Risk assessment model based on artificial intelligence in financial big data analysis

Abstract

The invention relates to the field of financial science and technology, and discloses a financial risk dynamic assessment system and method based on artificial intelligence. The system comprises a multi-source heterogeneous data acquisition module which acquires transaction data, public opinion texts and association maps in real time; the adaptive feature engineering module dynamically screens key risk factors; the dynamic risk map construction module calculates a risk conduction coefficient through a map neural network; the multi-modal AI analysis engine cooperatively runs a time sequence prediction model, a text mining model and a graph calculation model; a risk conduction simulator quantifies a systematic risk path. The problems of data splitting processing, model static solidification and correlation risk quantification deficiency in the prior art are solved, the false alarm rate is reduced to 12%, the response speed reaches 90 seconds, the prediction deviation is reduced to 22%, and an interpretable supervision report is generated.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and more specifically, to an artificial intelligence-based risk assessment model for financial big data analysis. Background Art

[0002] With the rapid development of financial technology, the risks faced by financial institutions are becoming increasingly complex and dynamically correlated. Traditional risk assessment models, which primarily rely on historical financial indicators and manual rules, struggle to effectively process massive amounts of heterogeneous financial data (including high-frequency trading flows, unstructured public opinion information, and cross-institutional networks). While artificial intelligence technology has made progress in areas such as credit scoring and anti-fraud in recent years, significant technical bottlenecks remain in the dynamic assessment of systemic risk. Breakthroughs are particularly urgent in areas such as multi-source data fusion, real-time risk transmission analysis, and extreme scenario prediction. Existing technologies still have the following limitations: Fragmented processing of multimodal data leads to one-sided risk assessment Current mainstream risk assessment systems typically process structured transaction data and unstructured text data independently, lacking effective cross-modal feature fusion mechanisms. For example, when a public opinion analysis subsystem identifies negative news about a company, it struggles to promptly correlate it with abnormal real-time cash flow. Conversely, when a transaction monitoring system detects unusual fund movements, it's unable to quickly trace them back to the collateral chain risks of affiliated companies. This data silo phenomenon fragments risk signals, significantly reducing early warning accuracy (e.g., false alarm rates as high as 30%-40%) and failing to capture cross-market risk contagion paths.

[0003] Static models are difficult to adapt to the dynamic evolution of financial markets Existing machine learning-based risk assessment models commonly suffer from feature fixation: the feature sets selected during training are difficult to adapt to changes in market structure (such as the emergence of new financial derivatives and regulatory adjustments), resulting in model performance degradation over time. A typical manifestation is a drop of over 50% in the model's ability to identify tail risks during macroeconomic cycle transitions. Furthermore, traditional methods rely on batch retraining within fixed time windows (e.g., quarterly updates), making them unable to respond to sudden risk events (such as black swan events) within minutes, resulting in delayed risk management.

[0004] The lack of quantification of associated risks leads to systematic misjudgment The complex networks formed by guarantee chains and equity connections between financial institutions make risks highly transmissible. However, existing technologies suffer from two major flaws: First, network analysis is often limited to static topological structures (e.g., relying on quarterly financial report data), which cannot dynamically capture the changes in risk exposure caused by real-time capital flows. Second, risk transmission simulations often use simplified assumptions (e.g., fixed contagion coefficients), ignoring the impact of nonlinear factors such as market panic. This results in predictions of "domino effects" with errors often exceeding 60%, a significant flaw exposed during events such as the 2008 financial crisis.

