Financial big data risk analysis and early warning system based on artificial intelligence

Abstract

The invention discloses a financial big data risk analysis and early warning system based on artificial intelligence, and belongs to the technical field of financial analysis. A data acquisition module is used for collecting financial data from a bank transaction system, a security transaction platform, a credit rating mechanism and a third-party financial data provider; information of transaction details, a debt sheet, market quotation data, macroeconomic indicators and customer credit records is covered; and the risk assessment module is used for fusing artificial intelligence algorithms such as logistic regression, a decision tree, a support vector machine, a neural network and a deep learning model and calculating a risk score in combination with a dynamic weight formula, and the dynamic weight formula dynamically adjusts the weight of each risk index according to parameters such as a fluctuation ratio, a market uncertainty index and a macroeconomic index. The method has the advantage of improving the communication efficiency among the modules and the task scheduling reasonability.

Description

Technical Field

[0001] The present invention belongs to the field of financial analysis technology, and in particular relates to a financial big data risk analysis and early warning system based on artificial intelligence. Background Art

[0002] In today's financial industry, accurate risk analysis and early warning are crucial to the stable operation of financial institutions. In practical applications, a key technical problem faced by the system is how to achieve efficient collaboration between modules while ensuring data processing efficiency.

[0003] Since the amount of data processed by each module is huge and complex, for example, the data acquisition module needs to obtain massive financial data from multiple data sources, and the data preprocessing module needs to perform complex cleaning and conversion operations on these raw data, which places extremely high demands on the system's performance and resource allocation. If the modules do not work well together, it may lead to problems such as data transmission delays and processing interruptions, which in turn affects the accuracy and real-time performance of the entire risk analysis and early warning system. How to optimize the system architecture and improve the communication efficiency and task scheduling rationality between modules has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention provides a financial big data risk analysis and early warning system based on artificial intelligence, which has the advantages of improving the communication efficiency between modules and the rationality of task scheduling, and solves the problems of the prior art.

[0005] The present invention is implemented as follows: a financial big data risk analysis and early warning system based on artificial intelligence includes a central processing module, and the central processing module is signal-connected to a data acquisition module, a data preprocessing module, a feature calculation module, a risk assessment module, an early warning module, a data storage module and a visualization module; wherein, Data collection module: collects financial data from bank trading systems, securities trading platforms, credit rating agencies, and third-party financial data providers, covering transaction details, balance sheets, market data, macroeconomic indicators, and customer credit records; Data preprocessing module: Use machine learning-based outlier detection algorithms, data filling algorithms, data conversion techniques, and normalization algorithms to clean, convert, and normalize raw data to improve data quality and availability; Feature calculation module: Based on financial theory and practical experience, it calculates key parameters such as solvency index, profitability index, market sensitivity index and liquidity index for risk assessment; Risk assessment module: Integrate artificial intelligence algorithms such as logistic regression, decision tree, support vector machine, neural network, and deep learning model, and calculate the risk score in combination with the dynamic weight formula. The dynamic weight formula dynamically adjusts the weights of each risk indicator according to parameters such as volatility, market uncertainty index, and macroeconomic indicators; Early warning module: Compare the risk score calculated by the risk assessment module with the preset risk threshold. When the risk score exceeds the threshold, send warning messages to relevant personnel through methods such as text messages, emails, system pop-up prompts, and voice broadcasts; Data storage module: Adopt distributed storage technology and an efficient database management system, such as Hadoop Distributed File System (HDFS) and NoSQL database, to securely and reliably store the collected original financial data, preprocessed data, and risk assessment results, and establish a data backup and recovery mechanism; Visualization module: Use data visualization tools such as Echarts, Tableau, and PowerBI to display financial data and risk assessment results in the form of intuitive and easy-to-understand charts, graphs, and dashboards, and provide interactive functions such as data filtering, indicator comparison, and trend analysis.

[0006] Preferably, the data acquisition module of the present invention includes: Interface docking unit: Establish data interfaces with bank trading systems, securities trading platforms, credit rating agencies, and third-party financial data providers to achieve real-time or scheduled data acquisition; Data scraping unit: Obtain financial data from specified data sources according to preset rules through web crawler technology.

