Financial panoramic risk management and control system

Abstract

The invention relates to the field of financial risk management, and discloses a financial panoramic risk management and control system, the system integrates a data layer, a machine learning layer, an application layer and an interaction layer, and aims to provide comprehensive and efficient risk management for enterprises, the data layer is responsible for collection, preprocessing and storage of multi-dimensional financial data, and the machine learning layer is responsible for interaction with the interaction layer. The machine learning layer trains a fund chain and supply chain risk prediction model by using the data to realize scientific risk assessment, the application layer performs real-time risk analysis and early warning to ensure that an enterprise timely deals with potential risks, and the interaction layer provides a user-friendly interface and strict authority management to ensure data security and convenient operation, so that the system has a wide application prospect. The system not only improves the efficiency and accuracy of risk management and control, but also strengthens the ability of an enterprise to control panoramic financial risks, effectively prevents omission and misjudgment, and assists the enterprise to stably operate in a complex and changeable market environment.

Description

Technical Field

[0001] The present invention relates to the field of financial risk management, and in particular to a financial panoramic risk management and control system. Background Art

[0002] With the continuous expansion of enterprise scale and increasingly fierce market competition, enterprises are facing more and more challenges in financial management and risk control. In order to effectively identify, evaluate and control financial risks, enterprises need a comprehensive and efficient risk management system. Existing financial risk management technology has many defects and cannot meet the needs of enterprises for panoramic financial risk management, as follows:

[0003] (1) Existing financial risk management systems often only focus on one aspect of financial data, such as accounts, cash flow or debt, and lack a comprehensive grasp of the company's financial status. This one-sided risk management approach can easily lead to the omission of potential risks, thereby causing unpredictable losses to the company.

[0004] (II) Traditional financial risk management methods mainly rely on manual analysis and judgment, which are inefficient and easily affected by subjective factors. As the amount of enterprise data continues to grow, manual analysis has become difficult to cope with the processing needs of large-scale data and is unable to detect and respond to potential risks in a timely manner.

[0005] (III) Existing technologies have obvious deficiencies in risk assessment and early warning. Due to the lack of scientific risk assessment models and efficient early warning mechanisms, enterprises are often unable to accurately judge the severity and urgency of risks, thus missing the best time to respond to risks.

[0006] In summary, the existing financial risk management technology cannot meet the enterprise's demand for comprehensive, efficient, scientific and safe risk management. Therefore, we propose a financial panoramic risk management system. Summary of the invention

[0007] The purpose of the present invention is to provide a financial panoramic risk management system to solve the problems raised in the above background technology.

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a financial panoramic risk management and control system, comprising a data layer, a machine learning layer, an application layer and an interaction layer;

[0009] The data layer includes a data acquisition module, a data preprocessing module and a data storage module;

[0010] The data acquisition module is used to extract historical data from the company's internal system at a monthly granularity. The extracted data includes accounting data, capital flow data, supplier data, inventory data, sales data, purchase data, debt data and human resources data at the end of each month. The data preprocessing module is used to perform preprocessing operations on the original data transmitted by the data acquisition module. The preprocessing includes data cleaning, data conversion and data standardization. The data storage module is used to store preprocessed multi-type data for efficient management and retrieval.

[0011] The machine learning layer is used to train a capital chain risk prediction model and a supply chain risk prediction model using machine learning technology. The capital chain risk prediction model is used to predict the possibility of a company's capital chain rupture risk by analyzing the company's financial data, credit status and debt structure; the supply chain risk prediction model is used to predict the possibility of a supply chain interruption risk by analyzing supplier data and inventory data; the machine learning layer includes a data marking module, a feature engineering module, a model training module and a model evaluation and optimization module; the data marking module receives data related to the model prediction results extracted from the data storage module, and marks these data according to preset marking rules. The marking content includes the category, level or specific risk point of the capital chain rupture risk or the supply chain interruption risk to form a marked data set; the feature engineering module constructs new meaningful feature data through combination and conversion based on the marked data set to form a feature data set; the model training module is used to construct and train a risk prediction model based on the feature data set and the machine learning algorithm; the model evaluation and optimization module is used to evaluate and optimize the trained risk prediction model so that its performance meets the actual use requirements;

[0012] The application layer includes a risk assessment module and a risk warning module;

[0013] The risk assessment module uses the trained capital chain risk prediction model and supply chain risk prediction model to conduct real-time analysis of the company's financial data and supply chain data, and outputs a comprehensive risk assessment report that lists in detail the level and potential impact of each risk; the risk warning module monitors the company's financial and supply chain status in real time based on the output results of the risk assessment module, and immediately triggers the warning mechanism once an abnormality is found or the preset risk threshold is reached;

[0014] The interaction layer includes a user interface module and a rights management module;

[0015] The user interface module is used to provide a user interface for users to view risk assessment results and receive risk warning information; the authority management module is used to authenticate users and control authority so that different users can only access data and functions within their authority range.

