Intelligent financial risk early warning method and system based on management decision

Abstract

The invention discloses an intelligent financial risk early warning method and system based on a management decision, and relates to the technical field of data intelligence, and the method comprises a multi-source heterogeneous data collection module which obtains enterprise financial data, supply chain data, market public opinion data and industry reference data in real time through an API interface, risk keywords are extracted from news, social media and policy documents through a natural language processing technology according to the market public opinion data; the streaming data processing engine is constructed based on an Apache Flink framework, performs windowing processing on the real-time data stream, calculates the dynamic fluctuation ratio of financial indexes by sliding a time window, and compares the dynamic fluctuation ratio with a preset industry risk threshold value; and a risk decision fusion model, a dynamic threshold adaptive module and a man-machine collaborative early warning terminal. According to the method, millisecond-level financial index fluctuation monitoring is realized through a streaming computing framework, a knowledge graph and a natural language processing technology are fused, a risk entity and a causal chain are extracted from unstructured data, and a multi-dimensional risk portrait is constructed.

Description

Technical Field

[0001] The present invention relates to the field of data intelligence technology, and in particular to an intelligent financial risk early warning method and system based on management decision-making. Background Art

[0002] With economic globalization and intensified market competition, the internal and external environments facing businesses are becoming increasingly complex, with a growing number of uncertainties, including adjustments to macroeconomic policies, shifts in market demand, and adjustments in competitor strategies. These factors can impact a company's financial situation and increase the likelihood of financial risk. Furthermore, with the increasing level of enterprise informatization, a vast amount of data, including financial statements, transaction records, and budget data, has accumulated in financial and related business systems. This data needs to be effectively utilized to extract valuable information to help business managers identify financial risks promptly.

[0003] Business managers need timely and accurate information about their company's financial status to make informed management decisions. Traditional financial analysis and risk warning methods often lag behind and fail to meet the real-time and accuracy requirements of modern businesses. Therefore, it is necessary to leverage advanced technologies to establish intelligent financial risk warning systems to provide managers with timely and accurate decision support. Summary of the Invention

[0004] In order to solve the above technical problems, an intelligent financial risk early warning method and system based on management decision-making is provided. This technical solution solves the above problems of insufficient real-time performance and dynamic adaptability.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] An intelligent financial risk early warning system based on management decision-making, including:

[0007] The multi-source heterogeneous data collection module acquires enterprise financial data, supply chain data, market sentiment data, and industry benchmark data in real time through API interfaces. Market sentiment data uses natural language processing technology to extract risk keywords from news, social media, and policy documents.

[0008] The streaming data processing engine, built on the Apache Flink framework, performs windowed processing on real-time data streams. By sliding time windows, it calculates the dynamic volatility of financial indicators and compares it with pre-set industry risk thresholds.

[0009] The risk decision-making fusion model integrates management decision rules and machine learning algorithms. The first sub-model, a time series prediction model based on an LSTM network, predicts a company's short-term debt repayment capacity and liquidity gap. The second sub-model, a decision tree model based on multi-objective optimization, generates a prioritized ranking of risk response paths.

[0010] Dynamic threshold adaptation module, which uses reinforcement learning algorithm to adjust risk warning threshold in real time and optimizes model sensitivity based on external environmental parameters;

[0011] The human-machine collaborative early warning terminal is equipped with a visual interactive interface to display multi-dimensional early warning results in the form of a risk heat map, and provide confidence scores for decision recommendations and historical case matching analysis.

[0012] Preferably, the multi-source heterogeneous data acquisition module specifically includes:

[0013] Enterprise financial data: including dynamic cash flow, balance sheet, income statement, accounts receivable, aging analysis, internal enterprise systems, connecting to SAP and Oracle ERP via OAuth2.0 protocol, calling RESTful API to obtain encrypted data streams; connecting to bank open platforms to obtain real-time transaction flow;

[0014] Supply chain data: Upstream data includes supplier credit ratings, raw material price fluctuations, and delivery on-time rates; downstream data includes customer order fulfillment rates, distributor inventory turnover days, and abnormal fluctuations in return rates. Logistics node status is tracked through RFID and GPS; access to the Hyperledger Fabric consortium chain verifies the authenticity of supplier contract performance.

[0015] Market sentiment data: including news media, social media, and policy documents; a risk semantic recognition engine fine-tuned based on the GPT-4 architecture, supporting entity recognition and sentiment analysis; combining text with PDF scans of policy documents to extract key terms through OCR and semantic segmentation;

[0016] Industry benchmark data: including industry average financial indicators, macroeconomic indicators, and competitor dynamics; protects data privacy by jointly training benchmark models across enterprise data pools; automatically triggers benchmark value iteration when the data of leading enterprises in the industry deviate from the mean.

[0017] Preferably, the multi-source heterogeneous data acquisition module specifically includes:

[0018] Heterogeneous data standardization unit: Financial data is converted into XBRL format and mapped to IFRS international standards; supply chain data is constructed by building an entity relationship model based on ontology and defining the "supplier-order-logistics" triple; public opinion data is associated with events, enterprises, and regulatory nodes through knowledge graphs to generate risk propagation links;

[0019] Real-time assurance unit: Streaming collection architecture, driven by events, uses Kafka message queues to partition and process different data sources; breakpoint resuming, using Apache Pulsar's persistent subscription mechanism; data credibility verification, key data hash values ​​are uploaded to the chain, supporting real-time inspection by regulatory agencies; identification of forged streams based on the isolation forest algorithm;

[0020] Risk keyword extraction unit: Multilingual processing: Chinese uses the BERT-wwm-ext model for word segmentation; English uses the SpaCy library for named entity recognition and annotates ORG and LAW tags.

[0021] Preferably, the streaming data processing engine specifically includes:

[0022] The data access layer connects to Kafka and Pulsar message queues, with financial data taking the highest priority.

[0023] The computing layer uses Flink's DataStream API to define sliding windows, volatility algorithms, and threshold comparison rules.

[0024] The output layer pushes warning signals to the risk decision model and writes them into Elasticsearch for visualization terminal calls;

[0025] The time window controller dynamically adjusts the window size to adapt to the characteristics of different financial indicators. Window types include high-frequency, medium-frequency, and low-frequency financial indicators. Event time processing uses a watermark strategy. The side output stream captures timeout data and triggers a secondary warning process.

[0026] Volatility calculation operator with built-in rolling standard deviation, coefficient of variation, and Holt-Winters seasonal forecast algorithms;

[0027] Threshold comparison engine, real-time access to industry risk database, supports multi-dimensional thresholds.

