Financial risk real-time early warning system based on neural network

Abstract

The invention relates to the technical field of financial risk early warning, in particular to a financial risk real-time early warning system based on a neural network. Weight division is performed according to importance of different risk factors and influence degrees on financial risks by introducing weight processing technologies such as an attention mechanism, so that a neural network model can focus on key factors during risk prediction. The method of dividing weights according to different conditions significantly improves the accuracy and rationality of risk prediction, ensures that the model can identify and evaluate financial risks more accurately, and provides more reliable decision support for financial institutions.

Description

Technical Field

[0001] The present invention relates to the technical field of financial risk early warning, and in particular to a real-time financial risk early warning system based on a neural network. Background Art

[0002] The financial industry, at the core of the modern economic system, involves a wide range of economic activities and significant capital flows. With the acceleration of global economic integration and the increasing complexity of financial markets, financial institutions face multiple risk challenges, including market volatility, credit defaults, and illiquidity. In recent years, the rapid development of financial technology and the continued advancement of financial innovation have led to a greater diversity in the types of financial products and trading methods, while also introducing new types of risks and uncertainties.

[0003] Financial risk refers to the potential for financial institutions to suffer losses or operational difficulties due to various internal and external factors during financial activities. Common financial risks include market risk (such as stock price fluctuations and exchange rate fluctuations), credit risk (such as corporate defaults and debtor defaults), and liquidity risk (such as poor capital turnover and insufficient market liquidity). These risks can not only cause direct economic losses to financial institutions but also trigger systemic risks, affecting the stable operation of the entire financial market.

[0004] Traditional financial risk prediction methods are usually difficult to accurately capture the complex relationships between different risk factors, and have limitations when processing multi-source data. The present invention introduces weight processing technologies such as attention mechanisms to dynamically assign weights based on the importance of different risk factors and the degree of their impact on financial risks. This method enables the model to adapt more flexibly to different market environments and risk scenarios, significantly improving the accuracy and pertinence of risk predictions. In financial risk warnings, the introduction of this weighting mechanism can not only help financial institutions identify potential risks more accurately, but also issue warnings in a timely manner when risks have not yet fully manifested, providing financial institutions with a valuable decision-making time window, thereby effectively reducing risk losses and improving the overall stability of the financial system. Summary of the Invention

[0005] To achieve the above objectives, the present invention proposes a real-time early warning system for financial risks based on neural networks, which includes a data collection layer, a data preprocessing layer, a feature extraction and weight processing layer, a risk assessment and early warning layer, and a user interaction layer;

[0006] The data collection layer includes financial market data collection, corporate financial statement data collection, and news information and public opinion data collection. It is used to collect financial market data, corporate financial statement data, news information and public opinion data;

[0007] The data preprocessing layer includes data cleaning, data denoising, data normalization and text data processing;

[0008] The feature extraction and weight processing layer includes neural network model selection, feature extraction and learning, weight processing mechanism, and weight optimization and adjustment;

[0009] The risk assessment and early warning layer includes the construction of a risk assessment indicator system, the calculation of risk scores, the setting and adjustment of risk thresholds, and the generation and release of early warning information. It is used to build a risk assessment indicator system, calculate risk scores, set and adjust risk thresholds, and generate and release early warning information;

[0010] The user interaction layer is used to design the user interface, provide risk query and analysis functions, and provide feedback and customization functions.

[0011] In one example, the data cleaning processes data with missing values, erroneous values, and duplicate values, selects interpolation method to fill missing values ​​based on the continuity and correlation of the data, sets reasonable data ranges and logical rules for error values ​​to detect and correct them, deduplicates duplicate values, and performs quality inspection on the cleaned data through a data quality assessment algorithm.

[0012] In one example, the data denoising uses a filtering algorithm to smooth the time series data, uses statistical methods to calculate the mean and standard deviation indicators, detects and removes outliers, and applies a wavelet transform denoising algorithm to further improve the purity of the data.

[0013] In one example, in the data normalization, a normalization method is used to uniformly map the data to a specific interval, and the comparability and consistency of the data are ensured through the normalization algorithm.

[0014] In one example, the text data processing performs a series of processing on the collected news information and public opinion text data to convert them into numerical features that can be recognized by neural networks. First, word segmentation technology is used to divide the text into words, and then word vector embedding technology is used to map the words to a high-dimensional vector space to generate a text feature matrix. The word segmentation algorithm and word vector training algorithm in natural language processing are used to achieve efficient conversion of text data.