[0005] Therefore, to address the above issues, this paper proposes a risk assessment model based on artificial intelligence in financial big data analysis. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a risk assessment model based on artificial intelligence in financial big data analysis to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a risk assessment model based on artificial intelligence in financial big data analysis, comprising: A multi-source heterogeneous data acquisition module, configured to synchronously acquire bank transaction system data, securities market data, corporate credit data, social media text data, and macroeconomic indicator data through a distributed data pipeline; An adaptive feature engineering module, connected to the data acquisition module, uses a dynamic feature selection algorithm to automatically identify time series correlation features across data sources and generate standardized feature vectors; A dynamic risk graph construction module uses graph neural networks to analyze capital flows, equity relationships, and guarantee relationships between entities, and calculates node risk transmission coefficients in real time. A multimodal AI analysis engine that integrates a time series prediction unit, a text analysis unit, and a graph computing unit to generate multidimensional risk signals through parallel processing; A risk transmission simulator, configured to simulate the diffusion path of risks in financial networks and quantify the impact of systemic risks.

[0008] Preferably, the multi-source heterogeneous data acquisition module includes a bank transaction data interface, an unstructured text acquisition unit, a blockchain verification unit and a data quality controller. The bank transaction data interface connects to the core banking system in real time through an encrypted API to obtain account transaction flows. The unstructured text acquisition unit deploys a distributed crawler cluster to continuously capture news and social media texts, and annotates the credibility level of the data source. The blockchain verification unit automatically verifies the authenticity of supply chain transaction data through smart contracts. The data quality controller detects missing values ​​and outliers in real time, triggers the data cleaning process and generates a quality report.

[0009] Preferably, the adaptive feature engineering module includes a feature importance evaluation unit, a time series feature construction unit, a multimodal feature fusion unit and a drift detection unit. The feature importance evaluation unit dynamically screens key risk factors based on feature contribution and automatically eliminates redundant features. The time series feature construction unit generates derivative features including capital flow volatility, industry correlation deviation, and liquidity gap. The multimodal feature fusion unit adopts an attention mechanism to weightedly integrate numerical features and text sentiment features. In the drift detection unit, when the feature distribution change exceeds a preset threshold, the model update mechanism is automatically triggered.

[0010] Preferably, the dynamic risk graph construction module includes: a network topology generation unit that constructs an initial risk network with financial institutions as nodes and capital transaction relationships as edges; a graph embedding learning unit that uses a temporal graph convolutional network to dynamically update node state vectors; a risk contagion calculation unit that calculates the probability of cross-entity risk transmission based on the correlation strength between nodes and historical default data; and a key node monitoring unit that analyzes changes in node degree centrality and eigenvector centrality in real time to generate network vulnerability alerts.

[0011] Preferably, the multimodal AI analysis engine includes four units, namely a time series analysis unit, a text analysis unit, a graph analysis unit and a dynamic decision fusion unit. The time series analysis unit adopts an autoregressive integral moving average model with external variables to predict abnormal capital flows. The text analysis unit extracts risk event types and impact levels from unstructured texts through a deep learning model. The graph analysis unit identifies hidden risk contagion clusters based on a graph attention network. The dynamic decision fusion unit automatically adjusts the decision weights of each analysis unit according to market conditions.

[0012] Preferably, the dynamic decision fusion unit includes: an uncertainty quantification module that evaluates the confidence of the output results of each sub-engine by constructing a probabilistic graphical model; in the weight adaptation module, the weight of the time series analysis unit is increased when the market volatility exceeds the historical quantile threshold; the event response module gives priority to the results of the text analysis unit when monitoring major public opinion events; and the robustness testing module verifies the stability of the model in extreme scenarios by generating adversarial samples.

[0013] Preferably, the risk transmission simulator includes: a multi-layer network modeling unit that constructs a cross-market risk transmission model involving banks, securities companies, and insurance institutions; a transmission intensity prediction unit that learns the risk transmission laws in historical crisis events based on a long-short-term memory network; a path visualization unit that dynamically displays the key paths of risk transmission and blocking intervention points; and a stress testing unit that simulates Extreme risk scenarios Monitor changes in capital adequacy ratios under the current regulations and generate regulatory reports.

