Financial management risk identification system and method

Abstract

The invention belongs to the technical field of financial management, and particularly relates to a financial management risk identification system and method, and the system comprises a data collection module which is used for obtaining the operation data of a main body; the local AI analysis engine comprises an AI analysis engine and an AI simulation investigation engine, the AI analysis engine is used for analyzing main body operation data in the system and data information acquired by the AI simulation investigation engine, and the AI simulation investigation engine acquires associated main body operation data to generate dynamic data information; analyzing the dynamic data information through an AI analysis engine so as to establish a database of a four-dimensional model construction model; and the four-dimensional model construction module is used for constructing a time dimension model, a space dimension model, a causal dimension model and an uncertainty dimension model. According to the invention, multi-dimensional identification of financial management risks can be realized, the diversification of analysis is improved, and the accuracy and timeliness of analysis are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of financial management, and particularly relates to a financial management risk identification system and method. BACKGROUND

[0002] The financial management risk identification system and method refers to a comprehensive system for identifying, analyzing and warning potential risks in enterprise financial activities through technical means and management processes. The core goal is to improve the accuracy of risk identification and reduce financial losses caused by market fluctuations, credit problems or operational errors.

[0003] Problems existing in the prior art: At present, the risk prediction mode is relatively single, and single time and time series are mostly used for analysis, while the analysis of space, causality and other dimensions is ignored, resulting in deviation in the prediction result. If non-single analysis is to be realized, it needs to be completed manually, which requires a large amount of work, incomplete data collection, low risk prediction accuracy, high false positive rate, especially for data analysis in a dynamic state. SUMMARY

[0004] The purpose of the present application is to provide a financial management risk identification system and method, which can identify financial management risks in multiple dimensions and improve the diversification of analysis, and improve the accuracy and timeliness of analysis.

[0005] The technical solutions adopted by the present application are as follows: A financial management risk identification system, comprising: A data acquisition module: the data acquisition module is used to acquire subject operation data; A local AI analysis engine: the local AI analysis engine comprises an AI analysis engine and an AI simulation research engine, the AI analysis engine is used to analyze the data information acquired by the system internal subject operation data and the AI simulation research engine, the AI simulation research engine acquires the associated subject operation data to generate dynamic data information, and the AI analysis engine is used to analyze the dynamic data information, so as to establish a four-dimensional model to build a model database; A four-dimensional model building module: the four-dimensional model building module is used to build a time dimension model, a space dimension model, a causality dimension model and an uncertainty dimension model; The time dimension model acquires the time dimension data in the local AI analysis engine database, and is used to analyze the historical data and future trend, and to predict risks; The space dimension model acquires the space dimension data in the local AI analysis engine database, and is used to analyze the external risks of the associated system internal subject operation data based on the AI simulation research engine; The causal dimension model mines risk driving factors through data correlation; The uncertainty dimension model is used to cope with data complexity and unknown risks. The AI analysis engine and the AI simulation research engine are combined to dynamically adjust the risk threshold in real time. The risk management framework emphasizes the management of unknown risks through a "risk identification-evaluation-response" closed loop.

[0006] The data collection module collects data in the following ways: uploading data reports and querying, obtaining, comparing, and predicting associated data of associated data reports through the AI simulation research engine; The associated data includes external industry data and associated industry data of the associated data reports.

[0007] The AI simulation research engine is at least one; The AI simulation research engine generates procurement models, market research models, market analysis models, and associated subject information collection models through machine learning, which are used to analyze procurement, market changes, market price changes, and the operating status information of associated subjects, and deliver the above information to the AI analysis engine for analysis and judgment.

[0008] A financial management risk identification system further comprises: The risk warning module determines the risk and realizes the warning based on the risk information obtained by the four-dimensional model construction module; The permission warning module dynamically adjusts the function permissions of the corresponding function module based on the dynamic data information obtained by the AI simulation research engine.