[0015] In one example, in the selection of the neural network model, the system flexibly selects different neural network models according to the type of financial data and the risk prediction target. In feature extraction and learning, the system inputs the preprocessed data into the selected neural network model for training, and the model automatically learns the key features in the data. In financial risk prediction, the importance of different risk factors varies. The attention mechanism weight processing technology is introduced in the weight processing mechanism to assign weights to different input features.

[0016] In one example, the risk score calculation is based on the feature weights obtained by the feature extraction and weight processing layer. The system combines the various indicator values ​​in the risk assessment indicator system and calculates the financial risk score by weighted summation. The weighted summation algorithm is used to comprehensively quantify the risk indicators to obtain an accurate risk score.

[0017] In one example, in the setting and adjustment of the risk threshold, a cluster analysis algorithm and a decision tree algorithm are used to achieve reasonable setting and dynamic adjustment of the risk threshold.

[0018] In one example, during the generation and release of the warning information, when the risk score reaches or exceeds a preset threshold, the system immediately triggers the warning mechanism and promptly releases the warning information to risk management personnel of the financial institution through multiple channels.

[0019] The neural network-based real-time financial risk early warning system proposed by the present invention can bring the following beneficial effects:

[0020] 1. This invention incorporates weighting techniques, such as the attention mechanism, to assign weights to different risk factors based on their importance and impact on financial risk, enabling the neural network model to focus on key factors when making risk predictions. This approach of assigning weights based on different circumstances significantly improves the accuracy and rationality of risk predictions, ensuring that the model can more accurately identify and assess financial risks, providing more reliable decision support for financial institutions.

[0021] 2. This invention integrates multiple types of data, including financial market data, corporate financial statement data, news information, and public opinion data, and uses advanced data processing technology for fusion and analysis. Compared with traditional single-source data analysis methods, this multi-source data fusion approach can provide more comprehensive and richer risk information, effectively avoiding the prediction blind spots caused by a single data source. By comprehensively utilizing this data, the system can capture market dynamics and potential risk factors more promptly and accurately, thereby significantly improving the comprehensiveness and timeliness of financial risk warnings and helping financial institutions better respond to various risk challenges in a complex market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 This is a system architecture diagram of a real-time early warning system for financial risks based on neural networks; DETAILED DESCRIPTION

[0024] In order to more clearly illustrate the overall concept of the present invention, a detailed description is given below in an exemplary manner in conjunction with the accompanying drawings.

[0025] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0026] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0027] In the present invention, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0028] In the present invention, unless otherwise clearly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the descriptions with reference to the terms "one scheme", "some schemes", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the scheme or example are included in at least one scheme or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same scheme or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more schemes or examples.

[0029] like Figure 1 As shown, the present invention proposes a real-time early warning system for financial risks based on neural networks, which includes a data collection layer, a data preprocessing layer, a feature extraction and weight processing layer, a risk assessment and early warning layer, and a user interaction layer.

[0030] The data collection layer includes financial market data collection, corporate financial statement collection, and news information and public opinion data collection. In the financial market data collection system, the system obtains accurate market data in real time by connecting to the official data interfaces of major stock exchanges, foreign exchange trading platforms, and financial data suppliers such as Bloomberg and Reuters. At the same time, it uses web crawler technology to capture supplementary data from public channels such as financial information websites at a preset time frequency.

[0031] Corporate financial statement collection regularly collects these data from corporate official websites, information disclosure platforms designated by securities regulatory authorities, and professional financial data service providers, and parses and standardizes financial statements in different formats through automated programs. With the help of data parsing and conversion algorithms, efficient processing of financial statement data is achieved.

[0032] The news information and public opinion data collection system uses web crawler technology to capture finance-related news articles, posts, comments and other data from major news websites, social media platforms, and financial information platforms, and uses text classification and keyword extraction algorithms in natural language processing technology to perform preliminary screening and classification of these text data and extract content containing specific financial keywords and topics.

[0033] The data preprocessing layer includes data cleaning, data denoising, data normalization and text data processing. Data cleaning processes data with missing values, erroneous values ​​and duplicate values. For missing values, interpolation method is selected to fill or samples are directly deleted based on the continuity and correlation of the data. Reasonable data ranges and logical rules are set for erroneous values ​​to detect and correct them. Duplicate values ​​are deduplicated. Through data quality assessment algorithms, the cleaned data is quality tested to ensure data reliability.

[0034] Data denoising uses filtering algorithms to smooth time series data, uses statistical methods to calculate indicators such as mean and standard deviation, detects and eliminates outliers, and applies denoising algorithms such as wavelet transform to further improve the purity of the data.