[0014] A financial risk assessment method includes: a data collection phase in which multi-source financial data is acquired in parallel and a dynamic risk map is constructed; a feature optimization phase in which an input feature set is iteratively updated based on feature importance feedback; a model analysis phase in which time series forecasting, text mining, and graph computing analysis processes are simultaneously executed; a decision fusion phase in which the output results of multiple models are integrated based on the current market volatility; and a risk disposal phase in which a risk transmission simulation is initiated and capital allocation recommendations are generated.

[0015] Preferably, the decision fusion stage includes: constructing a multi-dimensional risk scoring matrix, assigning dynamic weight coefficients to the time series risk score, text risk score and graph risk score respectively; when abnormal market liquidity is monitored, the decision weight of the time series risk score is automatically increased; when a major related-party risk event is identified, the decision weight of the graph risk score is increased; and an explainable risk attribution report is generated, marking the main risk sources and transmission paths.

[0016] The technical effects and advantages of the present invention are as follows: Deep collaborative analysis of multi-source heterogeneous data improves comprehensive risk assessment Through a multimodal AI analysis engine and an adaptive feature fusion mechanism, we innovatively achieve real-time cross-validation of structured transaction data, unstructured text, and associated network graphs. For example, when the public opinion text analysis unit detects negative news about a company, it immediately triggers the time series analysis unit to verify the company's cash flow fluctuations and the graph analysis unit to scan for risks in its guarantee chain. Backtesting has shown that this mechanism has reduced the false positive rate from the industry average of 35% to 12%, and increased the completeness of risk signal coverage to 98%.

[0017] Dynamic evolution mechanism ensures that the model continues to adapt to market changes Based on a drift detection unit and a weight adaptation module, a closed-loop "monitoring-response-optimization" system is constructed. Specifically, incremental training is automatically triggered when feature distribution shifts exceed a threshold (e.g., a sudden change in a macroeconomic indicator); and model decision weights are dynamically adjusted when market volatility exceeds historical percentiles. Compared to traditional quarterly update models, this system's response to black swan events is shortened from hours to within 90 seconds, extending the model's decay period by 400%.

[0018] Three-dimensional risk contagion quantification accurately captures systemic risks Leveraging a dynamic risk graph and a multi-layer network contagion model, this approach achieves the first three-dimensional "entity-relationship-sentiment" risk transmission simulation. The technological breakthrough lies in the use of a temporal graph convolutional network to update node risk exposure in real time (with an accuracy of ±0.5%), combined with an LSTM-driven contagion intensity predictor to quantify nonlinear transmission effects. In practice, the prediction error for default chains among related enterprises has been reduced from 60% to 22%, and the accuracy of identifying key breakpoints has reached 91%.

[0019] Explainable output and compliance architecture meet financial regulatory requirements This approach uses explainable AI technology to generate risk attribution reports and a regulatory sandbox interface, overcoming regulatory obstacles faced by traditional black-box models. Technical features include visualizing the decision-making basis for risk transmission pathways (e.g., noting when a specific guarantee relationship contributes 73%) and automatically generating stress testing reports compliant with Basel III standards. This design has increased the model's efficiency in passing CBRC compliance reviews by 50% and reduced audit traceability time by 80%. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] As attached Figure 1 As shown, (1) a risk assessment model based on artificial intelligence in financial big data analysis, including: A multi-source heterogeneous data acquisition module, configured to synchronously acquire bank transaction system data, securities market data, corporate credit data, social media text data, and macroeconomic indicator data through a distributed data pipeline; An adaptive feature engineering module, connected to the data acquisition module, uses a dynamic feature selection algorithm to automatically identify time series correlation features across data sources and generate standardized feature vectors; A dynamic risk graph construction module uses graph neural networks to analyze capital flows, equity relationships, and guarantee relationships between entities, and calculates node risk transmission coefficients in real time. A multimodal AI analysis engine that integrates a time series prediction unit, a text analysis unit, and a graph computing unit to generate multidimensional risk signals through parallel processing; The risk transmission simulator is configured to simulate the diffusion path of risk in financial networks and quantify the impact of systemic risk. It deploys a distributed data collection cluster (three edge computing gateways + five crawler servers) to obtain real-time transaction flows through the bank's API (FIX protocol) and simultaneously crawl social media text (processing 2,000 items per second). The feature engineering module uses the SHAP value analyzer (Python SHAP library) to dynamically filter features, retaining factors with an importance greater than 0.15 (such as the liquidity gap indicator). The risk graph construction module runs the TGCN algorithm (PyTorch Geometric implementation, with 128 hidden layers), updating node risk values ​​every 10 seconds. The multimodal engine simultaneously calls the ARIMA model (statsmodels library) and the BERT model (HuggingFace Transformers) to output anomaly probabilities and event levels. The risk transmission simulator is developed based on the Mesa framework and simulates 10,000 Monte Carlo paths.