[0009] The AI analysis engine analyzes the time series in the time dimension data, predicts potential risks based on the industry trend obtained by the AI simulation research engine, monitors the time sensitivity of the fund flow in real time, and sets the corresponding risk prediction coefficient according to the time sensitivity; The causal dimension model monitors potential risks and external risks based on the time series of the time dimension data and the spatial dimension data of the spatial dimension model, analyzes the risks through the AI simulation research engine, and locates the risk source.

[0010] A financial management risk identification method comprises the following steps: Obtain the operation data of the operation subject and the associated operation data of the associated operation subject; Analyze the operation data and the associated operation data, generate dynamic data information, and establish a database for constructing a four-dimensional model construction model; The four-dimensional model construction model combines the local AI analysis engine to analyze the data information in the database; Analyze and output the risk type and risk level of the operation subject data and the associated operation subject data.

[0011] The associated operation data is analyzed by using a local AI analysis engine, and the analysis method includes the following steps: Real-time acquisition of subject operation data and associated operation data of associated operation subjects; Feature extraction and classification of the associated data using an unsupervised learning algorithm to identify abnormal data patterns; Input the classified data into a four-dimensional model: Time dimension model: adopt time series analysis to predict future liquidity risk: Spatial dimension model: assess the impact of regional market environment on the subject operation; Causal dimension model: locate risk driving factors through association rule mining; Uncertainty dimension model: dynamically adjust the risk threshold based on reinforcement learning, and trigger the early warning when the real-time data deviation exceeds the preset range; According to the output result of the four-dimensional model, execute the "risk identification-evaluation-response" closed-loop management through the risk management framework, and synchronously adjust the function permission of the permission alarm module; The analysis result is fed back to the AI simulation research engine, the parameters of the procurement model and the market analysis model are optimized, and a dynamic iterative analysis closed loop is formed.

[0012] According to another aspect of the embodiment of the present application, an electronic device is also provided, which includes a memory and a processor; the memory is used to store a program; the processor executes the program to realize the method of any one of the preceding.

[0013] According to another aspect of the embodiment of the present application, a computer readable storage medium is also provided, which stores a computer program, and the computer program is executed by a processor to realize the method of any one of the preceding.

[0014] According to another aspect of the embodiment of the present application, a computer program product is also provided, which includes a computer program, and the computer program is executed by a processor to realize the method of any one of the preceding.

[0015] The technical effects obtained by the present application are: The application realizes the acquisition, identification and analysis of multi-dimensional risk factors, and realizes the risk identification-evaluation-response of various factors, so as to reduce the risk evaluation level in the financial management risk, eliminate the potential financial management risk, realize the optimization of each link through the feedback of the procurement, production and sales links, eliminate the influence on the financial aspect, and improve the timeliness of data analysis through real-time data acquisition of each link, and improve the accuracy of acquiring dynamic data and improve the judgment accuracy through the data acquisition module combined with the local AI analysis engine. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a system structure schematic diagram of the application; Figure 2 is a financial management risk identification method step flow chart in the application; Figure 3 is an analysis method flow chart of using a local AI analysis engine to analyze the related operation data in the application. DETAILED DESCRIPTION

[0017] In order to make the purpose and advantages of the application clearer and more apparent, the application will be specifically described below in combination with embodiments. It should be understood that the following text is only used to describe one or several specific embodiments of the application, and does not strictly limit the specific protection scope requested by the application.

[0018] It should be noted that the terms "first", "second" and the like in the specification and claims of the application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] According to the embodiment of the application, a method embodiment of a financial management risk identification method is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that herein.

[0020] AsFigure 1 As shown, a financial management risk identification system comprises: A data acquisition module: the data acquisition module is used to acquire subject operation data, which includes but is not limited to purchase orders, warehouse inventory, warehouse information, production progress, sales data and financial accounts; A local AI analysis engine: the local AI analysis engine includes an AI analysis engine and an AI simulation research engine, the AI analysis engine is used to analyze the data information acquired by the AI simulation research engine and the subject operation data inside the system, the AI simulation research engine acquires the related subject operation data to generate dynamic data information, and the analysis of the dynamic data information is realized through the AI analysis engine, so as to establish a four-dimensional model to build a model database; In addition, by splitting the AI simulation research engine into multiple role engines: For example: a procurement engine for procurement, which is used to acquire the price of the corresponding procurement raw materials and the fluctuation range of the price, and to monitor whether the procurement price is false.