[0007] Preferably, the data preprocessing module of the present invention includes: Data cleaning unit: Use outlier detection algorithms and data filling algorithms based on machine learning to remove noise, outliers, and duplicate data in the data, and reasonably fill in missing values; Data conversion unit: Adopt data conversion technology to convert unstructured data into structured data and encode non-numerical data into numerical form; Normalization processing unit: Use normalization algorithms to uniformly scale data with different magnitudes and distributions to a specific range to eliminate the dimensional difference between data; specifically adopt the minimum-maximum normalization algorithm, and the formula is: Where X is the original data, and are the minimum and maximum values in the dataset respectively, is the normalized data; and the ZScore standardization algorithm, the formula is: , where is the mean of the data set, is the standard deviation; when converting text data into numerical vectors, the word vector model Word2Vec is used, and its core formula is: , where E represents the loss function, W represents the target word, C represents the corpus, c represents the context word of the target word w, represents the probability that the context word c appears when the target word W is given.

[0008] Preferably, as the present invention, the feature calculation module includes: Solvency index calculation unit: According to financial theory, calculate solvency indexes such as asset-liability ratio and interest coverage ratio; Asset-liability ratio = total liabilities / total assets; Interest coverage ratio = earnings before interest and taxes / interest expenses; Profitability index calculation unit: Calculate profitability indexes such as return on net assets and gross profit margin; Return on net assets = net profit / net assets; Gross profit margin = (operating income - operating cost) / operating income; Market sensitivity index calculation unit: Obtain coefficient through regression analysis of the return on assets and the return on the market portfolio, and calculate market sensitivity indexes such as duration according to the cash flow and yield to maturity of the bond.

[0009] Preferably, as the present invention, the risk assessment module includes: Algorithm fusion unit: Fusion of artificial intelligence algorithms such as logistic regression, decision tree, support vector machine, neural network and deep learning model, using the advantages of different algorithms to process complex data and mine non-linear relationships; Parameter acquisition unit: Real-time acquisition of dynamic parameters such as volatility, market uncertainty index, and macroeconomic indicators; Weight calculation unit: According to the dynamic parameters, calculate the weights of each risk index through the dynamic weight formula, and the dynamic weight formula is: , where i = 1, 2, 3, V is volatility, U is the market uncertainty index, is a very small positive number, is a preset basic weight coefficient, and can be dynamically adjusted according to actual needs and market changes of the value; Risk score calculation unit: Multiply the calculated values of each risk index by the corresponding weights and accumulate to obtain the risk score.

[0010] Preferably, as the present invention, the warning module includes: Threshold comparison unit: Compare the risk score calculated by the risk assessment module with the preset risk threshold; Early warning sending unit: When the risk score exceeds the threshold, it sends early warning information to relevant personnel through text messages, emails, system pop-up prompts, and voice broadcasts.

[0011] Preferably, for the present invention, the distributed storage architecture building unit: adopts distributed storage technology, such as the Hadoop Distributed File System (HDFS), to build a data storage cluster, and dispersedly stores data on multiple nodes; Database management unit: Selects a suitable database management system, such as a NoSQL database, to securely and reliably store the collected original financial data, preprocessed data, and risk assessment results, and establishes a data backup and recovery mechanism.

[0012] Preferably, for the present invention, the visualization module includes: Tool selection and configuration unit: Selects data visualization tools such as Echarts, Tableau, PowerBI, etc., and performs corresponding configurations; Visualization display unit: Displays financial data and risk assessment results in the form of intuitive and easy-to-understand charts, graphs, dashboards, etc.; Interactive function implementation unit: Provides interactive functions such as data screening, indicator comparison, and trend analysis.

[0013] Preferably, for the present invention, the system has self-learning and self-adaptive capabilities, and can automatically optimize the artificial intelligence algorithm model and dynamic weight formula according to the changes in the financial market and historical risk data, continuously improving the accuracy of risk assessment and early warning.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: Efficient data processing: The data acquisition module obtains a large amount of financial data from multiple data sources, and the data preprocessing module uses advanced algorithms for complex cleaning and conversion operations, improving data quality and availability, providing a high-quality data foundation for subsequent modules, and ensuring data processing efficiency.

[0015] Accurate risk assessment: The risk assessment module integrates multiple artificial intelligence algorithms and combines a dynamic weight formula to calculate the risk score, which can more accurately assess risks and improve the accuracy of the risk analysis and early warning system.

