Commercial risk assessment management method and device and electronic equipment

Abstract

The invention provides a commercial risk assessment management method and device and electronic equipment, and belongs to the technical field of financial science and technology. The method comprises the following steps: collecting data sources from different channels, wherein the data sources comprise historical transaction records, market information, financial statements and social media feedback; defining a preset feature index according to the business demand, and constructing a risk assessment model by applying a machine learning algorithm; performing risk assessment on the data source by using the risk assessment model, calculating the numerical value of each risk index and generating a comprehensive risk score; and setting a risk level threshold, and triggering an early warning mechanism when the comprehensive risk score exceeds the risk level threshold. Through fusion of multi-source heterogeneous data and a machine learning algorithm, full-process intelligent evaluation and dynamic early warning of commercial risks are realized, and accuracy, response speed and decision support capability of risk identification are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of financial technology, and particularly relates to a commercial risk assessment management method and device and electronic equipment. BACKGROUND

[0002] In the current complex business environment, enterprises face challenges from market, financial, credit, public opinion and other aspects.

[0003] Traditional risk management methods mainly rely on manual analysis and experience judgment, which are difficult to meet the demand of massive, heterogeneous and real-time data processing, and have problems such as response lag, subjective evaluation and incomplete coverage, and cannot meet the actual needs of modern enterprises for efficient and intelligent risk identification and early warning. SUMMARY

[0004] In order to solve at least one of the technical problems in the background art, the application provides a commercial risk assessment management method, which realizes intelligent assessment and dynamic early warning of commercial risk by fusing multi-source heterogeneous data and machine learning algorithm, and improves the accuracy, response speed and decision support ability of risk identification.

[0005] The technical scheme adopted by the application is:

[0006] The first aspect of the application provides a commercial risk assessment management method, comprising:

[0007] Collecting data sources from different channels, the data sources including historical transaction records, market quotations, financial statements and social media feedback;

[0008] Defining preset feature indicators according to business requirements, and applying machine learning algorithm to build a risk assessment model;

[0009] Applying the risk assessment model to risk assessment of the data sources, calculating the values of various risk indicators and generating a comprehensive risk score;

[0010] Setting a risk level threshold, and triggering an early warning mechanism when the comprehensive risk score exceeds the risk level threshold.

[0011] According to the business risk assessment management method provided by the first aspect of the application, through the construction of a complete technical process from multi-source data collection, feature extraction, model training, risk scoring to dynamic early warning, efficient identification and real-time monitoring of various business risks are realized. First, the method integrates multi-channel heterogeneous data sources such as historical transaction records, market quotations, financial statements, and social media feedback, breaking through the bottleneck of single data dimension and limited source in traditional risk assessment, and providing a rich and comprehensive information base for subsequent modeling. Secondly, combined with business requirements, key feature indicators are defined, and various machine learning algorithms are applied for model construction and training, which not only improves the adaptability and prediction ability of the model, but also ensures the stability and generalization performance of the model through cross-validation and other means. Further, the method uses the trained risk assessment model to perform multi-dimensional risk prediction on the target object, outputs single risk indicators such as financial, market, credit, and public opinion, and generates a comprehensive risk score based on a dynamic weighted fusion algorithm, effectively solving the problem of large subjective judgment deviation and weak quantification in traditional assessment. Finally, by setting multi-level risk level thresholds and combining a feedback mechanism to dynamically adjust the early warning boundary, the method realizes graded response and intelligent early warning of risk events, can discover potential problems in a timely manner and notify relevant decision-makers before the risk occurs, significantly improving the timeliness and pertinence of risk response.

[0012] According to an embodiment of the application, the data sources from different channels are collected, including historical transaction records, market quotations, financial statements, and social media feedback, specifically:

[0013] The historical transaction records, financial statements of the enterprise, and real-time and historical quotation data of the external financial market are obtained through API interface or database connection;

[0014] User comments, public opinion information, and industry dynamic content on social media are captured using web crawler technology or third-party platform interfaces;

[0015] A unified data access intermediate layer is established to standardize the format of information from multiple heterogeneous data sources, and periodically or in real time synchronize to the central data warehouse or data lake to support subsequent risk analysis processes.

[0016] According to an embodiment of the application, the preset feature indicators are defined according to business requirements, and a risk assessment model is constructed using machine learning algorithms, specifically:

[0017] Based on the industry characteristics and risk management goals of the enterprise, key feature indicators reflecting financial health, market volatility sensitivity, credit risk level, and public opinion influence intensity are selected, including liquidity ratio, asset-liability ratio, revenue growth rate, social media sentiment score, and stock price volatility index;

[0018] The supervised learning algorithm selection strategy is adopted to select at least one of the following algorithms: logistic regression, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT), and deep neural network (DNN) for model training, and the accuracy, recall rate, and AUC value of each model are evaluated through cross-validation method;

[0019] The trained and verified risk assessment model is encapsulated as a callable service interface.