[0016] Accounting data includes accounting subjects, vouchers, account books and financial statements, which are used to reflect the company's financial status and operating results;

[0017] Fund flow data includes bank statements, cash flow statements, and payment records, which are used to track the inflow and outflow of company funds;

[0018] Supplier data includes basic supplier information, contract information, payment records and invoice information, which is used to manage the transaction relationship between the company and the supplier;

[0019] Inventory data includes inventory lists, inventory change records and inventory costs, which are used to monitor and manage the company's inventory situation;

[0020] Sales data includes sales orders, shipping records, and sales returns, which are used to analyze the company's sales and customer behavior;

[0021] Procurement data includes purchase orders, receipt records, and purchase returns, which are used to manage the company's procurement process and supplier performance;

[0022] Debt data includes debt type, debt amount, debt term, interest rate, repayment record and overdue status, which is used to comprehensively manage and analyze the company's debt situation;

[0023] Human resource data includes employee information, salary data, and attendance records, and is used to support human resource management and cost analysis.

[0024] Preferably, the capital chain risk prediction model and the supply chain risk prediction model are both constructed using a linear regression algorithm, and the prediction function of the linear regression model is:

[0025] f(χ)=w T x+b

[0026] Where w represents the weight vector, which is a multidimensional array whose dimension matches the number of features; χ represents the input feature vector, which is also a multidimensional array containing all the feature values ​​used for prediction; w T represents the transpose of the weight vector, that is, converting w from a column vector to a row vector so as to perform a dot product operation with the feature vector x; b represents the intercept, which is the bias term of the model and represents the predicted output value of the model when all eigenvalues ​​are zero; before the training starts, the initial weight and intercept values ​​are set for the linear regression model, and then the weight and intercept parameters of the linear regression model are iteratively updated by minimizing the mean square error between the predicted value and the actual value using the feature data set until the training stop condition is met; the calculation formula for minimizing the mean square error is:

[0027]

[0028] Among them, b is the number of feature samples, y i is the data labeling result of the i-th sample, x i is the eigenvector of the i-th sample, and MSE is the calculated minimized mean square error value.

[0029] Preferably, the data marking module marks each piece of data with a corresponding risk category, wherein the risk categories include "low risk", "medium risk" and "high risk", and annotates the time series data at key time points to indicate the association between these time points and risk changes, wherein the key time points include moments of abnormal fund flows and large fund transfers.

[0030] Preferably, the data preprocessing module converts non-numeric categorical data into numerical form by one-hot encoding to facilitate processing by a machine learning model.

[0031] Preferably, the data preprocessing module performs data standardization in the following manner:

[0032] The numerical data were scaled to conform to a standard normal distribution with a mean of 0 and a standard deviation of 1 by using the z-score standardization method;

[0033] The data is scaled to the specified range [0,1] using the min-max normalization method.

[0034] Preferably, the risk warning module monitors the company's financial and supply chain status in real time, and determines the level and potential impact of various risks based on the comprehensive risk assessment report output by the risk assessment module; when any risk indicator is found to exceed the preset risk threshold, the risk warning module immediately generates warning information; the warning information includes the risk type, level, potential impact and recommended response measures that trigger the warning; the warning information is displayed to the user through the user interface module, and the warning notification is sent to the preset recipient in the form of email, text message and mobile application push, so that relevant personnel can be informed of and deal with potential risks in a timely manner.

[0035] Preferably, the user interface module is implemented using front-end development technologies, including HTML, CSS and JavaScript; the user interface module communicates with the back-end service through an API interface to obtain risk assessment results and risk warning information; the risk assessment report displays detailed information in the form of charts, tables or text, including risk levels, potential impacts and recommended measures; risk warning notifications are presented in a striking manner, including pop-ups, sounds or vibrations, so that users can notice it in time and take corresponding actions.