[0021] For example: a sales market research engine for sales, which is used to monitor the fluctuation of the sales price of products and commodities, the sales quantity, etc., by detecting the sales quantity of e-commerce platforms, comparing the proportion of e-commerce channel sales in financial reports, predicting the sales average price and quantity of other channels, and comparing the out-of-stock sales information, the above information is fed back to the AI analysis engine to realize the comparison of the out-of-stock information, and the comparison and verification of the out-of-stock and market sales data.

[0022] For example: a production capacity monitoring engine for monitoring the production process, which is used to monitor the production quantity, the raw material in-out quantity, the scrap quantity, predict the output ratio, and detect the change of the actual reporting quantity according to the actual output ratio.

[0023] Through the analysis of the above data, for example, the change of the actual output ratio of production, if the output ratio changes greatly, the feedback is realized, the consumables, raw materials and equipment required for production are detected, the quality problems of raw materials are acquired, and the factors affecting the production quality are acquired in combination with the procurement and sales data, and the corresponding procurement data is researched to realize the analysis of the correlation of each link and the data reporting problem.

[0024] For example, through the analysis of the sales data, the change of the sales data is analyzed, and the change of the evaluation of the products after sales is analyzed to acquire the influence degree of the production, transportation, etc. on the products, and the upstream link causing the corresponding influence is evaluated and analyzed to acquire the reporting information of the production and procurement data, and the source problem is monitored to avoid the financial risk caused by false information.

[0025] According to the above embodiment, as for an entity enterprise or a software development enterprise that produces and sells products, the products are greatly affected by market public opinion. If problems in the procurement, generation, and sales links cannot be found in time, it will cause an impact on the financial level, such as stock price, revenue, and profit, when it reaches the market link.

[0026] Therefore, through the AI simulation research engine and the AI analysis engine, the internal and external collaborative data monitoring is realized, which is used to manage and predict the hidden influencing factors in the financial risk. Through real-time data analysis, the correction of each link from procurement to sales can be improved, and the improvement and iteration of the product can be realized in time.

[0027] In addition, each role class engine in the AI simulation research engine is trained by big data to obtain effective information, and through the selection of effective information, more accurate dynamic information is obtained to improve the judgment accuracy.

[0028] The above dynamic information is classified and integrated into a database through information normalization processing, denoising, etc., for retrieval and analysis of the four-dimensional model construction module.

[0029] The four-dimensional model construction module: The four-dimensional model construction module constructs a time dimension model, a space dimension model, a causal dimension model, and an uncertainty dimension model. The establishment method of the above four-dimensional model is to train the data by the local AI model, so that the model construction is in the calculation thinking mode of time, space, causality, and uncertainty, and through data feeding and training, the accuracy of data output is monitored. In addition, the model construction method also includes statistical models (such as linear regression models, time series models, etc.), machine learning models (such as decision tree models, random forest models, etc.), NLP models (such as BERT models, LSTM models, etc.), or business analysis models (such as survival analysis, RFM models, etc.). The above models can be optimized by targeted access to AI models or exist independently and be trained for different functions to ensure the accuracy and timeliness of the monitoring and analysis results. Further, the time dimension model obtains the time dimension data in the local AI analysis engine database to analyze historical data and future trends and predict risks to predict the periodic risks of financial risks. Further, the space dimension model obtains the space dimension data in the local AI analysis engine database to analyze the external risks of the correlation system based on the AI simulation research engine. Further, the causal dimension model mines risk driving factors through data correlation. Further, the uncertainty dimension model is used to cope with data complexity and unknown risks.