[0028] Preferably, the risk decision fusion model module specifically includes:

[0029] The first sub-model is a time series prediction model based on LSTM network;

[0030] Input layer: Time series data, including past short- to medium-term corporate cash flow, quick ratio, and interest coverage ratio, aligned at daily / weekly granularity; external factors, including industry prosperity index and changes in the central bank's benchmark interest rate;

[0031] Training strategy: Using QuantileLoss to optimize the prediction accuracy of different confidence intervals;

[0032] Dynamic calibration: Online learning: When the deviation between actual cash flow and predicted value exceeds the established range for multiple consecutive days, incremental model training is triggered; the Transformer attention mechanism is introduced to identify the impact weight of sudden policy events on debt repayment capacity.

[0033] Preferably, the risk decision fusion model module specifically includes:

[0034] The second sub-model is a decision tree model based on multi-objective optimization; multi-objective problem modeling includes decision variables and optimization objectives;

[0035] NSGA-III optimization framework: Reference point generation, defining reference vectors based on historical data clustering, and uniform distribution on the Pareto front; Constraint processing, using a penalty function method to eliminate solutions with excessive debt-to-asset ratios to the secondary population;

[0036] Decision tree construction: Use Gini importance ranking to retain the top key factors; node splitting rules, based on super volume gain, prioritize segmentation to simultaneously improve the characteristics of multiple targets;

[0037] Path priority sorting: TOPSIS comprehensive evaluation, calculating the Euclidean distance between each solution and the ideal solution / negative ideal solution;

[0038] Model fusion: Forward feedback: the liquidity gap predicted by LSTM serves as the input constraint of the decision tree module; reverse correction: the actual execution effect of the decision tree output feeds back to the LSTM training data.

[0039] Preferably, the dynamic threshold adaptive module specifically includes:

[0040] Input layer: Real-time financial indicators including cash flow volatility, quick ratio, and interest coverage ratio; historical warning records, types of false positives / missing negatives in the past short period and trigger thresholds; macroeconomic indicators including CPI, PMI, and the Treasury yield curve; industry dynamics including the volatility of the Shenwan First-Level Industry Index and the popularity of ESG controversial events;

[0041] Reinforcement learning framework: The state space includes the deviation of corporate financial indicators from industry benchmarks, external environmental risk scores, and historical threshold adjustment trajectories. The action space includes threshold adjustment directions such as increase, maintain, and decrease, the threshold adjustment amplitude, and the step size adjusted according to the risk level. This is calculated through a reward function.

[0042] Output layer: Generates elastic threshold intervals for each indicator and scenario through the early warning threshold matrix; quantifies the impact of external parameters on thresholds through sensitivity analysis reports;

[0043] Hierarchical reinforcement learning: The high-level strategy uses the PPO algorithm and regularly updates the global threshold strategy; the low-level execution is based on the current state and fine-tuned in real time;

[0044] Causal reasoning integration: Construction of causal graphs, identifying the causal relationship between thresholds and external factors through PC algorithms; counterfactual analysis, simulating the impact of events that have not occurred.

[0045] Preferably, the human-machine collaborative early warning terminal specifically includes:

[0046] Visual interactive interface: Risk Panorama Dashboard, including a real-time financial health index on the left, a supply chain network topology diagram in the middle, and a public opinion sentiment polarity radar chart on the right. Its dimensions include debt, compliance, ESG, market, supply chain, and management reputation.

[0047] Drill-down analysis function: Double-click the heat map area to display historical trends; voice command support, multi-dialect recognition based on the Whisper-V3 model;

[0048] Risk heat map generation: Spatial dimension, drill down by region and business unit; Time dimension, supports sliding windows of short-term time intervals to predict risk transmission paths; Object dimension, distinguishes legal entities and supply chain levels;

[0049] Decision recommendation confidence score: Input factors include data quality score, historical model accuracy, and external environment stability; expert rules are integrated through the Bayesian network to output the confidence interval; a dynamic attenuation mechanism is used, and if the data is not updated within a specified time, the score will be automatically reduced proportionally.

[0050] Furthermore, an intelligent financial risk early warning method based on management decision-making is used to implement the intelligent financial risk early warning system based on management decision-making as described above, comprising:

[0051] Synchronously acquire enterprise financial data, supply chain data, market sentiment data, and industry benchmark data through API interfaces; use natural language processing technology to extract risk keywords from unstructured text, and construct an entity-relationship-attribute triple database through knowledge graphs;

[0052] Windowed processing of real-time data streams based on the Apache Flink framework calculates the dynamic volatility of financial indicators; uses chi-square tests to identify abnormal fluctuations and trigger real-time warning signals;

[0053] Risk decision-making fusion modeling: The first prediction layer uses an LSTM network to predict a company's debt repayment capacity and liquidity gap, with input time series data including cash flow, industry prosperity index, and external interest rate changes. The second optimization layer uses a multi-objective optimization algorithm to generate a Pareto optimal solution set for risk response paths. The optimization objectives include maximizing the return on investment, minimizing the debt-to-asset ratio, and constraining strategic deviation.

[0054] Dynamic threshold adaptive adjustment, defining the reinforcement learning state space as the deviation between financial indicators and industry benchmarks, and the action space as the threshold increase and decrease range; based on the Q-Learning algorithm, iteratively optimize the threshold strategy, and the reward function integrates the false alarm rate, false negative rate, and sensitivity to environmental parameters;

[0055] Human-machine collaborative early warning output displays risk heat maps through a visual interface, marking risk transmission paths; provides decision-making recommendation confidence scores and historical case matching analysis.

[0056] Optionally, the dynamic analysis of streaming data specifically includes:

[0057] Window type selection: high-frequency trading data, including stock prices and exchange rates, to capture micro-sentiment fluctuations in the market; medium-frequency operational data, including accounts receivable turnover, to balance real-time performance with noise filtering; and low-frequency strategic data, including debt-to-asset ratios, to monitor long-term structural risks.

[0058] Time semantics and out-of-order processing adopt a watermark strategy, allowing the maximum delay to be within the established timeline. The side output stream captures timeout data, marks it as overdue events, and triggers secondary warnings; event time alignment is based on the actual time of occurrence of financial events.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] This paper proposes integrating enterprise financial, supply chain, and market sentiment data through standardized interfaces, combining natural language processing techniques to parse unstructured text, extract causal relationships between risk entities and events, and construct a dynamically updated knowledge graph. This graph supports implicit risk reasoning, transcending the limitations of traditional single-dimensional data and achieving a comprehensive understanding of risk factors.

[0061] Based on a streaming computing framework, we conduct windowed analysis of high-frequency financial indicators, employing a dual validation mechanism of statistical testing and machine learning to identify abnormal fluctuations. Compared to traditional batch processing, we achieve millisecond-level risk response, significantly reducing the risk of false positives and missed reports, and can also freeze high-risk trading orders.

[0062] Design a "prediction-optimization" dual-engine model: the time series prediction layer quantifies debt repayment capacity and liquidity gap through the LSTM network, and injects adversarial training to enhance adaptability to extreme scenarios; the optimization layer uses an improved multi-objective algorithm to generate a Pareto optimal solution, balance investment return rate, debt control and strategic consistency, and provide diversified disposal paths.

