Credit risk dynamic assessment and early warning method based on multi-dimensional data analysis

Abstract

The invention provides a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis, and relates to the technical field of financial risk control and big data analysis, and the method comprises the steps: obtaining multi-dimensional data from borrower basic information, financial statements, credit records and an external market environment, carrying out cleaning, standardization processing and storage on the multi-dimensional data; based on the multi-dimensional data, through a credit scoring model, cash flow analysis, liability ratio analysis and market environment risk assessment, generating a financial risk level of the borrower; the financial and market data of the borrowers are updated in real time, the dynamic change of the risk state of the borrowers is monitored in combination with a time sequence prediction model and risk correlation analysis, and a dynamic monitoring result is generated; and according to the risk level and the dynamic monitoring result, triggering a grading early warning mechanism, and generating a corresponding control measure suggestion so as to adjust a credit strategy or adopt a risk slow release means.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of financial risk control and big data analysis, and specifically relates to a credit risk dynamic evaluation and early warning method based on multi-dimensional data analysis. BACKGROUND

[0002] In traditional credit business, financial institutions mainly rely on manual experience or simple scorecard models to evaluate the financial risks of borrowers. This method usually only focuses on historical credit records and static financial data, and is difficult to fully reflect the comprehensive credit status of borrowers. At the same time, after the loan is issued, there is a lack of continuous monitoring mechanism for the risk status of borrowers, which leads to the inability to take effective measures in time when the financial status of borrowers deteriorates, increasing the possibility of loan defaults.

[0003] In recent years, with the development of big data and artificial intelligence technology, some financial institutions have begun to try to introduce machine learning models to evaluate the credit of borrowers, but the existing system still has the following problems:

[0004] Most systems only rely on limited financial information provided by borrowers, and fail to fully integrate external macroeconomic environment, industry policy changes and other influencing factors; the existing methods are mostly one-time evaluation, without considering the trend of changes in the financial status of borrowers over time; there is a lack of real-time monitoring and dynamic early warning function, which cannot provide early warning signals before risks occur. SUMMARY

[0005] The application provides a credit risk dynamic evaluation and early warning method based on multi-dimensional data analysis to solve one of the above technical problems.

[0006] The technical solution adopted by the application is:

[0007] The application embodiment provides a credit risk dynamic evaluation and early warning method based on multi-dimensional data analysis, comprising:

[0008] Multi-dimensional data is obtained from the basic information of borrowers, financial statements, credit records and external market environment, and the multi-dimensional data is cleaned, standardized and stored;

[0009] Based on the multi-dimensional data, the financial risk level of the borrower is generated through a credit scoring model, cash flow analysis, debt ratio analysis and market environment risk assessment;

[0010] Real-time update of borrower financial and market data, combined with time series prediction model and risk correlation analysis, to monitor the dynamic changes of the risk status of borrowers and generate dynamic monitoring results;

[0011] According to the risk level and the dynamic monitoring result, a hierarchical early warning mechanism is triggered, and corresponding control measure suggestions are generated to adjust the credit policy or take risk mitigation measures.

[0012] According to one embodiment of the present application, the multi-dimensional data is obtained from the borrower basic information, financial statements, credit records and external market environment, and the multi-dimensional data is cleaned, standardized and stored, including:

[0013] The borrower basic information includes identity information, operating time and industry category;

[0014] The financial statement data includes key indicators in the balance sheet, profit statement and cash flow statement;

[0015] The credit record includes the number of historical loan defaults, credit card overdue situations and guarantee information;

[0016] The external market data includes macroeconomic indicators, industry policy documents and competitor dynamics;

[0017] The missing data is interpolated or marked as abnormal;

[0018] Abnormal values are identified and removed by statistical methods;

[0019] Unstructured text is converted into structured fields, and time stamps and numerical units are unified.

[0020] According to one embodiment of the present application, based on the multi-dimensional data, the financial risk level of the borrower is generated through credit scoring model, cash flow analysis, debt ratio analysis and market environment risk assessment, including:

[0021] The qualitative indicators in the unstructured text are extracted by natural language processing technology, and the quantitative indicators including profitability indicators, solvency indicators and cash flow health indicators are calculated by combining expert knowledge base quantization scoring factors, using logistic regression model or random forest algorithm to generate credit score, and dividing risk level according to the score;

[0022] The cash flows of operating activities, investment activities and financing activities are modeled, the total amount of debt due within the next 6 months is predicted, and the net amount of operating activities cash flow in the same period is compared to judge the debt service coverage ability, and the cash flow gap under extreme market scenarios is simulated by stress test;

[0023] The asset-liability ratio and interest coverage multiple are calculated, and compared with the industry benchmark threshold. If the ratio deviates from the reasonable range for two consecutive periods, a high risk flag is triggered;

[0024] Build a regression model of macroeconomic factors and borrower financial indicators, quantify the impact of GDP growth rate and interest rate adjustment on solvency, analyze industry policies, and assess the potential impact on borrower cost structure.

[0025] According to one embodiment of the present application, the real-time update of borrower financial and market data, combined with time series prediction model and risk correlation analysis, monitors the dynamic changes of borrower risk state, generates dynamic monitoring results, including:

[0026] Synchronize credit data, bank flow and market environment data to the database through API interface every day;

[0027] Set threshold for key indicators, trigger automatic early warning when threshold is exceeded, key indicators include cash flow and debt ratio;

[0028] Use LSTM neural network to predict key financial indicators in the next 6 months;

[0029] Based on historical data, build a risk level migration matrix to simulate the probability of borrower credit rating changing over time;

[0030] Based on industrial and commercial registration, equity structure and guarantee relationship, build a borrower correlation graph;

[0031] When a node has a risk event, analyze the affected nodes along the graph path and calculate the risk propagation probability.

[0032] According to one embodiment of the present application, according to the risk level and the dynamic monitoring result, trigger a hierarchical early warning mechanism, and generate corresponding control measure suggestions to adjust the credit strategy or take risk mitigation measures, including:

[0033] According to the credit score, cash flow health and debt ratio, divide the risk level, including:

[0034] First level warning, no abnormality, only record;

[0035] Second level warning, pay attention, limit credit;

[0036] Third level warning, require additional collateral or limit new loans;

[0037] Fourth level warning, start the collection process;

[0038] Five level warning, transfer to the legal department for handling;

[0039] According to the risk level matching control measures;

[0040] Compare actual default data with prediction results every month to optimize credit scoring model parameters;

[0041] Periodically update industry policy and market environment data, retrain risk assessment model.

[0042] According to an embodiment of the present application, the credit scoring model comprises:

[0043] Extract non-numerical features of management stability, legal litigation records, and industry experience;

[0044] Identify negative event records from corporate announcements and news reports through sentiment analysis and keyword extraction techniques;

[0045] Assign weights to each qualitative factor and input them into the credit scoring model.

[0046] According to an embodiment of the present application, the cash flow analysis comprises:

[0047] Set up multiple macroeconomic scenarios;

[0048] Simulate the cash flow gap of the enterprise after operating income decreases and costs rise based on multiple macroeconomic scenarios;

[0049] Output the debt serviceability index under different scenarios as a supplementary basis for risk scoring.