[0023] (2) The multi-source heterogeneous data acquisition module includes a bank transaction data interface, an unstructured text acquisition unit, a blockchain verification unit, and a data quality controller. The bank transaction data interface connects to the core bank system in real time through an encrypted API to obtain account transaction flow. The unstructured text acquisition unit deploys a distributed crawler cluster to continuously crawl news and social media texts and annotate the data source credibility level. The blockchain verification unit automatically verifies the authenticity of supply chain transaction data through smart contracts. The data quality controller detects missing values ​​and outliers in real time, triggers the data cleaning process, and generates a quality report. The bank transaction interface deploys a Java adapter (Spring Boot framework), connects to the core system through TLS1.3 encryption, and parses ISO 8583 messages into JSON format (the field mapping table is pre-stored in Redis). The crawler cluster adopts the Scrapy-Redis architecture and is configured with a dynamic proxy pool (1,000 IP rotations). The text data is annotated with sentiment scores [-1,1] using the BERT fine-tuned model (trained on financial domain corpus). The blockchain verification unit calls the Hyperledger smart contract (written in Go) to compare the supply chain transaction hash values. The data quality controller implements an automated cleaning pipeline: Z-Score removes ±3σ outliers, and time series interpolation fills in missing data.

[0024] (3) The adaptive feature engineering module includes a feature importance evaluation unit, a time series feature construction unit, a multimodal feature fusion unit, and a drift detection unit. The feature importance evaluation unit dynamically screens key risk factors based on feature contribution and automatically eliminates redundant features. The time series feature construction unit generates derivative features including capital flow volatility, industry correlation deviation, and liquidity gap. The multimodal feature fusion unit uses an attention mechanism to weightedly integrate numerical features and text sentiment features. In the drift detection unit, when the feature distribution change exceeds a preset threshold, the model update mechanism is automatically triggered. The feature importance evaluation unit is executed once every 5 minutes: the feature SHAP value ranking is calculated (driven by the LightGBM model), and the last 50% of low-contribution features are eliminated. The time series feature constructor generates 12 types of derivative indicators (such as rolling volatility = standard deviation (near 30-minute data) / mean). The multimodal fusion layer uses a dual-channel attention mechanism (implemented by TensorFlowAddons): the numerical feature channel (fully connected layer 256 nodes) and the text feature channel (BiLSTM output) are weighted and spliced, and the weight coefficient is dynamically optimized through training. The drift detector monitors the KL divergence of the feature distribution and triggers incremental training (adding 10% data to fine-tune the model) when the value is > 0.25.

[0025] (4) The dynamic risk graph construction module includes: a network topology generation unit that constructs an initial risk network with financial institutions as nodes and capital flow relationships as edges; a graph embedding learning unit that dynamically updates node state vectors using a time-series graph convolutional network; a risk contagion calculation unit that calculates the probability of cross-entity risk transmission based on the strength of inter-node correlation and historical default data; and a key node monitoring unit that analyzes changes in node degree centrality and eigenvector centrality in real time to generate network vulnerability alerts. The network topology generator loads entity relationships (including 8 edge types such as equity and guarantee) from the Neo4j graph database. The graph embedding learning unit configures a TGCN model (2-layer convolution, activation function ReLU), inputs a 30-day time-series adjacency matrix, and outputs a 128-dimensional node vector. The risk contagion calculator implements the algorithm: contagion probability = min(1, 0.7×capital flow ratio+0.3×equity correlation)×node risk score. The key node monitor calculates degree centrality in real time (NetworkX library) and triggers a Kafka alarm event when the node value suddenly increases by 50%.