[0063] Transforming threshold management into a reinforcement learning problem, adaptively adjusting warning boundaries based on environmental deviations and risk levels, the system transitions from static rules to dynamic game-playing. The system continuously optimizes strategies through reward and penalty functions, rapidly adapting to macroeconomic fluctuations and industry cycles while reducing the cost of manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is an internal framework diagram of an intelligent financial risk early warning system based on management decisions;

[0065] Figure 2 This is the internal framework diagram of the multi-source heterogeneous data acquisition module;

[0066] Figure 3 This is the internal framework diagram of the streaming data processing engine;

[0067] Figure 4 This is a flowchart of an intelligent financial risk early warning method based on management decision-making. DETAILED DESCRIPTION

[0068] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0069] Reference Figure 1 As shown, an intelligent financial risk early warning system based on management decision-making includes:

[0070] The multi-source heterogeneous data collection module acquires enterprise financial data, supply chain data, market sentiment data, and industry benchmark data in real time through API interfaces. Market sentiment data uses natural language processing technology to extract risk keywords from news, social media, and policy documents.

[0071] The streaming data processing engine, built on the Apache Flink framework, performs windowed processing on real-time data streams. By sliding time windows, it calculates the dynamic volatility of financial indicators and compares it with pre-set industry risk thresholds.

[0072] The risk decision-making fusion model integrates management decision rules and machine learning algorithms. The first sub-model, a time series prediction model based on an LSTM network, predicts a company's short-term debt repayment capacity and liquidity gap. The second sub-model, a decision tree model based on multi-objective optimization, generates a prioritized ranking of risk response paths.

[0073] Dynamic threshold adaptation module, which uses reinforcement learning algorithm to adjust risk warning threshold in real time and optimizes model sensitivity based on external environmental parameters;

[0074] The human-machine collaborative early warning terminal is equipped with a visual interactive interface to display multi-dimensional early warning results in the form of a risk heat map, and provide confidence scores for decision recommendations and historical case matching analysis.

[0075] It should be noted that multi-source heterogeneous data: financial data (cash flow, debt ratio) → supply chain data (supplier rating, logistics delay rate) → public opinion data (policy keywords, competitor dynamics);

[0076] Dynamic calibration: Blockchain evidence storage ensures that data cannot be tampered with, and the isolation forest algorithm filters outliers;

[0077] Real-time computing layer: Flink engine, windowed processing of high-frequency data, and dynamic adaptation of sliding window step size to industry characteristics (1 hour for manufacturing, 10 seconds for finance). Volatility-threshold linkage, with industry benchmark thresholds updated quarterly. Deviations greater than 15% trigger reinforcement learning retraining of the threshold module.

[0078] Decision fusion layer: LSTM prediction outputs the probability distribution of liquidity gaps in the next three months (5% to 95% quantiles); multi-objective optimization, the Pareto front solution set is sorted using the TOPSIS algorithm, and management preferences (such as "maintaining cash flow first") are weighted to generate the top three solutions.

[0079] Human-computer interaction layer: Risk heat map, drill down by region (Yangtze River Delta / Pearl River Delta) and business line (production / sales), and mark the risk transmission chain (such as "supplier supply interruption → production stagnation → order default").

[0080] Reference Figure 2 As shown in the figure, the multi-source heterogeneous data acquisition module specifically includes:

[0081] Enterprise financial data: including dynamic cash flow, balance sheet, income statement, accounts receivable, aging analysis, internal enterprise systems, connecting to SAP and Oracle ERP via OAuth2.0 protocol, calling RESTful API to obtain encrypted data streams; connecting to bank open platforms to obtain real-time transaction flow;

[0082] Supply chain data: Upstream data includes supplier credit ratings, raw material price fluctuations, and delivery on-time rates; downstream data includes customer order fulfillment rates, distributor inventory turnover days, and abnormal fluctuations in return rates. Logistics node status is tracked through RFID and GPS; access to the Hyperledger Fabric consortium chain verifies the authenticity of supplier contract performance.

[0083] Market sentiment data: including news media, social media, and policy documents; a risk semantic recognition engine fine-tuned based on the GPT-4 architecture, supporting entity recognition and sentiment analysis; combining text with PDF scans of policy documents to extract key terms through OCR and semantic segmentation;

[0084] Industry benchmark data: including industry average financial indicators, macroeconomic indicators, and competitor dynamics. This data is trained jointly across enterprise data pools to protect data privacy. When the data of leading companies in the industry deviates from the mean, benchmark value iteration is automatically triggered.

[0085] Heterogeneous data standardization unit: Financial data is converted into XBRL format and mapped to IFRS international standards; supply chain data is constructed by building an entity relationship model based on ontology and defining the "supplier-order-logistics" triple; public opinion data is associated with events, enterprises, and regulatory nodes through knowledge graphs to generate risk propagation links;

[0086] Real-time assurance unit: Streaming collection architecture, driven by events, uses Kafka message queues to partition and process different data sources; breakpoint resuming, using Apache Pulsar's persistent subscription mechanism; data credibility verification, key data hash values ​​are uploaded to the chain, supporting real-time inspection by regulatory agencies; identification of forged streams based on the isolation forest algorithm;

[0087] Risk keyword extraction unit: Multilingual processing: Chinese uses the BERT-wwm-ext model for word segmentation; English uses the SpaCy library for named entity recognition and annotates ORG and LAW tags.

[0088] It should be noted that the aging analysis of corporate financial data has been enhanced:

[0089] Time series clustering is introduced to identify abnormal payment patterns, such as a customer's payment cycle suddenly extending by 2 standard deviations. Combined with supply chain data, a payment period-performance correlation map is constructed to quantify the impact of supplier delayed delivery on the bad debt rate.

[0090] Dynamic supplier rating based on supply chain data, building a three-tier credit assessment model:

[0091] Basic layer: historical fulfillment rate and financial health;

[0092] Dynamic layer: raw material price sensitivity;

[0093] Prediction layer: LSTM predicts supply stability in the next three months.

[0094] Emotional communication modeling, building a public opinion communication dynamics model, quantifying the diffusion speed of negative events, such as the hourly impact coverage of Twitter rumors = 12%, and the warning threshold is set as the transmission acceleration > 2σ.

[0095] The competitor monitoring subsystem of industry benchmark data uses knowledge graph reasoning to identify strategic shift signals from competitors; it also embeds antitrust compliance checks and automatically blocks sensitive data.

[0096] Reference Figure 3As shown, the streaming data processing engine specifically includes:

[0097] The data access layer connects to Kafka and Pulsar message queues, with financial data taking the highest priority.

[0098] The computing layer uses Flink's DataStream API to define sliding windows, volatility algorithms, and threshold comparison rules.