[0050] According to an embodiment of the present application, the risk relevance comprises:

[0051] Extract the relationship between the borrower and the associated entity based on business registration data;

[0052] Identify risk transmission links through graph path analysis and calculate the risk propagation probability of affected nodes;

[0053] Implement joint monitoring and risk isolation measures for highly associated groups of enterprises.

[0054] The second aspect embodiment of the present application provides a computer readable storage medium having a program stored thereon, which is executed by a processor to implement the steps in the method.

[0055] The third aspect embodiment of the present application provides an electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method.

[0056] As a result of adopting the above technical solutions, the present application has the following beneficial effects:

[0057] The application realizes comprehensive fusion and structured processing of multi-dimensional data, collects multi-source heterogeneous data of borrower basic information, financial statements, credit records and external market environment, and performs cleaning, standardization processing and unified storage, solving the problem of single data source and uneven quality in traditional systems, and improving the data basis quality of subsequent risk assessment.

[0058] The application constructs a multi-dimensional risk assessment system, improves the assessment accuracy, and comprehensively uses credit scoring model, cash flow health degree analysis, debt ratio calculation and market environment influence modeling and other assessment methods to assess the financial risk of the borrower from quantitative and qualitative two aspects, which can more accurately identify potential default risk compared with single index assessment method.

[0059] The application establishes a dynamic risk monitoring mechanism, enhances the timeliness of risk response, uses a time series prediction model to predict the trend of key financial indicators, and combines risk correlation analysis to identify risk transmission paths, realizes real-time data updating and dynamic risk state monitoring, can discover potential problems in advance before the risk appears, and improves the foresight and early warning ability of the system.

[0060] The application sets up a hierarchical early warning mechanism and control suggestion output, improves the risk response efficiency, automatically triggers the hierarchical early warning mechanism according to the risk level and dynamic monitoring result, and generates corresponding control measure suggestions (such as limiting credit, adding guarantee, starting the collection process, etc.), so that the credit personnel can quickly respond, reduce the non-performing loan rate, and improve the safety of funds.

[0061] The application improves the intelligent level and automation degree of credit decision, and the whole process from data collection to risk assessment, dynamic monitoring and control suggestion generation is highly automated and intelligent, reduces manual intervention, improves work efficiency, and reduces human judgment error. BRIEF DESCRIPTION OF DRAWINGS

[0062] The drawings described herein are used to provide further understanding of the application, constitute a part of the application, the illustrative embodiments of the application and the description thereof are used to explain the application, and do not constitute improper limitation on the application. In the drawings:

[0063] Figure 1 A flowchart of a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis provided by an embodiment of the application is shown in the drawings;

[0064] Figure 2 A structural schematic diagram of an electronic device provided by an embodiment of the application is shown in the drawings.

[0065] Reference signs:

[0066] 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0067] In order to more clearly illustrate the overall concept of the present application, the following will be described in detail with reference to the accompanying drawings.

[0068] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details and other implementations can be employed. In other instances, well-known methods have not been described in detail in order to avoid obscuring the present application. It will be appreciated that those who work in the art will be able to realize and implement alternatives to the embodiments and features specifically described herein. Without departing from the scope of the present application, the embodiments and features can be combined, replaced, or reordered.

[0069] In the present application, unless specifically defined and limited otherwise, the first feature is "on" or "under" the second feature can be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0070] Embodiment 1

[0071] As shown in the figure, a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis includes: Figure 1

[0072] Multi-dimensional data is obtained from the borrower's basic information, financial statements, credit records and external market environment, and the multi-dimensional data is cleaned, standardized and stored.

[0073] As described above, multi-dimensional data related to the financial status of the borrower is obtained through systematic means, and the original data is cleaned, standardized and structured to form a unified and high-quality data input source, providing a basis for subsequent risk assessment.

[0074] Specifically, the following processes are included:

[0075] Data collection: obtaining various types of information related to the borrower from multiple sources;

[0076] Data cleaning: identifying and processing missing values, outliers, duplicate records, etc.

[0077] ​Data Standardization: Convert data in different formats, units, or semantic expressions into a unified format.

[0078] Data Storage: Store processed data in a database according to time sequence or classification logic for subsequent call analysis.

[0079] This step emphasizes the control and management ability of data quality, which is the foundation for stable operation of the entire risk assessment system.

[0080] For example, from the enterprise registration information, extract the establishment time, registered capital, and industry category; for individual borrowers, collect basic information such as age, occupation, residence, and work experience.

[0081] Automatically parse the balance sheet, income statement, and cash flow statement provided by the enterprise to extract key financial indicators such as operating income, net profit, total assets, total liabilities, and net cash flow from operating activities.

[0082] By accessing the central bank credit investigation system and third-party credit investigation platform, obtain the borrower's loan history, overdue times, and guarantee conditions related to credit data.

[0083] Subscribe to macroeconomic data interfaces (such as GDP growth rate, CPI index), interest rate adjustment announcements, and industry policy release platforms to obtain external factors that may affect the borrower's debt repayment ability.

[0084] For example, if the "inventory" item is missing in a company's financial statements, the system automatically uses the previous year's data for interpolation filling; if the operating income of a certain month is much higher than the historical average, the system marks it as an outlier and prompts manual review.

[0085] Uniformly name the "liquidity ratio" field from different sources as "liquidity_ratio" and keep the numerical value to two decimal places; convert "annual income" to RMB ten thousand yuan unit.

[0086] Index by borrower ID and timestamp, and store the processed data into a relational database for subsequent query and modeling analysis.

[0087] It should be noted that in specific implementation scenarios, in addition to structured financial data, the system also supports automatic parsing of unstructured text such as enterprise announcements, news reports, and regulatory penalties on the basis of the above-mentioned solutions;

[0088] Use natural language processing (NLP) technology to identify negative event keywords (such as "environmental protection penalties" and "major lawsuits") and include them as qualitative scoring factors in the credit assessment model.

[0089] In specific implementation scenarios, different data update frequency strategies can be set based on the above-mentioned solutions, such as daily synchronization of bank flow data, weekly update of credit records, and monthly update of financial statements.

[0090] For data that cannot be automatically obtained, the system sets up a reminder mechanism to prompt the user to upload supplementary materials regularly.

[0091] In specific implementation scenarios, the system can automatically perform integrity checks after each data collection to determine whether all necessary fields have been collected based on the above-mentioned solutions.

[0092] If there are missing fields, the system can intelligently predict and complete them based on the existing data, or send a reminder to the operator for supplementary recording.

[0093] In specific implementation scenarios, the system has a multi-currency exchange rate conversion function for cross-border business scenarios, which can convert currency units such as US dollars and euros into Chinese yuan.

[0094] Support multiple languages for data input and output to meet international business needs.

[0095] In specific implementation scenarios, the system can set data access permissions according to different roles based on the above-mentioned solutions, such as risk control officers who can only view data for specific regional customers.

[0096] All data access and modification actions are recorded in audit logs to ensure data security and compliance.

[0097] In specific implementation scenarios, the system can interface with multiple third-party data service providers, such as tax systems, business registration platforms, and supply chain finance platforms, based on the above-mentioned solutions.

[0098] Real-time data grabbing is achieved through API interfaces to improve data acquisition efficiency and accuracy.

