Financial data risk monitoring and analysis method and system based on machine learning

Abstract

The invention relates to the technical field of machine learning, in particular to a financial data risk monitoring and analysis method and system based on machine learning, and the method comprises the steps: obtaining financial data of a target enterprise, and carrying out the preprocessing of the financial data; inputting the financial data into a pre-trained large-scale machine learning model for analysis; and generating a report containing potential business risk points, cash flow optimization suggestions and fund management improvement measures according to an analysis result. Accurate decision support can be provided for enterprises, and the problem that dynamic risk monitoring and deep analysis cannot be achieved in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of machine learning technology, and particularly to a method and system for financial data risk monitoring and analysis based on machine learning. Background Art

[0002] In recent years, with the development of informatization and intelligence, enterprise financial management has gradually evolved from the traditional manual bookkeeping mode to the direction of automation and intelligence. Especially for small and medium-sized enterprises, the demand for refined and compliant financial management is increasing day by day. However, due to the shortage of professional financial personnel or the limited professional capabilities of existing financial personnel in small and medium-sized enterprises, business owners often find it difficult to timely discover problems in financial management and may even overlook potential tax risks. In addition, financial management not only involves daily accounting processing, but also requires in-depth financial analysis to optimize cash flow management, identify financial risks, and formulate reasonable business decisions. Therefore, using intelligent technologies such as machine learning and data analysis to assist enterprises in financial management and risk identification has become an important development direction.

[0003] Currently, traditional financial management software mainly relies on manual input and preset accounting rules, and can generate basic financial statements such as balance sheets, income statements, and cash flow statements. Some systems also provide basic financial data comparison and analysis by presetting industry averages, ratio analysis, etc. Such solutions usually integrate, preprocess, and calculate static indicators for enterprise data according to a fixed process, covering links from data extraction, mapping, anomaly detection to basic industry comparison analysis, forming a relatively standardized data processing process.

[0004] However, the existing technology is limited to the basic processing and simple comparison of static data, and fails to achieve dynamic and intelligent risk monitoring and in-depth analysis of enterprise financial data. This limitation makes it difficult for enterprises to obtain targeted risk warnings and customized improvement suggestions in a short time, especially in tax risk identification and cash flow optimization. Therefore, how to achieve dynamic risk monitoring and in-depth analysis of enterprise financial data, so as to provide accurate and personalized risk identification and business optimization suggestions for small and medium-sized enterprises, is the current challenge. Summary of the Invention

[0005] This application provides a method and system for financial data risk monitoring and analysis based on machine learning, which can provide accurate decision-making support for enterprises and solve the problem that the existing technology cannot achieve dynamic risk monitoring and in-depth analysis. This application provides the following technical solutions:

[0006] In the first aspect, this application provides a method for financial data risk monitoring and analysis based on machine learning, and the method includes:

[0007] Obtain the financial data of the target enterprise and preprocess the financial data;

[0008] Input the financial data into a pre-trained large-scale machine learning model for analysis;

[0009] Generate a report containing potential business risk points, cash flow optimization suggestions, and fund management improvement measures based on the analysis results.

[0010] In a specific implementable solution, the obtaining of the financial data of the target enterprise and the preprocessing of the financial data include:

[0011] Extract key information from the balance sheet, income statement, and cash flow statement publicly disclosed by the target enterprise;

[0012] Use natural language processing algorithms to map financial statement fields in different formats to standard fields;

[0013] Perform basic cleaning on the imported financial data, including removing duplicates, filling in missing values, and unifying numerical formats.

[0014] In a specific implementable solution, the obtaining of the financial data of the target enterprise and the preprocessing of the financial data further include:

[0015] Adopt statistical methods to detect outliers in the imported financial data, identify and prompt financial data items that do not conform to industry norms;

[0016] Based on the historical data analysis of the enterprise, identify the data distribution trend and detect input errors or abnormal fluctuations.

[0017] In a specific implementable solution, the inputting of the financial data into a pre-trained large-scale machine learning model for analysis includes:

[0018] Collect the historical financial data of a large number of enterprises, and train the financial characteristic values of the enterprises and their corresponding business results using a neural network based on the supervised learning method;

[0019] Process the training data through an industry classification mechanism.