[0020] According to one embodiment of the present application, the risk assessment model is applied to the data source to calculate the values of various risk indicators and generate a comprehensive risk score, specifically:

[0021] Based on the trained risk assessment model, multi-dimensional risk prediction is performed on the target object, and the single risk indicator values including at least financial risk, market risk, credit risk, and public opinion risk are output;

[0022] According to the importance of each single risk indicator value, a weighting coefficient is set, and a linear or nonlinear fusion algorithm is used to generate a comprehensive risk score, wherein the weighting coefficient is dynamically adjusted according to industry standards, enterprise type, or the impact weight of historical risk events;

[0023] The calculated single risk indicator values and comprehensive risk score are output to a database and a user interface, and a timestamp and related context information are recorded for subsequent trend analysis and audit traceability.

[0024] According to one embodiment of the present application, a risk level threshold is set, and when the comprehensive risk score exceeds the risk level threshold, a warning mechanism is triggered, specifically:

[0025] A multi-level risk level system is established based on industry risk benchmarks and enterprise historical data, and the comprehensive risk score is divided into multiple risk level intervals, each interval corresponding to a different level of risk state, including low risk, medium risk, and high risk;

[0026] A corresponding threshold boundary is set for each risk level, and based on external environmental changes, policy adjustments, or market fluctuations, the threshold boundary is dynamically adjusted through a feedback mechanism to adapt to the risk management needs in different business scenarios;

[0027] When the comprehensive risk score of the target object enters a certain risk level interval, an early warning signal of the corresponding level is automatically triggered, and the high-risk level warning is preferentially pushed to the designated user terminal with risk details and response suggestions;

[0028] The early warning information is pushed to the client, the mail system or the third-party collaborative platform through the message notification system, and the early warning time, the scoring basis and the processing state are recorded in the database for subsequent audit and analysis.

[0029] According to one embodiment of the present application, the method further comprises:

[0030] The risk assessment results are presented to the user in the form of a chart through a graphical user interface to show the values of each single risk indicator and the trend of change over time;

[0031] An interactive control is provided to allow the user to adjust the view parameters on demand.

[0032] According to one embodiment of the present application, the method further comprises:

[0033] The feedback information of the user on the risk assessment results is received, and the model parameters are dynamically adjusted or the training data set is updated based on the feedback information;

[0034] New feature variables are automatically introduced based on external environmental changes to improve the prediction accuracy and adaptability of the model.

[0035] The second aspect embodiment of the present application provides a business risk assessment management device, comprising:

[0036] A data collection module is adapted to collect data sources from different channels, including historical transaction records, market quotations, financial statements and social media feedback;

[0037] A model construction module is adapted to define preset feature indicators according to business needs and apply machine learning algorithms to construct a risk assessment model;

[0038] An evaluation module is adapted to apply the risk assessment model to risk assessment of the data sources, calculate the values of each risk indicator and generate a comprehensive risk score;

[0039] A warning module is adapted to set a risk level threshold, and trigger a warning mechanism when the comprehensive risk score exceeds the risk level threshold.

[0040] The third aspect embodiment of the present application provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the business risk assessment management method in any of the embodiments of the first aspect.

[0041] The present application also provides a non-volatile computer storage medium having computer executable instructions stored thereon, wherein the computer program is executed by the processor to implement the business risk assessment management method in any of the embodiments of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0043] Figure 1 A flowchart of a business risk assessment management method provided by an embodiment of the application;

[0044] Figure 2 A structural diagram of a business risk assessment management device provided by an embodiment of the application;

[0045] Figure 3 A structural diagram of an electronic device provided by an embodiment of the application.

[0046] Reference signs:

[0047] 110, data collection module; 120, model construction module; 130, assessment module; 140, early warning module;

[0048] 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0049] In order to more clearly illustrate the overall concept of the application, the following will be described in detail with reference to the accompanying drawings.

[0050] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the application. However, the application can be practiced without the specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail since such can be readily understood by persons skilled in the art. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0051] In the present application, unless specifically defined and limited otherwise, the first feature is "on" or "under" the second feature, which can be direct contact between the first and second features, or indirect contact between the first and second features 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 application. In the 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.

[0052] As Figure 1As shown, the first aspect of the present application provides a business risk assessment management method, which comprises:

[0053] Step 100, collect data sources from different channels, including historical transaction records, market trends, financial statements, and social media feedback.

[0054] Step 200, define preset feature indicators according to business needs, and apply machine learning algorithms to build a risk assessment model.

[0055] Step 300, apply the risk assessment model to conduct risk assessment on the data sources, calculate the numerical values of each risk indicator, and generate a comprehensive risk score.

[0056] Step 400, set a risk level threshold, and trigger a warning mechanism when the comprehensive risk score exceeds the risk level threshold.