[0099] Based on the multi-dimensional data, a credit scoring model, cash flow analysis, debt ratio analysis, and market environment risk assessment are used to generate the financial risk level of the borrower.

[0100] As mentioned above, the credit scoring model: based on the borrower's historical credit records, repayment behavior, and other information, a scoring model is constructed to output a credit score.

[0101] Cash flow analysis: assesses the cash inflow and outflow of an enterprise or individual within a certain period to determine their short-term debt repayment ability.

[0102] Debt ratio analysis: Calculate the asset-liability ratio, liquidity ratio, quick ratio, and other key financial ratio indicators of the borrower to measure their long-term debt servicing ability.

[0103] Market environment risk assessment: Assess the potential impact of external factors such as macroeconomic changes and industry policy adjustments on the borrower's debt servicing ability.

[0104] Through the results of the above four assessment methods, the system finally outputs a comprehensive financial risk level (such as A to E) for subsequent risk warning and control suggestion generation.

[0105] For example, use logistic regression or random forest algorithm to train credit scoring model, input variables include overdue times, loan balance, credit card usage rate, guarantee situation, etc. For example, a borrower has no overdue history, credit card usage rate is 30%, no bad guarantee record, model output score is 820 points, corresponding to B level risk.

[0106] Assuming a company's average monthly net cash flow from operating activities is 500,000 yuan in the past year, and the total amount of principal and interest of debts to be paid in the next 6 months is 2,000,000 yuan. The system judges that the cash flow basically covers the debt, and marks it as "cash flow balance", but if the future forecast shows that the cash flow drops to 30,000 yuan / month, it may trigger a "cash flow tight" prompt.

[0107] The asset-liability ratio of a certain enterprise is 65%, while the average level of the same industry is 55%. The system determines that the enterprise's debt level is high according to the set threshold, and there is certain debt servicing pressure, so it adjusts the debt ratio item score downward.

[0108] If the central bank announces a 50 basis point interest rate hike, the system will automatically update the interest rate sensitivity model, recalculate the borrower's interest burden, and adjust their overall risk score accordingly. For example, a certain enterprise's original score is 750 points, after the interest rate rises, the score drops to 700 points, and the risk level rises from B level to C level.

[0109] The system weights and integrates the results of the above four types of assessment (for example, credit score accounts for 40% of the weight, cash flow accounts for 25%, debt ratio accounts for 20%, and market environment accounts for 15%), and finally generates a comprehensive risk level, such as "C level - medium-high risk".

[0110] It should be noted that in specific implementation scenarios, the system can also use the latest default sample data to retrain the credit scoring model on a regular basis to improve the model's prediction accuracy based on the above scheme;

[0111] Support online learning mechanism, allow model to update incrementally according to the latest data, adapt to short-term market fluctuations.

[0112] In specific implementation scenarios, the weight of each scoring factor can be dynamically adjusted based on the characteristics of different industries. For example, manufacturing enterprises pay more attention to inventory turnover, while retail enterprises place more emphasis on accounts receivable recovery efficiency;

[0113] In a specific economic cycle (such as an economic downturn), the system automatically increases the weight of the cash flow stability factor.

[0114] In specific implementation scenarios, the borrower's financial ratios can be compared with the average values of the same industry and excellent enterprises to identify their relative position in the industry based on the above-mentioned solutions;

[0115] Special prompts are given for cases deviating from the normal range of the industry to assist in risk level determination.

[0116] In specific implementation scenarios, in addition to the overall financial risk level, the system also provides sub-dimension scores such as "liquidity risk level" and "profitability risk level" based on the above-mentioned solutions;

[0117] Each sub-score can be used for special risk identification and management, facilitating targeted intervention by credit personnel.

[0118] In specific implementation scenarios, the system can automatically generate a structured scoring report containing scoring details, key risk points, trend change graphs, and other content based on the above-mentioned solutions;

[0119] The report supports export, printing, and online viewing, facilitating manual review and business communication.

[0120] In specific implementation scenarios, the system can provide scoring correction suggestions for special cases (such as newly established enterprises and enterprises in the industry transition period) based on the above-mentioned solutions;

[0121] Risk control personnel can manually adjust the scoring parameters based on the system's recommendations, and the system retains operation logs for audit.

[0122] In specific implementation scenarios, the system can support financial indicator conversion according to international accounting standards (IFRS) or local accounting standards for cross-border borrowers based on the above-mentioned solutions;

[0123] The system automatically adapts to different countries / regions' financial statement formats to ensure the applicability of the scoring model.

[0124] Real-time updating of borrower financial and market data, combined with time series prediction models and risk correlation analysis, monitors the dynamic changes in the borrower's risk status and generates dynamic monitoring results.

[0125] As mentioned above, based on the completion of the initial risk assessment, the latest financial and market data of the borrower is continuously obtained, combined with time series prediction model and risk correlation analysis means, the changing trend of the risk state of the borrower is monitored in real time, and the dynamic monitoring result with timeliness is generated.

[0126] The specific processing process includes the following two aspects:

[0127] Real-time data updating mechanism: the system periodically or irregularly obtains the latest financial status, credit record changes and external market environment information of the borrower through periodic or event-triggered mode;

[0128] Dynamic risk analysis mechanism:

[0129] Using time series prediction model (such as LSTM, ARIMA, etc.) to predict the trend of future key financial indicators (such as income, liability, cash flow);

[0130] Based on the correlation between the borrower and other entities (such as equity structure, guarantee chain, upstream and downstream transactions, etc.), a risk transmission map is constructed to identify potential risk diffusion paths;

[0131] Based on the above analysis results, the dynamic changes of the current risk state of the borrower are generated, which are used for subsequent early warning and control suggestion generation.

[0132] This step emphasizes the real-time response ability and forward-looking risk identification ability of the system, which is the key to the transformation of the entire financial risk assessment system from static assessment to dynamic management.

[0133] For example, the system automatically synchronizes the latest fund flow of the borrower from the bank flow interface every day; if it is found that there is no business income in the account for three consecutive days, it is marked as abnormal behavior and triggers a preliminary warning signal.

[0134] For the monthly net profit data of a certain enterprise, LSTM neural network model is used to predict the trend of the next 6 months. If the model prediction shows that the net profit will decrease month by month, and the sixth month may be lower than the warning value, the system judges that the financial health of the enterprise is deteriorating and needs to be paid attention to.

[0135] A certain enterprise A is a loan customer, and its main customer is enterprise B. If enterprise B is suspended for environmental protection punishment, the system identifies this risk transmission path based on the transaction relationship between the two enterprises, and gives a risk upgrade prompt to loan customer A in advance, reminding the credit personnel to pay attention to the accounts receivable recovery problem.

[0136] The system integrates the time series prediction results and correlation risk analysis results to output a dynamic monitoring report, for example: "the current risk level of borrower X is C level, and it is expected to rise to D level within 3 months, it is recommended to strengthen the frequency of post-loan inspection."

[0137] In the system interface, the key indicators such as the historical and predicted asset-liability ratio, net cash flow, credit score curve of the borrower are displayed in the form of a chart, helping risk control personnel intuitively understand the risk evolution trend.

