Artificial intelligence-based financial transaction risk dynamic prediction system

Abstract

The application relates to the technical field of financial risk prediction, and discloses a financial transaction risk dynamic prediction system based on artificial intelligence. A financial data acquisition unit of the system acquires real-time market quotation data streams and associated entity information streams of global multi-market transaction targets, and forms a financial space-time cube through space-time alignment processing; the data streams and the information streams contain multiple types of information such as target asset price fluctuation sequences and market depth change sequences; a risk factor separation unit performs deep tensor decomposition on the financial space-time cube, extracts a core factor tensor and an abnormal pattern indication tensor; a risk strategy generation unit generates a multi-market coordinated real-time risk prevention and control strategy containing target asset position adjustment and market exposure control instructions based on the core factor tensor and an institutional risk preference graph; and a dynamic strategy adjustment unit identifies a sudden risk event according to the abnormal pattern indication tensor, revises the strategy to generate a new prevention and control strategy in real time, and can effectively cope with dynamic risks of a financial market.

Description

Technical Field

[0001] This invention relates to the field of financial risk prediction technology, specifically to a dynamic prediction system for financial transaction risks based on artificial intelligence. Background Technology

[0002] In current financial transaction risk management practices, static or quasi-static monitoring systems based on historical data and specific risk models are commonly used. These technical solutions mostly focus on structured data from a single market or asset class, such as the time series of price and trading volume of the underlying asset. They measure and warn of risk through preset thresholds and rules, but their analytical dimensions are relatively singular, and their data foundation is also relatively isolated. This approach is unable to effectively capture the complex dynamic correlations and risk transmission effects between different financial markets, as well as between financial data and information from related entities. This results in serious lag and bias in the identification of systemic risks such as cross-market risk resonance and credit risk contagion.

[0003] The shortcomings of existing technologies lie in the limitations of their data foundation and analytical framework. Multi-source heterogeneous data are typically processed independently or simply overlaid, lacking a unified computational framework for spatiotemporal alignment and deep fusion, making it difficult to form a global and interconnected view of risk analysis. Traditional statistical analysis or machine learning models often use the same set of parameters or features to describe both the normal operating patterns of the market and attempt to capture sudden anomalies. This coupled design makes the model's steady-state parameters highly susceptible to contamination or failure when dealing with new risk patterns. Early warning mechanisms rely on post-event threshold adjustments, lacking the forward-looking and specialized detection capabilities for abnormal patterns, and failing to achieve intelligent, real-time dynamic revision of risk control strategies. Summary of the Invention

[0004] The purpose of this invention is to provide an artificial intelligence-based dynamic prediction system for financial transaction risks, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a dynamic prediction system for financial transaction risks based on artificial intelligence, the system comprising:

[0006] The financial data acquisition unit acquires real-time market data streams and related entity information streams of trading targets in multiple global markets. It performs spatiotemporal alignment processing on the real-time market data streams and related entity information streams to form a financial spatiotemporal cube. The real-time market data streams and related entity information streams include the underlying asset price fluctuation sequence, market depth change sequence, related entity credit status change sequence, cross-border capital flow rate, and macroeconomic event sequence.

[0007] The risk factor separation unit performs deep tensor decomposition on the financial spatiotemporal cube to extract the core factor tensor for risk status modeling and the abnormal pattern indication tensor for triggering early warning.

[0008] The risk strategy generation unit generates a multi-market collaborative real-time risk control strategy based on the core factor tensor and combined with a preset institutional risk preference map. The multi-market collaborative real-time risk control strategy includes underlying asset position adjustment instructions and market exposure control instructions.

[0009] The dynamic strategy adjustment unit identifies sudden risk events that occur during the incremental update of the financial spatiotemporal cube based on the abnormal pattern indication tensor, and revises the real-time risk prevention and control strategy of multi-market collaboration in real time according to the sudden risk events to generate new risk prevention and control strategies.

[0010] Preferably, the risk factor separation unit includes:

[0011] The multidimensional feature reconstruction module maps the financial spatiotemporal cube that has completed spatiotemporal alignment processing into a high-order feature tensor;

[0012] The tensor decomposition engine performs non-negative constraint tensor chain decomposition on the high-order feature tensors and outputs a low-rank core factor tensor and a sparse anomaly pattern indicator tensor.

[0013] The low-rank core factor tensor represents the market-based risk-driven structure after noise filtering, and the sparse anomaly pattern indicator tensor identifies the abnormal risk patterns corresponding to liquidity faults, credit contagion, and cross-market resonance.

[0014] Preferably, the risk strategy generation unit includes:

[0015] The risk status quantification module analyzes the volatility clustering characteristics, liquidity stratification characteristics, and credit correlation characteristics in the low-rank core factor tensor to generate multi-dimensional market risk status indicators.

[0016] The strategy decision engine integrates the multi-dimensional market risk status indicators with the preset institutional risk preference map, and generates target asset position adjustment instructions and market exposure control instructions through a constraint optimization algorithm, thus forming the multi-market collaborative real-time risk prevention and control strategy.

[0017] Preferably, the dynamic strategy adjustment unit includes:

[0018] The anomaly pattern monitoring module continuously scans the update status of the sparse anomaly pattern indicator tensor to identify sudden liquidity depletion events, entity credit downgrade events, and cross-market volatility transmission events.

[0019] The strategy reassessment module identifies affected market targets and related exposures based on the identified sudden risk events, dynamically modifies existing position adjustment instructions and exposure control instructions, and generates new risk control strategies.

[0020] Preferably, the dynamic strategy adjustment unit further includes:

[0021] The multi-event correlation analysis module detects the symbiotic relationship of multiple anomalous patterns in the sparse anomaly pattern indicator tensor within the same time window, and determines whether a cross-market risk contagion chain has been formed.

[0022] The cross-market linkage module executes multi-market coordinated hedging strategies for confirmed cross-market risk contagion chains, activates emergency liquidity reserves and cross-market hedging positions for affected targets, and simultaneously updates exposure control instructions for all related markets.

