order of the inspection items, and each bit in the status list is used to describe the corresponding status of the corresponding inspection item.
[0018] Step S302, checking the existence of the check items in turn, and modifying the status information corresponding to the status list.
[0019] Step S303: If the preset parameters support weight update, check the weight update and use the updated weight to calculate the weighted result of the state list.
[0020] The preset risk management parameters are a set of standards and indicators for assessing lending risks, determined based on the risk preferences, regulatory requirements and business experience of the financial institution.
[0021] Compared with the prior art, the present invention has the following beneficial effects: the present invention can efficiently process internal and external credit data, perform standardization, feature selection and representation, including dimensionality reduction of high-dimensional data and feature representation of text data using natural language processing technology. The established financial credit association analysis model integrates multiple factors to obtain accurate risk assessment results, and the risk management model is comprehensive, including risk behavior analysis, processing restrictions and result feedback. It can check the various aspects of the lending subject, assess risks according to preset risk management parameters, and obtain unique information of the lending subject through a dynamic risk management process for processing restrictions, feedback and update the lending subject information after risk behavior analysis, and achieve dynamic adjustment and continuous optimization. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1A flowchart of an exemplary step for establishing an association analysis model for the present invention.
[0023] Figure 2 A flow chart of an exemplary process for risk management for the present invention.
[0024] Figure 3 A flow chart of an exemplary process for obtaining processing restriction results for the present invention. DETAILED DESCRIPTION
[0025] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0026] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.
[0027] First, the optimal embodiment of the present invention is further described.
[0028] A financial credit correlation analysis model is established to obtain risk assessment results based on credit data. Its expression is:
[0029] R=α×f(E1)+β×g(E2)+γ×h(E3)+δ,
[0030] Wherein, R represents the risk assessment result, E1 represents the credit data entry vector, E2 represents the credit data associated entry vector, E3 represents the historical credit score vector, α, β, γ represent the weight coefficients corresponding to each vector respectively, and δ represents the bias term; f(E1) is the function representation after feature engineering processing of the credit data entry; g(E2) is the function representation after feature extraction and conversion of the associated credit data associated entries; h(E3) is the feature representation of the historical score after attenuation processing according to the time series; the credit data entry at least includes basic loan information, borrower basic information, borrower financial information, loan purpose information, and credit record related information; the credit data associated entries at least include associated enterprise information, guarantee information, and industry-related data information; a risk management model is established, and the risk assessment of the results of the financial credit association analysis model is performed through the risk management model.
[0031] The feature selection of the feature engineering mentioned above in this embodiment, for credit data items: use statistical methods, such as correlation analysis, to select the features most relevant to risk assessment. For example, analyze the correlation between various indicators in the borrower's financial information (such as debt-to-asset ratio, current ratio, etc.) and the loan default rate, and select indicators with high correlation as features. For high-dimensional data (such as data containing a large number of detailed financial indicators), use dimensionality reduction methods such as principal component analysis (PCA) to reduce the number of features. For example, in the borrower's financial data, multiple financial ratio indicators are converted into a few principal components through PCA. For credit data related items: for related enterprise information, select the credit rating, operating conditions (such as operating income growth rate, profit growth rate) of the related enterprise as key features. For guarantee information, select the value assessment of the collateral, the type of guarantee (such as mortgage, pledge, guarantee), etc. as features. For industry-related data information, select the industry prosperity index, industry average default rate, etc. as features.
[0032] like Figure 1 As shown, an exemplary step of establishing a financial credit correlation analysis model in this embodiment includes:
[0033] Step S101, data collection and preprocessing, obtaining internal and external credit data, cleaning, converting and integrating the credit data; using Z-score to standardize the data.
[0034] Step S102, feature selection is performed on the credit data according to statistical methods, dimensionality reduction is performed on high-dimensional data, and feature representation is performed on text data using natural language processing technology.
[0035] Step S103, model training and parameter optimization, establish a model according to the association analysis model expression, train the model, continuously adjust the model parameters, regularly use the validation set to evaluate the model performance, and finally obtain a model for association analysis.
[0036] Among them, the data standardization in the above steps is performed by standardizing the data through Z-score. For the numerical data in the credit data items, such as the borrower's income, the calculation formula is Z = (X-μ) / σ, where X is the original data, μ is the mean, and σ is the standard deviation.
[0037] Secondly, the Pearson correlation coefficient is used to measure the linear correlation between two variables. For example, the correlation between the borrower’s income and the loan default rate is calculated. If the correlation coefficient is high (such as greater than 0.7 or less than -0.7), income is an important risk assessment feature.
[0038] The risk management model includes risk behavior analysis, processing restrictions and result feedback. The risk behavior analysis credit data entries, the results of the credit analysis model, processing restrictions and preset risk management parameters determine how to continue credit risk management. The risk behavior analysis results include credit approval, credit rejection, and associated authorization; processing restrictions are used to check whether the borrower's information is up to date, whether the borrower is on the abnormal blacklist, whether the number of consecutive borrowings exceeds the limit, and whether the user is a new user.
[0039] This embodiment can further expand the processing restrictions, such as enterprises need to submit financial statements on time, financial institutions need to compare with blacklists, limit the number of loans for enterprises and individuals, and require more information for new users. The result feedback is to give corresponding notifications to the borrower for the three situations of credit approval, rejection and related authorization. In case of approval, detailed loan information is notified and issuance is arranged. In case of rejection, reasons and improvement suggestions are provided. In case of related authorization, relevant requirements are explained and the procedures are guided.
[0040] like Figure 2 The steps of performing risk management through the risk management model in this embodiment include:
[0041] Step S201, obtain and confirm the unique information of the lending subject, perform processing restrictions based on the unique information, and obtain the processing restriction results. The processing restriction results are the status list and weighted results of the inspection content. The status list is the existence of the processing restriction inspection content. If it exists, it is set to 1; if it does not exist, it is set to 0.
