A financial early warning system

Abstract

The application is suitable for the technical field of financial early warning, and provides a financial early warning system, comprising a heterogeneous data fusion module, a dynamic knowledge graph construction module, a risk perception and prediction module and a self-adaptive early warning generation module. The system solves the technical problem that the existing financial early warning system has a single data dimension, is difficult to depict a risk correlation network, leads to one-sided early warning, has insufficient early warning capability for "contagious" crises caused by associated risks, and has a static and rigid early warning model, which lacks self-adaptation and evolution capability, leading to gradual degradation of long-term early warning performance, poor explainability of early warning results, limited decision support, and inability to develop accurate and effective risk mitigation measures, greatly weakening the actual decision support value of the early warning system. The application achieves the technical effects of accurate, explainable and self-adaptive early warning of associated and dynamic financial risks.

Description

Technical Field

[0001] This invention relates to the field of financial early warning technology, and more specifically, to a financial early warning system. Background Technology

[0002] Financial early warning systems are a core component of modern financial institutions' risk management systems. Their goal is to identify potential risk signals in advance through the monitoring and analysis of relevant data, thereby gaining valuable time for risk prevention and disposal. With the increasing complexity of financial markets and the acceleration of globalization, the risks faced by enterprises and the financial system are increasingly characterized by their interconnectedness, concealment, and suddenness across entities, markets, and industries.

[0003] Currently, financial risk early warning systems or methods on the market mainly suffer from the following technical problems: Existing financial early warning systems suffer from limited data dimensions, making it difficult to depict risk correlation networks. This results in a one-sided early warning perspective and insufficient ability to warn of "contagious" crises triggered by risks from related parties. Furthermore, the static and rigid early warning models lack adaptability and evolutionary capabilities, which may lead to a gradual decline in their long-term early warning performance. Additionally, the poor interpretability of early warning results and limited decision support capabilities prevent the development of precise and effective risk mitigation measures, significantly weakening the actual decision support value of the early warning system. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a financial early warning system that solves the technical problems mentioned in the background section by employing a heterogeneous data fusion module, a dynamic knowledge graph construction module, a risk perception and prediction module, and an adaptive early warning generation module.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A financial early warning system includes a heterogeneous data fusion module, a dynamic knowledge graph construction module, a risk perception and prediction module, and an adaptive early warning generation module. The heterogeneous data fusion module collects multi-dimensional risk-related data of target entities from structured and unstructured data sources, and performs spatiotemporal alignment and semantic association. The heterogeneous data fusion module is communicatively connected to the dynamic knowledge graph construction module, which constructs a temporal knowledge graph containing target entities, related parties, industry elements, and risk event nodes and their dynamic relationships based on the multi-dimensional risk-related data. The dynamic knowledge graph construction module is also communicatively connected to the risk perception and prediction module, which includes a graph neural network submodule and a reinforcement learning decision submodule. The graph neural network submodule extracts structural and temporal features from the temporal knowledge graph, and the reinforcement learning decision submodule dynamically adjusts early warning strategy parameters based on the current risk status, historical early warning effects, and external environmental feedback. The adaptive early warning generation module integrates the outputs of the risk perception and prediction module.

[0006] The present invention is further configured such that: the dynamic knowledge graph construction module is specifically used to: identify and extract entities, attributes and relationships from the multi-dimensional risk-related data; Add a timestamp attribute to the relationship to characterize the time when the relationship was established, changed, or terminated; Incremental updates to the knowledge graph are performed based on event-driven mechanisms, where the events include at least one of the following: equity changes, major litigation, regulatory penalties, and changes in key personnel.

[0007] The present invention is further configured such that: the graph neural network submodule is a temporal graph attention network, which generates a dynamic representation vector of the target entity at the current moment by aggregating information of neighboring nodes in multiple historical time slices.

[0008] The present invention is further configured such that: the reinforcement learning decision submodule includes: The state space is defined as the current risk feature vector extracted from the temporal knowledge graph and graph neural network submodule; Action space is defined as the adjustment action of at least one parameter among the warning threshold, warning level classification rules, and push object combination; The reward function is calculated based on a combination of the accuracy and timeliness of the alert, and the positive and negative proportions of user feedback.