[0020] In a specific implementable solution, the inputting of the financial data into a pre-trained large-scale machine learning model for analysis further includes:

[0021] Construct a dynamically updated industry benchmark library, collect the financial data of a large number of similar enterprises in the industry, and calculate the average financial ratio of each industry as a standard reference for measuring the financial performance of individual enterprises;

[0022] Compare and analyze the financial indicators of the target enterprise with the industry average financial ratios, identify the advantages or disadvantages of the enterprise among its peers, and extract representative features through feature engineering to establish a mapping relationship library from historical feature values to business risk points;

[0023] Combine time series analysis and neural network algorithms to predict risks and output corresponding early warnings.

[0024] In a specific implementable solution, the inputting of financial data into a pre-trained large-scale machine learning model for analysis includes: The inputting of financial data into a pre-trained large-scale machine learning model for analysis further includes:

[0025] In the hybrid feature construction stage, by combining industry data and enterprise financial data, use the Z-score standardization method to ensure that all features are on the same scale and generate interaction features;

[0026] Through multi-level modeling, use industry data to train a high-level LSTM model to predict industry trends, and input the industry prediction results and enterprise financial data into a low-level LSTM model together;

[0027] Generate customized business optimization suggestions through comprehensive analysis.

[0028] In a specific implementable solution, the generating of a report including potential business risk points, cash flow optimization suggestions, and fund management improvement measures based on the analysis results includes:

[0029] Based on the financial data analysis results, identify the potential business risk points of the target enterprise, and generate a targeted risk assessment report in combination with industry benchmark analysis;

[0030] According to the financial characteristics and historical data of the enterprise, in combination with the industry prediction results, provide optimization suggestions and fund management improvement measures for the enterprise;

[0031] For each identified business problem, generate a specific action plan and provide an expected effect assessment of the improvement measures.

[0032] In a second aspect, the present application provides a financial data risk monitoring and analysis system based on machine learning, adopting the following technical solution:

[0033] A financial data risk monitoring and analysis system based on machine learning, comprising:

[0034] A data acquisition module, configured to acquire the financial data of the target enterprise and preprocess the financial data;

[0035] A model analysis module, configured to input the financial data into a pre-trained large-scale machine learning model for analysis;

[0036] A report generation module, configured to generate a report including potential business risk points, cash flow optimization suggestions, and fund management improvement measures based on the analysis results.

[0037] In a third aspect, the present application provides an electronic device, which includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement a method for monitoring and analyzing financial data risks based on machine learning as described in the first aspect.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium, in which a program is stored, and when the program is executed by a processor, it is used to implement a method for monitoring and analyzing financial data risks based on machine learning as described in the first aspect.

[0039] In summary, the beneficial effects of the present application at least include:

[0040] 1) Focus on extracting in-depth information from the core financial statements of enterprises. Different from traditional reliance on extensive data sources, it ensures the accuracy and pertinence of financial analysis, and is particularly suitable for small and medium-sized enterprises that usually lack complex financial systems or a large amount of transaction data. By analyzing the balance sheet, income statement, and cash flow statement, the financial health status of the enterprise can be accurately captured.

[0041] 2) By constructing a large-scale industry benchmark library with dynamic updates, the financial data of the enterprise is compared with the industry average in real time, providing users with intuitive and easy-to-understand comparison results. This enables enterprises to quickly identify their positions among their peers, clarify areas that need improvement, and make corresponding adjustments according to industry trends.

[0042] 3) Using machine learning algorithms to establish a mapping relationship between historical financial eigenvalue and potential business risk points can early warn of possible risks. This risk assessment method based on dynamic data and algorithms is more flexible and accurate than traditional static threshold setting, helps enterprises take preventive measures before risks occur, and improves the enterprise's business decision-making ability and risk management level.

[0043] By extracting key information from the public financial data of enterprises and performing preprocessing, using large-scale machine learning models and industry benchmark data for financial analysis, identifying potential risks and providing personalized optimization suggestions. By combining industry and enterprise micro-data, adopting Z-score standardization and interactive feature analysis, revealing the advantages and disadvantages of enterprises in the industry, and improving the prediction accuracy. Finally, the system generates a customized report including business risk points, cash flow optimization, and fund management improvement measures, providing accurate decision-making support for enterprises and solving the problem that the existing technology cannot achieve dynamic risk monitoring and in-depth analysis.