[0057] In step 100, "data sources" in this step refer to all original information sources for risk assessment, covering structured data (such as transaction records and financial statements in databases) and unstructured data (such as user comments on social media). Among them, "historical transaction records" reflect the past business behavior of enterprises; "market trends" include stock prices, interest rate changes, and other financial indicators; "financial statements" reflect the asset position and profitability of enterprises; "social media feedback" contains public opinion evaluation of enterprises.

[0058] This step is based on multi-source heterogeneous data collection technology, which obtains relevant data from enterprise internal systems and external platforms through API interface, web crawler and other methods, and uniformly accesses to the data processing intermediate layer. This data fusion method can effectively break the problem of data silos in traditional risk management, improve the information dimension and timeliness of risk identification. Compared with traditional risk analysis methods relying on a single data source, this step significantly enhances the data coverage and real-time response capability, providing high-quality and comprehensive basic data support for subsequent modeling.

[0059] In step 200, "feature indicators" refer to key variables extracted from raw data that can be used to predict risks, such as liquidity ratio, asset-liability ratio, revenue growth rate, social media sentiment score, etc. "Machine learning algorithm" refers to a class of automatic learning and prediction models based on data-driven, including logistic regression, random forest, support vector machine SVM, gradient boosting decision tree GBDT, deep neural network DNN, etc.

[0060] This step combines industry knowledge and data mining techniques to select representative risk features and train prediction models, achieving the transition from experience-based judgment to intelligent modeling. By methods such as cross-validation, the model parameters are optimized to ensure good generalization ability and stability. Compared to traditional risk assessment models based on rules or statistical methods, this step introduces machine learning mechanisms to improve the adaptability and accuracy of the model, especially in the face of complex, non-linear risk factors, demonstrating stronger predictive power. This provides a reliable technical foundation for subsequent dynamic risk assessment.

[0061] In step 300, the "risk assessment model" is a trained machine learning model with predictive power; the "risk indicator values" are the specific quantitative results of each risk dimension output by the model, such as credit risk values, market volatility risk values, etc.; the "comprehensive risk score" is the result of integrating multiple single risk indicators into a numerical representation of overall risk level through weighted fusion algorithms.

[0062] This step is based on the trained model to perform multi-dimensional risk prediction on target objects and generate a comprehensive score based on dynamic weight allocation strategies, achieving the transition from local risk identification to global risk assessment. By setting flexible fusion algorithms (such as linear or non-linear weighting), the scoring logic can be adjusted according to industry characteristics, enterprise types, etc., enhancing model applicability. This process not only improves the scientificity and objectivity of risk assessment, but also provides intuitive and actionable risk reference for management, helping to make quick decisions and develop appropriate prevention and control measures.

[0063] In step 400, "risk level threshold" refers to the boundary values between different risk levels set according to industry standards and historical data; "early warning mechanism" refers to the automatic notification process initiated by the system once a risk score exceeds the preset threshold, prompting relevant personnel to take action.

[0064] This step establishes a multi-level risk level system (such as low risk, medium risk, high risk) and sets corresponding threshold boundaries to achieve graded response and automated early warning of risk events. At the same time, the system supports dynamic adjustment of thresholds based on external environmental changes, maintaining the flexibility and adaptability of the early warning mechanism. Compared to traditional manual monitoring modes, this step greatly improves risk response speed and management efficiency, helping enterprises to intervene in time before risks occur, reducing potential losses, and ensuring operational safety.

[0065] According to the business risk assessment management method provided by the first aspect of the application, by constructing a complete technical process from multi-source data collection, feature extraction, model training, risk scoring to dynamic early warning, efficient identification and real-time monitoring of various business risks are realized. First, the method integrates multi-channel heterogeneous data sources such as historical transaction records, market quotations, financial statements, and social media feedback, breaking through the bottleneck of single data dimension and limited source in traditional risk assessment, and providing a rich and comprehensive information base for subsequent modeling. Secondly, combined with business requirements, key feature indicators are defined, and various machine learning algorithms are applied for model construction and training, which not only improves the adaptability and prediction ability of the model, but also ensures the stability and generalization performance of the model through cross-validation and other means. Further, the method performs multi-dimensional risk prediction on the target object through the trained risk assessment model, outputs single risk indicators such as financial, market, credit, and public opinion, and generates a comprehensive risk score based on a dynamic weighted fusion algorithm, effectively solving the problem of large subjective judgment deviation and weak quantification ability in traditional assessment. Finally, by setting multi-level risk level thresholds and combining a feedback mechanism to dynamically adjust the early warning boundary, the method realizes graded response and intelligent early warning of risk events, can discover potential problems in a timely manner and notify relevant decision-makers before the risk occurs, significantly improving the timeliness and pertinence of risk response.