[0036] Preferably, the permission management module implements user identity authentication and permission control by designing a comprehensive permission control system; the system includes three parts: user role management, permission allocation and access control; user role management allows system administrators to define different user roles and assign corresponding permissions to each role; permission allocation is to grant specific operating permissions to different user roles; access control is to check the role and permission of the user when the user tries to access system resources, so that the user can only access the data and functions for which he is authorized; the permission management module further adopts encryption technology to protect the security of user information in data network transmission.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This system realizes comprehensive monitoring and risk assessment of the company's financial status by integrating the data layer, machine learning layer, application layer and interaction layer. Compared with the one-sidedness of traditional financial risk management and control systems, this system can comprehensively consider multi-dimensional data such as accounts, capital flows, suppliers, inventory, sales, procurement, debts and human resources, so as to more comprehensively grasp the company's financial risks and effectively avoid the omission of potential risks. By introducing the machine learning layer, this system can automatically process and analyze large-scale financial data, greatly improving the efficiency of risk management. Compared with traditional methods that rely on manual analysis, this system not only reduces labor costs, but also avoids the influence of subjective factors on risk assessment, ensuring the objectivity and accuracy of the assessment results.

[0039] This system uses machine learning technology to train the capital chain risk prediction model and the supply chain risk prediction model, which can scientifically assess the severity and urgency of risks. At the same time, the risk warning module of the application layer can monitor the company's financial and supply chain status in real time. Once an abnormality is found or the preset risk threshold is reached, the warning mechanism is immediately triggered to ensure that the company can respond to risks in a timely manner and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is the overall structure diagram of the financial panoramic risk management and control system;

[0041] Figure 2 This is a diagram of the training steps for the capital chain risk prediction module. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] See also Figure 1-2 The present invention provides a technical solution: a financial panoramic risk management and control system, including a data layer, a machine learning layer, an application layer and an interaction layer; the system realizes the comprehensive identification, evaluation and control of the financial risks of the enterprise through the comprehensive effects of multiple layers. The functions of each layer will be described in detail below:

[0044] 1. Data layer:

[0045] The data layer is the data foundation of the entire system, including data acquisition module, data preprocessing module and data storage module. 1. Data acquisition module: This module is responsible for extracting historical data from the company's internal system at a monthly granularity. The extracted data covers multiple aspects, including accounting data, cash flow data, supplier data, inventory data, sales data, procurement data, debt data and human resources data, etc. These data provide a comprehensive data foundation for subsequent risk assessment. 2. Data preprocessing module: Since the original data may have problems such as inconsistent format, missing or abnormal data, the data preprocessing module is responsible for cleaning, converting and standardizing the original data to ensure data quality and consistency. 3. Data storage module: The preprocessed data will be stored in the data storage module for subsequent efficient management and retrieval.

[0046] 2. Machine Learning Layer:

[0047] The machine learning layer is the core part of the system, including data labeling module, feature engineering module, model training module and model evaluation and optimization module. 1. Data labeling module: This module extracts data related to the model prediction results from the data storage module and labels these data according to the preset labeling rules. The labeling content includes the category, level or specific risk point of the capital chain rupture risk or supply chain interruption risk to form a labeled data set. 2. Feature engineering module: Based on the labeled data set, the feature engineering module constructs new meaningful feature data through combination and transformation to extract more valuable information to form a feature data set. 3. Model training module: This module uses feature data sets and machine learning algorithms to build and train risk prediction models, including capital chain risk prediction models and supply chain risk prediction models. These two models are used to predict the possibility of the company's capital chain rupture risk and supply chain interruption risk respectively. 4. Model evaluation and optimization module: In order to ensure that the trained risk prediction model has practical application value, the model evaluation and optimization module is responsible for evaluating and optimizing the model so that its performance meets the actual use requirements.

[0048] 3. Application layer:

[0049] The application layer includes a risk assessment module and a risk warning module, which are responsible for applying the models trained by the machine learning layer to actual scenarios. 1. Risk assessment module: This module uses the trained capital chain risk prediction model and supply chain risk prediction model to analyze the company's financial data and supply chain data in real time, and outputs a comprehensive risk assessment report. The report lists the level and potential impact of each risk in detail, providing the company with a comprehensive risk view. 2. Risk warning module: Based on the output results of the risk assessment module, the risk warning module monitors the company's financial and supply chain status in real time. Once an abnormality is found or the preset risk threshold is reached, the warning mechanism is immediately triggered so that the company can take timely countermeasures.

[0050] 4. Interaction layer:

[0051] The interaction layer includes a user interface module and a permission management module, which are responsible for interacting with users and providing secure access control. 1. User interface module: This module provides an intuitive and easy-to-use user interface, allowing users to easily view risk assessment results and receive risk warning information. The user interface design is concise and clear, allowing users to quickly obtain the required information. 2. Permission management module: In order to ensure the security of the system and the confidentiality of data, the permission management module is responsible for user authentication and permission control. Through this module, different users can only access data and functions within their permission range, effectively preventing unauthorized access and data leakage risks.

[0052] The financial panoramic risk management and control system of the present invention realizes the comprehensive identification, assessment and control of enterprise financial risks through the synergy of multiple levels and modules, which not only improves the efficiency and accuracy of risk management, but also provides enterprises with a safer and more reliable risk management solution.