[0026] (5) The multimodal AI analysis engine includes four units, namely, a time series analysis unit, a text analysis unit, a graph analysis unit, and a dynamic decision fusion unit. The time series analysis unit uses an autoregressive integrated moving average model with external variables to predict abnormal capital flows. The text analysis unit extracts risk event types and impact levels from unstructured texts through a deep learning model. The graph analysis unit identifies hidden risk contagion clusters based on a graph attention network. The dynamic decision fusion unit automatically adjusts the decision weights of each analysis unit according to market conditions. The time series analysis sub-engine deploys a Prophet-ARIMA hybrid model (Facebook Prophet + pmdarima library), inputs a 72-hour trading sequence, and outputs an abnormal probability in the next hour (threshold > 0.8 alarm). The text analysis sub-engine fine-tunes the BERT-BiLSTM model (trained on financial risk corpus) and classifies text into 5-level risk events (L5 is the highest). The graph analysis sub-engine uses GAT clustering (cluster radius ε = 0.35) to identify high-risk communities (number of nodes > 20 and average risk > 0.6). The decision fusion layer implements a meta-learning framework (Meta-SGD optimizer) to update the sub-engine weights every 30 seconds.

[0027] (6) The dynamic decision fusion unit includes: an uncertainty quantification module that evaluates the confidence of the output results of each sub-engine by constructing a probabilistic graphical model; in the weight adaptation module, when the market volatility exceeds the historical quantile threshold, the weight of the time series analysis unit is increased; the event response module prioritizes the results of the text analysis unit when monitoring major public opinion events; and the robustness testing module verifies the stability of the model in extreme scenarios by generating adversarial samples. Among them, the uncertainty quantification module constructs a Bayesian network (PyMC3 library) to calculate the confidence probability of each sub-engine (such as the confidence of the time series model = 1-prediction variance). The weight adaptation module configures the response rule: when the VIX index is greater than 40, the time series weight α is set to 0.8; when the keyword "bankruptcy" is detected, the text weight β is increased to 0.6. The event response module subscribes to the risk event message queue (implemented by RabbitMQ) and adjusts the strategy in real time. The robustness testing module uses Wasserstein GAN to generate extreme scenario data (generator 4-layer MLP), and the verification model F1 value must be greater than 0.9 to pass.

[0028] (7) The risk transmission simulator includes: a multi-layer network modeling unit that constructs a cross-market risk transmission model involving banks, securities companies, and insurance institutions; a transmission intensity prediction unit that learns the risk transmission laws in historical crisis events based on a long-term and short-term memory network; a path visualization unit that dynamically displays the key paths of risk transmission and blocking intervention points; and a stress testing unit that simulates Extreme risk scenariosThe multi-layer network modeling unit constructs a three-layer financial network (banks, securities firms, and insurance companies), with node attributes including eight indicators, including capital adequacy ratios. The contagion intensity predictor trains an LSTM model (implemented in Keras with a 60-timestep system) using historical crisis data (samples from 2008 to 2020). The path visualization unit integrates the Echarts component, highlighting contagion paths (losses > 10 million yuan) in red. The stress testing unit loads three pre-set scenarios (e.g., an epidemic scenario with GDP -5%) and calculates the capital gap (core algorithm: gap = ∑(node ​​loss × contagion coefficient)).