[0099] The output layer pushes warning signals to the risk decision model and writes them into Elasticsearch for visualization terminal calls;

[0100] The time window controller dynamically adjusts the window size to adapt to the characteristics of different financial indicators. Window types include high-frequency, medium-frequency, and low-frequency financial indicators. Event time processing uses a watermark strategy. The side output stream captures timeout data and triggers a secondary warning process.

[0101] Volatility calculation operator with built-in rolling standard deviation, coefficient of variation, and Holt-Winters seasonal forecast algorithms;

[0102] Threshold comparison engine, real-time access to industry risk database, supports multi-dimensional thresholds.

[0103] It should be noted that multi-level priority control and QoS stratification are implemented: financial data (such as cash flow) is marked as the highest priority (QoS0), exclusively occupying independent Kafka partitions and dedicated bandwidth; supply chain data (such as logistics delays) is set to medium level (QoS1), and public opinion data is set to low level (QoS2), supporting burst traffic downgrade and discard.

[0104] Window intelligent adaptation:

[0105] High-frequency indicators (stock prices, exchange rates) use a 5-second sliding window, which is shortened to 2 seconds when the market volatility index (MVI) is greater than 60. Medium-frequency indicators (accounts receivable and payable) use a 1-hour rolling window, which switches to a 15-minute window when the payment period approaches (<30 days). Low-frequency indicators (assets and liabilities) use a 24-hour window, which is automatically extended to 72 hours at the end of the quarter to cover the settlement cycle.

[0106] Event time and watermark strategy: Watermark generation is based on the actual occurrence time of financial events (such as the invoice issuance timestamp), allowing a maximum out-of-order delay of 10 minutes. Side output stream processing, timeout data triggers the secondary warning pipeline, pushes it to the manual review queue, and records the delay cause classification (such as cross-border network congestion).

[0107] Volatility calculations using multiple algorithms: Rolling standard deviation is suitable for short-term liquidity monitoring, such as minute-by-minute cash flow fluctuations, sensitively capturing the risk of sudden debt repayments; the coefficient of variation is used to eliminate dimensional differences and horizontally compare fluctuations across indicators such as gross profit margin and inventory turnover. For example, a CV > 15% triggers an alert; Holt-Winters seasonality forecasts target cyclical financial indicators, such as peak holiday sales in the retail industry, and predict fluctuation trends over the next three periods.

[0108] Rolling standard deviation:

[0109] Where σ t is the standard deviation of the current time window t, x i is the i-th data point in the window, such as the cash flow per minute, is the arithmetic mean of the data in the window, N is the sliding window size, such as high-frequency indicators N = 30 seconds of data points;

[0110] Coefficient of variation:

[0111] Where, CV t is the relative amplitude of fluctuation of the current window data, σ t is the standard deviation of the current time window t, is the arithmetic mean of the data in the window;

[0112] Holt-Winters Seasonal Forecast:

[0113] Horizontal equation: L t =α(x t -S t -m)+(1-α)(L t-1 +B t-1 )

[0114] Trend equation: B t =β(L t -L t-1 )+(1-β)B t-1

[0115] Seasonal equation: S t =γ(x t -L t )+(1-γ)S t-m

[0116] Prediction equation:

[0117] Where, L t is the horizontal component at time t, B t is the trend component at time t, S tis the seasonal component of time t, m is the length of the seasonal cycle, such as quarterly data m = 4, α, β, γ are smoothing coefficients (0 to 1), through grid search optimization, is the predicted value for the next h period.

[0118] Multi-dimensional threshold library: In terms of industry, the debt-to-asset ratio threshold for manufacturing is set at 65%, and for retail at 55%; in terms of enterprise size, the cash flow warning threshold for small and medium-sized enterprises fluctuates by ±20%; and in terms of environmental coupling, when the PMI is <50, the overdue accounts receivable threshold is automatically lowered.

[0119] The risk decision fusion model module specifically includes:

[0120] The first sub-model is a time series prediction model based on LSTM network;

[0121] Input layer: Time series data, including past short- to medium-term corporate cash flow, quick ratio, and interest coverage ratio, aligned at daily / weekly granularity; external factors, including industry prosperity index and changes in the central bank's benchmark interest rate;

[0122] Training strategy: Using QuantileLoss to optimize the prediction accuracy of different confidence intervals;

[0123] Dynamic calibration: Online learning: When the deviation between actual cash flow and forecast value exceeds the established range for multiple consecutive days, incremental model training is triggered. The Transformer attention mechanism is introduced to identify the impact weight of sudden policy events on debt repayment capacity.

[0124] The second sub-model is a decision tree model based on multi-objective optimization; multi-objective problem modeling includes decision variables and optimization objectives;

[0125] NSGA-III optimization framework: Reference point generation, defining reference vectors based on historical data clustering, and uniform distribution on the Pareto front; Constraint processing, using a penalty function method to eliminate solutions with excessive debt-to-asset ratios to the secondary population;

[0126] Decision tree construction: Use Gini importance ranking to retain the top key factors; node splitting rules, based on super volume gain, prioritize segmentation to simultaneously improve the characteristics of multiple targets;

[0127] Path priority sorting: TOPSIS comprehensive evaluation, calculating the Euclidean distance between each solution and the ideal solution / negative ideal solution;

[0128] Model fusion: Forward feedback: the liquidity gap predicted by LSTM serves as the input constraint of the decision tree module; reverse correction: the actual execution effect of the decision tree output feeds back to the LSTM training data.

[0129] It should be noted that the LSTM time series prediction sub-model includes:

[0130] Time series input: internal indicators, including cash flow (daily), quick ratio (weekly), and interest coverage ratio (weekly) for the past 90 days. The data is normalized and then fed into the LSTM network.

[0131] External factors, such as the industry prosperity index (HHI, updated daily) and changes in the central bank's benchmark interest rate (event-driven updates), are encoded into time series vectors through the Embedding layer.

[0132] Heterogeneous alignment mechanism: external events (such as a central bank interest rate hike) are aligned to internal financial data streams through timestamps, and a dual-time axis attention mechanism is used to dynamically adjust the event impact window (such as the impact of an interest rate hike policy lasting 30 days).

[0133] QuantileLoss optimization:

[0134] Where L is QuantileLoss (quantile loss function), which is an indicator to measure the error between the predicted value and the true value. By optimizing the loss function, the predictions of different quantiles can be optimized at the same time to capture information at different locations in the data, including extreme risk scenarios. q is the quantile level, which takes values ​​of [0.05, 0.5, 0.95], namely 5%, 50%, and 95% quantiles. Different q values ​​correspond to the prediction emphasis on different locations of the data. For example, the 5% quantile can focus on the extreme situation of the left tail of the data, the 95% quantile focuses on the extreme situation of the right tail, and the 50% quantile corresponds to the median of the data. t It is a real value or observed value. In specific application scenarios, such as future liquidity gap prediction, it represents the actual value of the liquidity gap in the next 30 days. is the predicted value, which is the estimated value of yt made by the model based on the learned knowledge;

[0135] At the same time, the 5%, 50%, and 95% quantile forecasts are optimized to capture extreme risk scenarios (such as the probability of cash flow interruption); the forecast output is a probability distribution (such as the confidence level of the liquidity gap in the next 30 days in the range of [1 million, 5 million] [1 million, 5 million] is 80%).