[0044] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly and implement it in accordance with the content of the specification, the following takes the preferred embodiments of this application and combines with the drawings to elaborate in detail as follows. Description of the Drawings

[0045] Figure 1 It is a schematic flowchart of the method for monitoring and analyzing financial data risks based on machine learning in an embodiment of this application.

[0046] Figure 2 It is a block diagram of the structure of the system for monitoring and analyzing financial data risks based on machine learning in an embodiment of this application.

[0047] Figure 3 It is a block diagram of the electronic device for monitoring and analyzing financial data risks based on machine learning in an embodiment of this application. Detailed Embodiments

[0048] The following combines the drawings and embodiments to further describe in detail the specific embodiments of this application. The following embodiments are used to illustrate this application, but not to limit the scope of this application.

[0049] Optionally, this application takes the method for monitoring and analyzing financial data risks based on machine learning provided in each embodiment and applied to an electronic device as an example for illustration. The electronic device is a terminal or a server. The terminal can be a mobile phone, a computer, a tablet computer, etc. The type of the electronic device is not limited in this embodiment.

[0050] Refer to Figure 1 , which is a schematic flowchart of the method for monitoring and analyzing financial data risks based on machine learning provided in an embodiment of this application. The method at least includes the following steps:

[0051] Step S101, obtain the financial data of the target enterprise and preprocess the financial data.

[0052] Specifically, extract key information from the balance sheet, income statement and cash flow statement publicly disclosed by the enterprise, and automatically map the financial statement fields in different formats to standard fields. For example, correctly classify "operating income", "operating cost", etc. In order to improve data consistency, a field matching algorithm based on natural language processing is adopted to ensure that financial statements from different sources can be accurately parsed.

[0053] During the data import process, basic data cleaning is first performed, including removing duplicates, filling in missing values, and standardizing numerical formats. Subsequently, statistical methods are used for outlier detection, and users are prompted to check for potential errors by identifying financial data that does not conform to the industry's normal range (such as abnormal profit margins or current ratios). In addition, historical data is combined to analyze the data distribution trend, identify possible input errors or abnormal fluctuations, and provide correction suggestions to ensure the accuracy and reliability of the data.

[0054] Step S102: Input the financial data into a pre-trained large-scale machine learning model for analysis.

[0055] In step S102, the large-scale machine learning model is trained based on the historical financial cases of a large number of enterprises. Using the supervised learning method, the financial characteristic values of enterprises and their corresponding business results are learned through neural networks, thereby establishing an industry business model. The training data of the model covers multiple industries, and an industry classification mechanism is used to ensure that it can accurately identify the characteristic patterns of different industries, improving adaptability and prediction accuracy.

[0056] In addition, users are allowed to input different hypothetical conditions into the model (such as increasing marketing expenses, reducing inventory levels), and the possible effects after adopting a certain suggestion are estimated through simulation runs. For example, if a manufacturing enterprise considers investing in reproduction, predict how this will affect future capacity utilization, cost structure, and profit margins.

[0057] Specifically, first, by constructing a big data benchmark library, collect a large amount of historical financial statement data of many similar enterprises in the industry, and establish a dynamically updated financial database that covers historical records of multiple years and synchronously updates the current year's data, so as to calculate the average financial ratios of each industry (such as current ratio, net profit margin, etc.) as the standard reference for measuring the performance of individual enterprises. By comparing and analyzing the financial indicators of the target enterprise with these industry averages, it is possible to quickly identify the areas of strength or weakness of the enterprise among its peers and display the comparison results in an intuitive chart. At the same time, extract representative features (such as current ratio, net profit margin, etc.) from the enterprise's historical financial data through feature engineering, establish a mapping relationship library from historical feature values to possible business risk points (for example, high debt ratio is mapped to liquidity risk, and a decline in accounts receivable turnover is mapped to credit risk), and set dynamic risk thresholds according to industry characteristics and enterprise scale; use time series analysis techniques to track the change trends of each feature value, and combine neural network algorithms to predict possible future risks (such as a decline in net profit margin for several consecutive quarters indicating profitability risk). In addition, calculate the industry average indicators through simple average and weighted average (such as weighted by the number of employees: weight = enterprise operating income / number of enterprise employees; weighted by operating income: weight = enterprise operating income / ∑ operating income of all enterprises), then use principal component analysis (PCA) to construct a comprehensive industry index, and at the same time analyze the development stage of the industry (introduction period, growth period, maturity period, decline period) and the impact of policies and regulations on the industry's operating environment with the help of large language models to provide a basis for the future growth potential of the industry; then merge the industry data with the micro data of individual enterprises to construct a mixed feature set (ensuring that all features are on the same scale through Z-score standardization), and construct interaction features by calculating the difference or ratio between the enterprise's key ratios and the industry average level, so as to reveal the advantages or disadvantages of the enterprise relative to its peers and determine its ranking or percentile on specific indicators.