[0009] The present invention is further configured such that the heterogeneous data fusion module includes data cleaning, data transformation, data matching and alignment, and data quality assessment and processing.

[0010] The present invention is further configured such that: the explanation report generated by the adaptive early warning generation module includes: A visualization of the risk transmission path, showing how risks are transmitted from the source to the target entity; Ranking of key risk factors by contribution; This warning is compared with similar historical cases.

[0011] The present invention is further configured to include a privacy-preserving computing unit, which is used to perform risk feature calculation without exposing the original data when the heterogeneous data fusion module collects data involving the privacy of multiple parties, employing at least one of federated learning, secure multi-party computation, or homomorphic encryption technology.

[0012] The present invention is further configured such that: the multi-dimensional risk-related data from structured data sources and unstructured data sources are fused, and spatiotemporal alignment and semantic association are performed; Based on the aforementioned multi-dimensional risk-related data, a time-series knowledge graph reflecting the dynamic relationships between entities is constructed and maintained; Graph neural networks are used to extract spatiotemporal features from the temporal knowledge graph, and reinforcement learning dynamic decision-making mechanisms are combined to assess the current risk status and optimize the early warning strategy. Based on a dynamically optimized early warning strategy, tiered early warning signals and corresponding visual explanation reports are generated.

[0013] The advantages of this invention are: 1. Through a dynamic knowledge graph construction module, this invention not only establishes a vast relationship network covering target entities, related parties, industry elements, and risk events, but more importantly, it assigns timestamp attributes to all relationships, constructing a temporal knowledge graph. This enables the system to historically and dynamically track changes in risk relationships. Furthermore, combined with a graph neural network submodule, the system can aggregate risk information of target entities across multiple historical time slices and multi-hop related neighbors, thereby deeply capturing the spatiotemporal transmission patterns and lag effects of risks. This achieves a leap from "point-based" early warning to "networked, forward-looking" early warning, significantly improving the comprehensiveness and predictability of early warnings.

[0014] 2. This invention introduces a reinforcement learning decision-making submodule, modeling the early warning process as a dynamic decision problem. Through continuous trial and error and learning, the system can autonomously and dynamically adjust and optimize the early warning strategy parameters based on historical early warning effects and external environmental feedback, making them increasingly accurate. This achieves the transformation from "static rules" to "dynamic intelligent strategies," giving the system powerful self-adaptation and continuous evolution capabilities, and effectively reducing false alarms and missed alarms.

[0015] 3. This invention, through an adaptive early warning generation module, simultaneously generates a visual explanation report while outputting tiered early warning signals, thereby enhancing the interpretability and traceability of the early warning results. This provides a strong and operable basis for risk management and decision-making, and improves the practical value of early warning.

[0016] 4. This invention integrates data from different types and sources, such as financial reports, transactions, and news, into unified, high-quality, multi-dimensional risk-related data through a heterogeneous data fusion module. This solves the problem of data silos, lays a solid and comprehensive data foundation for subsequent in-depth analysis, and fully releases the collaborative value of multi-source data.

[0017] 5. By enabling privacy-preserving computing units and employing advanced cryptographic technologies such as federated learning and secure multi-party computation, this invention enables multiple parties to collaboratively complete risk feature calculations and model training without sharing the original plaintext data. This achieves "data usable but not visible," fully leveraging the value of data for risk warning while strictly ensuring data security and privacy compliance. It is particularly suitable for scenarios requiring cross-institutional data collaboration, such as supply chain finance and joint risk control. Attached Figure Description

[0018] Figure 1 This is a flowchart of a financial early warning system according to the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0021] In this invention, unless otherwise stated, the directional terms such as "up" and "down" generally refer to the directions shown in the accompanying drawings, or to the vertical, perpendicular, or gravitational direction; similarly, for ease of understanding and description, "left" and "right" generally refer to the left and right shown in the accompanying drawings; "inner" and "outer" refer to the inner and outer contours of each component itself, but the above directional terms are not intended to limit this invention.

