Dynamic risk control method and device, equipment and storage medium

Abstract

The invention relates to a dynamic risk control method and device, equipment and a storage medium. The method comprises the following steps: collecting and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; constructing a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculating a risk control model parameter set; performing risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; executing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; constructing a multi-layer threshold structure based on the comprehensive risk score and executing dynamic adjustment to obtain a personalized risk threshold; and according to the comprehensive risk score and the personalized risk threshold, triggering a risk control measure and executing a feedback mechanism to obtain a risk control execution result. The importance of different risk factors can be adjusted in real time, the market environment change is quickly responded, and the exploration capability of the system for unknown risks is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic risk control, and particularly to a dynamic risk control method, device, equipment and storage medium. Background Art

[0002] Traditional risk control methods generally use static rules and fixed thresholds for risk assessment, mostly relying on manual analysis and decision-making. This not only consumes a large amount of time and human resources, but also, due to the lack of integration and analysis of real-time data, it is difficult to accurately reflect the actual risk situation of customers. In a complex and ever-changing market environment, the lag and inaccuracy of risk assessment lead financial institutions to face a dilemma of capital losses or wrongly rejecting normal business, and the multi-level manual approval process further reduces the risk control efficiency and cannot meet the requirements of modern financial services for response speed.

[0003] With the deepening of digital transformation, financial risks show new characteristics of probability, dynamics and diversity. Traditional deterministic risk assessment methods are no longer fully applicable to this uncertain trading environment. Especially in emerging financial scenarios such as high-frequency trading, online payment and supply chain finance, risk factors are not only complex and changeable, but also interrelated, forming a risk network that is difficult to capture by simple rules. At the same time, the issues of automation and security of fund management are becoming increasingly prominent. The lack of coordination between systems leads to untimely monitoring of fund flows and prone to security vulnerabilities, while relying on manual reconciliation results in a serious lag in quota updates, affecting the timely response to fund demands. Summary of the Invention

[0004] The main object of the present invention is to provide a dynamic risk control method, device, equipment and storage medium. The present invention can adjust the importance of different risk factors in real time, quickly respond to changes in the market environment, and enhance the system's ability to explore unknown risks.

[0005] To achieve the above object, the present invention provides a dynamic risk control method, including the following steps: Collect and preprocess multi-source data to obtain a standardized multi-dimensional data matrix; Construct a dynamic risk control model according to the standardized multi-dimensional data matrix and calculate a risk control model parameter set; Perform risk factor sensitivity analysis using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; Perform iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; Construct a multi-level threshold structure based on the comprehensive risk score and perform dynamic adjustment to obtain a personalized risk threshold; Trigger a risk control measure and execute a feedback mechanism based on the comprehensive risk score and the personalized risk threshold to obtain the risk control execution result.

[0006] The present invention also provides a dynamic risk control device, including: An acquisition unit, configured to acquire and preprocess multi-source data to obtain a standardized multi-dimensional data matrix; A construction unit, configured to construct a dynamic risk control model according to the standardized multi-dimensional data matrix and calculate a risk control model parameter set; An analysis unit, configured to perform risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; An evaluation unit, configured to perform iterative risk evaluation based on the dynamic weight vector to obtain a comprehensive risk score; An adjustment unit, configured to construct a multi-layer threshold structure based on the comprehensive risk score and perform dynamic adjustment to obtain a personalized risk threshold; A feedback unit, configured to trigger a risk control measure and execute a feedback mechanism according to the comprehensive risk score and the personalized risk threshold to obtain the risk control execution result.

[0007] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0008] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0009] In summary, the technical solution provided by the present invention connects multi-source data through a standardized API interface protocol, combines a data verification program and a feature extraction algorithm, significantly reducing the interference of missing values and outliers on the risk control results. The multi-layer risk control model architecture based on a deep neural network, combined with the non-linear mapping ability of the ReLU activation function and the processing advantages of the LSTM network for time series data, enables the model to capture complex risk patterns and time-dependent characteristics. Through risk factor sensitivity analysis and dynamic weight calculation, the system can adjust the importance of different risk factors in real time and quickly respond to changes in the market environment. The combination of the Q-learning mechanism and the ε-greedy strategy realizes the automatic optimization selection of evaluation operators, while the cross-evaluation and mutation mechanisms enhance the system's exploration ability for unknown risks. The combined application of the multi-layer threshold structure and the PID controller enables the risk threshold to be dynamically adjusted according to the real-time risk score, and the particle swarm optimization algorithm ensures the optimization of control parameters. Through the optimal solution of the objective function, the system can achieve the best balance between risk control effect and resource consumption, and at the same time, the feedback mechanism prompts the entire system to continuously improve itself, forming a closed-loop optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a schematic diagram of the steps of the dynamic risk control method in an embodiment of the present invention; Figure 2 is a block diagram of the structure of the dynamic risk control device in an embodiment of the present invention; Figure 3 is a schematic block diagram of the structure of a computer device in an embodiment of the present invention.