[0138] It should be noted that in a specific implementation scenario, millisecond or second level data updates can also be supported on the basis of the above scheme, which is suitable for real-time monitoring of high-risk customers.

[0139] An instant notification mechanism is set for sudden negative events such as court seizure and public opinion exposure.

[0140] In a specific implementation scenario, the warning threshold of each risk indicator can be dynamically adjusted according to industry cycles, macroeconomic fluctuations and other factors on the basis of the above scheme.

[0141] For example, in the economic downturn period, the warning threshold of the cash flow to debt ratio is appropriately lowered to improve the warning sensitivity.

[0142] In a specific implementation scenario, the data sources from multiple business systems (such as ERP, CRM, and credit investigation platform) can be integrated on the basis of the above scheme to improve the comprehensiveness and accuracy of the data.

[0143] Integrated monitoring and unified analysis of multi-platform data are achieved.

[0144] In a specific implementation scenario, different monitoring frequencies and depths can be set for borrowers of different risk levels on the basis of the above scheme.

[0145] For example, daily data updates and hourly trend analysis are implemented for E-level high-risk customers, while A-level low-risk customers are only updated once a week.

[0146] In a specific implementation scenario, a risk level migration probability matrix can be established based on historical data on the basis of the above scheme to simulate the possible risk level change path of the borrower in the future.

[0147] A variety of risk evolution scenarios are generated by combining the Monte Carlo simulation method to assist in formulating long-term credit strategies.

[0148] According to the risk level and the dynamic monitoring result, a hierarchical warning mechanism is triggered, and corresponding control measure suggestions are generated to adjust the credit strategy or take risk mitigation measures.

[0149] As mentioned above, based on the financial risk level generated in the previous step and the dynamic monitoring results, the corresponding hierarchical early warning mechanism is triggered, and targeted control measures are generated to assist credit institutions in timely adjusting credit strategies or taking effective risk mitigation measures.

[0150] The processing process specifically includes the following two aspects:

[0151] Hierarchical early warning mechanism: According to the preset risk level (such as A to E) and dynamic change trend, set different levels of early warning rules. For example, when the risk level of the borrower rises from B to C, the system automatically triggers a three-level warning; if it further deteriorates to D, it is upgraded to a four-level warning and relevant risk control personnel are notified.

[0152] Control measure suggestion generation: The system automatically generates specific control suggestions such as limiting credit limit, adding collateral, repaying loans in advance, starting the collection process, etc. according to different risk levels and warning levels, combined with historical disposal experience and industry best practices, for credit personnel to refer to.

[0153] This step emphasizes the system's intelligent decision support capability and risk closed-loop management capability, which is the key landing point of the entire financial risk assessment system from identification to response.

[0154] For example, a certain enterprise has an initial risk level of B (medium-low risk), but in the latest dynamic monitoring, it is found that its cash flow has decreased for two consecutive periods, and the asset-liability ratio has risen to 70%. The system adjusts its risk level to C (medium-high risk) and triggers a three-level warning, prompting the credit manager to increase the frequency of post-loan checks.

[0155] For C-level risk customers, the system recommends the following control measures:

[0156] Require the borrower to provide additional collateral;

[0157] Freeze unused credit limit;

[0158] Increase the frequency of submitting quarterly financial statements;

[0159] Arrange on-site due diligence to verify the operating status.

[0160] Multi-level linkage early warning mechanism:

[0161] If a customer is determined to be D-level (high risk) in dynamic monitoring, the system not only triggers a four-level warning, but also automatically includes the customer's associated enterprises in the key monitoring range and synchronously pushes a special report to the risk management department.

[0162] Suggestion output diversification:

[0163] The system-generated control suggestions can include textual descriptions, operation guidelines, applicable scenario explanations, expected effect predictions, etc. For example: "Suggest freezing the newly added credit limit, which is expected to reduce potential losses by about 2 million yuan in the next 6 months."

[0164] Manual intervention and suggestion confirmation mechanism:

[0165] After the generation of control suggestions, the system supports credit personnel to view, modify or confirm the execution, and records all operation logs to ensure business compliance and responsibility traceability.

[0166] It should be noted that in specific implementation scenarios, in addition to triggering early warning based on risk level changes, other factors such as industry policy changes, related party risk events, and cash flow volatility can also be considered to trigger early warning.

[0167] Support multiple types of early warning definitions, such as "credit deterioration early warning", "liquidity crisis early warning", "market environment impact early warning", etc.

[0168] In specific implementation scenarios, in addition to the above solutions, the system can also have an in-built financial expert experience library containing typical control measures templates for different risk levels.

[0169] Customized suggestion options are provided for special industries or special customers (such as start-ups and cross-border enterprises).

[0170] In specific implementation scenarios, in addition to the above solutions, the system can simulate the impact of different control measures on risk levels during the suggestion generation stage to assist in selecting the optimal strategy.

[0171] After actual execution, the system periodically reviews the effect of the suggestion execution for model optimization and suggestion library update.

[0172] In specific implementation scenarios, in addition to the above solutions, machine learning algorithms can be used to train recommendation models to match the most suitable control measures for the current risk level based on historical successful cases and failure lessons.

[0173] The recommendation results are prioritized to facilitate quick decision-making by credit personnel.

[0174] In specific implementation scenarios, in addition to the above solutions, the system supports credit personnel to provide feedback on the execution of suggestions, such as "executed", "partially executed", "not executed", and the reasons.

[0175] All feedback data is used for subsequent model iteration and strategy optimization.

[0176] According to one embodiment of the present application, the multi-dimensional data is obtained from the borrower's basic information, financial statements, credit records and external market environment, and the multi-dimensional data is cleaned, standardized and stored, including:

[0177] The borrower's basic information includes identity information, business duration and industry category;

[0178] The financial statement data includes key indicators in the balance sheet, profit statement and cash flow statement;

[0179] The credit record includes the number of historical loan defaults, credit card overdue situations and guarantee information;

[0180] The external market data includes macroeconomic indicators, industry policy documents and competitor dynamics;

[0181] Interpolation or flagging of missing data;

[0182] Identify and remove outliers through statistical methods;

[0183] Convert unstructured text to structured fields and unify timestamps and numerical units.

[0184] As mentioned above, first, various types of data related to the borrower need to be obtained, which come from multiple different channels and types, including the borrower's basic information, financial status, credit behavior and the external market environment in which it is located. The system collects these multi-dimensional data and performs preprocessing operations such as cleaning and standardization to ensure the data quality and analysis accuracy of the subsequent risk assessment process.

[0185] First, in terms of borrower's basic information, the system collects information such as the borrower's identity information, such as enterprise name or personal name, unified social credit code or ID number, registered address or residence address; the borrower's business duration, such as the time of establishment of the enterprise, the duration of the main business; and the industry category, i.e. the specific industry field in which the borrower is located, such as manufacturing, retail, technology industry, etc. These information is used to identify the basic attributes of the borrower and provide background support for subsequent analysis.

[0186] Secondly, in terms of financial statement data, the system extracts key indicators from the balance sheet, profit statement and cash flow statement in the financial statements provided by the borrower. The balance sheet includes total assets, total liabilities, owner's equity and other items; the profit statement includes operating income, operating cost, net profit and other profit-related data; the cash flow statement includes operating cash flow, investment cash flow and financing cash flow and other information. These data reflect the financial structure, profitability and cash flow of the borrower, and are an important basis for assessing its debt servicing ability.