[0035] In data normalization, since the dimensions and value ranges of different financial data vary greatly, in order to enable the neural network to process this data efficiently, the system uses a normalization method to uniformly map the data to specific intervals such as [0,1] or [-1,1]. Through the normalization algorithm, the comparability and consistency of the data are ensured.

[0036] Text data processing: The system performs a series of processing on the collected news information and public opinion text data to convert them into numerical features that can be recognized by neural networks. First, word segmentation technology is used to divide the text into words or phrases, and then word vector embedding technology is used to map words to a high-dimensional vector space to generate a text feature matrix. In this process, the word segmentation algorithm and word vector training algorithm in natural language processing are used to achieve efficient conversion of text data.

[0037] The feature extraction and weight processing layer includes neural network model selection, feature extraction and learning, weight processing mechanism, and weight optimization and adjustment.

[0038] In the selection of neural network models, the system flexibly selects different neural network models according to the type of financial data and the risk prediction target. For image-based financial data, convolutional neural networks are an ideal choice; for time series data, recurrent neural networks and their variants can fully utilize their advantages in processing sequence data. By comparing the performance and applicability of different neural network models, the model architecture that best suits the current financial risk prediction task is selected.

[0039] During feature extraction and learning, the system feeds preprocessed data into a selected neural network model for training, allowing the model to automatically learn key features within the data. For example, in stock price prediction, the LSTM network selectively memorizes and updates feature information related to stock price changes through its internal gating mechanism. When processing news text data, the neural network learns semantic features, sentiment, and other characteristics within the text by adjusting the connection weights between neurons in the hidden layer. Using deep learning algorithms, the model automatically discovers potential features and patterns within the data.

[0040] In financial risk prediction, the importance of different risk factors varies. Weight processing technologies such as the attention mechanism are introduced into the weight processing mechanism to assign weights to different input features, so that the model can focus more on factors that have a greater impact on risks when making risk predictions. By optimizing the attention mechanism algorithm, the accuracy and rationality of weight allocation can be improved.

[0041] Weight optimization and adjustment. During the training process of the neural network model, the system uses backpropagation algorithm and optimization algorithm to optimize and adjust the weights. At the same time, to prevent overfitting, regularization technology is used to constrain and limit the weights, and optimization algorithms such as gradient descent algorithm are used to continuously adjust the weights to improve the prediction accuracy of the model.

[0042] The risk assessment and early warning layer includes the construction of a risk assessment indicator system, the calculation of risk scores, the setting and adjustment of risk thresholds, and the generation and release of early warning information.

[0043] In constructing a comprehensive and scientific risk assessment indicator system, we systematically built a comprehensive and scientific risk assessment indicator system covering multiple aspects, including market risk, credit risk, and liquidity risk. Market risk indicators include volatility and value-at-risk (VaR); credit risk indicators include a company's credit rating, probability of default (PD), and loss given default (LGD); and liquidity risk indicators include asset liquidity ratio and capital turnover rate. Through the algorithm used to construct the risk assessment indicator system, we ensure the integrity and scientific nature of the indicator system.

[0044] The risk score calculation is based on the feature weights obtained by the feature extraction and weight processing layer. The system combines the values ​​of various indicators in the risk assessment indicator system and calculates the financial risk score by weighted summation. The weighted summation algorithm is used to comprehensively quantify the risk indicators to obtain an accurate risk score.

[0045] In setting and adjusting risk thresholds, the system uses a combination of statistical analysis and machine learning algorithms to determine initial risk thresholds based on historical data, industry experience, and the financial institution's risk appetite and tolerance. The system then regularly adjusts these thresholds dynamically using newly collected data. Machine learning algorithms such as cluster analysis and decision tree algorithms are employed to achieve the appropriate setting and dynamic adjustment of risk thresholds.

[0046] During the generation and release of early warning information, when the risk score reaches or exceeds the preset threshold, the system immediately triggers the early warning mechanism and generates detailed and accurate early warning information. This information includes a description of the risk event, the specific value of the risk score, an analysis of the main risk factors and their weightings, the possible scope and extent of the impact, and corresponding risk response recommendations. This information is then promptly released to the risk management personnel of the financial institution through various channels. The early warning information release algorithm ensures the timeliness and effectiveness of early warning information.

[0047] The user interaction layer includes user interface design, risk query and analysis functions, and feedback and customization functions.