[0016] Real-time early warning: The early warning module compares the risk score with a preset threshold, and immediately sends early warning information through multiple methods once it exceeds the threshold, ensuring that relevant personnel can take measures in a timely manner and guaranteeing the real-time nature of the system.

[0017] Safe and reliable storage: The data storage module adopts distributed storage technology and an efficient database management system, and establishes a data backup and recovery mechanism to ensure the safe and reliable storage of data.

[0018] Intuitive Visualization Display: The visualization module uses data visualization tools to display financial data and risk assessment results in an intuitive and easy-to-understand form, and provides interactive functions to facilitate users' data analysis and decision-making. Description of the Drawings

[0019] Figure 1 It is a system block diagram of a financial big data risk analysis and early warning system based on artificial intelligence provided by an embodiment of the present invention. Detailed Implementation Manner

[0020] To further understand the content, features and effects of the present invention, the following embodiments are exemplified and described in detail in conjunction with the drawings as follows.

[0021] The structure of the present invention will be described in detail below in conjunction with the drawings.

[0022] As Figure 1 shown, the financial big data risk analysis and early warning system based on artificial intelligence provided by an embodiment of the present invention includes a central processing module, and the central processing module is signal-connected to a data acquisition module, a data preprocessing module, a feature calculation module, a risk assessment module, an early warning module, a data storage module and a visualization module; among them, Data Acquisition Module: Collect financial data from bank transaction systems, securities trading platforms, credit rating agencies, and third-party financial data providers, covering transaction details, balance sheets, market quotation data, macroeconomic indicators, and customer credit record information; Data Preprocessing Module: Use outlier detection algorithms, data filling algorithms, data conversion technologies, and normalization algorithms based on machine learning to clean, transform, and normalize the original data to improve data quality and usability; Feature Calculation Module: Calculate key parameters such as solvency indicators, profitability indicators, market sensitivity indicators, and liquidity indicators according to financial theory and practical experience for risk assessment; Risk Assessment Module: Integrate artificial intelligence algorithms such as logistic regression, decision tree, support vector machine, neural network, and deep learning model, and calculate the risk score in combination with a dynamic weight formula, and the dynamic weight formula dynamically adjusts the weights of each risk indicator according to parameters such as volatility, market uncertainty index, and macroeconomic indicators; Early Warning Module: Compare the risk score calculated by the risk assessment module with a preset risk threshold, and when the risk score exceeds the threshold, send warning information to relevant personnel through text messages, emails, system pop-up prompts, and voice broadcasts; Data storage module: Adopt distributed storage technology and an efficient database management system, such as the Hadoop Distributed File System (HDFS) and NoSQL databases, to securely and reliably store the collected original financial data, preprocessed data, and risk assessment results, and establish a data backup and recovery mechanism; Visualization module: Utilize data visualization tools, such as Echarts, Tableau, PowerBI, to display financial data and risk assessment results in the form of intuitive and easy-to-understand charts, graphs, dashboards, etc., and provide interactive functions such as data filtering, indicator comparison, and trend analysis.

[0023] Specifically, the data collection module: Collect financial data from bank trading systems, securities trading platforms, credit rating agencies, and third-party financial data providers, covering transaction details, balance sheets, market quotation data, macroeconomic indicators, and customer credit records, etc. Its working principle is as follows: Interface docking unit: Establish data interfaces with bank trading systems, securities trading platforms, credit rating agencies, and third-party financial data providers, follow unified data transmission protocols and format specifications, and achieve real-time or scheduled data collection. The interface docking unit will initiate data requests to the data source according to a preset time interval or event trigger mechanism. The data source responds and transmits the data to the interface docking unit, which then passes the data to the subsequent modules.

[0024] Data scraping unit: Use web crawler technology to extract financial data from the specified data source websites or pages according to pre-set rules. First, the crawler will parse the HTML or XML structure of the target web page, locate the tags or elements containing financial data, then extract this data, and organize and store it in a specified data format for subsequent processing.

[0025] Data preprocessing module: Apply outlier detection algorithms, data filling algorithms, data transformation techniques, and normalization algorithms based on machine learning to clean, transform, and normalize the original data, improving data quality and usability. Its working principle is as follows: Data cleaning unit: Based on outlier detection algorithms in machine learning, such as the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), identify and remove data points with significantly lower density than other regions by calculating the density of data points in space. The data filling algorithm fills in missing values reasonably using methods such as mean, median, and regression models according to the characteristics of the data and the distribution law of existing data, and at the same time removes duplicate data to improve the accuracy and integrity of the data.