[0023] Preferably, the system further includes:

[0024] The risk boundary constraint unit presets the maximum tolerable volatility threshold, liquidity depletion threshold, and credit default threshold under extreme market stress scenarios, thus forming the system's safe operation boundary.

[0025] The constraint injection module injects the system security operation boundary as a hard constraint condition during the operation of the strategy decision engine of the risk strategy generation unit.

[0026] Preferably, the risk factor separation unit further includes:

[0027] The market correlation matrix construction module generates a market risk coupling matrix based on the transmission path of historical financial crises. The market risk coupling matrix quantifies the intensity of risk contagion between different markets.

[0028] The tensor decomposition constraint module introduces cross-market correlation constraint terms corresponding to the market risk coupling matrix when the tensor decomposition engine performs tensor chain decomposition, thereby enhancing the ability to identify cross-market resonance risks.

[0029] Preferably, the risk strategy generation unit further includes:

[0030] The rule reasoning library stores historical crisis event handling rules, regulatory policy change rules, and market circuit breaker triggering rules.

[0031] The strategy compliance verification module, after generating the real-time risk prevention and control strategy for multi-market collaboration, calls the rule reasoning library to verify the strategy compliance and outputs a compliance verification flag.

[0032] Preferably, the dynamic strategy adjustment unit further includes:

[0033] When the tail risk handling module detects an extreme value pattern in the sparse anomaly pattern indicator tensor, it activates a preset tail risk handling protocol and generates a liquidity emergency channel configuration instruction and a stress test instruction.

[0034] The elastic strategy window module dynamically extends the validity period of key hedging instructions in the new risk control strategy based on the execution feedback of the liquidity emergency channel configuration instructions and stress test instructions.

[0035] Preferably, the system further includes:

[0036] The primary / backup system switching unit continuously monitors the operating status heartbeat signal of the risk strategy generation unit;

[0037] The backup strategy execution unit automatically loads a simplified rule and strategy library pre-generated based on historical extreme scenarios when the main / backup system switching unit detects an interruption in the operating status heartbeat signal, thus maintaining basic risk prevention and control functions.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] By constructing a "financial spatiotemporal cube" as a unified data foundation, multi-source heterogeneous data, including asset prices, market depth, credit status of related entities, cross-border capital flows, and macroeconomic events, are spatiotemporally aligned and fused. This high-dimensional tensor structure can fully preserve the original correlations of data across time, market, and feature dimensions, enabling the system to analyze complex interactions across markets and assets from a unified perspective. Its direct effect is to break down data silos in traditional systems, achieving a panoramic depiction of risk transmission paths and resonance effects. The comprehensiveness and relevance of risk analysis are fundamentally improved, providing a data foundation for identifying cross-market systemic risks that previous technologies lacked.

[0040] A deep tensor decomposition technique is employed to process the financial spatiotemporal cube, decoupling its inherent information into a core factor tensor describing the potential steady-state structure of the market and an anomaly pattern indicator tensor specifically designed to indicate sudden deviations. This decoupling design separates the normal modeling of risk from the early warning of abnormal situations. The core factor tensor focuses on capturing the long-term patterns and steady-state correlations of market operations, supporting the generation of robust benchmark risk control strategies; while the anomaly pattern indicator tensor acts as a highly sensitive dedicated filter, focusing on capturing new and sudden risk signals that cannot be explained by the steady-state structure in real time. This enables the system to achieve early and accurate detection of transient risk events without interfering with the steady-state model.

[0041] Based on the isolated anomaly pattern indication tensor, sudden risk events are identified, and the baseline risk control strategy generated from the core factor tensor is revised in real time accordingly. This process realizes the transformation of risk control strategies from static or periodic optimization to dynamic adaptive adjustment. The system no longer relies solely on preset rules or delayed manual intervention, but can automatically and instantly trigger strategy adjustment logic based on real-time detected anomaly risk patterns, generating new strategies to cope with the current specific risk state. This capability enhances the resilience and proactivity of the risk management system in the face of complex and rapidly evolving market environments. Attached Figure Description

[0042] Figure 1 This is a time sequence diagram of the AI-based dynamic prediction system for financial transaction risks described in this invention.

[0043] Figure 2 A flowchart illustrating the workflow of the risk factor separation unit;

[0044] Figure 3 A flowchart illustrating the basic workflow of the dynamic strategy adjustment unit;

[0045] Figure 4 The flowchart for the risk boundary constraint unit. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figure 1 This invention provides an artificial intelligence-based dynamic risk prediction system for financial transactions, the system comprising:

[0048] Through the collaborative operation of multi-level data processing and intelligent analysis modules, a complete global financial market risk monitoring and prevention system has been constructed. The system adopts a modular design, with each unit communicating via a high-throughput data bus to ensure the real-time nature and consistency of information transmission. Upon system startup, the financial data acquisition unit begins operation, acquiring real-time market data streams and related entity information streams from multiple heterogeneous data sources globally through distributed data acquisition nodes. Data sources include, but are not limited to, real-time quotation systems of stock exchanges, trading platforms in the interbank market, exchange rate data from foreign exchange trading centers, rating updates from credit rating agencies, event reports released by macroeconomic research institutions, and capital flow statistics from the Bank for International Settlements. The types of data collected cover: underlying asset price fluctuation sequences, including real-time price and volume data of financial instruments such as stocks, bonds, and derivatives; market depth change sequences, reflecting changes in order book buy and sell orders and liquidity conditions; related entity credit status change sequences, including credit rating adjustments of issuers, changes in default probabilities, and fluctuations in credit default swap spreads; cross-border capital flow rates, monitoring the speed and scale of international capital transfers between different markets; and macroeconomic event sequences, recording macroeconomic factors such as the release of important economic indicators, policy adjustments, and geopolitical events.