[0136] Online learning trigger conditions: When the actual value exceeds the prediction range (5% to 95% percentile) for three consecutive days, incremental training is triggered; sudden policy events (such as "chip export control") are calculated through Transformer to calculate the event embedding vector and weightedly injected into the LSTM hidden layer.

[0137] LSTM-Transformer hybrid architecture:

[0138] Bottom layer: 3-layer bidirectional LSTM (256 units) to extract long-term financial trends;

[0139] Top layer: Transformer encoder (4-head attention), capturing the dynamic relationship between policy events and financial indicators;

[0140] Output layer: Quantile regression layer, which simultaneously outputs multiple confidence interval prediction values.

[0141] Physical Constraint Embedding:

[0142] Hard rules (such as "the quick ratio must not be less than 1") are injected into the loss function through Lagrange multipliers to prevent the model from outputting solutions that violate financial common sense.

[0143] The multi-objective decision tree sub-model includes:

[0144] Modeling multi-objective problems:

[0145] Business decision variable table

[0146] Variable Type Example Constraints Financing decisions Bond issuance amount (million US dollars) Debt-to-asset ratio ≤ 70% Asset disposal Proportion of non-core business sales (%) Restrictions on non-compete agreements Supply chain adjustments Alternative supplier switching cycle (days) Minimum safety stock threshold

[0147] Optimization goal:

[0148] NSGA-III optimization framework:

[0149] Reference point generation is based on historical crisis case clustering (K-means++) to define reference vectors for six typical risk scenarios; the Pareto front solution set is evaluated using the hypervolume indicator to ensure the uniformity of solution distribution.

[0150] Constraint processing: hard constraints (such as "asset-liability ratio exceeds limit") adopt dynamic penalty function, and the secondary population solution participates in the next generation evolution through elite retention strategy to avoid the loss of high-quality genes.

[0151] Decision tree construction and path sorting:

[0152] When calculating Gini importance, multi-objective contribution is introduced to retain features that affect ≥2 objectives at the same time (such as “the joint impact of bond interest rate changes on f1 / f2”);

[0153] Node splitting rule optimization: Compare the hypervolume gains before and after the split and select the split point that maximizes the overall benefit of multiple objectives.

[0154] TOPSIS ranking:

[0155] In the formula, closeness is the degree of closeness between a solution and the ideal solution. The greater the closeness, the closer the solution is to the ideal solution and the more worthy it is to choose. + For the program and ideal solution +The Euclidean distance between them is used to measure the degree of deviation between the solution and the ideal solution in each target dimension. - is the solution and the negative ideal solution x - The Euclidean distance between them is used to measure the degree of deviation of the solution from the negative ideal solution in each target dimension. is the value of the ideal solution on the jth target, usually taking the theoretical optimal value of each target, that is, the best value that can be achieved in theory for each target. is the value of the negative ideal solution on the jth objective, taking the worst case in history, that is, the worst value of the objective in historical data or existing solutions, x ij is the normalized value of the i-th solution on the j-th target;

[0156] The ideal solution x+ takes the theoretical optimal value of each objective, and the negative ideal solution x- takes the worst historical case; the top 3 solutions are output, with an attached risk-return radar chart.

[0157] Positive feedback (LSTM → decision tree)

[0158] The predicted liquidity gap value serves as an input constraint for the decision tree: if the LSTM predicts a gap greater than $100 million in the next 30 days, the decision tree automatically eliminates inefficient options such as "short-term loan extension." The predicted confidence interval is used to quantify the risk level of the option.

[0159] Reverse correction (decision tree → LSTM)

[0160] The actual execution results are fed back into the training data: the changes in financial indicators after the decision tree solution is executed (such as a 2% reduction in financing costs) are added as new samples to the LSTM training set; extreme scenario data (such as "sudden war leading to supply chain disruptions") are synthesized through the adversarial generative network to enhance the robustness of the model.

[0161] The dynamic threshold adaptive module specifically includes:

[0162] Input layer: Real-time financial indicators including cash flow volatility, quick ratio, and interest coverage ratio; historical warning records, types of false positives / missing negatives in the past short period and trigger thresholds; macroeconomic indicators including CPI, PMI, and the Treasury yield curve; industry dynamics including the volatility of the Shenwan First-Level Industry Index and the popularity of ESG controversial events;

[0163] Reinforcement learning framework: The state space includes the deviation of corporate financial indicators from industry benchmarks, external environmental risk scores, and historical threshold adjustment trajectories. The action space includes threshold adjustment directions such as increase, maintain, and decrease, the threshold adjustment amplitude, and the step size adjusted according to the risk level. This is calculated through a reward function.

[0164] Output layer: Generates elastic threshold intervals for each indicator and scenario through the early warning threshold matrix; quantifies the impact of external parameters on thresholds through sensitivity analysis reports;

[0165] Hierarchical reinforcement learning: The high-level strategy uses the PPO algorithm and regularly updates the global threshold strategy; the low-level execution is based on the current state and fine-tuned in real time;

[0166] Causal reasoning integration: Construction of causal graphs, identifying the causal relationship between thresholds and external factors through PC algorithms; counterfactual analysis, simulating the impact of events that have not occurred.

[0167] It should be noted that the cash flow volatility is a robust volatility (anti-outlier interference) calculated based on the Huber loss function, and the formula is:

[0168]

[0169] Where σ Huber is the robust volatility calculated based on the Huber loss function. It is a robust indicator for measuring cash flow fluctuations and has a certain anti-interference ability against outliers. N is the sample size, that is, the number of cash flow data points used to calculate volatility. i is the value of the i-th cash flow data point, representing the cash flow at a certain time point or within a certain period, μ is the mean of the cash flow data, that is, the average cash flow, which is used to measure the center position of the data, ρ(x i -μ) is the Huber loss function in (x i -μ), which is used to measure the contribution of the difference between the i-th cash flow data point and the mean to the volatility. z is the input variable of the Huber loss function, usually expressed as the difference between the data point and the mean. δ is a parameter of the Huber loss function, with a value of 1.345, which is used to balance computational efficiency and robustness. It determines how outliers in the data are handled differently when calculating volatility: when the absolute difference between the data point and the mean does not exceed δ, a quadratic function is used to calculate the difference; when the absolute difference exceeds δ, a linear function is used to calculate the difference, thereby reducing the impact of outliers on volatility.

