Financial risk monitoring method

Abstract

The invention discloses a financial risk monitoring method. The method comprises the following steps: collecting financial data of each business system in real time; performing calculation analysis on the financial data by adopting a CNN-LSTM-Attention hybrid neural network model to obtain a calculation analysis result; and performing multi-dimensional matching on the calculation and analysis result, and generating early warning information according to a matching result. According to the method, the CNN-LSTM-Attention hybrid neural network model is adopted to perform real-time dynamic calculation and analysis on the acquired financial data, so that the risk monitoring effect is improved.

Description

Technical Field

[0001] This application belongs to the field of financial risk management technology, and in particular relates to a financial risk monitoring method. Background Technology

[0002] As enterprises deepen their digital transformation, financial risk monitoring has evolved from traditional ex-post supervision to real-time monitoring and predictive early warning.

[0003] Among related technologies, the traditional financial management system represented by SAP FI module is mainly used. However, the analytical capabilities of the traditional financial management system are limited to predefined analytical reports, lack dynamic analysis capabilities, and have poor risk monitoring effects. Summary of the Invention

[0004] The purpose of this application is to provide a financial risk monitoring method to solve the problem of poor risk monitoring effectiveness in related technologies.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: In a first aspect, embodiments of this application provide a financial risk monitoring method, comprising: real-time collection of financial data from various business systems; calculation and analysis of the financial data using a CNN-LSTM-Attention hybrid neural network model to obtain calculation and analysis results; multi-dimensional matching of the calculation and analysis results; and generation of early warning information based on the matching results.

[0006] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: This application embodiment collects financial data from various business systems in real time, employs a CNN-LSTM-Attention hybrid neural network model to calculate and analyze the collected financial data, obtains the calculation and analysis results, performs multi-dimensional matching on the calculation and analysis results, and generates early warning information based on the matching results. This application embodiment uses a CNN-LSTM-Attention hybrid neural network model to perform real-time dynamic calculation and analysis on the collected financial data, improving the effectiveness of risk monitoring. Attached Figure Description

[0007] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a financial risk monitoring method provided in one embodiment of this application; Figure 2 A flowchart illustrating a financial risk monitoring method provided for another embodiment of this application; Figure 3 A flowchart illustrating a financial risk monitoring method provided for another embodiment of this application; Figure 4 A flowchart illustrating a financial risk monitoring method provided for another embodiment of this application; Figure 5 An architecture diagram of a financial risk monitoring system provided as an embodiment of this application; Figure 6 A flowchart of a financial risk monitoring system provided as an embodiment of this application; Figure 7 A user interface design diagram of a financial risk monitoring system provided in one embodiment of this application; Figure 8 A schematic diagram of data flow processing for a financial risk monitoring system provided in one embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0009] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, "and / or" in this application indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship. It should be noted that all data involved in this application was obtained with the user's authorization.

[0010] The technical solutions provided in the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0011] Figure 1 This is a flowchart illustrating a financial risk monitoring method provided as an embodiment of this application. Figure 1 As shown, the financial risk monitoring method of this application embodiment may specifically include the following steps: S101 collects financial data from various business systems in real time.

[0012] In this embodiment, the financial risk monitoring method is implemented by a financial risk monitoring system, which can be installed in an electronic device. This electronic device can be a terminal device or a server. The terminal device can be a mobile phone, tablet computer, desktop computer, laptop, in-vehicle device, etc.; the server can be a standalone server or a server cluster composed of multiple servers. For example, in the insurance business field, this financial risk monitoring system can be installed in an insurance business platform.

[0013] Financial data can be collected in real time from multiple business systems (i.e., data sources) through data synchronization services. These business systems may include, but are not limited to, at least one of the following: sales operations system, core business system, financial management system, investment management system, and comprehensive management system. Financial data may include, but is not limited to, at least one of the following: time-series data (including real-time and historical data) and document data (such as configuration documents and analysis reports). The data synchronization service may employ distributed message queue technology to ensure reliable data transmission.

[0014] To address the diversity and complexity of financial data sources, this application proposes an intelligent data acquisition scheduling algorithm based on reinforcement learning. This algorithm supports multiple acquisition strategies, such as concurrent execution of timed acquisition, incremental acquisition, and event-triggered acquisition, to achieve comprehensive data acquisition. Furthermore, it dynamically adjusts the acquisition strategy by analyzing the historical acquisition patterns, data update frequency, and system load of the data source. For example, a Q-learning algorithm based on reinforcement learning can be used to analyze the historical acquisition patterns, data update frequency, and system load of the data source, optimize the acquisition time window, and combine it with a data importance assessment model to achieve intelligent allocation of acquisition resources, improving data acquisition efficiency and accuracy. The Q-learning algorithm is a model-independent reinforcement learning algorithm that directly optimizes an iteratively computeable Q-function.

[0015] Furthermore, such as Figure 2 As shown, after step S101 "real-time collection of financial data from various business systems", the financial risk monitoring method of this application embodiment may further include the following steps: S201, integrate and verify the financial data collected from various business systems.

[0016] In this embodiment, the multi-source data (i.e., financial data from various business systems) collected in step S101 undergoes rigorous format and integrity verification to ensure consistent data quality. Specifically: a multi-source data verification system based on data lineage is established to identify data anomalies and conflicts by comparing identical indicators from different data sources. A data quality assessment model is constructed using a combination of statistical testing and machine learning algorithms to automatically identify data anomalies, missing data, and duplication. A data quality scoring system is established by combining business rules and expert knowledge to provide a reliable data foundation for subsequent analysis.

[0017] For data that fails verification, a retry mechanism will be triggered to ensure the accuracy and integrity of the data. For data that passes verification, the next steps will be performed (i.e., step S202 will be executed).

[0018] S202, converts the format of the verified financial data.

[0019] In this embodiment, the verified data undergoes standardized transformation to ensure a uniform data format, laying the foundation for subsequent processing. Specifically, to address the format differences in data from different business systems, this embodiment proposes an adaptive data transformation algorithm based on pattern learning. This adaptive data transformation algorithm automatically generates transformation rules by analyzing the structure and semantic information of the source data, supporting complex data mapping and format conversion. It employs template matching and semantic analysis techniques to identify the correspondence between data fields, achieving intelligent data standardization processing.

[0020] S203 performs real-time data stream processing on the converted financial data to prevent data overload.

[0021] In this embodiment, a real-time data processing framework based on a stream computing engine is constructed. A backpressure control mechanism is employed to prevent data backlog, and incremental data processing is achieved through sliding window technology. By combining data compression and batch processing technologies, data transmission and storage efficiency are optimized to ensure stable system operation under high-concurrency scenarios.

[0022] Furthermore, after step S101 "collecting financial data from various business systems in real time" or step S203 "processing the converted financial data in real time data stream", the financial risk monitoring method of this application embodiment may also include the following data storage steps: storing the time-series data in the financial data into a distributed time-series database; storing the document data in the financial data into a document database.