[0030] Further, for various financial management risk factors, a four-dimensional model is constructed to build a time dimension model, a space dimension model, a cause and effect dimension model, and an uncertainty dimension model, to realize the acquisition, identification, and analysis of multi-dimensional risk factors, and to realize the risk identification, assessment, and response of various factors, so that the risk assessment level in the financial management risk can be reduced, the potential financial management risk can be eliminated, and through the feedback of the procurement, production, and sales links, the optimization of each link can be realized, so as to eliminate the impact on the financial aspect, and through real-time data acquisition of each link, the timeliness of data analysis can be improved, and through the data acquisition module combined with the local AI analysis engine, the accuracy of acquiring dynamic data and the judgment accuracy can be improved.

[0031] As an optional embodiment, the AI analysis engine performs financial data analysis on the time series in the time dimension data, combines the potential risks predicted by the AI simulation research engine according to the corresponding industry trends, monitors the time sensitivity of the fund flow in real time, and predicts the financial risk according to the time sensitivity, and sets the corresponding risk prediction coefficient.

[0032] As an optional embodiment, the cause and effect dimension model monitors the potential risks and external risks predicted by the corresponding industry trends according to the time series of the time dimension data and the space dimension data of the space dimension model, analyzes the potential risks and external risks through the AI simulation research engine, and locates the risk source.

[0033] As an optional embodiment, the time dimension model performs financial data analysis on the time series through the AI analysis engine, such as cash flow and debt ratio, identifies abnormal fluctuations, and at the same time, the data obtained by the AI simulation research engine is combined with the industry trend to predict potential risks, and the dynamic fund management technology also supports real-time monitoring of the time sensitivity of the fund flow; As an optional embodiment, the space dimension model covers cross-regional and cross-business risk identification based on the AI simulation research engine. Cross-border e-commerce enterprises need to evaluate the impact of different regions' policies and exchange rate fluctuations on the fund chain, and the risk management system needs to systematically identify multi-level risk sources inside and outside the organization, such as supply chain regional concentration, and the space dimension also involves spatial distribution analysis of business structure risks; As an optional embodiment, the cause and effect dimension through the AI analysis engine and the AI simulation research engine, for financial statement analysis, can reveal the causal relationship between high debt and profit decline, and the deep learning model can handle high-dimensional data, such as the mutual influence of market sentiment and financial indicators, identify nonlinear causal chains, and the management accounting tool locates the risk source through cost-benefit analysis; As an optional embodiment, the uncertainty dimension, coping with data complexity and unknown risks, the deep learning model processes the noise and uncertainty of financial market data through probability inference, the AI analysis engine and the AI simulation research engine combine to dynamically adjust the risk threshold based on real-time data, and the risk management framework emphasizes the management of unknown risks such as the impact of black swan events on financial resilience through the "risk identification-evaluation-coping" closed loop; As an optional embodiment, when the time dimension model and the space dimension model are combined, the AI simulation research engine and the AI analysis engine are used to realize data monitoring and market prediction in the corresponding region or industry; As an optional embodiment, when the time dimension model and the causal dimension model are combined, the AI analysis engine is used to monitor the subject operation data, and the potential financial management risks in the subject operation process are predicted through the operation data, the AI simulation research engine is used to monitor the external data related to the related subject operation data, and the development trend of the external environment is predicted through the external data, so as to realize the prediction of the future risks of the subject and the corresponding financial management risk control measures are formulated through the prediction; As an optional embodiment, when the time dimension model and the uncertainty dimension model are combined, the AI analysis engine and the AI simulation research engine are used to adjust the risk threshold in real time to realize the comparison and prediction of the risks in the corresponding time period; The method for adjusting the risk threshold comprises the following steps: S101, obtaining historical data and historical threshold, setting an alarm threshold based on the historical data, calculating the mean and standard value of the data obtained in real time, analyzing the historical threshold, generating a static threshold baseline, comparing the normal distribution and deviation distribution of the real-time data and the static threshold baseline, and setting a static risk threshold; S102, based on the risk input of the space dimension and the correlation analysis of the causal dimension, the external risks such as regional policies and market fluctuations are analyzed through the GIS system, the influence factors of the space dimension are quantified, such as the cost fluctuations caused by the changes caused by policies or uncontrollable factors, and the changes of the associated factors caused by the risks such as procurement and inventory caused by market factors, such as using association rules to mine and identify risk driving factors, using Apriori algorithm; S103, predict future risk trends using arima or LSTM model, dynamically correct threshold, realize correction of time series prediction, and through the output of comprehensive time (weight a), space (weight β), and causal (weight γ) dimensions, dynamically adjust the threshold through the weighted formula: dynamic threshold = a time threshold + β space threshold + γ causal threshold, and the weights are dynamically allocated according to real-time data fluctuations, such as reinforcement learning to optimize weights; S104, real-time monitoring and closed-loop feedback, compare real-time data with dynamic threshold, if the deviation exceeds the preset range, start the alarm, and iteratively optimize the threshold according to the false alarm rate / missed alarm rate feedback, and adjust the model parameters through reinforcement learning, for example, if the false alarm rate > the set maximum false alarm rate, reduce the weight β of the spatial dimension, and if the missed alarm rate > the set maximum missed alarm rate, increase the weight γ of the causal dimension; S105, by setting the mapping between risk threshold and permission, define the permission level according to the dynamic threshold, such as risk coefficient greater than the set value, freeze permissions such as procurement, and realize dynamic permission adjustment through RBAC model, permission adjustment methods include prohibition, tightening, relaxation, control, etc., and upload the above threshold adjustment data to the log to ensure compliance and audit traceability.