[0187] In terms of credit records, the system accesses credit investigation systems or other credit information platforms to obtain the borrower's historical loan default times, credit card overdue situations, and guarantee information. Loan default times are used to measure the borrower's past performance; credit card overdue situations reflect their short-term credit behavior; guarantee information includes whether they have provided guarantees for others and the fulfillment of their guarantee responsibilities. These information collectively constitute the borrower's credit profile and are important references for judging their credit risk level.

[0188] In terms of external market data, the system collects macroeconomic indicators such as GDP growth rate, inflation rate, interest rate changes, industry policy documents including government-issued industry support policies, new regulatory rules, environmental protection standards, and competitor dynamics such as changes in the operation of major competitors and changes in market share. These data are used to assess the macro and industry environment in which the borrower is located, assisting in judging the market risks they may face in the future.

[0189] After completing data collection, the system cleans and processes the obtained raw data. For missing data, the system uses interpolation filling methods such as using the last recorded or adjacent time period data to replace it; if it cannot be effectively filled, the field is marked as abnormal for manual review. For outliers, the system identifies them through statistical methods such as the 3σ principle or boxplot method to determine whether there are data points that significantly deviate from the normal range and are removed or corrected to avoid affecting the results of subsequent model calculations.

[0190] In addition, the system also processes unstructured text data. For example, the borrower's financial statement notes, corporate announcements, and news reports often contain important qualitative information. The system uses natural language processing techniques to extract keywords, event descriptions, and other content from these texts and converts them into structured fields that can be used for modeling, such as "management stability," "major litigation records," and "environmental penalty times." At the same time, the system formats all data timestamps uniformly to ensure consistency in the time dimension of data from different sources, and standardizes numerical units, such as converting currency units to RMB ten thousand yuan or rounding percentages to two decimal places, to improve data comparability and computational efficiency.

[0191] Finally, the cleaned and standardized data is stored in a database, indexed by borrower identifier and timestamp, for subsequent calling and analysis. The database supports multiple query methods to meet the real-time access needs of the risk assessment module, dynamic monitoring module, and early warning module.

[0192] According to one embodiment of the present application, the financial risk level of the borrower is generated based on the multi-dimensional data through a credit scoring model, cash flow analysis, debt ratio analysis and market environment risk assessment, including:

[0193] The qualitative indicators in the unstructured text are extracted through natural language processing technology, and the quantitative indicators including profitability indicators, debt serviceability indicators and cash flow health indicators are calculated by combining expert knowledge base quantitative scoring factors. A credit score is generated using a logistic regression model or a random forest algorithm, and the risk level is divided according to the score;

[0194] The cash flows of operating activities, investment activities and financing activities are modeled, the total amount of debt due within the next 6 months is predicted, and the debt service coverage ability is judged by comparing the net amount of operating activities cash flow in the same period. The cash flow gap under extreme market scenarios is simulated through stress testing;

[0195] The asset-liability ratio and interest coverage ratio are calculated, and compared with the industry benchmark threshold. If the ratio deviates from the reasonable range for two consecutive periods, a high-risk flag is triggered;

[0196] A regression model of macroeconomic factors and borrower financial indicators is constructed to quantify the impact of GDP growth rate and interest rate adjustment on debt serviceability, analyze industry policies, and assess the potential impact on the borrower's cost structure.

[0197] According to one embodiment of the present application, the borrower's financial and market data is updated in real time, and the dynamic changes of the borrower's risk state are monitored by combining time series prediction model and risk correlation analysis to generate dynamic monitoring results, including:

[0198] Daily synchronization of credit investigation data, bank flow and market environment data to the database through API interface;

[0199] Thresholds are set for key indicators, and automatic early warning is triggered when the thresholds are exceeded. Key indicators include cash flow and debt ratio;

[0200] LSTM neural network is used to predict key financial indicators in the next 6 months;

[0201] Based on historical data, a risk level migration matrix is constructed to simulate the probability of change of the borrower's credit level over time;

[0202] Based on industrial and commercial registration, equity structure and guarantee relationship, a borrower correlation graph is constructed;

[0203] When a risk event occurs at a certain node, the affected nodes along the graph path are analyzed, and the risk propagation probability is calculated.

[0204] As mentioned above, in order to achieve continuous tracking and dynamic management of the financial risks of borrowers, the system periodically acquires the latest financial information of the borrowers and external market environment data, and combines time series prediction models and risk correlation analysis methods to monitor the risk status of the borrowers in real time, thereby generating dynamic monitoring results with timeliness.

[0205] Firstly, the system synchronizes credit investigation data, bank flow data and market environment data to the database through API interface every day. These data include but are not limited to: the latest credit score, loan balance, overdue record and other credit investigation information of the borrower; daily inflow and outflow of enterprise or personal account, and other bank flow data; and macroeconomic indicators, industry policy changes, interest rate adjustments and other market environment information. Through the way of automatic interface, the system can ensure the timeliness and continuity of data update, providing basic support for subsequent analysis.

[0206] Secondly, the system sets thresholds for key financial indicators, and triggers an automatic early warning mechanism when the thresholds are exceeded. The key indicators of concern mainly include cash flow status and debt level. For example, if the net cash flow from operating activities of an enterprise is lower than a certain set lower limit for two consecutive periods, the system judges that it is in liquidity crisis and automatically issues a warning prompt; similarly, if the asset-liability ratio exceeds the preset safe upper limit, the system will trigger a warning signal to remind the relevant personnel to pay attention to the potential debt repayment pressure.

[0207] Further, the system uses LSTM neural network to predict the key financial indicators in the next 6 months. This model is based on the historical financial data of the borrower (such as operating income, net profit, total debt, etc.) to construct time series features, and trains a prediction model that can reflect the financial trend. The output of the model includes the change trend of key indicators such as predicted income, profit, and debt amount in the next few months. Through this prediction ability, the system can identify potential problems in advance before the risk occurs, improving the forward-looking nature of risk management.

[0208] At the same time, the system also builds a risk level migration matrix based on historical data to simulate the probability of change of the borrower's credit rating over time. This matrix establishes a probability model for the evolution of the borrower from the current risk level to other levels by statistically analyzing the transfer frequency between different risk levels in a large number of historical samples. For example, a B-level borrower has a 10% probability of rising to A-level and a 25% probability of falling to C-level in the next three months. This simulation method helps to assess the long-term credit trend of the borrower and assists in developing more scientific credit strategies.

[0209] In addition, the system also constructs a borrower correlation graph based on business registration, equity structure and guarantee relationship. This graph is used to show the relationship network between the borrower and its associated entities, including shareholders, subsidiaries, guarantors, upstream and downstream customers, etc. By graphically displaying the connection paths between nodes, the system can clearly identify the direction and scope of risk transmission.

[0210] When a risk event occurs at a certain node, for example, a company is listed as a dishonest person subject to enforcement by the court or a major default occurs, the system will analyze the affected nodes along the graph path and calculate the risk transmission probability. Through the graph analysis algorithm, the system identifies which associated parties may be affected and estimates the likelihood of their being affected based on historical similar cases. For example, if a core enterprise experiences an operational crisis, the default risk of its upstream suppliers and downstream customers will increase accordingly, and the system can thus prioritize monitoring of related borrowers in advance.