[0053] The following is a detailed description of accounting data, cash flow data, supplier data, inventory data, sales data, purchase data, debt data and human resources data and their functions in the system:

[0054] ①. Accounting data:

[0055] Accounting data is the basic data that reflects the company's financial status and operating results, including accounting subjects, vouchers, account books and financial statements. The functions of these data are mainly reflected in:

[0056] Accounting subjects: define the basic units for a company's financial accounting and provide the company with a clear and systematic financial accounting framework.

[0057] Voucher: It records the detailed information of each economic transaction of the company. It is the direct basis for accounting processing and the key to ensuring the accuracy and completeness of financial data.

[0058] Account book: Through the summary and classification of vouchers, it comprehensively reflects the changes in the company's assets, liabilities, owner's equity, as well as income, expenses and profits.

[0059] Financial statements: including balance sheet, income statement and cash flow statement, etc., are a comprehensive reflection of the company's financial status and operating results, and provide important decision-making basis for stakeholders such as management, investors and creditors.

[0060] ②. Fund flow data:

[0061] Fund flow data mainly includes bank statements, cash flow statements and payment records, which are used to:

[0062] Bank statements: Detailed records of changes in funds in the company's bank accounts, including income, expenditure, transfers, etc., are a true reflection of the company's capital flow.

[0063] Cash flow statement: reflects the company's cash inflows and outflows over a certain period of time, helping the company understand the source and use of cash, as well as the increase and decrease of cash.

[0064] Payment records: Records all the company's payment activities, including payment time, amount, object and other information, which helps the company monitor and analyze the use of funds.

[0065] ③. Supplier data:

[0066] Supplier data includes basic supplier information, contract information, payment records, and invoice information. Its functions are as follows:

[0067] Supplier basic information: Provides basic information about suppliers, such as name, address, contact information, etc., to facilitate the company's supplier management and communication.

[0068] Contract information: records the contract terms and agreements between the company and the supplier, and is an important basis for the company's procurement activities and supplier cooperation.

[0069] Payment records: Detailed records of the company's payments to suppliers, including payment time, amount and method, help the company understand payment progress and financial status.

[0070] Invoice information: Contains the details of the invoice issued by the supplier and is an important document for the company's financial accounting and tax processing.

[0071] ④. Inventory data:

[0072] Inventory data includes inventory lists, inventory change records, and inventory costs, and its main functions are:

[0073] Inventory List: A detailed list of all goods or materials currently in stock by the company, making it easier for the company to query and manage inventory.

[0074] Inventory change records: records the increase and decrease of inventory items, including warehousing, outbound and transfer operations, which helps the company understand inventory dynamics and conduct inventory management.

[0075] Inventory cost: reflects the cost information of inventory goods and is an important basis for the company to conduct cost accounting and cost control.

[0076] ⑤. Sales data:

[0077] Sales data includes sales orders, delivery records, and sales returns. Its functions are:

[0078] Sales Order: It records the detailed information of the goods or services purchased by the customer from the company and is a direct reflection of the company's sales activities.

[0079] Shipping records: Detailed records of the company's shipments to customers, including delivery time, quantity, logistics information, etc., which helps the company to grasp sales progress and customer service.

[0080] Sales Returns: records information on merchandise returned by customers for various reasons, and is an important reference for the company to improve product and service quality.

[0081] ⑥. Procurement data:

[0082] Procurement data mainly includes purchase orders, receipt records, and purchase returns, etc. Its functions are as follows:

[0083] Purchase Order: It records in detail the company's plan to purchase goods or services from suppliers and is the basis for the company's procurement activities.

[0084] Receiving records: reflect the company's receipt of goods from suppliers, including receipt time, quantity, quality and other information, which helps the company conduct procurement acceptance and inventory management.

[0085] Purchase returns: records the information of goods returned to suppliers due to quality issues or other reasons, and is an important basis for the company to evaluate and manage suppliers.

[0086] 7. Debt data:

[0087] Debt data includes debt type, debt amount, debt term, interest rate, repayment record and overdue status, etc. Its main functions are:

[0088] Debt Type and Amount: Detailed records of the various debt types and amounts undertaken by the company will help the company fully understand its debt situation.

[0089] Debt term and interest rate: reflects the debt repayment period and interest burden, and is an important basis for the company to conduct capital planning and risk control.

[0090] Repayment records and overdue status: Detailed records of the company's repayment status and whether there are any overdue payments will help the company identify problems in a timely manner and take corresponding measures to prevent risks.