[0026] Data Conversion Unit: By adopting data conversion technologies, for unstructured data such as market reviews and news reports in text form, lexical analysis, syntactic analysis and other methods in natural language processing technology (NLP) are used to convert it into structured data. For non-numerical data such as customer gender and industry classification, methods such as One-Hot Encoding or Label Encoding are used to encode and convert it into numerical form for subsequent analysis.

[0027] Normalization Processing Unit: Use normalization algorithms to eliminate the dimensional differences between data. The minimum-maximum normalization algorithm, the formula is: where X is the original data, and are the minimum and maximum values in the dataset respectively, is the normalized data; this algorithm uniformly scales the data to the interval [0, 1]. The ZScore standardization algorithm, the formula is: , where is the mean of the dataset, is the standard deviation; it converts the data into standard normal distribution data with a mean of 0 and a standard deviation of 1. When converting text data into numerical vectors, the word vector model Word2Vec is adopted, and its core formula is: , by continuously adjusting the word vectors, the co-occurrence probability of the target word and the context words is maximized, thus converting the text into numerical vectors.

[0028] Feature Calculation Module: According to financial theories and practical experiences, calculate key parameters such as solvency indicators, profitability indicators, market sensitivity indicators and liquidity indicators for risk assessment. Its working principle is as follows: Solvency Indicator Calculation Unit: According to financial theories, by obtaining the financial statement data of an enterprise or financial institution, calculate solvency indicators such as asset-liability ratio and interest coverage ratio. Asset-liability ratio = total liabilities / total assets, this indicator reflects the proportion of an enterprise's liabilities in its assets and measures the long-term solvency of the enterprise; Interest coverage ratio = earnings before interest and taxes / interest expenses, which reflects the enterprise's ability to pay interest and measures the short-term solvency of the enterprise.

[0029] Profitability Indicator Calculation Unit: By obtaining data such as an enterprise's operating income, operating cost, net profit, and net assets, calculate profitability indicators such as return on net assets and gross profit margin. Return on net assets = net profit / net assets, which reflects the return level of shareholders' equity and is used to measure the efficiency of a company's use of its own capital; Gross profit margin = (operating income - operating cost) / operating income, which reflects the initial profitability of an enterprise's products or services.

[0030] Market Sensitivity Indicator Calculation Unit: Obtained by performing a regression analysis on the asset return rate and the market portfolio return rate. In this process, methods such as the least squares method are used to find the optimal regression coefficient to determine the linear relationship between the asset return rate and the market portfolio return rate, thereby obtaining the β coefficient to measure the sensitivity of the asset to market fluctuations. Calculate the duration based on the cash flow and yield to maturity of the bond. By discounting the future cash flows of the bond and considering the weights of each period's cash flows, calculate the duration, which is the sensitivity indicator of the bond price to interest rate changes. Coefficient. In this process, methods such as the least squares method are used to find the optimal regression coefficient to determine the linear relationship between the asset return rate and the market portfolio return rate, thereby obtaining the β coefficient to measure the sensitivity of the asset to market fluctuations. Calculate the duration based on the cash flow and yield to maturity of the bond. By discounting the future cash flows of the bond and considering the weights of each period's cash flows, calculate the duration, which is the sensitivity indicator of the bond price to interest rate changes.

[0031] Risk Assessment Module: Integrates artificial intelligence algorithms such as logistic regression, decision tree, support vector machine, neural network, and deep learning model, and calculates the risk score in combination with the dynamic weight formula. The dynamic weight formula dynamically adjusts the weights of each risk indicator according to parameters such as volatility, market uncertainty index, and macroeconomic indicators. Its working principle is as follows: Algorithm Fusion Unit: Fuses artificial intelligence algorithms such as logistic regression, decision tree, support vector machine, neural network, and deep learning model. Different algorithms have different data processing methods and mining capabilities. For example, logistic regression is good at dealing with linear relationships, decision trees can handle non-linear relationships and can intuitively display the decision-making process, and neural networks and deep learning models have strong feature learning capabilities. By fusing these algorithms, comprehensively utilize their advantages in processing complex data and mining non-linear relationships to improve the accuracy of risk assessment.