[0066] In some embodiments of the present application, data sources from different channels are collected, including historical transaction records, market quotations, financial statements, and social media feedback, specifically:

[0067] The historical transaction records, financial statements of the enterprise and the real-time and historical quotation data of the external financial market are obtained through API interface or database connection method;

[0068] User comments, public opinion information and industry dynamic content on social media are captured using web crawler technology or third-party platform interface;

[0069] A unified data access intermediate layer is established to standardize the format of information from multiple heterogeneous data sources, and to synchronize to the central data warehouse or data lake regularly or in real time to support subsequent risk analysis processes.

[0070] In this specific data collection process, the accuracy and authority of the collected information are ensured by directly accessing data from internal enterprise systems and external financial markets through API interfaces or database connections. This approach relies on standardized data exchange protocols such as RESTful API, SOAP, or ODBC, enabling seamless data integration between different systems. For example, a financial institution can obtain real-time stock transaction records through the API interface of its trading platform and periodically synchronize the enterprise's financial statements through the database connection of the financial software, which not only improves data acquisition efficiency but also guarantees data integrity and consistency.

[0071] Using web crawler technology or third-party platform interfaces to capture user comments, public opinion information, and industry dynamic content on social media is an effective strategy for dealing with unstructured data sources. Web crawlers can automatically capture relevant information based on set keywords, frequencies, and ranges, while third-party platform interfaces (such as developer APIs provided by Weibo and Twitter) allow direct access to organized data streams. Taking a retail brand as an example, it can monitor consumer feedback on social media to understand market acceptance of its new products in a timely manner, which helps the enterprise quickly respond to market changes and adjust marketing strategies. In addition, this information is also valuable for identifying potential brand risks and social opinion trends.

[0072] Establishing a unified data access intermediate layer to standardize the format of information from multiple heterogeneous data sources and periodically or real-time synchronize to the central data warehouse or data lake is a basic step for efficient data analysis. This intermediate layer acts as a conversion hub, responsible for parsing various data formats and converting them into a standard format suitable for analysis. For example, different types of timestamps, currency units, and language codes need to be standardized at this stage. This eliminates data silos, promotes cross-departmental data sharing and collaboration, and provides high-quality data support for subsequent risk assessment models. In this way, enterprises can make more informed decisions based on comprehensive and up-to-date information, improving the effectiveness of risk management.

[0073] In some embodiments of the present application, pre-set feature indicators are defined according to business needs, and machine learning algorithms are applied to build risk assessment models, specifically:

[0074] Based on the industry characteristics and risk management goals of the enterprise, key feature indicators reflecting financial health, market volatility sensitivity, credit risk level, and public opinion influence intensity are selected, including liquidity ratio, asset-liability ratio, revenue growth rate, social media sentiment score, and stock price volatility index.

[0075] The supervised learning algorithm selection strategy is adopted to select at least one of the following algorithms: logistic regression, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT), and deep neural network (DNN) for model training, and the accuracy, recall rate, and AUC value of each model are evaluated through cross-validation method.

[0076] The trained and validated risk assessment model is encapsulated as a callable service interface.

[0077] In this step, first, based on the industry characteristics of the enterprise (such as manufacturing, financial services, or retail) and specific risk management goals (such as credit risk prevention and control, market volatility early warning, or public opinion crisis identification), key feature indicators that can effectively reflect the operating health of the enterprise and the impact of external environment are selected. These indicators include but are not limited to financial indicators such as liquidity ratio and asset-liability ratio, operating indicators such as revenue growth rate, public opinion indicators such as social media sentiment score, and market sensitivity indicators such as stock price volatility index. By incorporating these multi-dimensional features into the modeling process, the enterprise's risk profile can be more comprehensively described, and the accuracy and applicability of the model's predictions can be improved.

[0078] In the model construction phase, supervised learning algorithms are used for training, and cross-validation methods are used to evaluate the performance of various mainstream algorithms (such as logistic regression, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT), and deep neural network (DNN)) to select the optimal model. For example, when dealing with credit risk assessment tasks for small and medium-sized enterprises, random forest is preferred due to its good anti-overfitting ability and interpretability; while in the face of complex nonlinear financial time series data, deep neural networks may exhibit stronger modeling capabilities. Through this flexible selection strategy and model comparison mechanism, the generalization ability of the model is improved, and its adaptability and stability in different business scenarios are enhanced.

[0079] Finally, the trained and validated risk assessment model is encapsulated as a callable service interface (such as RESTful API) to realize the engineering deployment and business integration of the model. This approach allows front-end application systems, risk management platforms, or data analysis tools to directly call the model service, quickly obtain risk score results, and improve the response speed and scalability of the system. For example, a bank's loan approval system can call the credit risk assessment model in real-time through the API to assist credit personnel in quickly determining the credit rating of customers. This approach not only reduces the technical threshold for model deployment, but also facilitates subsequent model version updates and maintenance, forming a closed-loop process from model development to practical application, with good engineering practicality and promotional value.