[0023] Specifically, a hybrid storage architecture is adopted to optimize the storage and processing of time-series data and document data within the financial data. Specifically, a dual-track parallel storage mechanism is used to simultaneously write collected or verified time-series data (including real-time and historical data) into a distributed time-series database (TSDB), and a multi-level caching mechanism is implemented through a cache manager to improve data access efficiency. Simultaneously, document data (such as configuration documents and analysis reports, and other unstructured data) is stored in a document database to achieve unified management of unstructured data. This hybrid storage architecture ensures both efficient reading and writing of time-series data and flexible management of document data, providing a reliable data foundation for the high-performance operation of the system. TSDB is a database system specifically designed for processing time-series data.

[0024] Furthermore, regarding time-series data storage, this application's embodiments implement a two-dimensional intelligent sharding strategy based on time and indicator dimensions. By dynamically monitoring data access frequency, it automatically adjusts the shard distribution and supports automatic shard migration, effectively solving the problem of uneven data access. Correspondingly, as... Figure 3 As shown, the above step of "storing time-series data in financial data into a distributed time-series database" may specifically include the following steps: S301, Analyze the temporal and spatial locality of time series data, and use a clustering algorithm to divide the time series data into fragments to obtain fragmented time series data.

[0025] Specifically, considering the characteristics of financial time-series data, this application proposes an intelligent sharding algorithm based on data access patterns. This intelligent sharding algorithm analyzes the temporal and spatial locality of time-series data and uses a clustering algorithm (such as an improved K-means clustering algorithm) to shard the data. It can also combine data access frequency, data size, and query patterns to construct a multi-dimensional feature vector and use a genetic algorithm to optimize the sharding strategy, achieving an optimal balance between load balancing and query performance. Load balancing is a technique for distributing workloads across multiple computing resources.

[0026] S302 uses a consensus algorithm to process the consistency of the time-series data of each shard to obtain consensus data.

[0027] Specifically, addressing the extremely high consistency requirements of financial data, this application proposes an improved RAFT consensus algorithm to ensure data consistency in a distributed environment. High-concurrency read / write operations are achieved through a Multi-Version Concurrency Control (MVCC) mechanism. This improved RAFT consensus algorithm significantly reduces consensus latency by introducing a pre-voting mechanism and fast path optimization. Batch log replication technology is employed to merge multiple operations into a single log entry, improving data throughput. Combined with a heartbeat timeout adaptive adjustment mechanism, timeout parameters are dynamically adjusted based on network conditions, enhancing system stability in complex network environments. RAFT is a distributed consensus algorithm used to ensure data consistency and reliability in distributed systems. MVCC is a database concurrency control method.

[0028] S303 will generate cached data based on consensus data and store the cached data in a distributed time-series database.

[0029] Specifically, this application proposes a multi-level caching optimization strategy. By constructing a multi-level caching system and combining request routing based on consistent hashing with a dynamic weighted load assessment model, rapid data access response is achieved. Specifically, a smart caching algorithm based on a hybrid of Least Recently Used (LRU) and Least Frequently Used (LFU) algorithms can be constructed to dynamically adjust the caching strategy by analyzing data access patterns. Predictive caching technology can be employed to predict data that may be accessed in the future based on historical access patterns and pre-load it into the cache. Furthermore, data compression algorithms can be combined, using LZ4 compression technology to reduce storage space usage while ensuring decompression performance. LRU is a cache eviction algorithm that deletes items that have not been used for the longest time.

[0030] Furthermore, this application also proposes a fault detection and recovery mechanism. When a node failure occurs, the system can quickly complete fault detection and fault-tolerant switching to ensure service continuity. Specifically: a fault detection mechanism based on heartbeat detection and health checks is established, and a majority voting algorithm is used to quickly identify faulty nodes. Data integrity is ensured through data replication and checksum mechanisms, and incremental synchronization technology is used to achieve rapid data recovery. Combined with an automatic failover mechanism, when the master node fails, the system can quickly complete the switchover to ensure service continuity.

[0031] S102 uses a CNN-LSTM-Attention hybrid neural network model to calculate and analyze financial data, and obtains the calculation and analysis results.

[0032] In this embodiment of the application, to address the complexity of financial risk identification, a CNN-LSTM-Attention hybrid neural network model (or architecture) is designed. The CNN-LSTM-Attention hybrid neural network model includes: a Convolutional Neural Network (CNN) layer, a Long Short-Term Memory (LSTM) layer, and an attention mechanism layer. CNN is a deep learning model. LSTM is a special type of recurrent neural network. Attention is an important mechanism in deep learning used to highlight important features.

[0033] The CNN layer in the CNN-LSTM-Attention hybrid neural network model can be used to extract local features of financial data; the LSTM layer in the CNN-LSTM-Attention hybrid neural network model can be used to capture the temporal dependencies between local features; the Attention layer in the CNN-LSTM-Attention hybrid neural network model can be used to adaptively weight the temporal dependencies at key time points to obtain weighted feature data; and the calculation and analysis results are obtained based on the weighted feature data.

[0034] The CNN layer consists of three CNN structures with convolutional kernel sizes of 3×3, 5×5 and 7×7, respectively, used to extract short-term local features, medium-term local features and long-term local features of financial data. Each CNN structure is followed by ReLU activation function and batch normalization with a stride of 1 and padding of the same type.

[0035] The LSTM layer adopts a bidirectional LSTM structure (forward LSTM structure and backward LSTM structure), with 128 hidden units, which are used to capture the forward and backward temporal dependencies between local features, respectively. The random dropout rate is 0.3, and tanh and sigmoid activation functions are used.

[0036] The Attention layer employs an 8-head multi-head attention mechanism, which calculates the attention weight αij=softmax(QKT / √dk)V between the query vector Q, key vector K, and value vector V to achieve adaptive weighting at key time points, where dk is the dimension of the key vector, set to 64.

[0037] This CNN-LSTM-Attention hybrid neural network model optimizes the training effect of deep networks through residual connections, uses the Adam optimizer (learning rate 0.001) and cross-entropy loss function for training, and prevents overfitting by using random inactivation rate (0.3) and batch normalization, thus achieving accurate identification of financial risk patterns.

[0038] It should be noted here that before using the CNN-LSTM-Attention hybrid neural network model for analysis, the indicator calculation engine needs to be used to calculate the indicators based on the collected time series data, and the financial data after indicator calculation is analyzed using the CNN-LSTM-Attention hybrid neural network model.

[0039] Furthermore, such as Figure 4 As shown, the above step "obtaining the calculation and analysis results based on the weighted feature data" may specifically include the following steps: S401 employs a multi-scale feature fusion algorithm based on wavelet packet decomposition to fuse the weighted feature data and obtain the fused feature data.