[0022] Example 1, please refer to Figure 1This invention provides the following technical solution: a financial early warning system, specifically, a financial early warning system comprising a heterogeneous data fusion module, a dynamic knowledge graph construction module, a risk perception and prediction module, and an adaptive early warning generation module; the heterogeneous data fusion module is used to collect multi-dimensional risk-related data of target entities from structured and unstructured data sources, and perform spatiotemporal alignment and semantic association; the heterogeneous data fusion module is communicatively connected to the dynamic knowledge graph construction module, which is used to construct a temporal knowledge graph containing target entities, related parties, industry elements, and risk event nodes and their dynamic relationships based on the multi-dimensional risk-related data; the dynamic knowledge graph construction module is communicatively connected to the risk perception and prediction module, which includes a graph neural network submodule and a reinforcement learning decision submodule, the graph neural network submodule being used to extract structural and temporal features from the temporal knowledge graph, and the reinforcement learning decision submodule being used to dynamically adjust early warning strategy parameters based on the current risk status, historical early warning effects, and external environmental feedback; the adaptive early warning generation module is used to synthesize the output of the risk perception and prediction module.

[0023] Example 2, please refer to Figure 1This second embodiment improves upon the first embodiment as follows: Specifically, the dynamic knowledge graph construction module is used to: identify and extract entities, attributes, and relationships from multi-dimensional risk-related data; add timestamp attributes to relationships to characterize the establishment, modification, or termination time of relationships; incrementally update the knowledge graph based on event-driven processes, where events include at least one of: equity changes, major litigation, regulatory penalties, and changes in core personnel; the graph neural network submodule is a temporal graph attention network, which generates a dynamic representation vector of the target entity at the current moment by aggregating information from neighboring nodes across multiple historical time slices; the reinforcement learning decision-making submodule includes: a state space, defined as the current risk feature vector extracted from the temporal knowledge graph and the graph neural network submodule; an action space, defined as the adjustment action of at least one parameter among the warning threshold, warning level classification rules, and push object combination; a reward function, calculated comprehensively based on the accuracy, timeliness, and positive / negative ratio of user feedback; and a heterogeneous data fusion module including data cleaning, data... The system includes data transformation, matching and alignment, and data quality assessment and processing. The adaptive early warning generation module generates an explanatory report, including: a visualization of the risk transmission path, showing how the risk is transmitted from the source to the target entity; a ranking of the contribution of key risk factors; a comparative analysis of this early warning with similar historical cases; a privacy-preserving computation unit, used to calculate risk features without exposing the original data when collecting data involving the privacy of multiple parties in the heterogeneous data fusion module, employing at least one of federated learning, secure multi-party computation, or homomorphic encryption technology; fusing multi-dimensional risk-related data from structured and unstructured data sources, and performing spatiotemporal alignment and semantic association; constructing and maintaining a temporal knowledge graph reflecting the dynamic relationships between entities based on multi-dimensional risk-related data; extracting spatiotemporal features from the temporal knowledge graph using graph neural networks, and combining this with a reinforcement learning dynamic decision-making mechanism to assess the current risk status and optimize the early warning strategy; and generating tiered early warning signals and corresponding visual explanatory reports based on the dynamically optimized early warning strategy. The specific working principle of this invention is as follows: After the financial system is started, the heterogeneous data fusion module first collects data from various data sources in real time or at regular intervals. These data sources include: structured data sources (such as corporate financial statements, stock trading data, business registration information, court judgment databases, etc., which have clear fields and structures) and unstructured data sources (such as financial news, listed company announcements, social media information, analyst reports, etc., which exist in non-standardized forms such as text and images). Subsequently, the heterogeneous data fusion module begins to perform the following key processing on the raw data collected above. The specific processing process includes: data cleaning (removing invalid, duplicate, and abnormal data). According to, data transformation (extracting key entities (such as company names, personal names, and events) and sentiment from unstructured text data (such as news) using natural language processing (NLP) technology), spatiotemporal alignment and semantic association (aligning data from different sources and at different times around a unified "target entity" (such as the company to be monitored, Company A), and establishing semantic associations between it and "related parties" (such as supplier B and subsidiary C), "industry elements" (such as industry prosperity index), and "risk events" (such as litigation). For example, associating and integrating information such as stock price declines, negative news, and major shareholder reductions within the same time window to form a multi-dimensional risk-related data set). Next, the dynamic knowledge graph construction module receives the fused data, performs knowledge extraction and graph construction, and automatically identifies entities such as companies, people, products, and events from the data, as well as the relationships between them such as equity, transactions, litigation, and appointments. It records the attributes of each entity (such as registered capital and revenue) and