[0080] In some embodiments of the present application, a risk assessment model is applied to the data source to calculate the values of various risk indicators and generate a comprehensive risk score. Specifically:

[0081] Based on the trained risk assessment model, multi-dimensional risk prediction is performed on the target object, and single risk indicator values including at least financial risk, market risk, credit risk, and public opinion risk are outputted;

[0082] According to the importance of each single risk indicator value, a weighting coefficient is set, and a comprehensive risk score is generated through linear or nonlinear fusion algorithm, wherein the weighting coefficient is dynamically adjusted according to industry standards, enterprise type, or the impact weight of historical risk events;

[0083] The calculated single risk indicator values and comprehensive risk score are outputted to the database and user interface, and the time stamp and related context information are recorded for subsequent trend analysis and audit traceability.

[0084] In this step, based on the trained risk assessment model, multi-dimensional risk prediction is performed on the target object (such as an enterprise, a project, a counterparty, etc.), and single risk indicator values including financial risk, market risk, credit risk, and public opinion risk are outputted. These indicators reflect the potential risks that an enterprise may face at different levels: for example, financial risk can be reflected by liquidity ratio, asset-liability ratio, etc.; market risk is measured by stock price volatility index and industry sentiment changes; credit risk focuses on the solvency and historical default records of an enterprise; public opinion risk relies on social media sentiment score and news event heat analysis. This multi-dimensional modeling method breaks through the limitations of traditional single-dimensional risk assessment, and realizes the comprehensive description of the overall risk situation of an enterprise.

[0085] When generating a comprehensive risk score, the system sets a weighting coefficient according to the importance of each single risk indicator, and uses linear or nonlinear fusion algorithm for comprehensive calculation. For example, in the financial industry, credit risk may have a higher weight; while in the retail or consumer goods industry, the proportion of public opinion risk may be correspondingly increased. The setting of weighting coefficient not only considers industry standards and regulatory requirements, but also dynamically adjusts according to enterprise type (such as listed companies and small and medium-sized enterprises) and the impact of historical risk events, thereby realizing personalized and scenario-based scoring mechanism. This method improves the flexibility and adaptability of the scoring system, making it more accurately reflect the real risk level in a specific business context.

[0086] Finally, the system outputs the calculated single risk indicator values and the comprehensive risk score to the database and user interface for real-time viewing and analysis by risk management personnel. At the same time, the system also automatically records the timestamp of score generation and related context information (such as input data version, model version, score triggering conditions, etc.) to support subsequent trend analysis, anomaly detection, and audit traceability. For example, a financial institution can automatically generate a trend chart of customer credit risk scores at the end of each month to monitor changes in customer risk levels. In the event of a risk event, the score basis can also be traced back through the log to facilitate responsibility definition and model optimization. This mechanism not only improves the interpretability and visualization of risk assessment results, but also provides solid data support for building a closed-loop risk management process.

[0087] In some embodiments of the present application, a risk level threshold is set, and a warning mechanism is triggered when the comprehensive risk score exceeds the risk level threshold, specifically:

[0088] According to the industry risk benchmark and enterprise historical data, a multi-level risk level system is established, the comprehensive risk score is divided into multiple risk level intervals, each interval corresponds to a different level of risk state, including low risk, medium risk and high risk;

[0089] A threshold boundary is set for each risk level, and based on external environmental changes, policy adjustments or market fluctuations, the threshold boundary is dynamically adjusted through a feedback mechanism to adapt to the risk management needs in different business scenarios;

[0090] When the comprehensive risk score of the target object enters a certain risk level interval, the corresponding level of warning signal is automatically triggered, and the high risk level warning is preferentially pushed to the designated user terminal, with risk details and response suggestions;

[0091] The warning information is pushed to the client, email system or third party collaboration platform through the message notification system, and the warning time, score basis and processing status are recorded in the database for subsequent audit and analysis.

[0092] In this step, the system divides the comprehensive risk score into multiple intervals by establishing a multi-level risk level system, and sets a corresponding threshold boundary for each interval. For example, a comprehensive score below 60 points is a low risk interval, 60 to 80 points is a medium risk interval, and more than 80 points is a high risk interval. Different industries can adjust the risk tolerance according to their risk tolerance, such as financial institutions, which may have lower tolerance for credit risk, so they set lower medium and high risk thresholds. This grading mechanism helps risk management personnel quickly identify risk levels and take appropriate response strategies, improving the level and relevance of risk management.