[0091] ⑧. Human Resources Data:

[0092] Human resources data includes employee information, salary data, and attendance records. Its functions are:

[0093] Employee information: It records the personal information and work status of the company's employees in detail and is the basic data for the company to conduct human resources management.

[0094] Salary data: reflects the salary level and structure of the company's employees and is an important basis for the company to conduct salary management and cost control.

[0095] Attendance records: Detailed records of employee attendance and work performance, which helps the company evaluate and manage the work status of employees.

[0096] The present invention will be further described below in conjunction with Examples 1 to 3:

[0097] Embodiment 1:

[0098] The capital chain risk prediction model and the supply chain risk prediction model are both constructed using a linear regression algorithm, and the prediction function of the linear regression model is:

[0099] f(χ)=w T x+b

[0100] Where w represents the weight vector, which is a multidimensional array whose dimension matches the number of features; χ represents the input feature vector, which is also a multidimensional array containing all the feature values ​​used for prediction; w T represents the transpose of the weight vector, that is, converting w from a column vector to a row vector so as to perform a dot product operation with the feature vector x; b represents the intercept, which is the bias term of the model, indicating the predicted output value of the model when all eigenvalues ​​are zero.

[0101] The specific implementation method is as follows:

[0102] First, collect historical capital flow data, which includes input feature vector x and corresponding output feature vector y. x contains all feature values ​​used for prediction, such as bank flow, cash flow, etc.; y represents the capital chain risk status corresponding to the input feature vector, which can be expressed by risk level or specific risk value. In the data preprocessing stage, the collected raw data is cleaned, converted and standardized. Data cleaning mainly removes duplicate, invalid or erroneous data to ensure data quality. Data conversion converts raw data into a format suitable for linear regression model processing, such as converting categorical data into numerical data. Data standardization is to eliminate the dimensional differences between different features so that all data can be compared on the same scale.

[0103] Define the linear regression model function f(x) = w^Tx+b, where w represents the weight vector, b represents the intercept, and the dimension of the weight vector w matches the dimension of the input feature vector x. At the beginning of model construction, set the initial weight vector w and intercept b values ​​for the linear regression model. The initial values ​​can be determined by random assignment or empirical setting.

[0104] In the model training phase, the weight vector w is first converted from a column vector to a row vector so that it can be dot-producted with the input feature vector x. Then, using the collected feature data set, the weight vector w and intercept b of the linear regression model are iteratively updated by minimizing the mean square error (MSE) between the predicted value and the actual value. The formula for minimizing the mean square error is:

[0105]

[0106] Among them, n is the number of feature samples, y i is the data labeling result of the i-th sample, x i is the feature vector of the i-th sample, and MSE is the calculated minimized mean square error value; by continuously iteratively updating the weight vector w and the intercept b, the error between the model's predicted value and the actual value is gradually reduced. The iterative update process continues until the training stop condition is met, such as reaching the preset number of iterations, the MSE value is less than a certain threshold, or the model performance is no longer significantly improved. By setting reasonable training stop conditions, it can be ensured that the model achieves better performance while avoiding overfitting.

[0107] The data labeling module labels each piece of data with a corresponding risk category, including "low risk", "medium risk" and "high risk", and annotates the time series data at key time points to indicate the association between these time points and risk changes, including the moments of abnormal fund flow and large fund transfers. After the model training is completed, the final linear regression prediction model is constructed using the obtained optimal weight vector w and intercept b. For the new input feature vector x (i.e., the new fund flow data), the model f(x) = w^T x+b is used for prediction calculation, and the predicted fund chain risk category is output.

[0108] Before data labeling, a complete set of labeling rules needs to be set in advance. These rules are based on historical data, expert knowledge and business needs, and clearly define how to determine the risk category, level or specific risk point of the data based on its specific characteristics or patterns. For example, for capital chain risk, the rules can be set as follows: when the company's current ratio is lower than a certain threshold, it is marked as "high capital chain break risk"; when the accounts receivable turnover days exceed a certain standard, it is marked as "medium capital chain pressure", etc. Similarly, similar rules can be set for supply chain risks.

[0109] The data labeling module traverses each piece of data extracted from the data storage module according to the preset rules. During the traversal process, the module will identify key features in the data, such as financial indicators, supplier delivery on-time rate, inventory turnover rate, etc. According to the preset labeling rules and identified features, the module will make a risk assessment for each piece of data to determine its risk category, level or specific risk point. After the assessment is completed, the module will assign a corresponding label value to each piece of data, such as "high risk of capital chain rupture", "low risk of supply chain interruption", etc. After the above labeling process, the original data will be converted into a data set with risk labels. The data set not only contains all the information of the original data, but also adds the dimension of risk labeling, which provides more abundant and targeted information for subsequent feature engineering and model training.