[0040] Specifically, considering the multi-timescale characteristics of financial data, this application proposes a multi-scale feature extraction algorithm based on wavelet packet decomposition. This algorithm decomposes the weighted feature data into different frequency components using Morlet wavelet transform (a mathematical tool for signal processing and feature extraction). Specifically, it performs a wavelet transform on the input time-series data (i.e., the weighted feature data) x(t) W(a,b)=∫x(t)ψ*((tb) / a)dt, where ψ(t)=π^(-1 / 4)e^(iω0t)e^(-t² / 2) is the Morlet wavelet function, ω0 is the center frequency, a is the scale parameter, and b is the translation parameter. Then, it uses an adaptive thresholding technique, employing a soft thresholding function T(x)=sign(x)max(|x|-λ,0) for denoising (i.e., separating the signal and noise), where λ=σ√(2logN) is the adaptive threshold, σ is the noise standard deviation, and N is the data length. An improved Empirical Mode Decomposition (EMD) algorithm is employed to process non-stationary financial data. Instantaneous frequencies f(t) = dφ(t) / dt and amplitudes A(t) are extracted using Hilbert transform, constructing a multi-dimensional feature vector F = [f1(t), f2(t), ..., fn(t)], where fi(t) is the instantaneous frequency of the i-th intrinsic mode function (IMF) component. Finally, a weighted fusion mechanism W = α1W1 + α2W2 + α3W3 is used, where α1, α2, and α3 are weight coefficients, and W1, W2, and W3 are the short-term, medium-term, and long-term feature weight matrices, respectively, to achieve intelligent fusion of multi-scale features. This algorithm can simultaneously capture short-term fluctuations and long-term trends, balancing the influence of different time scales through an adaptive weighting mechanism W = α1W1 + α2W2 + α3W3, and optimizing through backpropagation.

[0041] S402 employs a financial indicator association graph construction algorithm to build a dynamic association graph based on the fused feature data.

[0042] Specifically, based on the correlation analysis of financial indicators using Graph Neural Networks (GNNs), this application proposes an algorithm for constructing a dynamic correlation graph. This algorithm calculates the mutual information I(X;Y)=∑∑p(x,y)log(p(x,y) / (p(x)p(y))) and the correlation coefficient r=∑(xi- (yi-) ) / √(∑(xi- )²∑(yi- The Granger causal relationship F = (RSSr - RSSu) / RSSu × (Tk) / k is used to construct a dynamic association graph G = (V, E, W), where V is the set of nodes (financial indicators), E is the set of edges (associations), and W is the weight matrix. A Graph Attention Network (GAT) is used to learn node representations, with attention coefficients αij = softmax(LeakyReLU(aT[Whi||Whj])), where a is the attention parameter vector, W is the weight matrix, and hi and hj are the feature representations of nodes i and j, respectively. A multi-head attention mechanism h'i = ||Kk=1σ(∑j∈NiαkijWkhj) captures associations at different levels, where K is the number of attention heads, σ is the activation function, and Ni is the set of neighbors of node i. An improved PageRank algorithm, PR(i) = (1-d) / N + d∑j∈MiPR(j) / L(j), is used to calculate the importance of indicators, where d is the damping coefficient, N is the total number of nodes, Mi is the set of nodes pointing to node i, and L(j) is the out-degree of node j. Prior constraints are constructed by incorporating knowledge from the financial domain, and Bayesian inference P(θ|D)∝P(D|θ)P(θ) is used to improve the accuracy of association analysis. GNN is a deep learning model used to process graph-structured data.

[0043] It should be noted that this application also employs an adaptive feature selection mechanism. Specifically, to address the problem of high dimensionality and redundant information in financial indicators, an adaptive feature selection algorithm based on information theory is proposed. This algorithm identifies redundant and key features by calculating the mutual information I(Xi;Xj) and conditional mutual information I(Xi;Xj|Y) between features. An improved maximum relevance minimum redundancy (mRMR) algorithm is used for feature selection, with the objective function being maxD-R, where D=∑I(Xi;Y) represents relevance and R=∑I(Xi;Xj) represents redundancy. Combining L1 regularization ||W||1=∑|wi| and Dropout technique P(xi=0)=p, sparse representation of features is achieved, improving the model's generalization ability. The loss function L=L0+λ1||W||1+λ2||W||2² is optimized through gradient descent, where L0 is the original loss and λ1 and λ2 are regularization coefficients, achieving adaptive feature selection.

[0044] Furthermore, this application also proposes a model update mechanism based on incremental learning (the ability of a model to continuously learn from new data) and knowledge distillation (a model compression and knowledge transfer technique). Specifically, a complete model update mechanism is established, and knowledge distillation technology is used to achieve continuous learning of the model. Through a teacher-student network architecture, the knowledge of the historical model is transferred to the new model. The knowledge distillation loss function is LKD=∑iKL(σ(ziT / T)||σ(ziS / T)), where ziT and ziS are the outputs of the teacher network and student network, respectively, and T is the temperature parameter. The Elastic Weight Consolidation (EWC) algorithm is used to prevent catastrophic forgetting. The regularization term is L(θ)=L(θ)+∑iλFi(θi-θi*)², where Fi is the Fisher information matrix, and θi* is the optimal parameter of the old task. The federated learning framework supports collaborative training among multiple institutions. The global model is updated using the FedAvg algorithm θt+1=∑k=1Knk / n·θkt+1, which improves model performance while protecting data privacy.

[0045] By establishing a complete model training and update mechanism, adopting a distributed parameter server (ParameterServer, a system architecture for distributed machine learning) architecture for model training, implementing incremental training through a sliding window, and utilizing model distillation technology to maintain knowledge accumulation, continuous optimization of model performance is ensured.

[0046] S403 employs a risk transmission analysis algorithm based on causal reasoning to identify the transmission path and impact intensity of financial risks based on a dynamic correlation diagram.

[0047] Specifically, a risk transmission analysis algorithm based on causal reasoning is employed. Based on a dynamic correlation graph, the algorithm uses Granger causality testing combined with a deep learning model (a branch of machine learning that uses multi-layer neural networks for data processing and feature learning) to identify the transmission path and impact intensity of financial risks. This risk transmission analysis algorithm first uses the Granger causality test F=(RSSr-RSSu) / RSSu×(Tk) / k to determine the causal relationship between indicators, where RSSr is the residual sum of squares of the restricted model, RSSu is the residual sum of squares of the unrestricted model, T is the number of samples, and k is the lag order. Then, a deep learning model is used to learn nonlinear causal relationships, and a CNN-LSTM-Attention architecture is used to extract temporal features, identifying key time points through an attention mechanism. Monte Carlo simulation is used to assess the confidence level of risk transmission. By generating N=10000 random samples, the risk transmission probability P(risk|indicator)=∑i=1NI(risk_i|indicator_i) / N is calculated, where I is the indicator function. A risk transmission network is constructed by combining graph neural networks. Node representations are learned through graph convolution operation h(l+1)=σ(Ãh(l)W(l)), where à is the normalized adjacency matrix, h(l) is the feature of the l-th layer node, W(l) is the weight matrix, and σ is the activation function, providing a scientific basis for risk prevention and control.

[0048] S103 performs multi-dimensional matching of the calculation and analysis results and generates early warning information based on the matching results.