[0034] According to the above steps, through the dynamic determination and adjustment of the risk threshold, real-time adjustment according to space, time, and causality can be realized, which can reduce data false alarms caused by changes in market and other factors compared with static threshold, improve the adaptability of the system to changes in market and other factors, and improve the risk coverage rate through multi-dimensional fusion, such as risk alarms caused by market changes, time period changes, and changes in associated subjects. The evidence provided by step S105 can reduce legal risks and trace problems through evidence preservation, and realize the accuracy and adjustment of data analysis through logs.

[0035] As an optional embodiment, when the spatial dimension model and the causal dimension model are combined, a corresponding time dimension model can also be combined to monitor the operation status of the external associated organization, provide alternative solutions for the selection of main raw materials and distributors according to the monitored data and the generated risk rating, and thus realize the early prediction of financial management risks and avoid the terminal of supply and demand links to ensure the normal operation of finance.

[0036] As an optional embodiment, the data collection module collects data in the following ways: data report uploading and querying, acquiring, comparing, and predicting associated data of associated data reports through an AI simulation research engine.

[0037] As an optional embodiment, the associated data includes external industry data and associated industry data of the associated data report, the data collection module includes a data collection system integrated in the AI analysis engine for obtaining the subject operation data, and a data collection system integrated in the AI simulation research engine for obtaining the associated subject operation data and a plurality of scripts in various scenes, the scripts preferably use Python script tools, JavaScript / Node.js tools, Shell tools, etc., in the running process of the AI simulation research engine.

[0038] As an optional embodiment, the AI simulation research engine is at least one.

[0039] As an optional embodiment, the AI simulation research engine generates a procurement model, a market research model, a market analysis model, and an associated subject information collection model through machine learning, for verifying and auditing procurement, market trend changes, market price changes, and associated subject operating status information, and delivering the above information to the AI analysis engine for analysis and judgment.

[0040] As an optional embodiment, a financial management risk identification system further comprises: A risk warning module determines risks and realizes early warning based on the risk information obtained by the four-dimensional model construction module; A permission warning module dynamically adjusts the function permissions of the corresponding function module based on the dynamic data information obtained by the AI simulation research engine.

[0041] As an optional embodiment, the permission level setting in the system stage sets the local AI analysis engine to only realize data reading permission, sets the local AI analysis engine internal data to be unable to be accessed by the outside, and sets the AI simulation research engine data to only have the permission of the local AI analysis engine for data reading.