[0093] To enhance the adaptability and intelligence of the system, the system also introduces a dynamic threshold adjustment mechanism. Based on external environmental changes (such as policy adjustments, economic cycle fluctuations), market emergencies or changes in the operating state of the enterprise, combined with user feedback and model output data, the risk threshold of each level is adaptively adjusted. For example, during the macroeconomic downturn, the system can automatically adjust the weight of the high and low risk intervals to early warn potential default risks; while in the stable growth period, the standard is appropriately relaxed to avoid false positives that interfere with normal operations. This mechanism effectively improves the flexibility and robustness of the system in complex business scenarios, making risk assessment more closely meet actual needs.

[0094] When the comprehensive risk score of the target object enters a certain risk level interval, the system will automatically trigger the corresponding level of early warning signal, and preferentially push the high-risk early warning information to the designated user terminal (such as the risk control supervisor, credit approval personnel, etc.), while attaching risk details (such as key risk indicators, public opinion event abstracts) and preliminary response suggestions (such as strengthening financial monitoring, suspending credit limit, etc.). Early warning information is pushed to the client through the message notification system, the email system or the third party collaboration platform (such as Dingding, WeChat for enterprise, Slack), and the complete early warning log is recorded in the database, including timestamp, scoring basis, processing status, etc., for subsequent audit traceability and model optimization. This way not only improves the efficiency and accuracy of risk response, but also provides strong support for building a closed-loop risk management process for the enterprise.

[0095] In some embodiments of the present application, the method further comprises:

[0096] Presenting the risk assessment results to the user in the form of a chart through a graphical user interface to show the values of each single risk indicator and its trend over time;

[0097] Providing interactive controls to allow the user to adjust the view parameters as needed.

[0098] This step presents the risk assessment results to the user in the form of a chart through a graphical user interface (GUI), making complex risk data easier to understand and analyze. The system can support multiple visualization forms, such as bar charts to display the values of each single risk indicator, line charts to reflect the trend of risk score over time, heat maps to reveal the risk distribution differences between different enterprises or projects, etc. For example, in the financial risk control scenario, the user can observe the fluctuations of a client's credit risk score in the past half year through the line chart to determine whether their credit status has deteriorated. This visualization presentation significantly improves the readability and communication efficiency of risk information.

[0099] To further enhance user experience and analysis flexibility, the system provides interactive controls that allow users to adjust view parameters according to their own needs. These controls include but are not limited to time range selectors, risk dimension filters, enterprise / project comparison functions, threshold marker settings, etc. For example, users can choose to "show only the last 30 days", "sort by financial risk", or "compare market risk trends of multiple clients", thereby quickly focusing on key objects and indicators of interest. This design not only enhances the usability of the system, but also meets the diverse viewing needs of different roles (such as executives, risk control officers, analysts) for risk information.

[0100] By introducing graphical display and interactive control mechanisms, this method effectively addresses the problems of single information presentation, high understanding threshold, and poor operational flexibility in traditional risk assessment systems. Users can obtain real-time dynamic risk assessment results and conduct in-depth analysis combined with historical trends to assist in making more forward-looking decisions. In addition, the visualization module can be linked with the early warning system to automatically mark risk trigger points in the chart and provide context explanations, further improving the intelligence and practicality of the system.

[0101] In some embodiments of the present application, the method further comprises:

[0102] Receiving user feedback on the risk assessment results and dynamically adjusting model parameters or updating the training data set based on the feedback information;

[0103] Based on external environmental changes, automatically introduce new feature variables to improve model prediction accuracy and adaptability.

[0104] This step realizes the dynamic optimization and continuous learning of the model by receiving user feedback on the risk assessment results. User feedback can include comments on risk scores, annotations of false positives / false negatives, or supplementary explanations of specific risk events, etc. The system collects this information through the feedback interface and uses it to adjust model parameters (such as feature weights, classification boundaries) or update the training data set, thereby improving the accuracy and adaptability of the model in specific business scenarios. For example, a risk control officer finds that a certain enterprise has been incorrectly rated as "low risk", but has actually experienced financial abnormalities. The system can retrain the model based on this feedback to enhance its ability to identify similar situations. This mechanism effectively addresses the limitations of static models in complex and changing environments, improving the practicality and interpretability of the model.

[0105] On this basis, the system also has the ability to automatically introduce new feature variables based on external environmental changes to further enhance the predictive performance of the model. External environmental changes include but are not limited to policy adjustments, macroeconomic fluctuations, industry trend evolution or unexpected events (such as epidemics, geopolitical conflicts) and the like. The system accesses external data sources (such as government announcements, industry reports, news information, market indices) for semantic analysis and trend identification, automatically extracts relevant features (such as "industry risk factors", "policy sensitivity score", "supply chain stability index" and the like) and incorporates them into the model training process. For example, after the central bank adjusts the interest rate policy, the system can automatically introduce the "interest rate sensitivity" indicator to more accurately assess the credit risk caused by changes in enterprise financing costs. This mechanism significantly improves the response capability of the model to external disturbances and enhances its robustness in dynamic environments.