[0049] In this embodiment, a complex event processing (CEP) rule engine based on a directed acyclic graph (DAG) can load early warning rules from an early warning rule base (providing a visual configuration interface for early warning rules) to perform multi-dimensional real-time matching of the calculation and analysis results in step S102. A rule execution flow is constructed based on DAG, enabling event correlation analysis based on time windows, supporting dynamic matching of complex event patterns, and adaptively adjusting rule priorities. CEP is a technology for real-time analysis and processing of multiple event streams, capable of identifying meaningful patterns within the event stream. A DAG is a data structure used to describe task dependencies.

[0050] When an alert condition is triggered, the alert level is automatically determined, and alert information is generated based on the configuration of the alert rule base. Simultaneously, the alert information is pushed to relevant recipients through the API gateway of the Application Programming Interface (API) service layer via the alert distribution service. This supports multiple alert methods and tiered distribution mechanisms, while also notifying the API service layer to ensure timely delivery through multiple channels, forming a complete closed loop of alert data. The entire process employs a complex event handling mechanism based on CEP technology, constructing a comprehensive risk monitoring system to ensure the accuracy and timeliness of alerts. The API gateway serves as the unified entry point for system services, responsible for request routing, combination, and protocol conversion.

[0051] The API service layer provides a unified service interface and security management mechanism through four components: Representational State Transfer Application Programming Interface (RESTful API), WebSocket service, access control, and API gateway. This layer uses the API gateway to uniformly manage data flow (including service routing and load balancing), implements fine-grained access control through access control, and provides flexible data access methods in conjunction with RESTful API and WebSocket services, ensuring the availability and security of system services. Specifically, the REST service handles regular data requests and performs necessary authentication, while the WebSocket service establishes persistent connections to enable real-time data push, and ensures the security and efficiency of data access through authentication and routing services. The caching service, in conjunction with the load balancing mechanism, optimizes data access performance and provides stable and reliable data services. The API service layer handles data distribution through both REST and WebSocket services. This dual-mode service mechanism meets the access needs of different scenarios while ensuring the real-time nature and security of data transmission. RESTful API is a software architecture style based on the Hypertext Transfer Protocol (HTTP) for designing network application programming interfaces. WebSocket is a network communication protocol that provides a full-duplex communication channel, supporting real-time data exchange between clients and servers. HTTP is a protocol for communication over computer networks.

[0052] To address the complexity of financial risk monitoring, this application's embodiments construct a "FTEC" four-dimensional early warning indicator system, covering four dimensions: Financial, Technical, Environmental, and Compliance, achieving comprehensive risk monitoring. Specifically, the FTEC four-dimensional early warning indicator system (corresponding to scoring models for the four dimensions) can be used to score the calculated analysis results, yielding risk scores. The four dimensions include: financial, technical, environmental, and compliance. Early warning information is generated based on the comparison between the risk score results and risk score thresholds.

[0053] The financial dimension scoring model constructs a dynamic weighted model based on core financial indicators such as cash flow, debt-to-equity ratio, and profitability, employing an improved Z-Score algorithm combined with industry benchmarks for risk scoring. Specifically, the cash flow risk score CFR = w1 × CFR1 + w2 × CFR2 + w3 × CFR3, where CFR1 = (CFO - CF_required) / CF_required is the cash flow adequacy ratio, CFR2 = CFO / Total_Debt is the cash flow solvency, CFR3 = CFO / Revenue is the cash flow revenue ratio, and w1, w2, and w3 are weighting coefficients. The debt-to-equity ratio risk score DAR = w1 × DAR1 + w2 × DAR2, where DAR1 = Total_Debt / Total_Assets is the debt-to-equity ratio, DAR2 = Current_Debt / Current_Assets is the current ratio, and w1 and w2 are weighting coefficients. The Profitability Risk Score (PRR) is calculated as follows: PRR = w1 × PRR1 + w2 × PRR2 + w3 × PRR3, where PRR1 = Net Income / Revenue (net profit margin), PRR2 = EBITDA / Revenue (EBITDA profit margin), and PRR3 = ROE (return on equity). w1, w2, and w3 are weighting coefficients. The Comprehensive Financial Risk Score (FRS) is calculated as follows: FRS = α × CFR + β × DAR + γ × PRR, where α, β, and γ are dimensional weights that can be dynamically adjusted based on company size, industry characteristics, and historical performance. Specifically, it can be obtained through training with historical data.

[0054] Technical Dimension Scoring Model: A monitoring system based on multi-level technical indicators such as system performance, data quality, and computational latency is established. Through real-time performance monitoring and anomaly detection algorithms, the impact of technical risks on the accuracy of financial data is identified. Specifically, the system performance risk score SPR = w1 × SPR1 + w2 × SPR2 + w3 × SPR3, where SPR1 = 1 - Availability (system unavailability rate), SPR2 = Response Time / Threshold (response time risk), SPR3 = Error Rate (error rate), and w1, w2, and w3 are weighting coefficients. The data quality risk score DQR = w1 × DQR1 + w2 × DQR2 + w3 × DQR3, where DQR1 = Missing Rate (missing rate), DQR2 = Duplicate Rate (duplicate rate), DQR3 = Inconsistency Rate (inconsistency rate), and w1, w2, and w3 are weighting coefficients. The delay risk score (CDR) is calculated as follows: CDR = w1 × CDR1 + w2 × CDR2, where CDR1 = Processing_Time / Threshold represents the processing time risk, CDR2 = Queue_Length / Threshold represents the queue length risk, and w1 and w2 are weighting coefficients. The comprehensive technical risk score (TRS) is calculated as follows: TRS = α × SPR + β × DQR + γ × CDR, where α, β, and γ are dimension weights. Environmental Dimension Scoring Model: This model integrates external environmental factors such as macroeconomic indicators, industry prosperity, and policy changes, and uses a multi-factor model to quantify the impact of environmental risks on financial indicators. Specifically, the macroeconomic risk score MER = w1 × MER1 + w2 × MER2 + w3 × MER3, where MER1 = |GDP_Growth - Expected_Growth| / Expected_Growth is the deviation from GDP growth, MER2 = |Inflation_Rate - Target_Rate| / Target_Rate is the deviation from inflation, MER3 = |Interest_Rate - Benchmark_Rate| / Benchmark_Rate is the deviation from interest rates, and w1, w2, and w3 are weighting coefficients. The Industry Prosperity Risk Score (ICR) is calculated as follows: ICR = w1 × ICR1 + w2 × ICR2, where ICR1 = |Industry_Growth - Expected_Growth| / Expected_Growth represents the industry growth deviation, and ICR2 = |Industry_Profit - Expected_Profit| / Expected_Profit represents the industry profit deviation. w1 and w2 are weighting coefficients. The Policy Change Risk Score (PCR) is calculated as follows: PCR = w1 × PCR1 + w2 × PCR2, where PCR1 = Policy_Change_Impact represents the impact of policy changes, and PCR2 = Regulatory_Risk represents regulatory risk. w1 and w2 are weighting coefficients. The Comprehensive Environmental Risk Score (ERS) is calculated as follows: ERS = α × MER + β × ICR + γ × PCR, where α, β, and γ are dimension weights. The compliance risk scoring model monitors compliance risks such as regulatory compliance, internal control implementation, and audit findings, and achieves intelligent identification of compliance risks through a rule engine and machine learning algorithms. Specifically, the regulatory compliance risk score CRR = w1 × CRR1 + w2 × CRR2, where CRR1 = Violation_Count / Total_Operations is the violation rate, CRR2 = Compliance_Score / 100 is the compliance score, and w1 and w2 are weighting coefficients. The internal control implementation risk score ICR = w1 × ICR1 + w2 × ICR2 + w3 × ICR3, where ICR1 = Control_Failure_Rate is the internal control failure rate, ICR2 = Control_Effectiveness is the internal control effectiveness, and ICR3 = Control_Coverage is the internal control coverage rate, and w1, w2, and w3 are weighting coefficients. The Audit Finding Risk Score (AFR) is calculated as follows: AFR = w1 × AFR1 + w2 × AFR2, where AFR1 = Audit_Finding_Count represents the number of audit findings, AFR2 = Audit_Risk_Level represents the audit risk level, and w1 and w2 are weighting coefficients. The Comprehensive Compliance Risk Score (CRS) is calculated as follows: CRS = α × CRR + β × ICR + γ × AFR, where α, β, and γ are the dimensional weights.