[0170] Dynamic weight of quick ratio: When the popularity of ESG controversy events in the industry exceeds 60 points, the quick ratio threshold weight will automatically increase by 20% (to cope with the risk of sudden bank runs).

[0171] Macro-industry coupling indicators and Treasury yield curve morphology encoding use cubic spline function to fit the curve, extract key features, and map them to the threshold sensitivity matrix. The industry index volatility transmission model calculates the beta coefficient between the Shenwan first-level industry index and the target company, and dynamically adjusts the threshold elasticity range. For example, when beta > 1.2, the threshold volatility tolerance is narrowed by 30%.

[0172] Reinforcement Learning Framework:

[0173] Environmental risk score: A weighted index integrating CPI (weight 30%), PMI (40%), and ESG heat (30%), mapped to the state space in 5 levels.

[0174] Reward function: R = w1·Accuracy-w2·False positive cost-w3·Miss positive loss

[0175] In the formula, R is the comprehensive reward value, which is the final output of the reward function. It is used to measure the pros and cons of a decision or behavior. It takes into account factors such as accuracy, false alarm cost and omission loss. It is an overall evaluation indicator used to guide the decision-making process so that the larger the reward value, the better; w1 is the weight coefficient of accuracy, which is used to measure the relative importance of accuracy in the calculation of comprehensive rewards. It determines the degree of influence of accuracy on the final reward value and determines the balance of various factors in the reward function together with other weight coefficients; w2 is the weight coefficient of false alarm cost, which reflects the importance of false alarm cost in the calculation of comprehensive rewards. By setting different w2 values, the degree of influence of false alarm cost on the final reward can be adjusted; w3 is the weight coefficient of omission loss, which indicates the weight of omission loss in the calculation of comprehensive rewards. It determines the influence of omission loss on the final reward value.

[0176] False alarm cost: graded by business impact, e.g., a level 1 false alarm (triggering an erroneous fund freeze) has a cost weight of w2 = 0.6w2 = 0.6;

[0177] Dynamic weight adjustment: When PMI < 50, the weight of underreporting loss w3w3 is increased by 50% (to prevent systemic risks during economic downturn).

[0178] Hierarchical Reinforcement Learning:

[0179] High-level strategies and global strategy updates are trained quarterly based on industry-wide data, outputting meta-strategies with threshold adjustments (such as "prioritizing cash flow protection during inflation cycles"); importance sampling optimization, using the Clip mechanism to control the strategy update amplitude and ensure training stability (ε=0.2).

[0180] The underlying real-time execution and fine-tuning mechanism immediately calls upon the detection of sudden policy events (such as a central bank reserve requirement ratio cut) to generate temporary thresholds, with a response delay of less than 200ms.

[0181] The experience replay pool stores the most recent 100,000 state-action-reward tuples, supporting fast fine-tuning with small samples.

[0182] Causal Inference Engine:

[0183] Through the conditional independence test (significance level α = 0.01), the causal chain between the threshold and the external factor is identified, for example: Treasury yield inversion → financing cost increase → 0.35 interest coverage threshold is lowered; Treasury yield inversion → financing cost increase → 0.35 interest coverage threshold is lowered;

[0184] Causal effect quantification, using the backdoor adjustment formula to calculate the intervention effect:

[0185]

[0186] Where P(threshold|do(CPI=3%)) is the probability of the threshold occurring when the CPI is intervened (i.e., forced to set) at 3%. This is the causal effect calculated by the backdoor adjustment formula, that is, the probability of the threshold occurring under the condition of CPI intervention; the threshold refers to a specific judgment value or standard value in the construction of the causal graph. When an indicator reaches this threshold, it will trigger the corresponding causal relationship or effect; CPI is the Consumer Price Index, a macroeconomic indicator that reflects the changes in the price level of consumer goods and services generally purchased by households; Z=z, where Z is a variable representing other variables or conditions used for adjustment in causal analysis, and z represents the specific value of the variable; P(z) is the marginal probability of variable Z taking the value z, which indicates the probability of variable Z taking the value z without considering other variables.

[0187] Counterfactual deduction:

[0188] Virtual scenario simulation: For example, "What would the current threshold be if no ESG controversy occurred?" GAN is used to generate adversarial samples and correct model biases.

[0189] Robustness verification: Hypothetically prune each edge in the causal graph (e.g., remove the “PMI→Quick Ratio” edge) and evaluate changes in threshold stability.

[0190] The human-machine collaborative early warning terminal specifically includes:

[0191] Visual interactive interface: Risk Panorama Dashboard, including a real-time financial health index on the left, a supply chain network topology diagram in the middle, and a public opinion sentiment polarity radar chart on the right. Its dimensions include debt, compliance, ESG, market, supply chain, and management reputation.

[0192] Drill-down analysis function: Double-click the heat map area to display historical trends; voice command support, multi-dialect recognition based on the Whisper-V3 model;

[0193] Risk heat map generation: Spatial dimension, drill down by region and business unit; Time dimension, supports sliding windows of short-term time intervals to predict risk transmission paths; Object dimension, distinguishes legal entities and supply chain levels;

[0194] Decision recommendation confidence score: Input factors include data quality score, historical model accuracy, and external environment stability; expert rules are integrated through the Bayesian network to output the confidence interval; a dynamic attenuation mechanism is used, and if the data is not updated within a specified time, the score will be automatically reduced proportionally.

[0195] It should be noted that the risk panorama dashboard:

[0196] Real-time Financial Health Index:

[0197] Calculation model: A dynamic weighted index that integrates cash flow volatility (30%), quick ratio (25%), interest coverage ratio (25%), and industry deviation (20%), updated every second;

[0198] Graded warning strategy table

[0199] Index range Indicator color Response Strategy ≥85 green Normal monitoring 70-84 yellow Department-level consultation ≤69 red Executive War Room activates emergency plan

[0200] Supply chain network topology diagram:

[0201] Node intelligent mapping:

[0202] Core suppliers are marked as hexagons (risk resistance ≥ AA level), and secondary suppliers are marked as circles (with transparency indicating on-time delivery rate);

[0203] Risk transmission path prediction: Based on the graph convolutional network (GCN), it highlights logistics links that may be disrupted in the next 72 hours (such as port nodes covered by typhoon paths).

[0204] Public opinion sentiment polarity radar chart:

[0205] Sentiment polarity quantification:

[0206] Debt dimension: Analyze the sentiment values ​​of keywords such as "extension" and "default" (VADER algorithm);

[0207] ESG dimension: Identify the semantic conflict between environmental penalties and carbon neutrality statements (BERT cross-attention score).