[0058] Specifically, a large amount of historical financial statement data of a large number of similar enterprises in the industry is collected by constructing a big data benchmark library to establish a dynamically updated database, and the average financial ratios of each industry (such as current ratio, net profit margin, etc.) are calculated as the measurement criteria. Next, hybrid feature construction is carried out: industry data provides a macro perspective, while the data of individual enterprises reflects the specific situation at the micro level. The combination of the two can help the model better understand the performance of a specific enterprise in the industry context. Z-score standardization is used to ensure that all features are on the same scale, avoiding some features dominating in training due to excessive magnitude differences. Industry features will also be added as additional columns to the feature matrix of individual enterprises. For example, in the original features of enterprises (such as revenue, cost, expenses, etc.), indicators such as the average revenue growth rate and average net profit margin of the industry are added, so as to provide more comprehensive information for subsequent analysis. At the same time, interaction features are also constructed: by calculating the difference between the key ratios of individual enterprises and the industry average level, the advantages or disadvantages of enterprises relative to their peers are revealed, which helps to identify the position and uniqueness of enterprises in the industry. Since some influencing factors may not be described by simple linear relationships, the interaction between various features needs to be considered. Interaction features can capture these complex non-linear patterns, thereby improving the performance of the model. For example, by calculating the net profit margin of an enterprise minus the industry average net profit margin or taking their ratio, the excess return or loss of the enterprise relative to the industry can be quantified. At the same time, rankings or percentiles can also be defined for enterprises on specific indicators to reflect their relative status among their peers. By combining the construction of features and interaction features, the pre-trained model can more comprehensively capture the intertwined effects of macro and micro information, improve the ability to understand enterprise financial data, and thus more accurately predict future key financial indicators in subsequent multi-level modeling, and finally output customized business optimization suggestions.

[0059] In the multi-level modeling stage, first, the high-level LSTM model is trained using industry data to capture the overall development trend of the industry, including the industry average net profit margin, return on assets, GDP growth rate, inflation rate, and the impact of policies and regulations, and the predicted values of key industry indicators for a period of time in the future are output as additional inputs for subsequent models; then, the low-level LSTM model is trained for each enterprise, taking the various financial indicators of the enterprise such as revenue, cost, expenses, assets, and liabilities, various indicators of the enterprise relative to the industry, and the predicted output of the industry-level model as inputs together to more accurately predict the future key financial indicators of the enterprise (such as revenue, profit, etc.); finally, the predicted indicators of the enterprise-level model are input into the pre-trained large model for comprehensive analysis, and specific conclusions about the enterprise's operating conditions, potential risks, and future development trends are obtained, and customized business optimization suggestions are generated, such as improving cash flow management, optimizing tax strategies, and adjusting cost structures, so as to provide accurate decision-making support for enterprises.

[0060] In addition, as a preference, in this embodiment, in order to enable the model to not only pursue numerical accuracy when predicting enterprise financial indicators, but also take into account the consistency between risk indicators and industry benchmarks, a new loss function is designed, and optimization algorithms such as gradient descent are used to continuously calculate this loss until the minimum value to update the model parameters. Specifically, during the training process, for each batch of samples, first use the neural network to obtain the predicted value and predicted risk indicators, then calculate the loss function to evaluate the deviation between the prediction result and the true value and industry benchmark, and then update the model parameters through backpropagation until the loss function converges.