[0032] Parameter Acquisition Unit: Real-time obtains dynamic parameters such as volatility, market uncertainty index, and macroeconomic indicators from channels such as financial market data and macroeconomic databases. These parameters reflect the real-time changes in the financial market and the macroeconomic environment, providing a basis for dynamic weight calculation.

[0033] Weight Calculation Unit: Calculates the weights of each risk indicator according to the dynamic parameters through the dynamic weight formula. The dynamic weight formula is: , where i = 1, 2, 3, V is volatility, U is the market uncertainty index, is a very small positive number, is a preset basic weight coefficient. This formula dynamically adjusts the weights of each risk indicator according to the influence degree of each dynamic parameter on risk, making the risk assessment result more in line with the actual risk situation, and can be dynamically adjusted according to actual needs and market changes.

[0034] Risk Score Calculation Unit: Multiplies the calculated values of each risk indicator by the corresponding weights and accumulates them to obtain the risk score. For example, the solvency indicator value of an enterprise is , and the corresponding weight is ; the profitability indicator value is , the corresponding weight is ; the market sensitivity index value is , the corresponding weight is , then the risk score , thus quantifying the risk level.

[0035] Early warning module: Compare the risk score calculated by the risk assessment module with the preset risk threshold. When the risk score exceeds the threshold, send warning messages to relevant personnel through methods such as text messages, emails, system pop-up prompts, and voice broadcasts. Its working principle is as follows: Threshold comparison unit: Compare the risk score calculated by the risk assessment module with the preset risk threshold in real time. These risk thresholds are determined based on the risk tolerance of financial institutions, business characteristics, and historical data analysis, etc., and are used as the criteria for judging whether the risk exceeds the acceptable range.

[0036] Warning sending unit: When the risk score exceeds the threshold, the system will trigger the warning sending mechanism. Through integration with the text message gateway, email server, and business system, send text message warnings to relevant personnel respectively. The text message content includes information such as the risk subject, risk level, and details of risk indicators; send a detailed risk warning report to the preset email address. The report includes risk analysis charts, risk cause analysis, and recommended countermeasures, etc.; pop up a warning window in the business system within the financial institution to remind operators to pay attention to the risk situation in a timely manner. At the same time, through the voice broadcast system, convey the warning information to relevant personnel in voice form.

[0037] Data storage module: Adopt distributed storage technology and an efficient database management system, such as the Hadoop Distributed File System (HDFS) and NoSQL database, to securely and reliably store the collected original financial data, preprocessed data, and risk assessment results, and establish a data backup and recovery mechanism. Its working principle is as follows: Distributed storage architecture building unit: Adopt distributed storage technology, such as the Hadoop Distributed File System (HDFS), to build a data storage cluster. HDFS divides data into multiple data blocks and dispersedly stores them on multiple nodes in the cluster, improving data reliability through redundant storage. The NameNode is responsible for managing the namespace and metadata of the file system, recording the mapping relationship between data blocks and DataNode nodes; the DataNode is responsible for actually storing data blocks. When a user requests data, the NameNode locates the DataNode node storing the data block according to the metadata information, and then reads the data from the corresponding node.

[0038] Database Management Unit: Select a suitable database management system, such as a NoSQL database. For structured data, its flexible data model and high efficient read and write performance can be utilized for storage; for unstructured data, such as market reviews and news reports in text form, the document - type storage structure of a NoSQL database can be adopted to facilitate data management and analysis. Meanwhile, establish a data backup and recovery mechanism, regularly back up the data and store it in a remote location or on other storage media. When data is lost or damaged, the backup data can be used for recovery to ensure data integrity and availability.

[0039] Visualization Module: Utilize data visualization tools, such as Echarts, Tableau, PowerBI, to display financial data and risk assessment results in intuitive and understandable forms such as charts, graphs, and dashboards, and provide interactive functions such as data filtering, indicator comparison, and trend analysis. Its working principle is as follows: Tool Selection and Configuration Unit: According to the characteristics of financial data and user requirements, select a suitable data visualization tool, such as Echarts, Tableau, PowerBI, etc. Then configure the selected tool, set the data source connection, and define the mapping relationship between data fields and chart elements according to the data type and display requirements, such as mapping the time field to the horizontal axis of the chart and mapping the risk indicator value to the vertical axis, etc.