[0049] These raw data streams undergo data cleaning and standardization. The data cleaning module removes outliers and noisy data, handles missing values, and standardizes data formats. The standardization module converts data from different sources into a unified unit of measurement and data format, ensuring data consistency and comparability. The spatiotemporal alignment module performs time synchronization and spatial mapping operations on the cleaned data. Time synchronization uses a high-precision clock protocol to align the timestamps of all data with millisecond-level precision, ensuring complete synchronization of data from different markets and assets in the time dimension. Spatial mapping establishes a unified market coding system and asset classification standards, categorizing assets from different global markets according to dimensions such as region, industry, and asset class, forming a standardized spatial coordinate system.

[0050] After spatiotemporal alignment, the data is organized into a financial spatiotemporal cube structure. This cube is a multidimensional dataset where the time dimension records data changes at millisecond granularity, the spatial dimension covers major global exchanges and over-the-counter markets, and the attribute dimension includes multiple risk indicators such as price, trading volume, and credit status. The financial spatiotemporal cube is stored in tensor form, supporting efficient multidimensional data querying and computation. The risk factor separation unit receives the financial spatiotemporal cube as input, performs deep tensor decomposition processing, extracts features from the spatiotemporal cube, and captures local patterns and global features in the data through convolution operations. Using a multi-level tensor decomposition algorithm, the high-dimensional data is decomposed into low-rank core factor tensors and sparse anomaly pattern indicator tensors. The core factor tensors capture basic risk drivers in the market, such as systemic risk factors like overall market volatility, liquidity levels, and credit risk status. The anomaly pattern indicator tensors extract signals of abnormal fluctuations and sudden events in the data, which often foreshadow potential market risks.

[0051] The risk strategy generation unit analyzes market risk status based on core factor tensors, employing deep learning models to analyze risk characteristics within these tensors, identifying risk clusters and transmission paths in the market. The unit reads a pre-set institutional risk preference map, which details an institution's tolerance for different types of risk, portfolio constraints, and risk-return objectives. Combining risk status analysis and risk preference information, the strategy generation unit generates real-time risk control strategies using a multi-objective optimization algorithm. These strategies include underlying asset position adjustment instructions, specifying the timing, quantity, and price range for buying and selling each asset; and market exposure control instructions, setting risk exposure caps and stop-loss thresholds for different markets and asset classes. All instructions support multi-market collaborative operations, ensuring consistency of risk control measures globally. The dynamic strategy adjustment unit monitors real-time changes in market conditions. This unit continuously monitors updates to the abnormal pattern indicator tensor and detects sudden risk events using pattern recognition algorithms. When significant market changes are identified, the unit immediately initiates a strategy reassessment process to dynamically revise the generated risk control strategies. The revision process takes into account the current market conditions, strategy implementation, and changes in risk parameters to generate new risk control strategies to ensure the timeliness and effectiveness of risk management.

[0052] The entire system adopts a distributed stream processing architecture, deployed on a cloud computing platform. Asynchronous message queues are used in the data processing pipeline to achieve data flow between modules, ensuring high availability and scalability. Computational nodes utilize a load balancing mechanism to dynamically allocate computing tasks, ensuring low latency and high throughput performance even during peak periods. The system also includes a monitoring and alarm module to monitor the operational status and performance indicators of each unit in real time, ensuring stable operation. Through the collaborative work of these units, the system achieves 24 / 7 real-time risk monitoring and dynamic control of the global financial market. The comprehensiveness of data collection, the real-time processing, and the depth of analysis enable the system to promptly identify market risks, generate effective control strategies, and dynamically adjust according to market changes, providing comprehensive risk management support for financial institutions. The modular design of the system also facilitates subsequent functional expansion and performance optimization, adapting to the ever-changing risk management needs of the financial market.

[0053] Example 1: See Figure 2 After the financial data acquisition unit completes the collection and processing of real-time market data and related entity information from multiple global markets, this data has been aligned in time and space to form a multi-dimensional data structure with a unified timestamp and spatial coordinates. This data structure, known as the financial time-space cube, is actually a multi-dimensional dataset. The time dimension records data change sequences in milliseconds, the spatial dimension covers trading venues in different countries and regions, and the attribute dimension contains various market indicators and risk parameters. When the risk factor separation unit starts working, it activates the multi-dimensional feature reconstruction module. The role of this module is to transform the raw data in the financial time-space cube into a higher-order feature form that is more suitable for machine learning algorithms. In this process, the module identifies potential patterns and correlations in the data, combining the original basic indicators such as market price and trading volume into more representative feature indicators. These feature indicators can better reflect the inherent operating rules and risk characteristics of the market. For example, the module may combine the price fluctuation of an asset with changes in market depth to form a composite indicator reflecting the liquidity risk of that asset. This feature reconstruction process requires the use of complex mathematical transformation methods to project the raw data into a higher-dimensional feature space, thereby capturing those risk features that are not obvious in the raw data but are very important. In this process, the module takes into account the correlation between different markets and the risk transmission effect at different time scales to ensure that the generated features can fully reflect the complex dynamic characteristics of the market.

[0054] Tensor decomposition engines perform non-negativity-constrained tensor chain decomposition on the reconstructed high-order feature tensors. This decomposition process can be understood as breaking down complex high-dimensional data into several relatively simple components, each representing a specific risk factor or pattern in the market. The non-negativity constraint is an important mathematical condition that ensures all values ​​obtained from the decomposition retain non-negativity, making the results more interpretable, as most indicators in financial markets, such as price and volume, are non-negative. Chain decomposition is a step-by-step decomposition method that breaks down data layer by layer according to specific orders and rules, yielding two main outputs: a low-rank core factor tensor and a sparse anomaly indicator tensor. The low-rank core factor tensor mainly contains stable factors representing the fundamental operating rules of the market, which typically have a sustained impact on the market over a long period, such as the overall economic environment and basic market liquidity. In contrast, the sparse anomaly indicator tensor focuses on capturing sudden, anomalous market events, which manifest in the data as special values ​​that significantly deviate from normal patterns. These anomalous signals may indicate special events or potential risks occurring in the market, such as a sudden shortage of market liquidity or a sharp deterioration in the creditworthiness of a major institution. When performing these operations, the tensor decomposition engine uses an iterative optimization approach to gradually improve the decomposition results, ensuring that the resulting factor tensors can explain the patterns of change in the original data to the greatest extent possible.