[0106] By introducing a user feedback mechanism and external environment perception capability, this method builds a closed-loop optimization system for a risk assessment model with adaptive optimization capability. On the one hand, user feedback enables the model to be close to actual business needs, improving the model's usability in specific scenarios; on the other hand, automatically introducing external feature variables enables the model to have forward-looking and generalization capabilities, enabling it to capture emerging risk factors in a timely manner. Compared with traditional static models, the technical means proposed in this step significantly improve the intelligence, flexibility and prediction accuracy of the model.

[0107] As shown in Figure 2 The second aspect of the present application provides a business risk assessment management device, comprising:

[0108] The data collection module 110 is adapted to collect data sources from different channels, including historical transaction records, market quotes, financial statements and social media feedback;

[0109] The model construction module 120 is adapted to define preset feature indicators according to business needs and apply machine learning algorithms to construct a risk assessment model;

[0110] The evaluation module 130 is adapted to apply the risk assessment model to evaluate the data sources, calculate the values of various risk indicators and generate a comprehensive risk score;

[0111] The early warning module 140 is adapted to set a risk level threshold, and trigger the early warning mechanism when the comprehensive risk score exceeds the risk level threshold.

[0112] The business risk assessment management device provided by the second aspect of the present application can implement the business risk assessment management method in any of the embodiments of the first aspect, and therefore can achieve any of the technical effects of the business risk assessment management method described above, which will not be repeated here.

[0113] The third aspect 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, wherein the processor executes the program to implement the business risk assessment management method in any of the embodiments of the first aspect.

[0114] Figure 3 An example of a schematic diagram of the physical structure of an electronic device is shown in Figure 3 As shown, 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 communicate with each other through the communication bus 840. The processor 810 can invoke the logical instructions in the memory 830 to execute the business risk assessment management method in any of the embodiments of the first aspect, which comprises:

[0115] Step 100, collect data sources from different channels, including historical transaction records, market quotes, financial statements and social media feedback.

[0116] Step 200, define preset feature indicators according to business requirements, and apply machine learning algorithm to build risk assessment model.

[0117] Step 300, apply the risk assessment model to the data source for risk assessment, calculate the numerical value of each risk indicator and generate a comprehensive risk score.

[0118] Step 400, set a risk level threshold, and trigger a warning mechanism when the comprehensive risk score exceeds the risk level threshold.

[0119] 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 part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory, random access memory, magnetic disk or optical disk and various program code storage media.

[0120] 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 the computer program being executable by a processor to cause a computer to execute the business risk assessment management method provided by the above-mentioned method, which comprises:

[0121] Step 100, collecting data sources from different channels, the data sources comprising historical transaction records, market quotations, financial statements and social media feedback.

[0122] Step 200, defining preset feature indexes according to business requirements, and constructing a risk assessment model by applying a machine learning algorithm.

[0123] Step 300, applying the risk assessment model to perform risk assessment on the data sources, calculating values of various risk indexes and generating a comprehensive risk score.

[0124] Step 400, setting a risk level threshold, and triggering a warning mechanism when the comprehensive risk score exceeds the risk level threshold.

[0125] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, the computer program being executable by a processor to implement the business risk assessment management method provided by the above-mentioned method, which comprises:

[0126] Step 100, collecting data sources from different channels, the data sources comprising historical transaction records, market quotations, financial statements and social media feedback.

[0127] Step 200, defining preset feature indexes according to business requirements, and constructing a risk assessment model by applying a machine learning algorithm.

[0128] Step 300, applying the risk assessment model to perform risk assessment on the data sources, calculating values of various risk indexes and generating a comprehensive risk score.

[0129] Step 400, setting a risk level threshold, and triggering a warning mechanism when the comprehensive risk score exceeds the risk level threshold.

[0130] Finally, the present application also provides a non-volatile computer storage medium, which stores computer executable instructions, the computer program being executable by a processor to implement the business risk assessment management method provided by the above-mentioned method, which comprises:

[0131] Step 100, collecting data sources from different channels, the data sources comprising historical transaction records, market quotations, financial statements and social media feedback.

[0132] Step 200, define preset characteristic indexes according to business requirements, and apply machine learning algorithm to build risk assessment model.

[0133] Step 300, apply risk assessment model to risk assessment of data source, calculate numerical value of each risk index and generate comprehensive risk score.

[0134] Step 400, set risk level threshold, and trigger early warning mechanism when comprehensive risk score exceeds risk level threshold.

[0135] The places not mentioned in the application can be realized by using or referring to the existing technology.

[0136] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly explains the difference from other embodiments.

[0137] The above is only an embodiment of the application and is not used to limit the application. For those skilled in the art, the application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A business risk assessment and management method, characterized in that, include: Collect data sources from various channels, including historical transaction records, market data, financial statements, and social media feedback; Define preset feature indicators based on business needs, and apply machine learning algorithms to build a risk assessment model; The risk assessment model is applied to assess the risk of the data source, and the values ​​of various risk indicators are calculated to generate a comprehensive risk score. A risk level threshold is set, and an early warning mechanism is triggered when the comprehensive risk score exceeds the risk level threshold.