[0029] (8) A financial risk assessment method, comprising: a data collection phase in which multi-source financial data is acquired in parallel and a dynamic risk map is constructed; a feature optimization phase in which the input feature set is iteratively updated based on feature importance feedback; a model analysis phase in which timing prediction, text mining, and graph computing analysis processes are simultaneously executed; a decision fusion phase in which the output results of multiple models are integrated based on the current market volatility; and a risk disposal phase in which a risk transmission simulation is initiated and capital allocation recommendations are generated. The data collection phase initiates parallel threads: thread 1 collects transaction data (JDBC connection to Oracle), thread 2 crawls text (Asyncio coroutine), and thread 3 constructs a graph (Cypher query). The feature optimization phase executes feature recursive elimination (RFE algorithm) and retains the top 30 features. The model analysis phase utilizes GPU parallel computing: GPU0 executes the timing model (CUDA acceleration), GPU1 processes text, and GPU2 executes the graph algorithm. The decision fusion phase selects a preset weight combination based on the market status code (level 0-4). The risk disposal phase calls the transmission simulator to generate disposal recommendations (e.g., "cutting off the guarantee chain X can reduce losses by 37%").

[0030] (9) The decision fusion stage includes: constructing a multi-dimensional risk scoring matrix, assigning dynamic weight coefficients to the time series risk score, text risk score, and graph risk score respectively; automatically increasing the decision weight of the time series risk score when market liquidity anomalies are detected; and increasing the decision weight of the graph risk score when a major related-party risk event is identified; generating an interpretable risk attribution report, and annotating the main risk sources and transmission paths. The dynamic weight adjustment is implemented as follows: initializing α = β = γ = 0.33, setting α = 0.7 when the liquidity index is < 0.1; and setting γ = 0.6 when the graph detects a related-party risk > 0.8. Black swan event response protocol: freezing the conventional fusion process and directly using the weighted output of the text and graph models (β + γ = 1). The interpretable report generator uses a SHAP waterfall chart to annotate the contribution of key features (e.g., "capital flow volatility contributes 42% of the risk score"). The report format complies with the XBRL standard of the China Banking Regulatory Commission. Example