[0055] During the risk strategy generation phase, the risk status quantification module begins analyzing various risk characteristics within the low-rank core factor tensor. It employs multiple statistical analyses and machine learning methods to identify and quantify the risk information contained within the core factor tensor. The identification of volatility clustering characteristics is achieved by analyzing the time-series characteristics of market volatility, which shows that market volatility tends to remain at high or low levels over a period of time. The analysis of liquidity stratification characteristics requires examining the liquidity differences between different markets or assets, as well as the persistence and changing patterns of these differences. The mining of credit correlation characteristics involves analyzing the mutual influence of credit conditions between different entities, which requires examining the transmission patterns of credit events in historical data. Through these analyses, the module generates a series of multi-dimensional market risk status indicators, which quantify the market risk status from different perspectives. The strategy decision engine then begins operation. The core function of this engine is to combine market risk status with the specific risk preferences of institutions to generate practical risk control strategies. The institutional risk preference map is an important input parameter, defining in detail the financial institution's tolerance and preference settings for various types of risk. The constrained optimization algorithm used by the engine comprehensively considers market risk indicators and institutional risk preferences to find the optimal risk control solution. This optimization process needs to meet several constraints, including risk limits, liquidity requirements, and compliance regulations. The generated strategies include specific underlying asset position adjustment instructions, which clearly specify which assets require adjustments, as well as the magnitude and timing of those adjustments; they also include market exposure control instructions, which specify the maximum permissible risk exposure across different markets or asset classes. All these instructions need to be clearly actionable for practical execution.

[0056] The system's operation involves multiple auxiliary processes and quality control mechanisms. The data quality monitoring module continuously checks the completeness and accuracy of input data to ensure the reliability of analysis results. The performance evaluation module periodically assesses the effectiveness of strategy execution, providing a reference for adjusting system parameters. An anomaly handling mechanism can handle various unexpected situations, ensuring stable system operation. These auxiliary functions work in conjunction with the core analysis module to ensure the effectiveness and practicality of the entire risk prediction system. The system also possesses learning and evolution capabilities, continuously optimizing its analysis models and strategy generation algorithms based on historical experience and new market data to adapt to the ever-changing financial environment. This continuous improvement mechanism enables the system to maintain the long-term effectiveness of its risk prediction and prevention, providing reliable risk management support for financial institutions.

[0057] Example 2: See Figure 3During system operation, the anomaly pattern monitoring module continuously scans the update status of the sparse anomaly pattern indicator tensor at high frequency. This scanning process employs a sliding time window technique, with each window covering data changes within a specific time period. The module identifies anomaly signals by comparing the current tensor value with historical benchmark patterns, which are reference standards formed through long-term observation of normal market operation. When a sudden liquidity crunch is detected, the module analyzes the numerical mutations in the liquidity dimension of the anomaly tensor. These mutations typically manifest as a sharp drop in market depth indicators and a significant widening of bid-ask spreads, which is verified by combining real-time market depth change sequences. For identifying entity credit downgrade events, the module correlates credit status change sequences, observing sudden widening of credit default swap spreads or credit rating downgrade signals, and confirms the severity of the event by analyzing abnormal fluctuations in the credit dimension of the anomaly tensor. When detecting cross-market volatility transmission events, the module tracks the correlation changes between market dimensions in the anomaly tensor, observing the path and intensity of volatility propagation from one market to other markets. This propagation often manifests as a sudden increase in the synchronicity of price fluctuations between different markets.

[0058] Upon receiving the output from the anomaly pattern monitoring module, the strategy reassessment module immediately begins locating the affected market targets and related exposures. It queries the currently active risk control strategy database to retrieve all executing position adjustment and exposure control orders. An impact propagation model is used to analyze the potential impact of sudden risk events on these orders. This model simulates the transmission effects of events across different markets and asset classes. For example, when a liquidity crunch is detected in a market, the module calculates the impact of the liquidity change on related asset prices and the extent to which this impact might propagate to other markets through asset correlation. Based on the impact analysis results, the module dynamically modifies existing orders. These modifications may include adjusting the position ratio of affected targets, reducing the weighting of high-risk assets; modifying risk limits in exposure control orders to tighten risk exposure restrictions to affected markets; or introducing new hedging orders to establish protective positions targeting specific risk factors. All these modifications must be completed within a very short timeframe to ensure that the risk control strategy can respond promptly to market changes. The modified order set forms a new risk control strategy and is transmitted to the execution unit in real time via the system interface.

[0059] The multi-event correlation analysis module is responsible for detecting the symbiotic relationships of multiple anomalous patterns in the sparse anomaly pattern indicator tensor within the same time window. It employs a graph-based complex network analysis method to construct a correlation network between anomalous events. In this network, nodes represent the detected anomalous events, and edges represent the statistical correlation between events. The module uses mutual information computation and Granger causality tests to determine the dependencies between events. Mutual information measures the statistical dependence between two events, while Granger causality tests help determine whether the occurrence of one event can predict the occurrence of another. When multiple anomalous patterns exhibit high temporal synchronicity and cross-market propagation characteristics, the module determines that these events form a cross-market risk contagion chain. The cross-market linkage module initiates a multi-market collaborative response mechanism for the confirmed risk contagion chain. This module uses a risk contagion model to analyze the strength and direction of risk transmission between markets in the contagion chain. This analysis needs to consider factors such as the strength of market correlation, the scale of capital flows, and the speed of information propagation. Based on the analysis results, the module generates a multi-market collaborative hedging strategy. This strategy typically involves simultaneously establishing hedging positions in multiple markets to offset the negative impact of risk contagion. For example, the module might simultaneously establish short positions in the source market of the volatility spillover and corresponding protective positions in the affected markets. It will also activate emergency liquidity reserves for affected assets, instructing the trading system to allocate additional liquidity resources to address potential funding pressures. Simultaneously, the module will establish cross-market hedging positions, the size of which will be dynamically adjusted based on the intensity of risk contagion to ensure that the hedging effect matches the level of risk exposure. The module will synchronously update exposure control instructions for all related markets to ensure that the overall system's risk exposure level remains within a safe range. The entire process needs to meet timeliness requirements, typically completing all analysis and decision-making steps within a very short time after an anomaly is detected.