[0061] The designed loss function is called "risk-adjusted mean squared error loss function", and its formula is as follows:

[0062]

[0063] where y i represents the true financial indicator value of the i-th sample, which is extracted from the enterprise financial statements. represents the predicted financial indicator value of the i-th sample. is the risk level of the i-th sample predicted by the model according to the input features. is the industry benchmark risk level corresponding to the i-th sample, which is calculated through the big data benchmark library. η is the scaling coefficient of the risk penalty term, which is used to regulate the impact of risk deviation on the overall loss. δ is the normalization constant, which is used to adjust the scale of the input value of the tanh function, so that the numerical value of the risk deviation is smoothed within a reasonable range. N is the total number of samples in the training batch. λ·||θ|| 2 is the regularization term (such as L2 regularization), which is used to prevent the model from overfitting, and θ represents the model parameters.

[0064] This loss function introduces a risk deviation penalty factor in the calculation of the basic mean squared error. When the gap between the risk predicted by the model and the industry benchmark is large, the deviation is mapped into a smooth penalty value through the tanh function and the basic error is amplified, so as to prompt the model to pay more attention to the accurate matching of risk indicators during the parameter update process. The entire training process continuously calculates this loss function and updates the model parameters through gradient descent until the loss value reaches the minimum, thereby improving the overall prediction accuracy of the model for enterprise financial indicators and their risk levels.

[0065] Step S103: Generate a report containing potential business risk points, cash flow optimization suggestions, and fund management improvement measures according to the analysis results.

[0066] In step S103, the analysis result will generate a detailed report, including potential business risk points, cash flow optimization suggestions, and fund management improvement measures. Based on industry benchmark analysis and the specific prediction characteristics of the enterprise, combined with the pre-input enterprise attributes, the report will provide personalized business suggestions. For example, if the gross profit margin of a retail enterprise is lower than the industry average and shows a downward trend, the report will put forward suggestions on optimizing supply chain management or adjusting the product mix from both local and retail industry perspectives.

[0067] In addition, for each identified business problem, the report will provide a specific action plan to help the enterprise formulate improvement measures. For example, in the case of tight cash flow, the report may suggest accelerating the collection of accounts receivable or proposing other optimization measures. At the same time, each improvement suggestion will be accompanied by an expected effect assessment to help the enterprise understand the specific benefits or improvement effects after taking a certain measure. For example, by optimizing supply chain management, a certain percentage of costs are expected to be saved; improving the efficiency of the sales team may lead to an increase in sales.

[0068] In summary, by extracting key information from the enterprise's public financial data and preprocessing it, using large-scale machine learning models and industry benchmark data for financial analysis, identifying potential risks and providing personalized optimization suggestions. By combining industry and enterprise micro data, adopting Z-score standardization and interaction feature analysis, revealing the advantages and disadvantages of the enterprise in the industry, and improving the prediction accuracy. Finally, the system generates a customized report containing business risk points, cash flow optimization, and fund management improvement measures, providing accurate decision-making support for the enterprise and solving the problem that the existing technology cannot achieve dynamic risk monitoring and in-depth analysis.

[0069] Figure 2 It is a structural block diagram of a financial data risk monitoring and analysis system based on machine learning provided by an embodiment of the present application. The system at least includes the following modules:

[0070] A data acquisition module, configured to acquire the financial data of the target enterprise and preprocess the financial data;

[0071] A model analysis module, configured to input the financial data into a pre-trained large-scale machine learning model for analysis;

[0072] A report generation module, configured to generate a report containing potential business risk points, cash flow optimization suggestions, and fund management improvement measures according to the analysis result.

[0073] For related details, refer to the above method embodiment.

[0074] Figure 3 It is a block diagram of an electronic device provided by an embodiment of the present application. The device at least includes a processor 401 and a memory 402.

[0075] The processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 401 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0076] The memory 402 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 402 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 402 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 401 to implement the method for monitoring and analyzing financial data risks based on machine learning provided in the method embodiments of the present application.

[0077] In some embodiments, the electronic device may further optionally include: a peripheral device interface and at least one peripheral device. The processor 401, the memory 402, and the peripheral device interface may be connected through a bus or signal lines. Each peripheral device may be connected to the peripheral device interface through a bus, signal lines, or a circuit board. Schematically, the peripheral devices include, but are not limited to: a radio frequency circuit, a touch display screen, an audio circuit, and a power supply, etc.

[0078] Of course, the electronic device may also include fewer or more components, and this embodiment does not limit this.