[0211] Finally, all the above analysis results are integrated into dynamic monitoring results, including the risk change trend of the borrower, the early warning level, the expected financial indicator trend, the associated risk transmission path, etc. These information can not only be used to trigger subsequent hierarchical early warning mechanism, but also serve as an important basis for generating control measures recommendations, achieving a full-process closed-loop management from risk identification to response.

[0212] According to one embodiment of the present application, the hierarchical early warning mechanism is triggered and the corresponding control measures recommendations are generated according to the risk level and the dynamic monitoring results, to adjust the credit strategy or take risk mitigation measures, including:

[0213] The risk level is divided according to the credit score, cash flow health and debt ratio, including:

[0214] Level 1 warning, no abnormality, only record;

[0215] Level 2 warning, pay attention, limit credit;

[0216] Level 3 warning, require additional collateral or limit new loans;

[0217] Level 4 warning, start the collection process;

[0218] Level 5 warning, transfer to the legal department for handling;

[0219] Match control measures according to risk level;

[0220] Compare actual default data with prediction results every month to optimize credit score model parameters;

[0221] Regularly update industry policy and market environment data and retrain the risk assessment model.

[0222] As mentioned above, based on the pre-generated borrower financial risk level and dynamic monitoring results, the system will automatically determine whether the preset warning conditions are met, and accordingly trigger the corresponding hierarchical warning mechanism. At the same time, the system will also match and generate targeted control measures suggestions according to different risk levels, to guide the credit institutions to timely adjust the credit strategy or take effective risk mitigation measures, so as to realize the closed-loop management of the borrower's risk.

[0223] Firstly, the system divides the borrower into multiple risk levels by comprehensively considering the credit score, cash flow health, and debt ratio, etc. Each level corresponds to a different risk level, and sets corresponding warning levels and disposal rules. For example:

[0224] First-level warning (low risk): indicating that the borrower is currently in normal state without obvious abnormal conditions. At this time, the system only records relevant data and does not make additional interventions;

[0225] Second-level warning (low-medium risk): indicating that the borrower has slight fluctuations, such as short-term cash flow tension or slight decrease in credit score. The system prompts relevant personnel to pay attention, and can limit the borrower's new credit limit;

[0226] Third-level warning (medium-high risk): indicating that the borrower has certain debt repayment pressure, such as continuous increase in debt ratio or occurrence of overdue records. The system will require additional collateral or suspend new loan approval;

[0227] Fourth-level warning (high risk): indicating that the borrower has shown obvious default tendency, such as increasing cash flow disruption risk or multiple overdue payments. The system will start the collection process, including phone reminders, on-site visits, etc;

[0228] Fifth-level warning (extremely high risk): indicating that the borrower has committed substantial default behavior or has been listed as a dishonest person subject to enforcement. The system will automatically transfer it to the legal department for legal treatment.

[0229] Secondly, after triggering the warning, the system will automatically generate corresponding control measures suggestions according to the current risk level. These control measures not only include standard operating guidelines, but also can be intelligently recommended in combination with historical disposal experience and industry best practices.

[0230] For example, for a third-level warning customer, the system may suggest:

[0231] Requiring new guarantor or increasing collateral assets;

[0232] Suspend the use of credit limit;

[0233] Increase the frequency of financial statement submission;

[0234] Arrange for on-site inspection after loan.

[0235] In addition, to continuously improve the accuracy and adaptability of the risk assessment model, the system also has a model optimization mechanism. Specifically, the system will compare the actual default events with the prediction results every month, identify samples with large prediction deviations, and optimize the parameter settings of the credit scoring model accordingly to improve the accuracy of subsequent predictions.

[0236] At the same time, the system will regularly update industry policies and market environment data, and incorporate the latest data into the training set to retrain the risk assessment model. For example, when a certain industry is affected by newly introduced regulatory policies, the system will update the weights of relevant feature variables or introduce new influencing factors to ensure that the model accurately reflects the impact of external environmental changes on the risk of borrowers.

[0237] Finally, all the above warning information, control suggestions, and model optimization actions will be recorded by the system and support query and export to facilitate internal audit, compliance review, and business review.

[0238] According to an embodiment of the present application, the credit scoring model comprises:

[0239] Extracting non-numerical features such as management stability, legal litigation records, and industry experience;

[0240] Identifying negative event records from enterprise announcements and news reports through sentiment analysis and keyword extraction techniques;

[0241] Assigning weights to each qualitative factor and inputting them into the credit scoring model.

[0242] As mentioned above, the credit scoring model used not only relies on traditional quantitative financial indicators, but also introduces multiple non-numerical qualitative feature factors to improve the comprehensiveness and accuracy of credit risk assessment of borrowers. These qualitative factors mainly come from the management background, legal compliance situation, and maturity of the industry of the borrower.

[0243] Firstly, the system will extract non-numerical features such as management stability, legal litigation records, and industry experience from the information provided by the borrower or public information. For example:

[0244] Management stability is used to measure whether the core management personnel of the enterprise changes frequently, usually by counting the tenure of senior management, frequency of replacement, etc.

[0245] Legal litigation records reflect whether the enterprise has a history of unresolved or lost cases, and whether it involves significant administrative penalties;

[0246] Industry experience reflects the employment time and project experience of the enterprise and its main managers in the industry, which helps to judge their operating ability and risk tolerance.

[0247] Secondly, to further tap the potential risk signals of borrowers, the system will also identify negative event records from unstructured texts such as corporate announcements and news reports through sentiment analysis and keyword extraction techniques. For example:

[0248] Using natural language processing techniques to analyze the semantics of annual reports, board announcements, and other content published by companies, negative keywords such as "losses," "layoffs," and "regulatory penalties" are identified.

[0249] Automatically scraping and analyzing news from mainstream financial media and government regulatory platforms, negative public opinion events related to borrowers are identified, such as "under investigation," "suspected of violating regulations," and "contract disputes."

[0250] The system scores based on the sentiment orientation of the text content (e.g., negative, neutral, positive), and the relevant events are used as important supplementary data for the credit scoring model.

[0251] Subsequently, the system assigns weights to each qualitative factor extracted above. The determination of weights can be based on historical default data analysis, expert experience judgment, or machine learning model training results, ensuring that the influence of different factors in the final score matches their actual risk contribution. For example:

[0252] If historical data shows that companies with frequent management changes are more likely to default, the system will assign a higher weight to "management stability."

[0253] If a certain type of negative public opinion event (such as environmental penalties) has a strong correlation with default rates, the scoring weight of the corresponding keyword will also be increased.

[0254] Finally, all processed qualitative factors and their corresponding weights are input into the credit scoring model, along with quantitative financial indicators, to participate in comprehensive scoring calculations. The model output can be used to generate a credit score for the borrower and further map it to a corresponding risk level, providing a basis for subsequent early warning mechanisms and control recommendations.

[0255] By introducing non-numerical qualitative factors and combining text intelligent analysis techniques, the risk identification ability of the credit scoring model is effectively improved, making risk assessment more comprehensive and accurate, especially suitable for new enterprises lacking complete financial data or individual borrowers with insufficient information disclosure.