[0042] Step S202, checking whether the status list and the weighted results meet the risk management conditions according to the preset risk management parameters, obtaining a first risk management result, and performing risk behavior analysis on the first risk management result to obtain a second risk management result; the first risk management result is the result of whether the risk management conditions of the preset risk management parameters are met, and the second risk management result is the final result of the loan risk management.
[0043] Step S203: Feedback the second risk management result and update the borrower information with the second risk management result.
[0044] like Figure 3 An exemplary step of obtaining the processing restriction result in this embodiment includes:
[0045] Step S301, obtain the inspection items of the processing restrictions, generate a status list according to the entry order of the inspection items, and each bit in the status list is used to describe the corresponding status of the corresponding inspection item.
[0046] Step S302, checking the existence of the check items in turn, and modifying the status information corresponding to the status list.
[0047] Step S303: If the preset parameters support weight update, check the weight update and use the updated weight to calculate the weighted result of the state list.
[0048] In the above steps, if the preset parameters support weight update and the weight is updated: Assume that the above four check items are assigned weights w1, w2, w3, w4 respectively, and the state list is [s1, s2, s3, s4], where s i It is 0 or 1, indicating the status of the i-th check item.
[0049] The preset risk management parameters are a set of standards and indicators used to assess lending risks, based on the risk appetite, regulatory requirements and business experience of financial institutions. Risk appetite includes conservative, prudent and aggressive tendencies, with different standards in terms of loan applicant credit scores, corporate debt-to-asset ratios, collateral value requirements and industry risk assessment. Regulatory requirements, such as capital adequacy requirements and loan concentration limits, are used to ensure financial market stability and protect the interests of investors and consumers.
[0050] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims
1. Financial credit intelligent analysis and risk management method based on artificial intelligence, characterized by: A financial credit correlation analysis model is established to obtain risk assessment results based on credit data. Its expression is: R=α×f(E1)+β×g(E2)+γ×h(E3)+δ, Wherein, R represents the risk assessment result, E1 represents the credit data entry vector, E2 represents the credit data associated entry vector, E3 represents the historical credit score vector, α, β, γ represent the weight coefficients corresponding to each vector respectively, and δ represents the bias term; the f(E1) is the function representation after feature engineering processing of the credit data entry; the g(E2) is the function representation after feature extraction and conversion of the associated credit data associated entries; the h(E3) is the feature representation after the historical score is attenuated according to the time series; the credit data entry at least includes basic loan information, borrower basic information, borrower financial information, loan purpose information, and credit record related information; the credit data associated entries at least include associated enterprise information, guarantee information, and industry-related data information; a risk management model is established, and the results of the financial credit association analysis model are risk assessed through the risk management model.
2. The method for intelligent analysis and risk management of financial credit based on artificial intelligence according to claim 1, characterized in that: The steps to establish a financial credit correlation analysis model include: Step S101, data collection and preprocessing, obtaining internal and external credit data, cleaning, converting and integrating the credit data; using Z-score to standardize the data; Step S102, feature selection is performed on the credit data according to statistical methods, dimension reduction is performed on high-dimensional data, and feature representation is performed on text data using natural language processing technology; Step S103, model training and parameter optimization, establish a model according to the association analysis model expression, train the model, continuously adjust the model parameters, regularly use the validation set to evaluate the model performance, and finally obtain a model for association analysis.
3. The method for intelligent analysis and risk management of financial credit based on artificial intelligence according to claim 1, characterized in that: The risk management model includes risk behavior analysis, processing restrictions and result feedback. The risk behavior analysis credit data entries, the results of the credit analysis model, processing restrictions and preset risk management parameters determine how to continue credit risk management. The risk behavior analysis results include credit approval, credit rejection, and associated authorization; the processing restrictions are used to check whether the borrower's information is up to date, whether the borrower is on the abnormal blacklist, whether the number of consecutive borrowings exceeds the limit, and whether the user is a new user.
4. The method for intelligent analysis and risk management of financial credit based on artificial intelligence according to claim 1, characterized in that: The steps of risk management through the risk management model include: Step S201, obtaining and confirming the unique information of the lending subject, performing processing restrictions according to the unique information, and obtaining the processing restriction result, wherein the processing restriction result is a status list and a weighted result of the check content, wherein the status list is the existence of the processing restriction check content, and existence is set to 1; If it does not exist, it is set to 0; Step S202, checking whether the status list and the weighted result meet the risk management conditions according to the preset risk management parameters, obtaining a first risk management result, and performing risk behavior analysis on the first risk management result to obtain a second risk management result; The first risk management result is the result of whether the risk management condition of the preset risk management parameters is met, and the second risk management result is the final result of the loan risk management; Step S203: Feedback the second risk management result and update the borrower information with the second risk management result.
5. The method for intelligent analysis and risk management of financial credit based on artificial intelligence according to claim 4, characterized in that: The steps to obtain the processing restriction results include: Step S301, obtaining the inspection items of the processing restriction, generating a status list according to the order of entry of the inspection items, and each bit in the status list is used to describe the corresponding status of the corresponding inspection item; Step S302, checking the existence of the check items in turn, and modifying the status information corresponding to the status list; Step S303: If the preset parameters support weight update, check the weight update and use the updated weight to calculate the weighted result of the state list.
6. The method for intelligent analysis and risk management of financial credit based on artificial intelligence according to claim 4, characterized in that: The preset risk management parameters are a set of standards and indicators for assessing lending risks, determined based on the risk preferences, regulatory requirements and business experience of the financial institution.