[0048] In the user interface design, the user interface is mainly divided into data display area, risk assessment result display area, warning information display area and operation control area. The data display area displays original financial data, pre-processed data and feature extraction results in the form of tables and charts. The risk assessment result display area presents financial risk scores, risk trends, weight distribution of various risk factors and other information in intuitive visual charts. The warning information display area displays in detail the risk events that trigger the warning, warning content and response suggestions, etc. The operation control area provides users with control buttons and input boxes for system parameter setting, data query, threshold adjustment and other functions. The user interface design algorithm is used to improve the friendliness and ease of use of the interface.

[0049] The system provides users with a more in-depth risk analysis tool through risk query and analysis functions. Users can query historical risk data and assessment results by time, risk type, financial institution, industry, and other dimensions through the query function provided by the interface. The system also provides risk factor correlation analysis and risk scenario simulation tools. Using data analysis and mining algorithms, multi-dimensional query and analysis of risk data is possible.

[0050] Feedback and customization functions: the system allows users to input actual risk processing results and feedback information into the system and incorporate them into the model retraining process. In addition, the system provides customization functions, and users can customize the risk indicator system, weight distribution, warning thresholds, etc. according to their own risk management needs and preferences. Through feedback mechanism algorithms and customized algorithms, the system can be personalized and continuously optimized.

[0051] The specific operation steps of the system include the following steps:

[0052] Step 1: Data collection and preprocessing

[0053] S1.1 Data collection strategy formulation: The system formulates a detailed data collection strategy based on the goals and needs of financial risk forecasting, and clarifies key elements such as the scope, frequency, and source of data collection. In this process, the data collection strategy planning algorithm is used to ensure the rationality and effectiveness of the strategy.

[0054] S1.2 Data collection and execution: In accordance with the preset data collection strategy, the system obtains financial-related data from various data sources regularly or in real time through technical means such as web crawlers and financial data interfaces, performs preliminary format checks and organizes the data, and uses data collection and organization algorithms to improve the efficiency and quality of data collection.

[0055] S1.3 Data Preprocessing: Collected data enters the data preprocessing module. For text data, the system performs word segmentation and word embedding techniques to generate a text feature matrix. For time series data, the system performs normalization and serialization. At the same time, all data types are cleaned and denoised. Data preprocessing algorithms ensure data availability and consistency.

[0056] Step 2: Feature extraction and weight learning

[0057] S2.1 Neural Network Model Training Preparation: The preprocessed data is divided into training, validation, and test sets. Based on the data characteristics and risk prediction objectives, the system selects an appropriate neural network model architecture and initializes the model parameters. A model initialization algorithm is used to ensure the model's initial state is reasonable.

[0058] S2.2 Model Training Process: The training set data is input into the neural network model for training. During training, the model automatically learns the features in the data and adjusts the weights of different features based on the goal of financial risk prediction. The model output is calculated through forward propagation, and the error between the predicted results and the actual risk situation is calculated. The error is then propagated back layer by layer using the backpropagation algorithm to update the model parameters. During training, the model is regularly evaluated using the validation set, and the model hyperparameters are adjusted based on the performance indicators on the validation set. Deep learning training algorithms are used to achieve efficient model training and optimization.

[0059] S2.3 Model Evaluation and Optimization: When model training reaches a certain number of iterations or meets specific stopping conditions, a comprehensive performance evaluation of the model is performed using the test set. Based on the evaluation results, if the model performance does not meet the requirements, the system will further optimize the model. Through model evaluation and optimization algorithms, the accuracy and generalization ability of the model are improved.

[0060] Step 3: Risk Assessment and Early Warning

[0061] S3.1 Real-time data acquisition and preprocessing: During system operation, in order to achieve real-time risk assessment and early warning, the system continuously obtains new financial data from various data sources. These real-time data undergo the same preprocessing process as the training data to ensure that the format and quality of the real-time data are consistent with the training data. Real-time data processing algorithms are used to achieve timely data acquisition and preprocessing.

[0062] S3.2 Risk assessment calculation: The preprocessed real-time data is input into the trained neural network model. The model calculates the financial risk score based on the previously learned features and weights, and uses the risk assessment algorithm to accurately quantify and evaluate the risk.

[0063] S3.3 Warning Triggering and Information Generation: The system compares the calculated risk score with the preset risk threshold in real time. When the score reaches or exceeds the threshold, the risk warning mechanism is immediately triggered. The system quickly generates detailed risk warning information based on the warning level and risk type. Using warning triggering and information generation algorithms, we ensure the timeliness and accuracy of warnings.

[0064] S3.4 Early warning information release: The generated risk warning information is promptly released to relevant personnel through multiple channels, and the efficient transmission of early warning information is achieved through information push algorithms.