[0042] As an optional embodiment, according to the warning level of the risk warning module, the highest level of risk can be blocked in the financial permission, such as a certain amount of capital outflow, a plurality of batches of small amount of capital outflow, any one of the uncertainty dimension reporting as a risk, the cause and effect dimension reporting as a risk, the space dimension reporting as a risk, and the time dimension reporting as a risk, the corresponding permission is blocked, the personnel permission of the corresponding level is blocked according to the risk level, if the permission exceeds the amount, it will cause (uncertainty dimension reporting as a risk, cause and effect dimension reporting as a risk, space dimension reporting as a risk, time dimension reporting as a risk), the permission is limited, the warning information is reported to the next level and recorded in the log, and whether to decide to release the limitation according to the instruction.

[0043] It should be noted that the above various modules can be realized by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: all the above modules are located in the same processor; or the above various modules are located in different processors in any combination.

[0044] As an optional embodiment, refer to the attached Figure 2 A financial management risk identification method, comprising the following steps: S1, acquiring operation data of an operation subject and associated operation data of an associated operation subject; S2, analyzing the operation data and the associated operation data to generate dynamic data information and establish a database for constructing a four-dimensional model construction model; S3, analyzing the data information in the database by the four-dimensional model construction model combined with a local AI analysis engine; S4, analyzing and outputting the risk type and risk level for the operation data of the operation subject and the associated operation data of the associated operation subject.

[0045] As an optional embodiment, refer to the attached Figure 3 The associated operation data is analyzed by using a local AI analysis engine, and the analysis method comprises the following steps: S201, acquiring operation data of a subject and associated operation data of an associated operation subject in real time, which realizes data acquisition and analysis by using a local AI analysis engine; S202, using an unsupervised learning algorithm to extract features and classify the associated data, and identifying abnormal data patterns; S203, inputting the classified data into a four-dimensional model: (1) Time dimension model: using time series analysis to predict future liquidity risk: (2) Spatial dimension model: evaluating the influence degree of each regional market environment on the operation of the subject, which can be combined with geographic information system and external industry data and other factors; (3) Causal dimension model: locating risk driving factors by using association rule mining; (4) Uncertainty dimension model: dynamically adjusting the risk threshold based on reinforcement learning, and triggering an early warning when the real-time data deviation exceeds the preset range; S204, according to the output result of the four-dimensional model, executing the "risk identification-evaluation-response" closed-loop management through a risk management framework, and synchronously adjusting the function permissions of the permission alarm module; S205, feeding back the analysis result to an AI simulation research engine, optimizing the parameters of the procurement model and the market analysis model, and forming a dynamic iterative analysis closed loop.

[0046] According to a further aspect of the embodiments of the present application, an electronic device is also provided, which includes a memory and a processor; the memory is configured to store a program; and the processor is configured to execute the program to implement the method of any one of the preceding method embodiments.

[0047] According to a further aspect of the embodiments of the present application, a computer readable storage medium is also provided, which stores a computer program; and the computer program, when executed by a processor, implements the method of any one of the preceding method embodiments.

[0048] According to a further aspect of the embodiments of the present application, a computer program product is also provided, which includes a computer program; and the computer program, when executed by a processor, implements the method of any one of the preceding method embodiments.

[0049] The above merely illustrates the preferred embodiments of the present application, and it should be noted that, for those skilled in the art, some improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as falling within the protection scope of the present application. The structures, devices and operation methods not specifically described and explained in the present application are implemented according to the conventional means in the art, unless otherwise specified and limited.