[0256] According to one embodiment of the present application, the cash flow analysis includes:

[0257] Setting up multiple macroeconomic scenarios;

[0258] Simulate the cash flow gap after the decline in operating income and the rise in costs under various macroeconomic scenarios;

[0259] Output the solvency index under different scenarios as a supplementary basis for risk scoring.

[0260] As mentioned above, the cash flow analysis module not only focuses on the current cash flow status of the borrower, but also further introduces a macroeconomic scenario simulation mechanism to assess the financial stability and solvency of the borrower under different economic environments that may occur in the future.

[0261] First, the system will set up various macroeconomic scenarios. These scenarios represent different external economic conditions that may affect the operating status of the borrower, such as:

[0262] Baseline scenario: assumes that the macro economy remains stable, market supply and demand are balanced, interest rates, raw material prices, etc. maintain normal levels;

[0263] Mild downward scenario: economic growth slows down, market demand decreases slightly, and some industries face mild overcapacity;

[0264] Severe recession scenario: the economy declines significantly, consumption shrinks, and enterprises face declining revenues and financing difficulties;

[0265] Inflation shock scenario: prices continue to rise, labor and raw material costs rise, and enterprise profit margins are squeezed;

[0266] Policy regulation scenario: the government introduces restrictive industry policies or stricter environmental regulations, resulting in increased operating costs for enterprises.

[0267] Second, under each macroeconomic scenario, the system simulates the changes in the cash flow of the enterprise under the conditions of declining operating income and rising costs. For example:

[0268] Under the "inflation shock scenario", the system assumes that the procurement cost of the enterprise increases by 10%, while the product sales price cannot be increased simultaneously, resulting in a decrease in gross profit;

[0269] Under the "severe recession scenario", the system predicts that the operating income of the enterprise will decrease by 20%, the accounts receivable recovery period will be extended, and short-term financing will be increased to maintain operations;

[0270] The system combines the historical operating data of the enterprise, the average level of the industry, and the current financial structure to calculate the cash flow gap under various scenarios, i.e. the part of the expected cash inflow that is insufficient to cover the cash outflow in a certain period of time.

[0271] Subsequently, the system outputs the solvency index under different macroeconomic scenarios based on the simulation results described above. This index reflects the ability of the enterprise to fulfill its debt obligations in a specific economic environment, and is usually calculated by considering the following factors:

[0272] The adequacy of the enterprise's disposable cash flow over the next 6 to 12 months;

[0273] The ratio between the total amount of short-term debt and the available cash flow of the enterprise;

[0274] Whether the enterprise has sufficient liquid assets to meet emergency repayments;

[0275] Whether the enterprise has the possibility to obtain external financial support in a stressful environment.

[0276] Finally, these solvency indices under different scenarios are used as important supplementary evidence for credit risk scoring, to optimize the overall risk assessment model. For example, if an enterprise has good current financial status, but its solvency index under the "severe recession scenario" decreases significantly, the system will appropriately increase its risk level in the risk rating, prompting credit personnel to pay attention to potential vulnerabilities.

[0277] By introducing macroeconomic scenario simulation and cash flow gap analysis mechanisms, the ability to predict the long-term solvency of borrowers is effectively improved, making risk assessment more comprehensive, dynamic, and with stronger forward-looking and adaptability.

[0278] According to one embodiment of the present application, the risk relevance includes:

[0279] Extracting the relationship between the borrower and the associated entities based on business registration data;

[0280] Identifying risk transmission links through graph path analysis and calculating the risk propagation probability of affected nodes;

[0281] Implementing joint monitoring and risk isolation measures for highly associated enterprise groups.

[0282] As mentioned above, in order to more comprehensively identify and manage the credit risk of borrowers, the system introduces a risk relevance analysis module to identify the association between the borrower and other enterprises or individuals, and to assess the possible transmission paths and impact range of risks among these associated entities.

[0283] First, the system will extract the relationship between the borrower and the associated entities based on business registration data. These data usually come from public enterprise registration information, equity structure disclosure materials, etc., and the system identifies the borrower's main shareholders, subsidiaries, holding companies, legal representatives and other enterprises controlled by them, etc. For example:

[0284] If a company A holds more than 50% of the shares of company B, the system determines that company B is a related party of A;

[0285] If the legal representatives of two companies are the same person, or there is a cross-shareholding relationship, the system will also include it in the correlation graph;

[0286] The system also identifies non-equity-related information such as guarantee relationships and upstream and downstream transaction relationships to build a more complete risk correlation network.

[0287] Secondly, on the basis of establishing the correlation relationship, the system will identify the risk transmission link through graph path analysis and calculate the risk propagation probability of the affected nodes. For example:

[0288] If a related enterprise is listed as a person subject to public credit information due to major default, the system will automatically identify its upstream and downstream customers, guarantors, and potential affected nodes such as co-borrowers based on the correlation graph;

[0289] Through graph theory algorithms such as shortest path analysis and influence propagation model, the system determines the possibility and path of risk spreading from one node to other nodes;

[0290] The system estimates the risk propagation probability under different paths based on historical similar case data and assesses the impact degree of each correlation node accordingly.

[0291] Finally, for those groups of enterprises with complex correlation relationships and dense risk transmission paths, the system will implement joint monitoring and risk isolation measures. For example:

[0292] For groups of enterprises that exist in close association or supply chain core enterprises, the system will monitor them as a whole;

[0293] When a member enterprise shows a risk signal, the system automatically increases the monitoring frequency of the entire correlation group and conducts risk checks on other related enterprises in advance;

[0294] In necessary cases, the system suggests that credit institutions take risk isolation measures for high-risk related enterprises, such as limiting new credit, freezing funds, and suspending joint credit arrangements, to prevent the risk from further spreading within the group.

[0295] Through the above mechanisms, the system realizes the systematic identification and dynamic management of the correlation risk of borrowers, improves the coverage and accuracy of risk early warning, and is especially suitable for preventing regional or systemic financial risks caused by single enterprise risks.

[0296] The second aspect embodiment of the application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the method in any of the embodiments of the first aspect.

[0297] Figure 2 An example of a schematic diagram of the physical structure of an electronic device is shown in Figure 2 The electronic device can include a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 can communicate with each other through the communication bus 840. The processor 810 can invoke the logical instructions in the memory 830 to execute the method in any of the embodiments of the first aspect, which comprises:

[0298] Multi-dimensional data is obtained from the borrower's basic information, financial statements, credit records, and external market environment, and the multi-dimensional data is cleaned, standardized, and stored;

[0299] Based on the multi-dimensional data, a credit scoring model, cash flow analysis, debt ratio analysis, and market environment risk assessment are used to generate the financial risk level of the borrower;

[0300] The borrower's financial and market data are updated in real time, and a time series prediction model and risk correlation analysis are used to monitor the dynamic changes in the borrower's risk state and generate dynamic monitoring results;

[0301] According to the risk level and the dynamic monitoring results, a graded early warning mechanism is triggered, and corresponding control measure suggestions are generated to adjust the credit strategy or take risk mitigation measures.