[0055] The final four-dimensional comprehensive risk score TRS = wF×FRS + wT×TRS + wE×ERS + wC×CRS, where wF, wT, wE, and wC are the weights of the four dimensions, determined through historical data and expert knowledge.

[0056] Among them, the risk scoring threshold can be automatically adjusted based on the reinforcement learning mechanism, according to the historical early warning effect and business feedback.

[0057] Specifically, addressing the difficulty in setting risk scoring thresholds for financial indicators, a dynamic threshold optimization algorithm based on reinforcement learning is proposed. This algorithm automatically adjusts the warning thresholds for each dimension of indicators through a Q-learning mechanism, based on historical warning effects and business feedback. The Q-learning update formula is Q(s,a)←Q(s,a)+α[r+γmaxa'Q(s',a')-Q(s,a)], where α is the learning rate, γ is the discount factor, r is the reward, s is the state, and a is the action. The state space S includes the current indicator value, historical trends, market environment, etc., and the action space A includes the direction and magnitude of threshold adjustment. The reward function R=w1×accuracy-w2×false_positive-w3×false_negative, where w1, w2, and w3 are weight coefficients, accuracy is the accuracy rate, false_positive is the false positive rate, and false_negative is the false negative rate. The algorithm selects actions using an ε-greedy strategy, where ε = ε0 × decay^episode, ε0 is the initial exploration rate, decay is the decay factor, and episode is the number of training rounds. Based on historical warning performance and business feedback, the algorithm automatically adjusts the warning thresholds for various metrics, achieving a balanced optimization between warning accuracy and timeliness.

[0058] Furthermore, the financial risk monitoring method in this application embodiment may also include the following steps: visually displaying early warning information and / or financial data on multiple terminals.

[0059] Specifically, the presentation layer provides users with four functional modules: a web console, a mobile application, a report generator, and a visualization engine, enabling visualization across multiple terminals. The web console employs a responsive design to adapt to various devices, displaying the system's operational status in real time through monitoring dashboards and visual charts. The mobile application receives push notifications and updates alert status, ensuring monitoring capabilities anytime, anywhere. The report generator supports flexible report customization, while the visualization engine, based on WebGraphics Library (WebGL) technology, provides high-performance data visualization capabilities, collectively constructing a complete user interaction system. This multi-terminal collaborative display mechanism allows users to grasp the system's operational status and risk status anytime, anywhere. WebGL is a JavaScript API used to render interactive 3D and 2D graphics in web browsers.

[0060] In summary, the financial risk monitoring method of this application embodiment achieves the following technical effects: 1) Regarding improved data processing performance, distributed data acquisition technology and a hybrid storage architecture were adopted to achieve efficient collection and storage of financial data. A two-dimensional sharding scheme based on time and indicator dimensions, combined with an improved RAFT algorithm, ensured data consistency and significantly improved the system's data processing capabilities. Through intelligent sharding algorithms and adaptive caching strategies, the system's query response time and storage space utilization showed significant advantages compared to traditional solutions.

[0061] 2) Regarding the improvement of accuracy in intelligent analysis, deep learning-based Artificial Intelligence (AI) analysis models, especially those employing a CNN-LSTM-Attention hybrid neural network architecture, can automatically identify complex risk patterns and perform real-time dynamic calculation and analysis on collected financial data, thereby improving risk monitoring effectiveness. Through learning and analyzing massive amounts of historical data, complex relationships between financial indicators can be accurately captured. Combined with incremental learning and online update mechanisms, the accuracy and timeliness of risk identification are continuously improved, enabling the model to adapt to dynamically changing business environments. Through multi-scale time-series feature fusion algorithms and financial indicator correlation graph construction algorithms, the risk identification accuracy and false alarm rate have significant advantages compared to traditional methods. AI refers to the ability of computer systems to simulate human intelligence.

[0062] 3) Regarding improved risk monitoring efficiency, a real-time monitoring mechanism was implemented using DAG-based CEP technology, and a "FTEC" four-dimensional early warning indicator system was constructed, enabling multi-dimensional risk early warning. Real-time risk assessment was conducted through a complex event processing engine, supporting dynamic matching of complex event patterns. Furthermore, early warning thresholds were optimized through deep reinforcement learning, significantly improving the efficiency of risk prevention and control and substantially reducing the false alarm rate. Through financial risk transmission path identification algorithms and dynamic threshold optimization algorithms, the timeliness and accuracy of early warnings showed significant advantages compared to traditional methods.

[0063] 4) Regarding improved information transmission efficiency, a multi-terminal display and multi-channel early warning distribution mechanism has been implemented, providing users with monitoring capabilities anytime, anywhere through a web console and mobile terminals. The system supports various early warning methods, including in-system messages, emails, SMS, and mobile push notifications, and has established a hierarchical distribution and confirmation mechanism for early warning information to ensure that risk information can be delivered to relevant responsible persons in a timely and accurate manner, enabling rapid response and handling. Through intelligent data acquisition and scheduling algorithms and multi-source data fusion verification mechanisms, data acquisition efficiency and data quality scoring have significant advantages compared to traditional methods.

[0064] 5) In terms of enhancing decision support capabilities, a hybrid computing model is adopted to support real-time analysis and in-depth data mining. This meets the needs of real-time monitoring while also enabling complex trend prediction and risk assessment. Through parallel computational analysis processes, combining real-time stream computing and batch offline computing, comprehensive data support is provided for risk management decisions, helping enterprises achieve proactive risk management and improving the scientific nature and effectiveness of risk management. Through adaptive feature selection mechanisms and knowledge distillation techniques, the model training efficiency and prediction accuracy show significant advantages compared to traditional methods.