[0060] During processing, the system uses a specific mathematical model to quantify the correlation strength of abnormal patterns. For the abnormal mode indicator tensor, Indicates the first The first time point The first market The strength index of anomaly patterns. The correlation strength between multiple anomaly patterns can be calculated using the following formula:

[0061]

[0062] Where: Γ represents the overall correlation strength of the abnormal patterns, This represents the length of the observation time window. It is the total number of markets. This represents the total number of exception mode types. It is the first The first market Weighting coefficients for exception patterns Indicates the rate of change of the intensity of the anomalous pattern. This is a function that measures the correlation between two anomalous patterns. This formula helps the system quantify the degree of correlation between different anomalous patterns, providing a quantitative basis for determining whether a risk contagion chain has formed. Weighting coefficient Dynamically adjust the rate of change based on the importance of various anomaly patterns in historical data. Correlation function reflects the rate of evolution of abnormal patterns. This process captures the statistical dependencies between different anomaly patterns. Through this calculation, the system can objectively assess the strength of the correlation between anomaly patterns, providing data support for subsequent risk prevention and control decisions. The entire implementation process of Example 2 demonstrates the system's ability to respond to complex market situations, ensuring the timely detection and handling of cross-market risk contagion issues through multi-level monitoring and analysis mechanisms.

[0063] Example 3: See Figure 4 The system enhances the accuracy of risk identification by pre-setting parameters for extreme stress scenarios and historical risk transmission patterns. The risk boundary constraint unit defines three key threshold parameters: the maximum tolerable volatility threshold is set based on the 99th quantile of historical extreme volatility distributions, reflecting the maximum price fluctuation range an institution can withstand under extreme market conditions; the liquidity depletion threshold is determined based on the historical lowest level of the market depth indicator, specifically manifested as an order book bid-ask spread expanding to more than three times the normal level and a depth decrease of 80%; the credit default threshold is calibrated by analyzing the correlation between rating downgrades and default probabilities in historical credit events, typically corresponding to a credit rating downgrade exceeding three levels or a default probability rising above 20%. These threshold parameters collectively constitute the system's safe operating boundary, stored digitally in the risk parameter database. The constraint injection module invokes these boundary values ​​in real-time during the strategy decision engine's operation, transforming them into constraints for the optimization problem. In the mathematical optimization model, the maximum tolerable volatility threshold is transformed into a portfolio volatility upper limit constraint, the liquidity depletion threshold into a liquidity coverage ratio lower limit requirement, and the credit default threshold is reflected as a credit value adjustment limit. These constraints, acting as hard boundaries, are injected into the optimization algorithm of the strategy decision engine to ensure that all generated risk control strategies strictly meet the organization's risk tolerance range. The constraint injection module employs a real-time monitoring mechanism to continuously compare strategy parameters with boundary values, and immediately triggers the strategy recalculation process when a potential risk of exceeding the limits is detected.

[0064] The market correlation matrix construction module generates a risk coupling matrix based on market data from historical financial crises. It collects inter-market transmission data from major financial crises over the past two decades, including changes in yield correlation, capital flow scale, and information dissemination patterns among markets during major events such as the 2008 global financial crisis, the 2010 European sovereign debt crisis, and the 2015 Chinese stock market volatility. The module employs a dynamic conditional correlation model to calculate the intensity of risk contagion among markets during crises. This model captures the time-varying characteristics of market correlation during periods of stress. The calculation process uses rolling time windows, with each window covering a complete crisis evolution cycle. The contagion intensity is quantified by calculating the correlation coefficient of inter-market returns, the volatility spillover index, and the degree of extreme risk dependence. Data sources include real-time trading data from various exchanges, cross-border capital data from capital flow monitoring systems, and risk event reports from news and public opinion systems. The calculated risk coupling matrix is ​​stored in square matrix form, with matrix elements... Indicates market With the market The risk contagion strength coefficient between the two markets ranges from 0 to 1, with a higher value indicating stronger risk transmission between the two markets. The matrix is ​​updated dynamically based on market volatility, normally once a quarter, with an emergency update procedure initiated when market volatility exceeds a specific threshold.

[0065] The tensor decomposition constraint module introduces a cross-market correlation constraint term based on the risk coupling matrix when performing tensor chain decomposition. This is achieved by modifying the objective function of tensor decomposition, adding a regularization constraint to the original decomposition error minimization, penalizing decomposition results inconsistent with historical risk contagion patterns. Specifically, this regularization term requires that markets with high correlation strength in the risk coupling matrix within the decomposed anomalous pattern indication tensor should exhibit higher anomalous pattern synchronicity. This constraint mechanism enables the system to better identify cross-market resonance risks, especially during periods of market stress, significantly improving the system's sensitivity to cross-market risk transmission. The weights of the constraint term are dynamically adjusted according to market volatility, assigning lower weights during calm periods to avoid over-constraint, and increasing weights during periods of heightened market volatility to enhance risk identification capabilities. The module also includes a conditional judgment mechanism for constraint activation; cross-market correlation constraints are only activated when market volatility exceeds a preset threshold, ensuring that decomposition accuracy is not affected under normal market conditions.