Claims

1. A financial management risk identification system, characterized in that: include: Data collection module: The data collection module is used to obtain the main body of the operation data; Local AI analysis engine: The local AI analysis engine includes an AI analysis engine and an AI simulation research engine. The AI ​​analysis engine is used to analyze the operating data of the main body within the system and the data information obtained by the AI ​​simulation research engine. The AI ​​simulation research engine obtains the operating data of the related entities to generate dynamic data information. The dynamic data information is analyzed by the AI ​​analysis engine to establish a database for the four-dimensional model construction model; Four-dimensional model construction module: constructing a time dimension model, a space dimension model, a causal dimension model and an uncertainty dimension model through the four-dimensional model construction module; The time dimension model obtains time dimension data from the local AI analysis engine database for analyzing historical data and future trends to predict risks; The spatial dimension model obtains spatial dimension data from the local AI analysis engine database, and obtains and analyzes external risks of the operating data of the internal entities of the associated system based on the AI ​​simulation research engine; The causal dimension model mines risk drivers through data correlation; The uncertainty dimension model is used to deal with data complexity and unknown risks. The AI ​​analysis engine and AI simulation research engine dynamically adjust risk thresholds based on real-time data. The risk governance framework emphasizes managing unknown risks through a closed-loop "risk identification-assessment-response." 2. A financial management risk identification system according to claim 1, characterized in that: The data collection module collects data in the following ways: uploading data reports and querying, obtaining, comparing and predicting the related data of related data reports through the AI ​​simulation research engine; The associated data includes external industry data and associated industry data of the associated data report.

3. A financial management risk identification system according to claim 1, characterized in that: At least one AI simulation research engine is provided; The AI ​​simulation research engine generates procurement models, market research models, market analysis models, and related entity information collection models through machine learning, which are used to verify and review procurement, market changes, market price change analysis, and related entity operating status information, and transmit the above information to the AI ​​analysis engine for analysis and judgment.

4. A financial management risk identification system according to claim 1, characterized in that: Also includes: A risk warning module, which determines risks and issues warnings based on the risk information obtained by the four-dimensional model building module; The permission alarm module dynamically adjusts the functional permissions of the corresponding functional modules based on the dynamic data information obtained by the AI ​​simulation research engine.

5. A financial management risk identification system according to claim 1, characterized in that: The AI ​​analysis engine performs financial data analysis on the time series in the time dimension data, combines the corresponding industry trends obtained by the AI ​​simulation research engine to predict potential risks, monitors the time sensitivity of capital flows in real time, and predicts financial risks based on time sensitivity, and sets corresponding risk prediction coefficients; The causal dimension model monitors the corresponding industry trends and predicts potential risks and external risks based on the time series of the time dimension data and the spatial dimension data of the space dimension model, and analyzes the potential risks and external risks through the AI ​​simulation research engine to locate the root causes of the risks.

6. A method for identifying financial management risks, using the system according to any one of claims 1 to 5, characterized in that: The steps include: Obtaining the operating data of the operating entity and the associated operating data of the associated operating entities; Analyze operational data and related operational data, generate dynamic data information, and establish a database for building a four-dimensional model; Analyze the data information in the database by building a model through a four-dimensional model and combining it with the local AI analysis engine; Analyze and output the risk type and risk level for the operating entity data and related operating entity data.

7. A financial management risk identification method according to claim 6, characterized in that: The associated operation data is analyzed using a local AI analysis engine, and the analysis method includes the following steps: Obtain the main operating data and the related operating data of related operating entities in real time; Use unsupervised learning algorithms to extract features and classify related data to identify abnormal data patterns; Input the classified data into the four-dimensional model: Time dimension model: Use time series analysis to predict future liquidity risks: Spatial dimension model: assesses the impact of each regional market environment on the main body's operations; Causal dimension model: locate risk drivers through association rule mining; Uncertainty Dimension Model: Dynamically adjusts risk thresholds based on reinforcement learning, triggering warnings when real-time data deviations exceed preset ranges; Based on the output of the four-dimensional model, a closed-loop management system of "risk identification-assessment-response" is implemented through the risk governance framework, and the functional authority of the authority alarm module is adjusted simultaneously. The analysis results are fed back to the AI ​​simulation research engine to optimize the parameters of the procurement model and market analysis model, forming a dynamic iterative analysis closed loop.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 6 or 7.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to claim 6 or 7 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to claim 6 or 7 is implemented.

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