[0065] To clearly illustrate the financial risk monitoring method of this application embodiment, the following is combined with... Figure 5 The architecture diagram of the financial risk monitoring system shown is described in detail. Figure 5 As shown, the financial risk monitoring system of this application adopts a layered architecture design, which includes, from bottom to top: a data acquisition layer, a storage layer, a computing engine layer, a monitoring and early warning layer, an API service layer, and a presentation layer, as specifically implemented as follows: 1) Data Acquisition Layer As the foundational layer of the system, the data acquisition layer enables real-time acquisition and processing of data from multiple business systems through four core components: data synchronization service, real-time acquisition engine, data verification module, and data converter.

[0066] (1) Intelligent data acquisition and scheduling algorithm: Based on reinforcement learning, it collects financial data in real time from multiple business systems (i.e., data sources) through data synchronization services, such as sales operations system, business core system, financial management system, investment management system and comprehensive management system. It supports multiple acquisition strategies, such as timed acquisition, incremental acquisition and event-triggered acquisition.

[0067] (2) Multi-source data fusion verification mechanism: Based on the data lineage, the multi-source data verification system performs strict format verification and integrity verification on the collected multi-source data (i.e., financial data of various business systems) to ensure the uniformity of data quality.

[0068] (3) Adaptive data transformation engine: Based on pattern learning, the adaptive data transformation algorithm performs standardized transformation on the verified data to ensure that the data format is consistent.

[0069] (4) Real-time data stream processing optimization: Real-time data stream processing is performed on the financial data after format conversion to prevent data compression.

[0070] 2) Storage layer A hybrid storage architecture is adopted to optimize the storage and processing of time-series data and document data in financial data separately.

[0071] (1) Time series storage The verified time-series data is simultaneously written into the distributed time-series database, and multi-level caching is implemented through a cache manager to improve data access efficiency.

[0072] Adaptive sharding algorithm: Based on the time dimension and the indicator dimension, it performs two-dimensional intelligent sharding of time series data.

[0073] Improved RAFT consensus algorithm: The improved RAFT consensus algorithm is used to perform consistency processing on the time-series data of each shard to obtain consensus data.

[0074] Multi-level storage optimization strategy: A multi-level caching system was built, which, combined with a request routing based on consistent hashing and a load assessment model with dynamic weights, enabled fast data access response.

[0075] Fault detection and recovery mechanism: When a node failure occurs, the system can quickly complete fault detection and fault-tolerant switching to ensure service continuity.

[0076] (2) Document storage Document data (such as configuration documents and analysis reports, which are unstructured data) is stored in a document database to achieve unified management of unstructured data.

[0077] 3) Computation Engine Layer (1) Indicator calculation: The indicator calculation engine is used to calculate the indicators of the time series data.

[0078] (2) AI analysis CNN-LSTM-Attention Hybrid Neural Network: The CNN-LSTM-Attention hybrid neural network is used to extract local features, temporal dependencies and adaptive weighting of key time points of the index data to obtain weighted feature data.

[0079] Multi-scale temporal feature fusion algorithm: A multi-scale feature fusion algorithm based on wavelet packet decomposition is adopted to fuse the weighted feature data to obtain the fused feature data.

[0080] Financial Indicator Correlation Graph Construction Algorithm: The financial indicator correlation graph construction algorithm is adopted to construct a dynamic correlation graph by calculating the mutual information, correlation coefficient and Granger causality between the fused feature data.

[0081] Risk Transmission Analyzer: Employing a risk transmission analysis algorithm based on causal reasoning, this analyzer uses a dynamic correlation graph and combines Granger causality tests with a deep learning model to identify the transmission path and impact intensity of financial risks.

[0082] Model training and update mechanism: A distributed parameter server architecture is used for model training, incremental training is achieved through a sliding window, and model distillation technology is used to maintain knowledge accumulation, ensuring continuous optimization of model performance.

[0083] 4) Monitoring and Early Warning Layer (1) FTEC four-dimensional early warning indicator system: adopts a scoring model with four dimensions: financial dimension, technical dimension, environmental dimension and compliance dimension. The calculation and analysis results output by the calculation engine layer are risk-scored respectively, and the four-dimensional comprehensive risk score result is obtained.

[0084] (2) Dynamic threshold optimization algorithm: Through the Q-learning mechanism, the warning thresholds of each dimension indicator are automatically adjusted according to the historical warning effect and business feedback, so as to achieve a balance between the accuracy and timeliness of the warning.

[0085] (3) Warning rule engine: When the warning condition is triggered, the DAG-based CEP rule engine loads the warning rule from the warning rule library and generates the warning information.

[0086] (4) Early warning distribution service: Supports multiple early warning methods and hierarchical distribution mechanisms.

[0087] 5) API Service Layer The API service layer provides a unified service interface and security management mechanism through four components: RESTful API, WebSocket service, access control, and API gateway.

[0088] 6) Presentation layer The presentation layer provides users with four functional modules: a web console, a mobile application, a report generator, and a visualization engine, enabling visualization across multiple terminals.

[0089] The layered architecture described above, through clear division of responsibilities and standardized interface definitions, achieves decoupling and collaboration among the various functional modules of the system. Asynchronous communication mechanisms are used between layers, and the data flow forms a complete closed loop from bottom-level acquisition to top-level display, ensuring both the system's scalability and maintainability, as well as the efficiency and reliability of data processing.

[0090] To clearly illustrate the financial risk monitoring method of this application embodiment, the following is combined with... Figure 6 The workflow diagram of the financial risk monitoring system shown is described in detail. Figure 6 As shown, the workflow of the financial risk monitoring system in this application embodiment is divided into the following stages: 1) Data Acquisition Phase Data is collected from multiple business systems via a data synchronization service, employing three parallel strategies: scheduled collection, incremental collection, and event-triggered collection, to achieve comprehensive data acquisition. The collected data undergoes rigorous format validation and integrity checks. Data failing validation triggers a retry mechanism to ensure accuracy and completeness. Validated data is then standardized and transformed to lay the foundation for subsequent real-time data stream processing.

[0091] 2) Data storage stage A dual-track parallel storage mechanism is adopted, simultaneously writing verified time-series data to a distributed time-series database and updating the real-time cache, while storing unstructured data such as configuration documents and analysis reports in a document database. This hybrid storage architecture ensures both efficient reading and writing of time-series data and flexible management of document data, providing a reliable data foundation for the high-performance operation of the system.

[0092] 3) Calculation and Analysis Stage This phase simultaneously initiates three computational and analytical processes: indicator calculation, AI analysis, and predictive analysis. Indicator calculation performs real-time calculations of basic and composite indicators; AI analysis utilizes deep learning models to identify risk patterns; and predictive analysis generates trend predictions based on historical data and assesses risk transmission. This multi-dimensional parallel analysis ensures that the system can comprehensively and promptly identify potential risks.