[0066] The system implements the tensor decomposition process of risk coupling constraints through the following mathematical model, where Θ represents the high-order feature tensor after feature reconstruction, and the objective function of tensor decomposition is modified as follows:

[0067]

[0068] Where: Ω represents the constraint optimization objective value. Representing the Explanatory power of the factor model The regularization coefficient is a weight used to balance the coupling constraint between decomposition accuracy and risk. This represents the risk contagion strength coefficient between markets m and n. This indicates the market in the abnormal patterns obtained from the decomposition. and The actual correlation strength between them. This optimization objective requires the system to maintain the explanatory power of tensor decomposition while ensuring that the market correlation patterns in the decomposition results are consistent with the historical risk coupling matrix. The regularization coefficient λ is dynamically adjusted according to the market volatility state, taking a smaller value during periods of market calm and automatically increasing its weight during periods of heightened market volatility to enhance the effect of risk coupling constraints. Through this constraint mechanism, the system can more accurately identify risk patterns that may trigger cross-market resonance, especially during periods of market stress, where the system's early warning capability for cross-market risk transmission is strengthened. The entire implementation process demonstrates the system's ability to learn from historical risk transmission patterns, enhancing the foresight and accuracy of risk identification by transforming historical experience into mathematical constraints.

[0069] The system incorporates a continuous learning and updating mechanism. The risk coupling matrix is ​​updated regularly, incorporating the latest market crisis event data to ensure that the matrix reflects the risk transmission characteristics of the current market structure. The update process uses an incremental learning algorithm, gradually adjusting matrix parameters while maintaining historical patterns. Boundary threshold parameters are dynamically adjusted based on changes in institutional risk appetite and market environment evolution, ensuring the timeliness of risk constraints. The adjustment process considers the latest risk tolerance assessment results of institutions and changes in market volatility characteristics, determining new threshold levels through multi-factor weighted calculations. The constraint injection mechanism employs flexible constraint technology, allowing for moderate relaxation of constraints during extreme market volatility to avoid excessive restrictions, while maintaining rigid constraints on core risk control boundaries. This design enables the system to maintain the effectiveness of risk control while possessing the flexibility to adapt to changes in the market environment. The entire process combines historical experience with real-time analysis, enhancing the system's risk identification and prevention capabilities through a data-driven constraint mechanism. The system also establishes a constraint effectiveness evaluation mechanism, regularly analyzing the impact of constraints on strategy performance and optimizing constraint parameter settings based on the evaluation results.

[0070] Example 4: The system ensures the compliance and effectiveness of risk control strategies by integrating historical experience rules and real-time regulatory requirements. The rule reasoning library uses a multi-level structure to store various rule data, including rules for handling historical crisis events, rules for changes in regulatory policies, and rules for triggering market circuit breakers. Rules for handling historical crisis events are derived from summaries of experience in dealing with past major financial crises, such as liquidity support measures taken by central banks during the 2008 financial crisis and temporary intervention measures during the abnormal stock market fluctuations in 2015. These rules detail the best handling solutions and operational procedures for different crisis scenarios. Rules for changes in regulatory policies are updated in real-time by accessing policy updates released by major global regulatory agencies, including the latest revisions to Basel III, adjustments to trading rules by securities regulators in various countries, and changes in foreign exchange management policies. The system automatically parses key clauses in regulatory documents using natural language processing technology and transforms them into executable rule conditions. Rules for triggering market circuit breakers integrate the circuit breaker mechanism parameters of major global exchanges, including specific provisions such as circuit breaker trigger thresholds, suspension durations, and conditions for resuming trading. All these rules are stored in a uniform format in the rule database. Each rule contains fields such as rule number, rule type, effective time, applicable market, triggering conditions, and execution action.

[0071] The strategy compliance verification module initiates its verification process immediately after the risk strategy generation unit outputs a real-time risk control strategy involving multiple markets. It converts the strategy content into a standardized format recognizable by the rule inference library. This includes parsing elements such as the underlying asset information, trading direction, and quantity size from position adjustment instructions, and extracting parameters such as market scope, limit indicators, and constraints from exposure control instructions. The converted strategy data is then matched against conditions in the rule library. The matching process employs a rule engine-based inference algorithm to check each strategy instruction for violations of any current rules. For example, when a strategy is found to contain an instruction to increase holdings of a high-risk asset, the module queries the rule library for the latest regulatory requirements for that asset to confirm whether it violates position ratio limits or investment threshold regulations. When market exposure settings are found in the strategy, the module compares them with the circuit breaker rules of the relevant markets to ensure that exposure control remains at a safe distance from the circuit breaker threshold. During the verification process, the module generates a detailed compliance detection report, recording the matching results and violations of each rule. If the strategy passes all rule checks, the module outputs a compliance verification flag and allows the strategy to proceed to the execution phase. If a rule violation is found, the module generates correction suggestions and feeds them back to the strategy decision engine for recalculation. The correction suggestions include specific rule violations, a severity assessment, and guidance on modification directions, helping the strategy decision engine quickly adjust strategy parameters.

[0072] When handling extreme risk scenarios, the system activates a tail risk management mechanism. When the anomaly monitoring module detects an extreme value pattern in the sparse anomaly indicator tensor—that is, when the anomaly indicator exceeds the historical extreme event threshold—the tail risk management module automatically activates a preset management protocol. This protocol includes a tiered response mechanism, initiating different levels of response measures based on the severity of the anomaly. The initial response involves generating a liquidity emergency channel configuration instruction, requiring the trading system to establish emergency fund transfer channels with multiple liquidity providers to ensure rapid access to funding during periods of market liquidity stress. The intermediate response involves generating a stress test instruction; the system initiates a real-time stress test scenario to simulate the performance of the current portfolio under extreme market conditions, with test indicators including maximum potential loss, liquidity gap, and collateral adequacy ratio. The advanced response triggers a comprehensive risk review procedure, reassessing all positions and initiating emergency hedging operations. The flexible strategy window module dynamically adjusts strategy parameters based on the execution feedback of the management instructions. This module monitors the usage status of the liquidity emergency channel and stress test results in real time. When feedback data shows a continued deterioration in the risk situation, the module automatically extends the validity period of key hedging instructions to ensure the continuity of risk coverage. At the same time, the module will adjust the elastic parameters of the strategy, including expanding the trading price range, extending the order execution time limit, and adding backup trading channels, in order to cope with the execution difficulties in extreme market conditions, see Table 1.