[0110] The feature engineering module receives labeled data sets output from the data labeling module. These data sets have been labeled with risk categories according to preset labeling rules and contain information related to financial risks and supply chain risks. The feature engineering module combines multiple original features into new features to capture more complex patterns. For example, a new feature of sales growth rate can be obtained by dividing the sales of a certain month by the sales of the previous month. After combining the features, the original features are mathematically transformed or encoded to make them more consistent with the model's assumptions or improve the model's performance. For example, for features with skewed distributions, logarithmic transformation or Box-Cox transformation can be performed to make them closer to normal distribution. Based on indicators such as feature importance, relevance, or information content, select the features that are most helpful for model prediction.

[0111] Embodiment 2:

[0112] During the data preprocessing process, for non-numeric categorical data (such as text, labels, etc.) in the original data, the data preprocessing module uses One-Hot Encoding to convert them. One-Hot Encoding is a method of converting categorical variables into a format that is easy for machine learning algorithms to use. Specifically, for each categorical feature, One-Hot Encoding creates a new binary column representing each unique category in the original data. If a sample in the original data belongs to a certain category on the categorical feature, the corresponding binary column is 1, otherwise it is 0. For example, the "Debt Rating" field in the dataset contains the following four possible categories: "Low", "Medium", "High" and "Extremely High". These categories are non-numeric, so they need to be converted to numerical form through One-Hot Encoding to facilitate processing by the machine learning model.

[0113] The data preprocessing module performs data standardization in the following manner:

[0114] The numerical data were scaled to conform to a standard normal distribution with a mean of 0 and a standard deviation of 1 by using the z-score standardization method;

[0115] Implementation of the z-score normalization method:

[0116] 1. Calculate the mean and standard deviation:

[0117] For given numerical data, the data preprocessing module first calculates the mean and standard deviation of the data.

[0118] 2. Data scaling:

[0119] Next, the module scales the data using the z-score normalization formula. The formula is as follows:

[0120]

[0121] Among them, χ is the original data, μ is the mean, and σ is the standard deviation. After this transformation, the mean of the data becomes 0 and the standard deviation becomes 1, which conforms to the standard normal distribution.

[0122] The data is scaled to the specified range [0,1] using the min-max normalization method.

[0123] Implementation of the min-max normalization method:

[0124] 1. Determine the data range:

[0125] The data preprocessing module first determines the maximum value (max) and the minimum value (min) of the numerical data.

[0126] 2. Data scaling:

[0127] Then, the module uses the min-max normalization formula to scale the data to the [0,1] interval range. The formula is as follows:

[0128]

[0129] Among them, x is the original data, min is the minimum value, and max is the maximum value. After this conversion, all data will be scaled between 0 and 1.

[0130] After completing the above two standardization processes, the data preprocessing module will output the standardized data set.

[0131] Embodiment 3:

[0132] The risk warning module monitors the company's financial and supply chain status in real time, determines the level of each risk and its potential impact based on the comprehensive risk assessment report output by the risk assessment module, and issues a timely warning when the risk indicator exceeds the preset threshold.

[0133] The risk warning module is connected to the system's data acquisition module and receives the company's financial data and supply chain data in real time. The risk warning module regularly receives the comprehensive risk assessment report output by the risk assessment module, which analyzes in detail the various risks and their levels currently faced by the company. According to the company's historical data, industry standards and expert advice, reasonable thresholds are set for various risk indicators, which represent the risk level that the company can bear. The risk warning module constantly compares real-time data with risk thresholds. Once any risk indicator exceeds its corresponding threshold, the warning mechanism is triggered. When the risk indicator exceeds the standard, the risk warning module immediately generates warning information. The information includes the risk type, level, potential impact and recommended response measures that trigger the warning. The warning information is first displayed to the user in an intuitive way through the user interface module. At the same time, the system also sends the warning information to the preset recipients through email, SMS and mobile application push to ensure that relevant personnel can be informed and deal with potential risks in the first time.

[0134] The user interface module is mainly responsible for interacting with the user. This module is implemented using front-end development technology, including HTML (Hypertext Markup Language), CSS (Cascading Style Sheets) and JavaScript. HTML is used to build the structure and content of a web page, and defines the elements and layout of a web page. CSS is used to set the style of web page elements, including color, font, layout, etc., making the user interface more beautiful and easy to use. JavaScript is used to implement interactive functions of web pages, such as responding to user operations and dynamically updating page content.