[0093] 4) Monitoring and early warning stage The monitoring and early warning layer adopts the FTEC four-dimensional early warning indicator system. The calculated and analyzed results are used to score risks, resulting in a comprehensive four-dimensional risk score. A dynamic threshold optimization algorithm automatically adjusts the early warning thresholds for each dimension. Early warning rules are loaded from the early warning rule library, and multi-dimensional rule matching is performed on the calculated and analyzed results. When an early warning condition is triggered, the system automatically determines the early warning level and generates early warning information. Simultaneously, the early warning information (notification content) is pushed to relevant recipients through the early warning distribution service. The entire process employs a complex event handling mechanism based on CEP technology to ensure the accuracy and timeliness of early warnings.

[0094] 5) Service processing stage The API service layer handles data distribution through two methods: REST interface service and WebSocket push service. The REST interface service is responsible for handling regular data access requests and performing necessary authorization and authentication checks; the WebSocket service establishes persistent connections to achieve real-time data distribution. This dual-mode service mechanism satisfies the access needs of different scenarios while ensuring the real-time nature and security of data transmission.

[0095] 6) Data Presentation Stage The presentation layer displays the processed data in a visual manner on both web and mobile devices. The web interface displays the system's operational status in real time through monitoring dashboards and visual charts; the mobile interface receives push notifications and updates alert status. This multi-terminal collaborative display mechanism allows users to monitor the system's operational status and risk conditions anytime, anywhere.

[0096] 7) User interaction stage As the final step in the workflow, the user interaction phase involves the system responding to various user requests, including receiving and confirming alerts, viewing and analyzing monitoring data, reviewing and processing risk reports, and executing risk mitigation measures. Through standardized interaction processes and a user-friendly interface, the system ensures users can quickly and accurately complete various risk management tasks.

[0097] The aforementioned workflow, through its rigorous phase division and clear responsibilities, achieves end-to-end management from data collection to risk management. Asynchronous communication mechanisms and parallel processing strategies are employed between each phase, ensuring both real-time data processing and the system's high efficiency and reliability.

[0098] To clearly illustrate the financial risk monitoring method of this application embodiment, the following is combined with... Figure 7 The user interface design diagram of the financial risk monitoring system shown is described in detail. Figure 7 As shown, the interface design of the financial risk monitoring system in this application adopts a multi-layered architecture, mainly including: API service layer, login interface, main console, core function area and mobile application, etc., and the specific implementation is as follows: 1) API service layer components The API service layer manages system interface access through an API gateway. Authentication management ensures access security, routing and distribution enable intelligent request scheduling, and real-time push handles instant data transmission. This layer provides reliable data support and service guarantees to upper-layer interfaces through standardized interface definitions and strict access control.

[0099] 2) Login Interface The login interface uses a unified identity authentication portal, integrating three authentication methods: username and password, SMS verification code, and QR code login. With a simple and intuitive design style, combined with multiple security verification mechanisms, the interface ensures both the security of system access and a flexible and convenient login experience.

[0100] 3) Main console The main console consists of a top navigation bar and a left-side function menu, forming the system's main framework. The top navigation bar centrally displays global functions such as user information, message center, and system settings; the left-side function menu provides quick access to core functions such as real-time monitoring, risk warning, AI analysis, predictive analysis, and report center, creating a well-organized and user-friendly interface.

[0101] 4) Core Functional Area The core functional area comprises three main modules: a monitoring dashboard, an AI analysis center, and predictive analytics tools. The monitoring dashboard displays the system's operational status from multiple dimensions through indicator dashboards, alert lists, trend charts, and risk maps. The AI ​​analysis center integrates intelligent diagnostics and intelligent decision-making functions, providing end-to-end analysis from anomaly identification to handling recommendations. The predictive analytics tools support advanced analytical functions such as trend prediction, scenario analysis, and sensitivity testing, providing a scientific basis for risk management decisions.

[0102] 5) Mobile Applications The mobile application utilizes two main interfaces: a mobile homepage and mobile functions, to bring the system's core functions to mobile devices. The mobile homepage prominently displays key metrics and alert notifications, ensuring users are promptly informed of important information. The mobile functions interface provides features such as alert handling, AI insights, and quick operations, enabling users to monitor and manage risks anytime, anywhere.

[0103] The interface design described above adopts a multi-layered, modular design concept, achieving seamless data interaction between various interface modules through an API service layer. This design not only cleverly presents the system's complex functions to users in a concise and intuitive way, but also ensures the independence of each interface module and the smooth flow of data. Through this design approach, the system successfully creates a professional and efficient risk monitoring platform, enabling users to easily perform various risk monitoring operations.

[0104] To clearly illustrate the financial risk monitoring method of this application embodiment, the following is combined with... Figure 8 The data flow processing diagram of the financial risk monitoring system shown is described in detail below. Figure 8 As shown, the data flow design of the financial risk monitoring system in this application embodiment forms a complete data processing closed loop, mainly including the following data flow processes: 1) Data source access and transfer The system collects raw data from multiple data sources, including sales, business, financial, investment, and management data, through its data access module. This data undergoes processing such as data verification, transformation, and distribution to ensure accuracy and standardization, forming the system's foundational data flow and laying the groundwork for subsequent analysis and processing.

[0105] 2) Data flow in the storage layer The storage layer employs a dual-track parallel data processing mechanism, with real-time and historical data written to a time-series database respectively. A multi-level cache manager further enhances data access efficiency. Configuration and report data are stored in a document database, enabling unified management of unstructured data. This hybrid storage architecture ensures both efficient data access and flexible management of different data types.

[0106] 3) Data flow in the computing engine The data flow of the computing engine layer is divided into three parallel processing channels: the indicator calculation channel reads data from the time-series database and performs real-time calculations; the AI ​​analysis channel combines the training database to train models and identify risks; and the predictive analysis channel generates prediction results based on historical data. The calculation results of the three channels are transmitted to the monitoring and early warning layer through a data distribution mechanism, while the analysis results are stored in the document database.

[0107] 4) Monitoring and early warning data flow The monitoring and early warning layer receives real-time data from the computing engine, performs multi-dimensional matching through the rule engine, and generates early warning information based on the configuration of the early warning rule base. The early warning distribution service pushes this information to relevant users through the API gateway, while the notification service ensures timely delivery through multiple channels, forming a complete closed loop of early warning data.

[0108] 5) Data flow in the API service layer The API service layer centrally manages data flow through a gateway, handles regular data requests with a REST service, and enables real-time data push via a WebSocket service. Authentication and routing services ensure secure and efficient data access. A caching service, combined with a load balancing mechanism, optimizes data access performance and provides a stable and reliable data service.

[0109] 6) Data flow in the presentation layer The data flow in the presentation layer is divided into two directions: web and mobile. The web console displays real-time data and analysis results through a monitoring dashboard, alert center, report generator, and visualization engine; mobile devices receive push data through real-time monitoring and alert notification modules. Data on both ends is kept synchronized to ensure users receive a consistent data experience across different devices.