[0073] Table 1: Rules stored in the rule reasoning library.

[0074] Rule Number Rule Type Applicable Market Triggering conditions Execute action R2023001 Position Limit Rules Stock Market Holding more than 15% of total assets in a single stock Automatically generate instructions to reduce positions to 12%. R2023002 Circuit Breaker Response Rules Futures Market Market volatility hits 80% of the circuit breaker threshold. Initiating the pre-circuit breaker liquidation procedure R2023003 Liquidity rules Foreign exchange market The bid-ask spread has tripled. Enable alternative liquidity providers R2023004 Credit Risk Rules bond market The issuer's credit rating was downgraded to below BB+. Triggering the bond reduction process R2023005 Cross-border transaction rules Global Markets Daily cross-border capital outflow exceeds the limit Initiate capital flow control

[0075] The system's rule maintenance mechanism ensures the timeliness and accuracy of rules. The rule base is updated regularly, automatically retrieving the latest regulatory policy changes daily, summarizing market mechanism adjustments weekly, and comprehensively updating historical event handling rules monthly. Rule version management employs a strict control mechanism, recording the effective and expiration dates of each rule to ensure that the latest valid version is always used. A rule conflict detection mechanism identifies contradictions between different rules and resolves conflicts through weight allocation and priority settings. A rule simulation testing environment allows new rules to be virtually tested before going live, verifying their applicability and effectiveness in real-world market environments. The entire rule management system is tightly integrated with the risk control strategy generation process, forming a complete compliance assurance system. The strategy compliance verification module is also equipped with a learning function, learning optimization directions from past strategy adjustments to gradually improve the coverage and accuracy of the rule base. Tail-end risk handling protocols are regularly evaluated and updated based on actual implementation results to ensure that responses remain synchronized with market developments. The parameter settings of the flexible strategy window also have a dynamic optimization mechanism, automatically adjusting elastic parameters based on market liquidity and instruction execution success rates to ensure the executability of strategies in extreme market environments.

[0076] Example 5: To ensure the continuity of risk control functions, the primary / backup system switching unit continuously monitors the operational status heartbeat signal of the risk policy generation unit. This heartbeat signal is a periodically sent system status message containing key performance parameters such as the computational load, memory usage, processing latency, and output queue status of the policy generation unit. The monitoring frequency is set to multiple checks per second to ensure timely detection of system anomalies. Redundant communication channels are used for heartbeat signal transmission, including a primary high-speed internal bus and a backup independent network link to prevent misjudgments due to communication problems. When the primary / backup system switching unit detects an interruption in the heartbeat signal or performance indicators exceeding the normal range, a multi-level confirmation mechanism is initiated to check for transient faults. Consistency judgments over multiple consecutive detection cycles determine whether a real system failure has occurred. After confirming the fault, the switching unit sends a takeover command to the backup policy execution unit and simultaneously notifies the system monitoring platform to record the fault event and trigger an alarm mechanism. The entire switching process is designed to be automated, requiring no manual intervention, ensuring the primary / backup system conversion is completed in a very short time. The system uses the following mathematical model to quantify the reliability indicators of the switching process:

[0077]

[0078] in: Indicates time The overall system reliability index Representing the Failure rate parameters of each component It is the detection time interval. Indicates the success rate of the switching unit. Indicates the first The availability status of each backup component. This formula helps the system quantitatively assess the reliability level of the switching mechanism, providing a basis for system fault-tolerant design. Failure rate parameter The success rate is updated regularly based on the lifespan data of each hardware component and the stability records of the software modules. Availability status was determined based on historical switchover test data. The reliability of the switching mechanism is determined by the self-test results of the backup components. This calculation model allows the system to assess the reliability of the switching mechanism in real time and issue early warnings when indicators fall below a safety threshold.

[0079] The standby strategy execution unit activates immediately upon receiving a takeover command. This unit is pre-loaded with a simplified rule strategy library generated based on historical extreme scenarios. This library was built during the system development phase by analyzing numerous historical market crisis events and contains a set of validated basic risk control rules. These rules employ relatively simple logical condition judgments and execution actions, such as automatically reducing risk asset holdings when market volatility exceeds a specific threshold, or initiating capital protection measures when liquidity indicators deteriorate. The rule library is categorized and stored by market type and asset class, with each rule clearly specifying its applicable conditions, judgment parameters, and execution actions. When executing these rules, the standby unit obtains the latest snapshot of current market data, including real-time market conditions, liquidity status, and credit environment indicators for major markets. It then matches and judges these conditions according to the rules, generating corresponding risk control instructions. While these instructions may not be as granular as the strategies generated by the main system, they ensure that a minimum level of risk protection is maintained during periods of severe market volatility, preventing catastrophic losses. The standby unit's execution efficiency is guaranteed through optimized algorithms, enabling rule matching and instruction generation within milliseconds, ensuring timely risk control.

[0080] The system also establishes a comprehensive recovery and regression mechanism. Once the primary system is troubleshooted and returns to normal operation, the primary / backup system switching unit coordinates and executes a regression process. This process includes verifying the stability of the primary system, confirming its normal operation through a period of observation, and gradually switching the risk control functions from the backup unit back to the primary system. During the regression process, the system adopts a gradual switching strategy, initially allowing the primary system to process a portion of the market data, gradually expanding its processing scope, while the backup unit remains on standby for unforeseen circumstances. Throughout the process, the system ensures the continuity and consistency of risk control instructions, avoiding a risk management vacuum caused by system switching. To ensure the effectiveness of the backup strategy, the system regularly updates and tests the simplified rule strategy library, adjusting rule parameters according to changes in market structure and new regulatory requirements, and verifying the applicability of the rules by simulating historical extreme scenarios. Simultaneously, the system records event logs and performance data for each primary / backup switch for subsequent analysis and system optimization. This high-availability design enables the system to maintain continuous operation of risk control functions under various abnormal conditions, providing reliable technical support for financial institutions. The system also establishes a fault prediction mechanism, which analyzes the operating status trend data of each component to identify potential fault risks in advance and take preventive maintenance measures to further reduce the probability of system interruption.