[0302] In addition, the logical instructions in the memory 830 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes instructions to make a computer device (which can be a personal computer, a server, or a network device) execute all or part of the steps of the method described in the embodiments of the application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0303] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program being stored in a non-transitory computer-readable storage medium and executable by a processor to enable a computer to execute the method provided by any of the above methods, and the method comprises:

[0304] obtaining multi-dimensional data from the borrower's basic information, financial statements, credit records and external market environment, and performing cleaning, standardization processing and storage on the multi-dimensional data;

[0305] based on the multi-dimensional data, generating the financial risk level of the borrower through a credit scoring model, cash flow analysis, debt ratio analysis and market environment risk assessment;

[0306] updating the financial and market data of the borrower in real time, combining a time series prediction model and risk correlation analysis to monitor the dynamic changes of the risk state of the borrower, and generating a dynamic monitoring result;

[0307] according to the risk level and the dynamic monitoring result, triggering a hierarchical early warning mechanism, and generating corresponding control measure suggestions to adjust the credit strategy or take risk mitigation measures.

[0308] In another aspect, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the cigarette box image recognition method provided by any of the above methods, and the method comprises:

[0309] obtaining multi-dimensional data from the borrower's basic information, financial statements, credit records and external market environment, and performing cleaning, standardization processing and storage on the multi-dimensional data;

[0310] based on the multi-dimensional data, generating the financial risk level of the borrower through a credit scoring model, cash flow analysis, debt ratio analysis and market environment risk assessment;

[0311] updating the financial and market data of the borrower in real time, combining a time series prediction model and risk correlation analysis to monitor the dynamic changes of the risk state of the borrower, and generating a dynamic monitoring result;

[0312] according to the risk level and the dynamic monitoring result, triggering a hierarchical early warning mechanism, and generating corresponding control measure suggestions to adjust the credit strategy or take risk mitigation measures.

[0313] The places not mentioned in the present application can be realized by using or referring to the existing technology.

[0314] The various embodiments in the specification are described in progressive manner, and the same or similar parts between the various embodiments can be mutually referred to, and each embodiment focuses on the difference from other embodiments.

[0315] The above only describes the embodiments of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for dynamic assessment and early warning of credit risk based on multi-dimensional data analysis, characterized in that, include: The system obtains multi-dimensional data from borrowers' basic information, financial statements, credit records, and the external market environment, and then cleans, standardizes, and stores this multi-dimensional data. Based on the aforementioned multi-dimensional data, the borrower's financial risk level is generated through credit scoring models, cash flow analysis, debt ratio analysis, and market environment risk assessment. Real-time updates of borrowers' financial and market data, combined with time series forecasting models and risk correlation analysis, monitor dynamic changes in borrowers' risk status and generate dynamic monitoring results; Based on the risk level and the dynamic monitoring results, a tiered early warning mechanism is triggered, and corresponding control measures are recommended to adjust credit strategies or take risk mitigation measures.

2. The method according to claim 1, characterized in that, The process of acquiring multi-dimensional data from borrower basic information, financial statements, credit records, and the external market environment, and then cleaning, standardizing, and storing this multi-dimensional data, includes: Borrower basic information includes identity information, years of operation, and industry category; Financial statement data includes key indicators from the balance sheet, income statement, and cash flow statement; Credit history includes the number of past loan defaults, credit card delinquencies, and guarantee information; External market data includes macroeconomic indicators, industry policy documents, and competitor activities; Fill in missing data with interpolation or mark it as an anomaly; Outliers are identified and removed using statistical methods; Transform unstructured text into structured fields and unify timestamps and numerical units.

3. The method according to claim 1, characterized in that, Based on the aforementioned multi-dimensional data, the borrower's financial risk level is generated through credit scoring models, cash flow analysis, debt ratio analysis, and market environment risk assessment, including: Qualitative indicators are extracted from unstructured text using natural language processing technology, and quantitative indicators are calculated by combining them with expert knowledge base to quantify scoring factors. These quantitative indicators include profitability indicators, solvency indicators, and cash flow health indicators. Credit scores are generated using logistic regression models or random forest algorithms, and risk levels are classified based on the scores. The system classifies and models the cash flows of operating, investing, and financing activities, predicts the total amount of debt due in the next six months, compares it with the net cash flow from operating activities in the same period, assesses the debt repayment capacity, and simulates the cash flow gap under extreme market scenarios through stress testing. Calculate the debt-to-equity ratio and interest coverage ratio, compare them with industry benchmark thresholds, and if the ratio deviates from the reasonable range for two consecutive periods, a high-risk flag is triggered. We construct regression models between macroeconomic factors and borrowers' financial indicators to quantify the impact of GDP growth rate and interest rate adjustments on debt repayment capacity, analyze industry policies, and assess the potential impact on borrowers' cost structure.

4. The method according to claim 1, characterized in that, The system updates borrowers' financial and market data in real time, combines time series forecasting models and risk correlation analysis, monitors the dynamic changes in borrowers' risk status, and generates dynamic monitoring results, including: Credit data, bank statements, and market environment data are synchronized to the database daily via API interface; Set thresholds for key indicators and trigger automatic alerts when the thresholds are exceeded. Key indicators include cash flow and debt ratio. Use LSTM neural networks to predict key financial indicators for the next 6 months; A risk level migration matrix is ​​constructed based on historical data to simulate the probability of changes in a borrower’s credit rating over time. Construct a borrower relationship graph based on business registration, equity structure, and guarantee relationships; When a risk event occurs at a certain node, the affected nodes are analyzed along the graph path to calculate the probability of risk propagation.

5. The method according to claim 1, characterized in that, The step of triggering a tiered early warning mechanism based on the risk level and the dynamic monitoring results, and generating corresponding control measure recommendations to adjust credit strategies or take risk mitigation measures, includes: Risk levels are categorized based on credit score, cash flow health, and debt ratio, including: Level 1 alert, no abnormalities, only record; Level 2 warning, indicating a need for attention and restriction of credit lines; A Level 3 warning requires additional collateral or restricts new loans. Level 4 warning, initiate collection process; Level 5 warning, transferred to the legal department for handling; Match control measures according to risk level; Compare actual default data with predicted results monthly to optimize credit scoring model parameters; Regularly update industry policies and market environment data, and retrain the risk assessment model.

6. The method according to claim 3, characterized in that, The credit scoring model includes: Extract non-numerical features such as management stability, legal litigation records, and industry experience; By using sentiment analysis and keyword extraction techniques, negative event records can be identified from corporate announcements and news reports; Each qualitative factor is assigned a weight and then fed into the credit scoring model.

7. The method according to claim 3, characterized in that, The cash flow analysis includes: Set up multiple macroeconomic scenarios; Simulates the cash flow gap of enterprises under various macroeconomic scenarios, resulting in decreased operating revenue and increased costs. Output the debt repayment capacity index under different scenarios as a supplementary basis for risk scoring.

8. The method according to claim 3, characterized in that, The aforementioned risk correlation includes: Extracting the relationship between borrowers and related entities based on business registration data; Risk transmission links are identified through graph path analysis, and the probability of risk propagation at affected nodes is calculated. Implement joint monitoring and risk isolation measures for highly interconnected enterprise groups.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-8.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-8.

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