adds precise timestamp attributes (such as the start date and change date of "shareholding relationship") to each "relationship". This transforms the static relationship network into a time-series knowledge graph that reflects historical evolution. Then, through event-driven incremental updates, the system continuously monitors new data. When a predefined major event occurs (such as equity change, major litigation, regulatory penalties, or changes in core personnel), the incremental update of the knowledge graph is automatically triggered, inserting new nodes and new relationships, and updating the status of existing relationships (such as updating "ongoing litigation" to "judged"). This ensures that the graph always maintains a dynamic depiction of the latest risk-related network. Next, the system begins risk perception and decision-making based on graph neural networks and reinforcement learning. The risk perception and prediction module is the core of the system's intelligence. It takes a temporal knowledge graph as input and performs deep analysis. During the analysis, the graph neural network submodule (spatiotemporal feature extraction) employs a temporal graph attention network. This network not only focuses on the current direct associations of the target entity but also aggregates information from its neighboring nodes (such as related companies and individuals) across multiple historical time slices. Through this mechanism, the model can capture the dynamic patterns of risk propagation along the association network and the time lag effect, ultimately generating a dynamic representation vector that comprehensively characterizes the risk status of the target entity at the current moment. Subsequently, the reinforcement learning decision-making submodule (adaptive policy optimization) simulates the learning process of an agent, aiming to find the optimal early warning policy. The "dynamic representation vector" generated in the previous step describes the current environment (risk status), where reinforcement learning decision-making... The "actions" mentioned in the submodule refer to adjustable warning parameters, such as raising or lowering the warning threshold, modifying the warning level classification rules (e.g., tightening the threshold value of a certain indicator), and adjusting the target combination of warning pushes (e.g., adding pushes to the risk control director). Then, the "rewards" mentioned in the reinforcement learning decision-making submodule are calculated based on the actual effect after each warning action. The reward function comprehensively considers the accuracy of the warning (whether it hits the real risk), timeliness (whether it is issued before the risk occurs), and the positive / negative feedback ratio of users. At the same time, the "decision loop" mentioned in the reinforcement learning decision-making submodule is that the system selects an "action" to adjust the strategy and generate a warning based on the current "state". Subsequently, the effectiveness of the action is evaluated based on the "reward" generated by the warning, and its decision model is updated. Through continuous trial and error and learning, the system can dynamically adjust its warning strategy parameters, making it increasingly adaptable to the complex and ever-changing market environment and reducing false alarms and missed alarms. Following this, the adaptive early warning generation module receives the decision results (i.e., the optimized early warning strategy and risk assessment) from the risk perception and prediction module, and generates tiered early warning signals. Simultaneously, based on dynamically optimized thresholds and rules, it determines the risk level of the target entity and generates clear tiered early warning signals (e.g., "Red - Extremely High Risk," "Orange - High Risk," "Yellow - Attention," etc.), which are pushed to relevant user terminals. A visual explanatory report is then generated. To address the "black box" problem of traditional early warning systems, this module automatically generates a detailed explanatory report. The system includes: a visualization of the risk transmission path (intuitively showing how the identified risks are transmitted from the source (such as a supplier in crisis) to the target entity through equity chains, guarantee chains, transaction chains, etc.), a ranking of the contribution of key risk factors (quantifying and listing the factors that have the greatest impact on the warning results (such as "sharp increase in the debt-to-asset ratio" contributing 35%, and "the actual controller being listed as an enforcement target" contributing 28%), helping users quickly grasp the key points), and a comparative analysis of similar historical cases (comparing the current situation with similar historical risk cases stored in the knowledge graph, providing historical handling results as a reference to assist decision-making). Next, when the system needs to process data involving customer privacy, trade secrets, or sensitive data from multiple parties (such as transaction data of various participants in supply chain finance), it can enable a privacy-preserving computing unit. This unit uses one or more of the following technologies: federated learning, secure multi-party computation, or homomorphic encryption. This allows the data from all parties to be processed collaboratively without leaving the local area or being aggregated in plaintext. This fully leverages the value of the data while strictly ensuring data privacy and security compliance. Finally, the system's learning process does not end after the warning is issued. At this point, the user's confirmation, ignoring, or feedback on the warning (such as "effective" or "false alarm"), as well as the actual occurrence of subsequent risk events, will all be collected as new "historical warning effects and external environment feedback" and fed back into the reward function of the reinforcement learning decision-making submodule. At the same time, the newly occurring risk events themselves will also serve as new data, triggering incremental updates to the knowledge graph. This forms a complete closed loop from "perception, decision-making, action, and feedback," driving the entire system to continuously evolve and constantly improve the accuracy and adaptability of the warnings. Through the above overall process, the process of processing raw data into intelligent early warning is automated, dynamic, and interpretable, significantly improving the timeliness, accuracy, and decision support capabilities of financial risk early warning.