[0079] Optionally, the present application further provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the method for monitoring and analyzing financial data risks based on machine learning in the above method embodiments.

[0080] Optionally, the present application further provides a computer product, which includes a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the method for monitoring and analyzing financial data risks based on machine learning in the above method embodiments.

[0081] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0082] The above embodiments only express several implementation manners of the present application, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A financial data risk monitoring and analysis method based on machine learning, characterized in that: The method comprises: Obtain the target company's financial data and pre-process the financial data; Input financial data into pre-trained large-scale machine learning models for analysis; Generate a report based on the analysis results, including potential business risk points, cash flow optimization suggestions and fund management improvement measures.

2. The method for financial data risk monitoring and analysis based on machine learning according to claim 1, characterized in that: The obtaining of the target enterprise's financial data and preprocessing of the financial data includes: Extract key information from the target company's publicly available balance sheet, income statement and cash flow statement; Use natural language processing algorithms to map financial statement fields in different formats to standard fields; Perform basic cleaning on imported financial data, including removing duplicates, filling missing values, and unifying numerical formats.

3. The financial data risk monitoring and analysis method based on machine learning according to claim 2 is characterized in that: The obtaining of the target enterprise's financial data and preprocessing of the financial data also includes: Use statistical methods to detect outliers in imported financial data, identify and prompt financial data items that are inconsistent with industry norms; Analyze data distribution trends based on corporate historical data to identify input errors or abnormal fluctuations.

4. The financial data risk monitoring and analysis method based on machine learning according to claim 1 is characterized in that: The step of inputting financial data into a pre-trained large-scale machine learning model for analysis includes: Collect a large amount of historical financial data of enterprises, and use neural networks based on supervised learning methods to train the financial characteristic values ​​of enterprises and their corresponding operating results; The training data is processed through an industry classification mechanism.

5. The method for financial data risk monitoring and analysis based on machine learning according to claim 4 is characterized in that: The inputting of financial data into the pre-trained large-scale machine learning model for analysis also includes: Build a dynamically updated industry benchmark database, collect financial data of a large number of similar companies in the industry, and calculate the average financial ratio of each industry as a standard reference for measuring the financial performance of individual companies; Compare and analyze the target company's financial indicators with the industry's average financial ratios to identify the company's strengths or weaknesses among its peers, extract representative features through feature engineering, and establish a mapping relationship library from historical feature values ​​to business risk points; Combining time series analysis and neural network algorithms, risks are predicted and corresponding warnings are output.

6. The method for financial data risk monitoring and analysis based on machine learning according to claim 5 is characterized in that: The inputting of financial data into a pre-trained large-scale machine learning model for analysis includes: The inputting of financial data into a pre-trained large-scale machine learning model for analysis also includes: In the hybrid feature construction stage, by combining industry data and corporate financial data, the Z-score standardization method is used to ensure that all features are on the same scale and generate interactive features; Through multi-level modeling, industry data is used to train a high-level LSTM model to predict industry trends, and the industry forecast results and corporate financial data are input into a low-level LSTM model; Generate customized business optimization suggestions through comprehensive analysis.

7. The financial data risk monitoring and analysis method based on machine learning according to claim 1 is characterized in that: The report generated based on the analysis results, which includes potential business risk points, cash flow optimization suggestions and fund management improvement measures, includes: Based on the results of financial data analysis, we identify the potential business risks of target companies and generate targeted risk assessment reports in combination with industry benchmark analysis; Provide optimization suggestions and fund management improvement measures for enterprises based on their financial characteristics and historical data, combined with industry forecast results; For each identified business problem, a specific action plan is generated, providing an evaluation of the expected effects of the improvement measures.

8. A financial data risk monitoring and analysis system based on machine learning, characterized in that: include: The data acquisition module is used to obtain the financial data of the target enterprise and pre-process the financial data; Model analysis module, which is used to input financial data into pre-trained large-scale machine learning models for analysis; The report generation module is used to generate reports containing potential business risk points, cash flow optimization suggestions and fund management improvement measures based on the analysis results.

9. An electronic device, characterized in that: The device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement a financial data risk monitoring and analysis method based on machine learning as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, which, when executed by a processor, is used to implement a financial data risk monitoring and analysis method based on machine learning as described in any one of claims 1 to 7.

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