[0208] Decision Confidence Engine:

[0209] Input factor quantization:

[0210] Factor Type Calculation method Weight Data quality analysis Missing rate ≤ 5% → 100 points, 5 points will be deducted for every 1% exceeding the limit 35％ Model historical accuracy Rolling 90-day F1-score average 40％ External environment stability Calculating the volatility of macroeconomic indicators using the entropy method 25％

[0211] Reference Figure 4 As shown, an intelligent financial risk early warning method based on management decision-making includes:

[0212] Synchronously acquire enterprise financial data, supply chain data, market sentiment data, and industry benchmark data through API interfaces; use natural language processing technology to extract risk keywords from unstructured text, and construct an entity-relationship-attribute triple database through knowledge graphs;

[0213] Windowed processing of real-time data streams based on the Apache Flink framework calculates the dynamic volatility of financial indicators; uses chi-square tests to identify abnormal fluctuations and trigger real-time warning signals;

[0214] Risk decision-making fusion modeling: The first prediction layer uses an LSTM network to predict a company's debt repayment capacity and liquidity gap, with input time series data including cash flow, industry prosperity index, and external interest rate changes. The second optimization layer uses a multi-objective optimization algorithm to generate a Pareto optimal solution set for risk response paths. The optimization objectives include maximizing the return on investment, minimizing the debt-to-asset ratio, and constraining strategic deviation.

[0215] Dynamic threshold adaptive adjustment, defining the reinforcement learning state space as the deviation between financial indicators and industry benchmarks, and the action space as the threshold increase and decrease range; based on the Q-Learning algorithm, iteratively optimize the threshold strategy, and the reward function integrates the false alarm rate, false negative rate, and sensitivity to environmental parameters;

[0216] Human-machine collaborative early warning output displays risk heat maps through a visual interface, marking risk transmission paths; provides decision-making recommendation confidence scores and historical case matching analysis.

[0217] It should be noted that the visual deduction and risk heat map rendering:

[0218] Spatial dimension: OpenLayers map overlaying supply chain node risk scores (red→yellow→green gradient);

[0219] Time dimension: TensorFlow.js drives sliding window predictions, dynamically playing risk diffusion simulations for the next seven days;

[0220] For historical case matching, the DTW algorithm is used to calculate the temporal similarity between the current situation and historical crises.

[0221] In summary, the advantages of the present invention are:

[0222] A streaming computing framework enables millisecond-level monitoring of financial indicator fluctuations. The integration of knowledge graphs and natural language processing technologies extracts risk entities and causal chains from unstructured data, building a multi-dimensional risk profile. Dynamic window analysis and chi-square tests are integrated to improve the capture rate of abnormal events. Reinforcement learning-driven threshold management enables autonomous evolution of warning boundaries based on environmental deviations and reward and punishment mechanisms, addressing the rigidity of static rules. Combined with an improved multi-objective optimization algorithm, Pareto-optimal solutions are simultaneously generated that balance financial security and strategic objectives. A visual war room interface is constructed, supporting drill-down on risk heat maps, voice interaction, and historical case matching. The decision confidence engine integrates Bayesian networks and expert rules to output a quantifiable and verifiable scoring system. The causal reasoning module transcends the limitations of black boxes, revealing strategic blind spots through counterfactual reasoning. This improves executive decision adoption and forms a closed-loop risk prevention and control system.

[0223] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent financial risk early warning system based on management decision-making, characterized by: include: The multi-source heterogeneous data collection module acquires enterprise financial data, supply chain data, market sentiment data, and industry benchmark data in real time through API interfaces. Market sentiment data uses natural language processing technology to extract risk keywords from news, social media, and policy documents. The streaming data processing engine, built on the Apache Flink framework, performs windowed processing on real-time data streams. By sliding time windows, it calculates the dynamic volatility of financial indicators and compares it with pre-set industry risk thresholds. The risk decision-making fusion model integrates management decision rules and machine learning algorithms. The first sub-model, a time series prediction model based on an LSTM network, predicts a company's short-term debt repayment capacity and liquidity gap. The second sub-model, a decision tree model based on multi-objective optimization, generates a prioritized ranking of risk response paths. Dynamic threshold adaptation module, which uses reinforcement learning algorithm to adjust risk warning threshold in real time and optimizes model sensitivity based on external environmental parameters; The human-machine collaborative early warning terminal is equipped with a visual interactive interface to display multi-dimensional early warning results in the form of a risk heat map, and provide confidence scores for decision recommendations and historical case matching analysis.

2. The intelligent financial risk early warning system based on management decision-making according to claim 1 is characterized in that: The multi-source heterogeneous data acquisition module specifically includes: Enterprise financial data: including dynamic cash flow, balance sheet, income statement, accounts receivable, aging analysis, internal enterprise systems, connecting to SAP and Oracle ERP via OAuth2.0 protocol, calling RESTful API to obtain encrypted data streams; connecting to bank open platforms to obtain real-time transaction flow; Supply chain data: Upstream data includes supplier credit ratings, raw material price fluctuations, and delivery on-time rates; downstream data includes customer order fulfillment rates, distributor inventory turnover days, and abnormal fluctuations in return rates. Logistics node status is tracked through RFID and GPS; access to the Hyperledger Fabric consortium chain verifies the authenticity of supplier contract performance. Market sentiment data: including news media, social media, and policy documents; a risk semantic recognition engine fine-tuned based on the GPT-4 architecture, supporting entity recognition and sentiment analysis; combining text with PDF scans of policy documents to extract key terms through OCR and semantic segmentation; Industry benchmark data: including industry average financial indicators, macroeconomic indicators, and competitor dynamics; protects data privacy by jointly training benchmark models across enterprise data pools; automatically triggers benchmark value iteration when the data of leading enterprises in the industry deviate from the mean.

3. The intelligent financial risk early warning system based on management decision-making according to claim 2 is characterized in that: The multi-source heterogeneous data acquisition module specifically includes: Heterogeneous data standardization unit: Financial data is converted to XBRL format and mapped to IFRS international standards; supply chain data is constructed by building an entity relationship model based on ontology and defining the "supplier-order-logistics" triple; public opinion data is associated with events, enterprises, and regulatory nodes through knowledge graphs to generate risk propagation links; Real-time assurance unit: Streaming collection architecture, driven by events, uses Kafka message queues to partition and process different data sources; breakpoint resuming, using Apache Pulsar's persistent subscription mechanism; data credibility verification, key data hash values ​​are uploaded to the chain, supporting real-time inspection by regulatory agencies; identification of forged streams based on the isolation forest algorithm; Risk keyword extraction unit: Multilingual processing: Chinese uses the BERT-wwm-ext model for word segmentation; English uses the SpaCy library for named entity recognition and annotates ORG and LAW tags.