[0024] Obviously, the embodiments described above are merely some, not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0027] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0028] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A financial early warning system, characterized in that: It includes a heterogeneous data fusion module, a dynamic knowledge graph construction module, a risk perception and prediction module, and an adaptive early warning generation module; The heterogeneous data fusion module is used to collect multi-dimensional risk-related data of target entities from structured and unstructured data sources, and to perform spatiotemporal alignment and semantic association. The heterogeneous data fusion module is communicatively connected to the dynamic knowledge graph construction module. The dynamic knowledge graph construction module is used to construct a time-series knowledge graph containing target entities, related parties, industry elements, and risk event nodes and their dynamic relationships based on the multi-dimensional risk-related data. The dynamic knowledge graph construction module is communicatively connected to the risk perception and prediction module. The risk perception and prediction module includes a graph neural network submodule and a reinforcement learning decision submodule. The graph neural network submodule is used to extract structural features and temporal features from the temporal knowledge graph. The reinforcement learning decision submodule is used to dynamically adjust the warning strategy parameters based on the current risk status, historical warning effects, and external environmental feedback. The adaptive early warning generation module is used to integrate the output of the risk perception and prediction module.

2. The financial early warning system according to claim 1, characterized in that: The dynamic knowledge graph construction module is specifically used to: identify and extract entities, attributes and relationships from the multi-dimensional risk-related data; Add a timestamp attribute to the relationship to characterize the time when the relationship was established, changed, or terminated; Incremental updates to the knowledge graph are performed based on event-driven mechanisms, where the events include at least one of the following: equity changes, major litigation, regulatory penalties, and changes in key personnel.

3. A financial early warning system according to claim 2, characterized in that: The graph neural network submodule is a temporal graph attention network, which generates a dynamic representation vector of the target entity at the current moment by aggregating information from neighboring nodes across multiple historical time slices.

4. A financial early warning system according to claim 3, characterized in that: The reinforcement learning decision-making submodule includes: The state space is defined as the current risk feature vector extracted from the temporal knowledge graph and graph neural network submodule; Action space is defined as the adjustment action of at least one parameter among the warning threshold, warning level classification rules, and push object combination; The reward function is calculated based on a combination of the accuracy and timeliness of the alert, and the positive and negative proportions of user feedback.

5. A financial early warning system according to claim 4, characterized in that: The heterogeneous data fusion module includes data cleaning, data transformation, data matching and alignment, and data quality assessment and processing.

6. A financial early warning system according to claim 5, characterized in that: The explanation report generated by the adaptive early warning generation module includes: A visualization of the risk transmission path, showing how risks are transmitted from the source to the target entity; Ranking of key risk factors by contribution; This warning is compared with similar historical cases.

7. A financial early warning system according to any one of claims 1-6, characterized in that: It also includes a privacy-preserving computing unit, which is used to perform risk feature calculations without exposing the original data when the heterogeneous data fusion module collects data involving the privacy of multiple parties, using at least one of federated learning, secure multi-party computation, or homomorphic encryption technology.

8. A financial early warning system according to claim 7, characterized in that: The system integrates multi-dimensional risk-related data from both structured and unstructured data sources, and performs spatiotemporal alignment and semantic association. Based on the aforementioned multi-dimensional risk-related data, a time-series knowledge graph reflecting the dynamic relationships between entities is constructed and maintained; Graph neural networks are used to extract spatiotemporal features from the temporal knowledge graph, and reinforcement learning dynamic decision-making mechanisms are combined to assess the current risk status and optimize the early warning strategy. Based on a dynamically optimized early warning strategy, tiered early warning signals and corresponding visual explanation reports are generated.

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