[0135] The user interface module communicates with the backend service through the API (application programming interface) interface. The backend service includes the risk assessment module and the risk warning module. Through the API interface, the user interface module can obtain the risk assessment results and risk warning information in real time. The user interface module sends a data acquisition request to the backend service through HTTP (Hypertext Transfer Protocol) request; after the backend service processes the request, it returns the risk assessment results and risk warning information to the user interface module in JSON (JavaScript Object Notation) or other formats. The user interface module displays the detailed information of the risk assessment report in the form of charts, tables or texts. This information includes risk level, potential impact and recommended measures. It uses chart libraries (such as Chart.js, ECharts, etc.) to generate intuitive risk level distribution charts to help users quickly understand the risk status. The detailed data of each risk is listed in a table, including risk name, level, potential impact, etc., which is convenient for users to check and compare; provide detailed risk descriptions and recommended measures, and present them to users in text form for reference and decision-making. When the risk warning module issues a warning message, the user interface module presents this information to the user in a striking prompting manner to ensure that the user can notice it in time and take corresponding actions, including:

[0136] Pop-up prompt: A window containing warning information pops up on the user interface to attract the user's attention.

[0137] Sound prompt: Play the preset alarm sound to remind users to check the warning information.

[0138] Vibration reminder: On mobile devices, vibration is used to remind users to check warning information.

[0139] The authority management module implements user authentication and authority control by designing a comprehensive authority control system; the system includes three parts: user role management, authority allocation and access control; user role management allows system administrators to define different user roles and assign corresponding authority to each role; authority allocation is to assign specific operation authority to different user roles; access control is to check the role and authority of the user when the user tries to access the system resources, so that the user can only access the data and functions authorized by the user; the authority management module further adopts encryption technology to protect the security of user information in data network transmission. System administrators can define different user roles according to organizational structure and business needs, such as ordinary users, department administrators, system administrators, etc. Each role has its specific responsibilities and authority scope; after defining the user role, the system administrator needs to assign corresponding operation authority to each role, which can include specific operations such as data viewing, data editing, data deletion, report generation, etc. Through fine authority allocation, it can be ensured that each role can only perform the operations authorized by it; when a user tries to access system resources, the access control mechanism will check the user's role and the authority it has. Only when the user has the authority to access specific resources or perform specific operations, the system will allow it to access or operate. Otherwise, the system will deny the user's request and may display an appropriate error message.

[0140] When user information needs to be transmitted in the network, the permission management module will use an encryption algorithm (such as AES, RSA, etc.) to encrypt the data. In this way, even if the data is intercepted during transmission, attackers cannot easily decrypt and obtain sensitive information.

[0141] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0142] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A panoramic financial risk management and control system, characterized by: Includes data layer, machine learning layer, application layer and interaction layer; The data layer includes a data acquisition module, a data preprocessing module and a data storage module; The data acquisition module is used to extract historical data from the company's internal system at a monthly granularity. The extracted data includes accounting data, capital flow data, supplier data, inventory data, sales data, purchase data, debt data and human resources data at the end of each month. The data preprocessing module is used to perform preprocessing operations on the original data transmitted by the data acquisition module. The preprocessing includes data cleaning, data conversion and data standardization. The data storage module is used to store preprocessed multi-type data for efficient management and retrieval. The machine learning layer is used to train a capital chain risk prediction model and a supply chain risk prediction model using machine learning technology. The capital chain risk prediction model is used to predict the possibility of a company's capital chain breaking risk by analyzing the company's financial data, credit status and debt structure; the supply chain risk prediction model is used to predict the possibility of a supply chain interruption risk by analyzing supplier data and inventory data; the machine learning layer includes a data labeling module, a feature engineering module, a model training module and a model evaluation and optimization module; The data labeling module receives data related to the model prediction results extracted from the data storage module, and labels these data according to preset labeling rules. The labeling content includes the category, level or specific risk point of the risk of capital chain rupture or supply chain interruption, so as to form a labeling data set; the feature engineering module constructs new and meaningful feature data through combination and conversion based on the labeling data set to form a feature data set; the model training module is used to construct and train a risk prediction model based on the feature data set and machine learning algorithm; the model evaluation and optimization module is used to evaluate and optimize the trained risk prediction model so that its performance meets the actual use requirements; The application layer includes a risk assessment module and a risk warning module; The risk assessment module uses the trained capital chain risk prediction model and supply chain risk prediction model to conduct real-time analysis of the company's financial data and supply chain data, and outputs a comprehensive risk assessment report that lists in detail the level and potential impact of each risk; the risk warning module monitors the company's financial and supply chain status in real time based on the output results of the risk assessment module, and immediately triggers the warning mechanism once an abnormality is found or the preset risk threshold is reached; The interaction layer includes a user interface module and a rights management module; The user interface module is used to provide a user interface for users to view risk assessment results and receive risk warning information; The authority management module is used to authenticate users and control their authority so that different users can only access data and functions within their authority scope.