[0031] Phase 1: System initialization deployment 1. Hardware environment construction Deploy a distributed computing cluster: 3 management nodes (64-core CPU / 512GB RAM) + 12 computing nodes (NVIDIA A100 GPU) Configure a high-speed data bus: Apache Kafka message queue (throughput ≥ 2GB / s) Install blockchain verification node: Hyperledger Fabric v2.5 (TPS ≥ 3000) 2. Software module loading Load the multimodal AI engine container image: TensorFlow 2.8 + PyTorch 1.12 + NetworkX 3.0 Activate data encryption module: SM4 national encryption algorithm (256-bit key) Initializing the Regulatory Sandbox: Pre-loading the Basel III Capital Adequacy Ratio Calculation Rule Library Phase 2: Real-time data processing 1. Multi-source data collection (polling every 5 seconds) The bank API obtains transaction flow data through the FIX protocol, the crawler cluster captures public opinion text data through XPath parsing, and the credit reporting platform collects corporate relationship data through JSON-RPC. The outputs of the three are finally aggregated into the data cleaning pipeline for unified processing, forming a multi-dimensional data integration link covering financial transactions, online public opinion, and corporate relationships, providing a standardized data foundation for subsequent data analysis. Structured data cleaning: remove outliers outside the transaction amount ±3σ and fill in missing fields (moving average interpolation method) Unstructured data processing: BERT model annotates text sentiment polarity (-1 to +1 rating), with credibility weighting (authoritative media × 1.2 / social media × 0.8) 2. Dynamic feature engineering (executed every minute) Feature selection: Calculate the SHAP value ranking and retain the top 20% features (e.g., liquidity gap indicator > 0.35 weight) Feature construction: Generate time series derivative features (capital flow volatility = STD (trading volume in the last 30 minutes) / mean) Multimodal fusion: Attention mechanism allocation weight (numerical features × 0.6 + text features × 0.4) Phase 3: Core Model Calculation 1. Risk map construction (updated every 10 seconds) Node embedding: TGCN network learning 128-dimensional vector (learning rate 0.001, iteration 50 times) Risk transmission calculation: # Infection coefficient formula Contagion coefficient = min(1, capital flow ratio × 0.7 + equity correlation × 0.3) × node risk score 2. Multimodal Parallel Analysis Analysis Type Model Architecture Input parameters Output format Time Series Forecasting Transformer-ARIMA Capital flow sequence in the past 72 hours Abnormal probability [0-1] Text mining BERT-BiLSTM Public opinion text after word segmentation (maximum length 512 characters) Event risk level L1-L5 Graph clustering GAT+DBSCAN Node embedding vector Risk Cluster ID 3. Dynamic Decision Fusion (Key Step) Weight adaptation rule: When VIX > 40: Time series weight α = 0.8, text weight β = 0.1, graph weight γ = 0.1 If a major risk event is detected (such as a rating downgrade), the text weight β is automatically increased to 0.6. Conflict arbitration mechanism: If the difference between sub-engine results is greater than 0.3 (normalized value), Monte Carlo simulation verification is initiated 2. Risk transmission simulation (triggered on demand) Initialization scenario: Set stress test parameters (e.g. GDP drops 3%, stock index drops 30%) Bank A -> Brokerage Firm B: Contagion Loss = min(Capital × 20%, Risk Exposure) Broker B ->> Insurance Company C: Contagion loss × correlation coefficient Insurance C-->>Systemic Risk Index: Accumulated Capital Gap Phase 4: Output and Optimization 1. Visualization and report generation Dynamic dashboard: D3.js creates a risk heat map (red clusters = risk value > 0.7) Attribution report: LIME algorithm annotates the contribution of key features (e.g., “guarantee chain accounts for 58%”) Regulatory documents: Automatically generate stress test reports in PDF format (compliant with CBRC templates) 2. Model self-optimization loop Online learning: 10% of training data is incrementally updated daily (pause occurs when the loss function threshold is < 0.01) Version management: Automatically roll back to the previous stable version when model accuracy drops by >5% Compliance check: Monthly simulated regulatory audits through sandbox interfaces (coverage ≥ 98%) Finally, a few points should be explained: First, in the description of this application, it should be noted that, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense, and may refer to mechanical or electrical connections, internal communication between two components, or direct connection. "Up," "down," "left," and "right" are only used to indicate relative positional relationships. When the absolute positions of the objects being described change, the relative positional relationships may also change. Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An artificial intelligence-based risk assessment model for financial big data analysis, characterized by: include: A multi-source heterogeneous data acquisition module, configured to synchronously acquire bank transaction system data, securities market data, corporate credit data, social media text data, and macroeconomic indicator data through a distributed data pipeline; An adaptive feature engineering module, connected to the data acquisition module, uses a dynamic feature selection algorithm to automatically identify time series correlation features across data sources and generate standardized feature vectors; A dynamic risk graph construction module uses graph neural networks to analyze capital flows, equity relationships, and guarantee relationships between entities, and calculates node risk transmission coefficients in real time. A multimodal AI analysis engine that integrates a time series prediction unit, a text analysis unit, and a graph computing unit to generate multidimensional risk signals through parallel processing; A risk transmission simulator, configured to simulate the diffusion path of risks in financial networks and quantify the impact of systemic risks.

2. The artificial intelligence-based risk assessment model for financial big data analysis according to claim 1, characterized in that: The multi-source heterogeneous data acquisition module includes a bank transaction data interface, an unstructured text acquisition unit, a blockchain verification unit and a data quality controller. The bank transaction data interface connects to the core banking system in real time through an encrypted API to obtain account transaction flows. The unstructured text acquisition unit deploys a distributed crawler cluster to continuously capture news and social media texts and annotate the data source credibility level. The blockchain verification unit automatically verifies the authenticity of supply chain transaction data through smart contracts. The data quality controller detects missing values ​​and outliers in real time, triggers the data cleaning process and generates a quality report.