[0040] Visualization Display Unit: According to the configuration information, convert financial data and risk assessment results into intuitive and understandable forms such as charts, graphs, and dashboards. For example, display the change trend of risk indicators over time as a line chart, and visually present the dynamic changes of risks through the undulations of the line; use a bar chart to compare the risk status of different financial institutions or business segments, and compare the risk levels through the heights of the bars; display the current risk score and risk level in real - time with a dashboard, making it clear at a glance for users.

[0041] Interactive Function Implementation Unit: Provide interactive functions such as data filtering, indicator comparison, and trend analysis. Users can trigger the data filtering function by selecting conditions such as time range, risk indicators, and financial institutions on the visualization interface. The system re - queries and filters the data according to the user's selection and updates the visualization chart; the indicator comparison function allows users to select multiple risk indicators for comparison and display in the same chart to facilitate the analysis of the relationships between different indicators; the trend analysis function shows the change trend of risk indicators through the analysis of historical data, and users can deeply analyze the details of the trend through operations such as zooming and panning.

[0042] Exemplarily, the whole process is as follows: Data collection Interface Docking Unit: Establish a real-time data interface with a large bank's trading system to obtain millions of transaction details data daily. For example, on October 1, 2024, 5 million transaction records covering various businesses such as savings, loans, and transfers were successfully collected. At the same time, it is regularly docked with a well-known securities trading platform to obtain market quotation data of stocks, bonds, etc. every 5 minutes.

[0043] Data Crawling Unit: Through web crawler technology, crawl macroeconomic indicator data from the websites of third-party financial data providers according to preset rules. For example, at the same time, the quarterly GDP growth rate is obtained as 3.5% and the inflation rate is 2.0% and other macro data.

[0044] Data Preprocessing Data Cleaning Unit: Use an outlier detection algorithm based on machine learning to identify an abnormal transfer record in the bank transaction data. The amount is as high as 1 billion yuan and the transfer time is 3 am, which significantly deviates from the normal transaction pattern and is removed. At the same time, use a data filling algorithm to reasonably fill in some missing customer age data.

[0045] Data Transformation Unit: Convert unstructured text data such as market comments into numerical vectors using the word vector model Word2Vec. For example, convert the text "The market outlook is optimistic" into a specific numerical vector for subsequent analysis. For non-numerical data such as customer gender, use one-hot encoding to convert it into a numerical form.

[0046] Normalization Processing Unit: Perform normalization processing on the transaction amount data. Assume that in the original transaction amount data, the minimum value is 10 yuan, the maximum value is 100,000 yuan, and a certain transaction amount X is 5,000 yuan. Use the min-max normalization algorithm, . For some financial indicator data that follows a normal distribution, use the ZScore standardization algorithm. Assume that the mean of a certain financial indicator dataset is 50, the standard deviation is 10, and a certain data point X is 60, then .

[0047] Feature Calculation Debt Repayment Ability Indicator Calculation Unit: Calculate the debt repayment ability indicators of a certain enterprise. The total debt of the enterprise is 50 million yuan, and the total assets are 100 million yuan, then the asset-liability ratio = 50 million / 100 million = 0.5; the earnings before interest and taxes is 8 million yuan, and the interest expense is 2 million yuan, then the interest coverage ratio = 8 million / 2 million = 4.

[0048] Profitability Index Calculation Unit: Calculate the profitability index of the enterprise. The net profit is 15 million yuan, the net assets are 80 million yuan, and the return on net assets = 15 million / 80 million = 0.1875; the operating income is 120 million yuan, and the operating cost is 90 million yuan, and the gross profit margin = (120 million - 90 million) / 120 million = 0.25 Market Sensitivity Index Calculation Unit: Through the regression analysis of the enterprise's return on assets and the return on the market portfolio, it is obtained that The coefficient is 1.2. For the bonds held by the enterprise, the duration is calculated to be 5 years based on its cash flow and yield to maturity.

[0049] Risk Assessment Algorithm Fusion Unit: Fusion of neural network and deep learning models to analyze the preprocessed and feature-calculated data.

[0050] Parameter Acquisition Unit: The current market volatility V is obtained in real time as 0.12, and the market uncertainty index U is 0.18.