[0110] The aforementioned data stream processing employs an advanced event-driven architecture design. By establishing a multi-level caching mechanism and a distributed processing framework, it achieves efficient real-time data flow and optimized access. The system not only supports multi-channel data distribution strategies to ensure timely and accurate delivery of information to various terminals, but also guarantees the reliability of the entire data flow process through a strict consistency control mechanism. This comprehensive data flow design constructs a complete processing chain from data acquisition to final display, providing a high-performance and highly reliable data support foundation for the intelligent financial risk monitoring system.

[0111] This application also provides an electronic device. For example... Figure 9 As shown, the electronic device 900 can vary considerably due to differences in configuration or performance. It may include one or more processors 901 and memory 902, with memory 902 storing one or more programs or instructions. Memory 902 may be temporary or permanent storage. The program stored in memory 902 may include one or more modules (not shown), each module including a series of computer-executable instructions for the electronic device 900. Furthermore, processor 901 may be configured to communicate with memory 902, executing the series of programs or computer-executable instructions stored in memory 902 on the electronic device 900. The electronic device 900 may also include one or more power supplies 903, one or more wired or wireless network interfaces 904, one or more input / output interfaces 905, and one or more keyboards 906.

[0112] Specifically, in the embodiments of this application, the electronic device includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of any of the above-described financial risk monitoring method embodiments.

[0113] The electronic device in this application improves data processing performance by employing distributed data acquisition technology and a hybrid storage architecture, achieving efficient collection and storage of financial data. Regarding improved accuracy in intelligent analysis, a deep learning-based AI analysis model, particularly employing a CNN-LSTM-Attention hybrid neural network architecture, can automatically identify complex risk patterns and perform real-time dynamic calculation and analysis on the collected financial data, thus enhancing risk monitoring effectiveness. In terms of improved risk monitoring efficiency, a "FTEC" four-dimensional early warning indicator system is constructed, enabling multi-dimensional risk warnings. Regarding improved information transmission efficiency, a multi-terminal display and multi-channel early warning distribution mechanism is implemented, providing users with anytime, anywhere monitoring capabilities through a web console and mobile terminals. Finally, in terms of enhanced decision support capabilities, a hybrid computing model is adopted, supporting real-time analysis and in-depth mining, meeting both real-time monitoring needs and complex trend prediction and risk assessment.

[0114] This application also proposes a readable storage medium storing one or more computer programs or instructions that, when executed by a processor in an electronic device, enable the processor in the electronic device to perform the steps of any of the above-described financial risk monitoring method embodiments.

[0115] The readable storage medium of this application embodiment improves data processing performance by employing distributed data acquisition technology and a hybrid storage architecture, achieving efficient collection and storage of financial data. Regarding improved accuracy in intelligent analysis, a deep learning-based AI analysis model, particularly employing a CNN-LSTM-Attention hybrid neural network architecture, can automatically identify complex risk patterns and perform real-time dynamic calculation and analysis on the collected financial data, thus improving risk monitoring effectiveness. In terms of improved risk monitoring efficiency, a "FTEC" four-dimensional early warning indicator system is constructed, enabling multi-dimensional risk warnings. Regarding improved information transmission efficiency, a multi-terminal display and multi-channel early warning distribution mechanism is implemented, providing users with anytime, anywhere monitoring capabilities through a web console and mobile terminals. Finally, in terms of improved decision support capabilities, a hybrid computing mode is adopted, supporting real-time analysis and in-depth mining, meeting both real-time monitoring needs and complex trend prediction and risk assessment.

[0116] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0117] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0123] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0124] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0125] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0126] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0127] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A financial risk monitoring method, characterized in that, include: Real-time collection of financial data from various business systems; The financial data was calculated and analyzed using a CNN-LSTM-Attention hybrid neural network model to obtain the calculation and analysis results; The calculation and analysis results are matched in multiple dimensions, and early warning information is generated based on the matching results.

2. The method according to claim 1, characterized in that, The financial data is calculated and analyzed using a CNN-LSTM-Attention hybrid neural network model to obtain the calculation and analysis results, including: The convolutional neural network (CNN) layer in the CNN-LSTM-Attention hybrid neural network model is used to extract local features from the financial data. The Long Short-Term Memory (LSTM) layer in the CNN-LSTM-Attention hybrid neural network model is used to capture the temporal dependencies between the local features. The attention layer of the CNN-LSTM-Attention hybrid neural network model is used to adaptively weight the temporal dependency at key time points to obtain weighted feature data. The calculation and analysis results are obtained based on the weighted feature data.

3. The method according to claim 2, characterized in that, The CNN layer includes a three-layer CNN structure with convolutional kernel sizes of 3×3, 5×5 and 7×7, which are used to extract short-term local features, medium-term local features and long-term local features of the financial data, respectively. The LSTM layer adopts a bidirectional LSTM structure with 128 hidden units, which are used to capture the positive and negative temporal dependencies between the local features, respectively. The Attention layer employs an 8-head multi-head attention mechanism.

4. The method according to claim 3, characterized in that, The calculation and analysis results obtained based on the weighted feature data include: A multi-scale feature fusion algorithm based on wavelet packet decomposition is used to fuse the weighted feature data to obtain the fused feature data. A financial indicator correlation graph construction algorithm is used to construct a dynamic correlation graph based on the fused feature data. A risk transmission analysis algorithm based on causal reasoning is used to identify the transmission path and impact intensity of financial risks based on the dynamic correlation diagram.

5. The method according to claim 1, characterized in that, The step of performing multi-dimensional matching on the calculation and analysis results and generating early warning information based on the matching results includes: A four-dimensional early warning indicator system is used to score the risk of the calculation and analysis results, resulting in a risk score. The four dimensions include: financial dimension, technological dimension, environmental dimension, and compliance dimension. The warning information is generated based on the comparison between the risk score result and the risk score threshold.

6. The method according to claim 5, characterized in that, Also includes: Based on reinforcement learning, the risk scoring threshold is automatically adjusted according to historical early warning effects and business feedback.

7. The method according to claim 1, characterized in that, Also includes: The warning information and / or the financial data will be visualized on multiple terminals.

8. The method according to claim 1, characterized in that, After real-time collection of financial data from various business systems, the process also includes: The financial data collected by each of the business systems is integrated and verified; Convert the format of the verified financial data; Real-time data stream processing is performed on the converted financial data to prevent data overload.

9. The method according to claim 1, characterized in that, After real-time collection of financial data from various business systems, the process also includes: The time-series data in the financial data is stored in a distributed time-series database; The document data in the financial data is stored in the document database.

10. The method according to claim 9, characterized in that, The step of storing the time-series data in the financial data into a distributed time-series database includes: The temporal and spatial locality of the time series data are analyzed, and a clustering algorithm is used to partition the time series data into fragmented time series data. A consensus algorithm is used to process the time-series data of each fragment to obtain consensus data. Cache data will be generated based on consensus data, and the cache data will be stored in the distributed time-series database.

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