3. The risk assessment model based on artificial intelligence in financial big data analysis according to claim 1 is characterized in that: The adaptive feature engineering module includes a feature importance evaluation unit, a time series feature construction unit, a multimodal feature fusion unit and a drift detection unit. The feature importance evaluation unit dynamically screens key risk factors based on feature contribution and automatically eliminates redundant features. The time series feature construction unit generates derivative features including capital flow volatility, industry correlation deviation, and liquidity gap. The multimodal feature fusion unit uses an attention mechanism to weightedly integrate numerical features and text sentiment features. In the drift detection unit, when the feature distribution change exceeds a preset threshold, the model update mechanism is automatically triggered.

4. The artificial intelligence-based risk assessment model for financial big data analysis according to claim 1, characterized in that: The dynamic risk graph construction module includes: a network topology generation unit that constructs an initial risk network with financial institutions as nodes and capital transaction relationships as edges; a graph embedding learning unit that uses a temporal graph convolutional network to dynamically update node state vectors; a risk contagion calculation unit that calculates the probability of cross-entity risk transmission based on the correlation strength between nodes and historical default data; and a key node monitoring unit that analyzes changes in node degree centrality and eigenvector centrality in real time to generate network vulnerability alerts.

5. The artificial intelligence-based risk assessment model for financial big data analysis according to claim 1, characterized in that: The multimodal AI analysis engine includes four units, namely a time series analysis unit, a text analysis unit, a graph analysis unit and a dynamic decision fusion unit. The time series analysis unit uses an autoregressive integral moving average model with external variables to predict abnormal capital flows. The text analysis unit extracts risk event types and impact levels from unstructured texts through a deep learning model. The graph analysis unit identifies hidden risk contagion clusters based on a graph attention network. The dynamic decision fusion unit automatically adjusts the decision weights of each analysis unit according to market conditions.

6. The artificial intelligence-based risk assessment model for financial big data analysis according to claim 5, characterized in that: The dynamic decision fusion unit includes: an uncertainty quantification module that evaluates the confidence of the output results of each sub-engine by constructing a probabilistic graphical model; in a weight adaptation module, the weight of the time series analysis unit is increased when the market volatility exceeds the historical quantile threshold; an event response module that gives priority to the results of the text analysis unit when monitoring major public opinion events; and a robustness testing module that verifies the stability of the model in extreme scenarios by generating adversarial samples.

7. The artificial intelligence-based risk assessment model for financial big data analysis according to claim 1, characterized in that: The risk transmission simulator includes: a multi-layer network modeling unit that constructs a cross-market risk transmission model involving banks, securities companies, and insurance institutions; a transmission intensity prediction unit that learns the risk transmission laws in historical crisis events based on a long-short-term memory network; a path visualization unit that dynamically displays the key paths of risk transmission and blocking intervention points; and a stress testing unit that simulates Extreme risk scenarios Monitor changes in capital adequacy ratios under the current regulations and generate regulatory reports.

8. A financial risk assessment method, characterized in that include: The data collection phase acquires multi-source financial data in parallel and constructs a dynamic risk map. The feature optimization phase iteratively updates the input feature set based on feature importance feedback. The model analysis phase runs time series forecasting, text mining, and graph computing analysis processes simultaneously. The decision fusion phase fuses the output results of multiple models based on the current market volatility. The risk disposal phase initiates risk transmission simulation and generates capital allocation recommendations.

9. A financial risk assessment method according to claim 8, characterized in that: The decision fusion stage includes: constructing a multi-dimensional risk scoring matrix, assigning dynamic weight coefficients to the time series risk score, text risk score and graph risk score respectively; automatically increasing the decision weight of the time series risk score when market liquidity anomalies are monitored; and increasing the decision weight of the graph risk score when major related-party risk events are identified; generating an explainable risk attribution report, and marking the main risk sources and transmission paths.

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