2. A financial panoramic risk management and control system according to claim 1, characterized in that: Accounting data includes accounting subjects, vouchers, account books and financial statements, which are used to reflect the company's financial status and operating results; Fund flow data includes bank statements, cash flow statements, and payment records, which are used to track the inflow and outflow of company funds; Supplier data includes basic supplier information, contract information, payment records and invoice information, which is used to manage the transaction relationship between the company and the supplier; Inventory data includes inventory lists, inventory change records and inventory costs, which are used to monitor and manage the company's inventory situation; Sales data includes sales orders, shipping records, and sales returns, which are used to analyze the company's sales and customer behavior; Procurement data includes purchase orders, receipt records, and purchase returns, which are used to manage the company's procurement process and supplier performance; Debt data includes debt type, debt amount, debt term, interest rate, repayment record and overdue status, which is used to comprehensively manage and analyze the company's debt situation; Human resource data includes employee information, salary data, and attendance records, and is used to support human resource management and cost analysis.

3. A financial panoramic risk management and control system according to claim 2, characterized in that: The capital chain risk prediction model and the supply chain risk prediction model are both constructed using a linear regression algorithm, and the prediction function of the linear regression model is: f(x)=w T x+b Where w represents the weight vector, which is a multidimensional array whose dimension matches the number of features; x represents the input feature vector, which is also a multidimensional array containing all the feature values ​​used for prediction; w T represents the transpose of the weight vector, that is, converting w from a column vector to a row vector so as to perform a dot product operation with the feature vector x; b represents the intercept, which is the bias term of the model and represents the predicted output value of the model when all eigenvalues ​​are zero; before the training starts, the initial weight and intercept values ​​are set for the linear regression model, and then the weight and intercept parameters of the linear regression model are iteratively updated by minimizing the mean square error between the predicted value and the actual value using the feature data set until the training stop condition is met; the calculation formula for minimizing the mean square error is: Among them, n is the number of feature samples, y i is the data labeling result of the i-th sample, x i is the eigenvector of the i-th sample, and MSE is the calculated minimized mean square error value.

4. A financial panoramic risk management and control system according to claim 3, characterized in that: The data labeling module labels each piece of data with a corresponding risk category, which includes "low risk", "medium risk" and "high risk", and annotates the time series data at key time points to indicate the association between these time points and risk changes, which include moments of abnormal fund flows and large fund transfers.

5. A financial panoramic risk management and control system according to claim 4, characterized in that: The data preprocessing module converts non-numeric categorical data into numerical form by one-hot encoding to facilitate processing by machine learning models.

6. A financial panoramic risk management and control system according to claim 5, characterized in that: The data preprocessing module performs data standardization in the following manner: The numerical data were scaled to conform to a standard normal distribution with a mean of 0 and a standard deviation of 1 by using the z-score standardization method; The data is scaled to the specified range [0,1] using the min-max normalization method.

7. A financial panoramic risk management and control system according to claim 6, characterized in that: The risk warning module monitors the company's financial and supply chain status in real time, and determines the level and potential impact of various risks based on the comprehensive risk assessment report output by the risk assessment module; when any risk indicator is found to exceed the preset risk threshold, the risk warning module immediately generates warning information; The warning information includes the risk type, level, potential impact and recommended response measures that trigger the warning; The warning information is displayed to users through the user interface module, and warning notifications are sent to preset recipients via email, SMS and mobile application push, so that relevant personnel can be informed and deal with potential risks in a timely manner.

8. A financial panoramic risk management and control system according to claim 7, characterized in that: The user interface module is implemented using front-end development technologies, including HTML, CSS and JavaScript; the user interface module communicates with the back-end service through an API interface to obtain risk assessment results and risk warning information; the risk assessment report displays detailed information in the form of charts, tables or text, including risk levels, potential impacts and recommended measures; Risk warning notifications are presented in a striking manner, including pop-ups, sounds or vibrations, so that users can notice them in time and take appropriate actions.

9. A financial panoramic risk management and control system according to claim 8, characterized in that: The authority management module implements user identity authentication and authority control by designing a comprehensive authority control system; the system includes three parts: user role management, authority allocation and access control; User role management allows system administrators to define different user roles and assign corresponding permissions to each role; Permission allocation is to grant specific operating permissions to different user roles; access control is to check the role and permissions of users when they try to access system resources, so that users can only access the data and functions they are authorized to. The permission management module further uses encryption technology to protect the security of user information during data network transmission.

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