4. The intelligent financial risk early warning system based on management decision-making according to claim 3 is characterized in that: The streaming data processing engine specifically includes: The data access layer connects to Kafka and Pulsar message queues, with financial data taking the highest priority. The computing layer uses Flink's DataStream API to define sliding windows, volatility algorithms, and threshold comparison rules. The output layer pushes warning signals to the risk decision model and writes them into Elasticsearch for visualization terminal calls; The time window controller dynamically adjusts the window size to adapt to the characteristics of different financial indicators. Window types include high-frequency, medium-frequency, and low-frequency financial indicators. Event time processing uses a watermark strategy. The side output stream captures timeout data and triggers a secondary warning process. Volatility calculation operator with built-in rolling standard deviation, coefficient of variation, and Holt-Winters seasonal forecast algorithms; Threshold comparison engine, real-time access to industry risk database, supports multi-dimensional thresholds.

5. The intelligent financial risk early warning system based on management decision-making according to claim 4 is characterized in that: The risk decision fusion model module specifically includes: The first sub-model is a time series prediction model based on LSTM network; Input layer: Time series data, including past short- to medium-term corporate cash flow, quick ratio, and interest coverage ratio, aligned at daily / weekly granularity; external factors, including industry prosperity index and changes in the central bank's benchmark interest rate; Training strategy: Using QuantileLoss to optimize the prediction accuracy of different confidence intervals; Dynamic calibration: Online learning: When the deviation between actual cash flow and predicted value exceeds the established range for multiple consecutive days, incremental model training is triggered; the Transformer attention mechanism is introduced to identify the impact weight of sudden policy events on debt repayment capacity.

6. The intelligent financial risk early warning system based on management decision-making according to claim 5 is characterized in that: The risk decision fusion model module specifically includes: The second sub-model is a decision tree model based on multi-objective optimization; multi-objective problem modeling includes decision variables and optimization objectives; NSGA-III optimization framework: Reference point generation, defining reference vectors based on historical data clustering, and uniform distribution on the Pareto front; Constraint processing, using a penalty function method to eliminate solutions with excessive debt-to-asset ratios to the secondary population; Decision tree construction: Use Gini importance ranking to retain the top key factors; node splitting rules, based on super volume gain, prioritize segmentation to simultaneously improve the characteristics of multiple targets; Path priority sorting: TOPSIS comprehensive evaluation, calculating the Euclidean distance between each solution and the ideal solution / negative ideal solution; Model fusion: Forward feedback: the liquidity gap predicted by LSTM serves as the input constraint of the decision tree module; reverse correction: the actual execution effect of the decision tree output feeds back to the LSTM training data.

7. The intelligent financial risk early warning system based on management decision-making according to claim 6 is characterized in that: The dynamic threshold adaptive module specifically includes: Input layer: Real-time financial indicators including cash flow volatility, quick ratio, and interest coverage ratio; historical warning records, types of false positives / missing negatives in the past short period and trigger thresholds; macroeconomic indicators including CPI, PMI, and the Treasury yield curve; industry dynamics including the volatility of the Shenwan First-Level Industry Index and the popularity of ESG controversial events; Reinforcement learning framework: The state space includes the deviation of corporate financial indicators from industry benchmarks, external environmental risk scores, and historical threshold adjustment trajectories. The action space includes threshold adjustment directions such as increase, maintain, and decrease, the threshold adjustment amplitude, and the step size adjusted according to the risk level. This is calculated through a reward function. Output layer: Generates elastic threshold intervals for each indicator and scenario through the early warning threshold matrix; quantifies the impact of external parameters on thresholds through sensitivity analysis reports; Hierarchical reinforcement learning: The high-level strategy uses the PPO algorithm and regularly updates the global threshold strategy; the low-level execution is based on the current state and fine-tuned in real time; Causal reasoning integration: Construction of causal graphs, identifying the causal relationship between thresholds and external factors through PC algorithms; counterfactual analysis, simulating the impact of events that have not occurred.

8. The intelligent financial risk early warning system based on management decision-making according to claim 7 is characterized in that: The human-machine collaborative early warning terminal specifically includes: Visual interactive interface: Risk Panorama Dashboard, including a real-time financial health index on the left, a supply chain network topology diagram in the middle, and a public opinion sentiment polarity radar chart on the right. Its dimensions include debt, compliance, ESG, market, supply chain, and management reputation. Drill-down analysis function: Double-click the heat map area to display historical trends; voice command support, multi-dialect recognition based on the Whisper-V3 model; Risk heat map generation: Spatial dimension, drill down by region and business unit; Time dimension, supports sliding windows of short-term time intervals to predict risk transmission paths; Object dimension, distinguishes legal entities and supply chain levels; Decision recommendation confidence score: Input factors include data quality score, historical model accuracy, and external environment stability; expert rules are integrated through the Bayesian network to output the confidence interval; a dynamic attenuation mechanism is used, and if the data is not updated within a specified time, the score will be automatically reduced proportionally.

9. An intelligent financial risk early warning method based on management decision-making, according to the intelligent financial risk early warning system based on management decision-making according to claims 1-8, characterized in that: include: Synchronously obtain enterprise financial data, supply chain data, market sentiment data and industry benchmark data through API interfaces; Use natural language processing technology to extract risk keywords from unstructured text and build an entity-relationship-attribute triple database through knowledge graph; Use the Apache Flink framework to perform window processing on real-time data streams and calculate the dynamic volatility of financial indicators. Identify abnormal fluctuations through chi-square tests and trigger real-time warning signals; Risk decision fusion modeling, the first prediction layer, uses the LSTM network to predict corporate debt repayment ability and liquidity gap. Input time series data includes cash flow, industry prosperity index, and external interest rate changes. The second optimization layer uses a multi-objective optimization algorithm to generate a Pareto optimal solution set for risk response paths. The optimization objectives include maximizing the return on investment, minimizing the debt-to-asset ratio, and constraining strategic deviation. Dynamic threshold adaptive adjustment, defining the reinforcement learning state space as the deviation between financial indicators and industry benchmarks, and the action space as the threshold increase and decrease range; based on the Q-Learning algorithm, iteratively optimize the threshold strategy, and the reward function integrates the false alarm rate, false negative rate, and sensitivity to environmental parameters; Human-machine collaborative early warning output, displaying risk heat maps through a visual interface and marking risk transmission paths; Provide decision-making recommendation confidence scores and historical case matching analysis.

10. The intelligent financial risk early warning method based on management decision-making according to claim 9, characterized in that: The dynamic analysis of streaming data specifically includes: Window type selection: high-frequency trading data, including stock prices and exchange rates, to capture micro-sentiment fluctuations in the market; medium-frequency operational data, including accounts receivable turnover, to balance real-time performance with noise filtering; and low-frequency strategic data, including debt-to-asset ratios, to monitor long-term structural risks. Time semantics and out-of-order processing adopt a watermark strategy, allowing the maximum delay to be within the established timeline. The side output stream captures timeout data, marks it as overdue events, and triggers secondary warnings; event time alignment is based on the actual time of occurrence of financial events.

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