2. The business risk assessment and management method according to claim 1, characterized in that, The data sources collected are from various channels, including historical transaction records, market data, financial statements, and social media feedback, specifically: Obtain internal historical transaction records, financial statements, and real-time and historical market data from external financial markets through API interfaces or database connections; Use web crawler technology or third-party platform interfaces to capture user comments, public opinion information, and industry news from social media. Establish a unified data access middleware layer to standardize the format of information from multiple heterogeneous data sources and synchronize it to the central data warehouse or data lake regularly or in real time to support subsequent risk analysis processes.

3. The business risk assessment and management method according to claim 1, characterized in that, The process of defining preset feature indicators based on business needs and applying machine learning algorithms to construct a risk assessment model specifically includes: Based on the characteristics of the industry in which the enterprise operates and its risk management objectives, key characteristic indicators reflecting financial health, market volatility sensitivity, credit risk level and the intensity of public opinion influence are selected. These key characteristics include current ratio, debt-to-equity ratio, revenue growth rate, social media sentiment score and stock price volatility index. A supervised learning algorithm selection strategy was adopted, and at least one of the following algorithms was selected for model training: logistic regression, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT), and deep neural network (DNN). The accuracy, recall, and AUC of each model were evaluated by cross-validation. The trained and validated risk assessment model is encapsulated into a callable service interface.

4. The business risk assessment and management method according to claim 3, characterized in that, The application of the risk assessment model to perform a risk assessment on the data source, calculates the values ​​of various risk indicators, and generates a comprehensive risk score, specifically as follows: Based on the trained risk assessment model, multi-dimensional risk prediction is performed on the target object, and the output includes at least the values ​​of individual risk indicators such as financial risk, market risk, credit risk and public opinion risk. Weighting coefficients are set according to the importance of each individual risk indicator value, and a comprehensive risk score is generated through a linear or nonlinear fusion algorithm. The weighting coefficients are dynamically adjusted according to industry standards, enterprise type, or the impact weight of historical risk events. The calculated values ​​of the individual risk indicators and the comprehensive risk score are output to the database and user interface, and the timestamp and relevant context information are recorded for subsequent trend analysis and audit traceability.

5. The business risk assessment and management method according to claim 1, characterized in that, The aforementioned risk level threshold is set, and an early warning mechanism is triggered when the comprehensive risk score exceeds the risk level threshold. Specifically: A multi-level risk rating system is established based on industry risk benchmarks and enterprise historical data. The comprehensive risk score is divided into multiple risk level intervals, each interval corresponding to a different level of risk status, including low risk, medium risk and high risk. A corresponding threshold boundary is set for each risk level, and the threshold boundary is dynamically adjusted through a feedback mechanism based on changes in the external environment, policy adjustments, or market fluctuations to adapt to the risk management needs of different business scenarios. When the comprehensive risk score of the target object enters a certain risk level range, the corresponding level of early warning signal is automatically triggered. Among them, the high-risk level early warning is pushed to the designated user terminal first, along with risk details and response suggestions. The system pushes early warning information to clients, email systems, or third-party collaborative platforms through a message notification system, and records the early warning time, scoring criteria, and processing status in the database for subsequent auditing and analysis.

6. The business risk assessment and management method according to any one of claims 1 to 5, characterized in that, The method also includes: The risk assessment results are presented to users in the form of charts through a graphical user interface to show the values ​​of each individual risk indicator and their trends over time. Provide interactive controls to allow users to adjust view parameters as needed.

7. The business risk assessment and management method according to any one of claims 1 to 5, characterized in that, The method also includes: Receive user feedback on risk assessment results and dynamically adjust model parameters or update training dataset based on the feedback. New feature variables are automatically introduced based on changes in the external environment to improve the model's prediction accuracy and adaptability.

8. A business risk assessment and management device, characterized in that, include: The data collection module is suitable for collecting data sources from different channels, including historical transaction records, market data, financial statements, and social media feedback. The model building module is suitable for defining preset feature indicators according to business needs and applying machine learning algorithms to build risk assessment models; The assessment module is adapted to apply the risk assessment model to assess the data source, calculate the values ​​of various risk indicators, and generate a comprehensive risk score. The early warning module is suitable for setting a risk level threshold, and triggers an early warning mechanism when the comprehensive risk score exceeds the risk level threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the business risk assessment and management method as described in any one of claims 1 to 7.

10. A non-volatile computer storage medium storing computer-executable instructions thereon, characterized in that, When the computer program is executed by a processor, it implements the business risk assessment and management method as described in any one of claims 1 to 7.

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