[0081] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0082] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based dynamic prediction system for financial transaction risk, characterized in that, include: The financial data acquisition unit acquires real-time market data streams and related entity information streams of trading targets in multiple global markets. It performs spatiotemporal alignment processing on the real-time market data streams and related entity information streams to form a financial spatiotemporal cube. The real-time market data streams and related entity information streams include the price fluctuation sequence of the underlying assets, the market depth change sequence, the credit status change sequence of related entities, the cross-border capital flow rate, and the macroeconomic event sequence. The risk factor separation unit performs deep tensor decomposition on the financial spatiotemporal cube to extract the core factor tensor for risk status modeling and the abnormal pattern indication tensor for triggering early warning. The risk strategy generation unit generates a multi-market collaborative real-time risk control strategy based on the core factor tensor and combined with a preset institutional risk preference map. The multi-market collaborative real-time risk control strategy includes underlying asset position adjustment instructions and market exposure control instructions. The dynamic strategy adjustment unit identifies sudden risk events that occur during the incremental update of the financial spatiotemporal cube based on the abnormal pattern indication tensor, and revises the real-time risk prevention and control strategy of multi-market collaboration in real time according to the sudden risk events to generate new risk prevention and control strategies. 2.The AI-based dynamic financial transaction risk prediction system of claim 1, wherein, The risk factor separation unit includes: The multidimensional feature reconstruction module maps the financial spatiotemporal cube that has completed spatiotemporal alignment processing into a high-order feature tensor; The tensor decomposition engine performs non-negative constraint tensor chain decomposition on the high-order feature tensors and outputs a low-rank core factor tensor and a sparse anomaly pattern indicator tensor. The low-rank core factor tensor represents the market-based risk-driven structure after noise filtering, and the sparse anomaly pattern indicator tensor identifies the abnormal risk patterns corresponding to liquidity faults, credit contagion, and cross-market resonance. 3.The AI-based dynamic financial transaction risk prediction system of claim 2, wherein, The risk strategy generation unit includes: The risk status quantification module analyzes the volatility clustering characteristics, liquidity stratification characteristics, and credit correlation characteristics in the low-rank core factor tensor to generate multi-dimensional market risk status indicators. The strategy decision engine integrates the multi-dimensional market risk status indicators with the preset institutional risk preference map, and generates target asset position adjustment instructions and market exposure control instructions through a constraint optimization algorithm, thus forming the multi-market collaborative real-time risk prevention and control strategy. 4.The AI-based dynamic financial transaction risk prediction system of claim 3, wherein, The dynamic strategy adjustment unit includes: The anomaly pattern monitoring module continuously scans the update status of the sparse anomaly pattern indicator tensor to identify sudden liquidity depletion events, entity credit downgrade events, and cross-market volatility transmission events. The strategy reassessment module identifies affected market targets and related exposures based on the identified sudden risk events, dynamically modifies existing position adjustment instructions and exposure control instructions, and generates new risk control strategies. 5.The AI-based dynamic financial transaction risk prediction system according to claim 4, wherein, The dynamic strategy adjustment unit further includes: The multi-event correlation analysis module detects the symbiotic relationship of multiple anomalous patterns in the sparse anomaly pattern indicator tensor within the same time window, and determines whether a cross-market risk contagion chain has been formed. The cross-market linkage module executes multi-market coordinated hedging strategies for confirmed cross-market risk contagion chains, activates emergency liquidity reserves and cross-market hedging positions for affected targets, and simultaneously updates exposure control instructions for all related markets. 6.The AI-based dynamic financial transaction risk prediction system of claim 2, wherein, The system also includes: The risk boundary constraint unit presets the maximum tolerable volatility threshold, liquidity depletion threshold, and credit default threshold under extreme market stress scenarios, thus forming the system's safe operation boundary. The constraint injection module injects the system security operation boundary as a hard constraint condition during the operation of the strategy decision engine of the risk strategy generation unit.

7. The artificial intelligence-based dynamic financial transaction risk prediction system according to claim 6, wherein, The risk factor separation unit further includes: The market correlation matrix construction module generates a market risk coupling matrix based on the transmission path of historical financial crises. The market risk coupling matrix quantifies the intensity of risk contagion between different markets. The tensor decomposition constraint module introduces cross-market correlation constraint terms corresponding to the market risk coupling matrix when the tensor decomposition engine performs tensor chain decomposition, thereby enhancing the ability to identify cross-market resonance risks. 8.The AI-based dynamic financial transaction risk prediction system of claim 7, wherein, The risk strategy generation unit also includes: The rule reasoning library stores historical crisis event handling rules, regulatory policy change rules, and market circuit breaker triggering rules. The strategy compliance verification module, after generating the real-time risk prevention and control strategy for multi-market collaboration, calls the rule reasoning library to verify the strategy compliance and outputs a compliance verification flag. 9.The AI-based dynamic financial transaction risk prediction system of claim 8, wherein, The dynamic strategy adjustment unit further includes: When the tail risk handling module detects an extreme value pattern in the sparse anomaly pattern indicator tensor, it activates a preset tail risk handling protocol and generates a liquidity emergency channel configuration instruction and a stress test instruction. The flexible strategy window module dynamically extends the validity period of key hedging instructions in the new risk control strategy based on the execution feedback of the liquidity emergency channel configuration instructions and stress test instructions. 10.The AI-based dynamic financial transaction risk prediction system of claim 1, wherein, The system also includes: The primary / backup system switching unit continuously monitors the operating status heartbeat signal of the risk strategy generation unit; The backup strategy execution unit automatically loads a simplified rule and strategy library pre-generated based on historical extreme scenarios when the main / backup system switching unit detects an interruption in the operating status heartbeat signal, thus maintaining basic risk prevention and control functions.

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