[0051] Weight Calculation Unit: Assume the basic weight coefficient , extremely small positive number

[0052] Risk Score Calculation Unit: The debt-paying ability index value of the enterprise is 0.6, the profitability index value is 0.7, and the market sensitivity index value is 0.8. Then the risk score = 0.1×0.6 + 0.13×0.7 + 0.1×0.8 = 0.231.

[0053] Early Warning Threshold Comparison Unit: The preset risk threshold is 0.3, and the calculated risk score of 0.231 is compared with it.

[0054] Early Warning Sending Unit: Since the risk score does not exceed the threshold, no early warning information is sent for the time being. If the risk score exceeds the threshold, such as 0.4, the system will immediately notify the relevant risk management personnel by text message. The text message content is "[Enterprise Name] Risk Level Upgraded to Medium Risk. Please view the email for details of risk indicators", and at the same time send a detailed risk early warning report to the specified email address, and pop up an early warning window in the internal business system of the financial institution.

[0055] Data Storage Distributed Storage Architecture Building Unit: Use the Hadoop Distributed File System (HDFS) to build a data storage cluster, and disperse and store 5 million bank transaction records collected, macroeconomic data captured, and various processed indicator data on multiple nodes.

[0056] Database Management Unit: Select the MongoDB database to store these data safely and reliably, and establish a weekly data backup mechanism and an emergency recovery plan.

[0057] Visualization Tool Selection and Configuration Unit: Select the Echarts data visualization tool and perform corresponding configurations.

[0058] Visualization Display Unit: Display the change trend of the enterprise's risk indicators in the past year with a line chart, compare the risk status of different enterprises in the same industry with a bar chart, and display the enterprise's current risk score and risk level in real time with a dashboard.

[0059] Interactive Function Implementation Unit: Users can select a time range of the past three months and a risk indicator of profitability indicators to dynamically generate corresponding visualization charts and deeply analyze the change in the enterprise's profitability during this period.

[0060] Working Principle of the Present Invention: The system has self-learning and self-adaptive capabilities. It can automatically optimize the artificial intelligence algorithm model and the dynamic weight formula according to the changes in the financial market and historical risk data, and continuously improve the accuracy of risk assessment and early warning. Its working principle is that the system will regularly collect the latest data of the financial market and historical risk data, use these data to retrain the artificial intelligence algorithm model, and adjust the parameters of the model, such as the connection weights of the neural network, etc., to make it better adapt to the changes in the financial market. For the dynamic weight formula, the system will analyze the correlation between each risk indicator and the actual risk according to the characteristics and risk status of the financial market in different periods, so as to automatically adjust the value of the basic weight coefficient to optimize the accuracy of risk assessment and early warning.

[0061] It should be noted that in this article, relational terms such as first and second 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0062] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Financial big data risk analysis and early warning system based on artificial intelligence, characterized by: It includes a central processing module, which is signal-connected to a data acquisition module, a data preprocessing module, a feature calculation module, a risk assessment module, an early warning module, a data storage module and a visualization module; wherein, Data collection module: collects financial data from bank trading systems, securities trading platforms, credit rating agencies, and third-party financial data providers, covering transaction details, balance sheets, market data, macroeconomic indicators, and customer credit records; Data preprocessing module: Use machine learning-based outlier detection algorithms, data filling algorithms, data conversion techniques, and normalization algorithms to clean, convert, and normalize raw data to improve data quality and availability; Feature calculation module: Based on financial theory and practical experience, it calculates solvency index, profitability index, market sensitivity index and liquidity index for risk assessment; Risk Assessment Module: It integrates artificial intelligence algorithms such as logistic regression, decision tree, support vector machine, neural network and deep learning model, and calculates risk scores with dynamic weight formula. The dynamic weight formula dynamically adjusts the weight of each risk indicator according to the parameters of volatility, market uncertainty index and macroeconomic indicators; Early warning module: Compare the risk score calculated by the risk assessment module with the preset risk threshold. When the risk score exceeds the threshold, an early warning message is sent to relevant personnel through SMS, email, system pop-up window prompts and voice broadcasts; Data storage module: It uses distributed storage technology and an efficient database management system to store collected raw financial data, pre-processed data, and risk assessment results securely and reliably, and establishes a data backup and recovery mechanism; Visualization module: Use data visualization tools such as Echarts, Tableau, and PowerBI to display financial data and risk assessment results in the form of charts, graphs, and dashboards.

2. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 1 is characterized in that: The data acquisition module comprises: Interface docking unit: establish data interfaces with bank trading systems, securities trading platforms, credit rating agencies, and third-party financial data providers to achieve real-time or scheduled data collection; Data crawling unit: obtains financial data from designated data sources according to preset rules through web crawler technology.

3. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 1 is characterized in that: The data preprocessing module comprises: Data cleaning unit: Use outlier detection algorithms and data filling algorithms based on machine learning to remove noise, outliers, and duplicate data from the data, and fill in missing values ​​reasonably; Data conversion unit: uses data conversion technology to convert unstructured data into structured data and encodes non-numerical data into numerical form; Normalization processing unit: Use the normalization algorithm to uniformly scale data of different magnitudes and distributions to a specific range to eliminate the dimensional differences between the data; specifically, the minimum and maximum normalization algorithm is used, and the formula is: Where X is the original data, and are the minimum and maximum values ​​in the data set, respectively. is the normalized data; and the ZScore standardization algorithm, the formula is: ,in is the mean of the data set, is the standard deviation; when text data is converted into numerical vectors, the word vector model Word2Vec is used, and its core formula is: , where E represents the loss function, W represents the target word, C represents the corpus, and c represents the context word of the target word w. It represents the probability of the context word c appearing when the target word W is given.

4. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 1 is characterized in that: The feature calculation module comprises: Debt-paying ability indicator calculation unit: Based on financial theory, calculate the debt-paying ability indicators of asset-liability ratio and interest coverage ratio; asset-liability ratio = total liabilities / total assets; interest coverage ratio = profit before interest and tax / interest expense; Profitability index calculation unit: calculate profitability indexes of return on net assets and gross profit margin; return on net assets = net profit / net assets; gross profit margin = (operating income-operating costs) / operating income; Market sensitivity index calculation unit: The beta coefficient is obtained by performing regression analysis on the asset yield and the market portfolio yield, and the market sensitivity index of duration is calculated based on the bond's cash flow and yield to maturity.

5. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 1 is characterized in that: The risk assessment module includes: Algorithm Fusion Unit: Integrates artificial intelligence algorithms such as logistic regression, decision tree, support vector machine, neural network, and deep learning models, and takes advantage of different algorithms to process complex data and mine nonlinear relationships; Parameter acquisition unit: real-time acquisition of volatility, market uncertainty index, and dynamic parameters of macroeconomic indicators; Weight calculation unit: according to the dynamic parameters, the weight of each risk indicator is calculated by a dynamic weight formula, and the dynamic weight formula is: , where i=1, 2, 3, V is volatility, U is the market uncertainty index, is a very small positive number, It is a pre-set basic weight coefficient, which can be adjusted dynamically according to actual needs and market changes. The value of Risk score calculation unit: multiply the calculated risk indicator values ​​by the corresponding weights and add them up to obtain the risk score.

6. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 1 is characterized in that: The early warning module comprises: Threshold comparison unit: compares the risk score calculated by the risk assessment module with the preset risk threshold; Warning sending unit: When the risk score exceeds the threshold, warning information is sent to relevant personnel via SMS, email, system pop-up prompts and voice broadcasts.

7. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 1 is characterized in that: The data storage module comprises: Distributed storage architecture building unit: Use distributed storage technology to build a data storage cluster and store data in multiple nodes; Database management unit: Select a suitable database management system to store the collected original financial data, pre-processed data and risk assessment results safely and reliably, and establish a data backup and recovery mechanism.

8. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 1 is characterized in that: The visualization module comprises: Tool selection and configuration unit: Select at least one data visualization tool from Echarts, Tableau, and PowerBI, and configure it accordingly; Visualization display unit: presents financial data and risk assessment results in the form of intuitive and easy-to-understand charts, graphs and dashboards; Interactive function implementation unit: provides interactive functions of data screening, indicator comparison, and trend analysis.

9. The financial big data risk analysis and early warning system based on artificial intelligence according to claim 5 is characterized in that: The system has self-learning and adaptive capabilities, and can automatically optimize artificial intelligence algorithm models and dynamic weight formulas based on changes in the financial market and historical risk data, thereby continuously improving the accuracy of risk assessment and early warning.

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