[0065] Step 4: Results display and feedback

[0066] S4.1 Visualization of Risk Results: The user interface presents financial risk forecast results and warning information in a variety of visual charts. For example, a line chart shows the trend of risk scores over time, a bar chart compares the scores and weight distribution of different risk types, and a map visualizes the risk distribution of different regions or industries. The interface also provides detailed data tables and reports, allowing users to view specific risk indicator values, warning event details, and more. Data visualization algorithms are used to improve the intuitiveness and aesthetics of the results presentation.

[0067] S4.2 User Feedback Collection and System Optimization: After receiving early warning information and performing risk management, users can input their actual risk management results and feedback into the system. The system regularly collects and organizes this feedback, incorporating it into the model retraining process to make targeted adjustments and optimizations to the neural network model and risk warning strategy. Through feedback collection and system optimization algorithms, the system can achieve continuous improvement and upgrades.

[0068] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0069] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A real-time early warning system for financial risks based on neural networks, characterized by: It includes data collection layer, data preprocessing layer, feature extraction and weight processing layer, risk assessment and early warning layer and user interaction layer; The data collection layer includes financial market data collection, corporate financial statement data collection, and news information and public opinion data collection. It is used to collect financial market data, corporate financial statement data, news information and public opinion data; The data preprocessing layer includes data cleaning, data denoising, data normalization and text data processing; The feature extraction and weight processing layer includes neural network model selection, feature extraction and learning, weight processing mechanism, and weight optimization and adjustment; The risk assessment and early warning layer includes the construction of a risk assessment indicator system, the calculation of risk scores, the setting and adjustment of risk thresholds, and the generation and release of early warning information. It is used to build a risk assessment indicator system, calculate risk scores, set and adjust risk thresholds, and generate and release early warning information; The user interaction layer is used to design the user interface, provide risk query and analysis functions, and provide feedback and customization functions.

2. The neural network-based real-time early warning system for financial risks according to claim 1, characterized in that: The data cleaning process is carried out for data with missing values, erroneous values ​​and duplicate values, interpolation method is selected to fill missing values ​​according to the continuity and correlation of the data, reasonable data range and logical rules are set for erroneous values ​​to detect and correct them, duplicate values ​​are deduplicated, and the cleaned data is quality tested through a data quality assessment algorithm.

3. The neural network-based real-time early warning system for financial risks according to claim 1, characterized in that: The data denoising adopts a filtering algorithm to smooth the time series data, uses statistical methods to calculate the mean and standard deviation indicators, detects and removes outliers, and uses a wavelet transform denoising algorithm to further improve the purity of the data.

4. The neural network-based real-time early warning system for financial risks according to claim 1, characterized in that: In the data normalization, a normalization method is used to uniformly map the data to a specific interval, and the comparability and consistency of the data are ensured through the normalization algorithm.

5. The neural network-based real-time early warning system for financial risks according to claim 1, characterized in that: The text data processing performs a series of processing on the collected news information and public opinion text data to convert them into numerical features that can be recognized by the neural network. First, the text is divided into words using word segmentation technology, and then the words are mapped to a high-dimensional vector space using word vector embedding technology to generate a text feature matrix. The word segmentation algorithm and word vector training algorithm in natural language processing are used to achieve efficient conversion of text data.

6. The neural network-based real-time early warning system for financial risks according to claim 1, characterized in that: In the selection of the neural network model, the system flexibly selects different neural network models according to the type of financial data and the risk prediction target. In feature extraction and learning, the system inputs the preprocessed data into the selected neural network model for training. The model automatically learns the key features in the data. In financial risk prediction, the importance of different risk factors varies. The attention mechanism weight processing technology is introduced in the weight processing mechanism to assign weights to different input features.

7. The neural network-based real-time financial risk early warning system according to claim 1, characterized in that: The risk score calculation is based on the feature weights obtained by the feature extraction and weight processing layer. The system combines the values ​​of various indicators in the risk assessment indicator system and calculates the financial risk score by weighted summation. The weighted summation algorithm is used to comprehensively quantify the risk indicators to obtain an accurate risk score.

8. The neural network-based real-time early warning system for financial risks according to claim 1, characterized in that: In the setting and adjustment of the risk threshold, cluster analysis algorithm and decision tree algorithm are used to achieve reasonable setting and dynamic adjustment of the risk threshold.

9. The neural network-based real-time early warning system for financial risks according to claim 1, characterized in that: In the generation and release of the warning information, when the risk score reaches or exceeds the preset threshold, the system immediately triggers the warning mechanism and releases the warning information to the risk management personnel of the financial institution in a timely manner through multiple channels.

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