[0011] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0013] Referring to Figure 1 , this embodiment provides a dynamic risk control method, including the following steps: S1, collect and preprocess multi-source data to obtain a standardized multi-dimensional data matrix; Among them, the data interface module is connected to multiple data sources through a standardized API interface protocol, including structured transaction databases, unstructured text databases, real-time data streams, and external public databases, to establish a stable and efficient data transmission channel. This process requires ensuring that the data interface can be compatible with the formats and protocols of different data sources, enabling data to be transmitted and processed under a unified standard, thereby avoiding information loss or transmission delays caused by incompatible data formats. At the same time, adopting a standardized API protocol helps to improve the stability of data transmission, ensure the integrity and consistency of data, and enable the entire risk control system to operate efficiently. Based on the data transmission channel, multi-source raw data streams including transaction amount, transaction frequency, transaction timestamp, and transaction object identifier are collected. According to different business requirements, the dimensions of data collection are extended, such as introducing the geographical location information of users, device information, social network data, and historical credit scores, etc., to form a more comprehensive risk assessment system. A data verification program is executed on the multi-source raw data stream to ensure the quality and reliability of the data. The data verification program includes steps such as integrity check, consistency detection, outlier filtering, and missing value filling, using a rule engine or machine learning algorithms to identify abnormal data, such as detecting whether the transaction amount exceeds a reasonable range, whether the transaction frequency fluctuates abnormally, and whether the timestamp conforms to normal logic, etc., and cleaning the data that does not meet the specifications. The cleaned data set is converted into a unified data structure to ensure that all data fields conform to the same format and storage specifications to form a standardized data set. The conversion process includes data type conversion, field alignment, time format standardization, etc. Feature extraction is performed on the standardized data set to construct a risk assessment feature vector set with business value. The feature extraction process is carried out in various ways, including rule-based methods, statistical analysis methods, and machine learning techniques. For example, calculating the transaction behavior patterns of users, such as average transaction amount, coefficient of variation of transaction amount, proportion of cross-border transactions, etc.; extracting time series features, such as the change trend of daily transaction times, distribution of transaction interval times, etc.; adopting natural language processing techniques to extract information such as user credit comments and complaint records from unstructured text databases, and converting them into quantifiable feature values through methods such as sentiment analysis or keyword extraction. Through these feature extraction methods, the redundancy of the original data can be effectively reduced, and the model's ability to identify key risk factors can be improved. Dimensionality reduction processing is performed on the feature vector set to reduce the data dimension, improve the calculation efficiency, and avoid the overfitting problem caused by high-dimensional data. The feature vector set after dimensionality reduction processing constitutes a standardized multi-dimensional data matrix.

[0014] S2. Construct a dynamic risk control model based on the standardized multi-dimensional data matrix and calculate the risk control model parameter set; Specifically, a basic risk assessment layer is constructed through a deep neural network. The standardized multi-dimensional data matrix is used as the input data, and a network structure matching the data feature dimensions is designed to ensure that the model fully learns and represents the risk features in the data. Since risk assessment involves various complex non-linear relationships, using a deep neural network can better capture the implicit patterns in the data and improve the ability to identify risk factors and prediction accuracy. The input layer of this risk assessment benchmark model corresponds to the standardized multi-dimensional data matrix, and an appropriate hidden layer structure is adopted in the middle layer to ensure the expressive ability of the network. At the same time, a preliminary risk assessment result is generated at the output layer. While constructing the risk assessment benchmark model, non-linear mapping processing is performed on the model. The ReLU activation function is used to enhance the non-linear expression ability of the model, enabling it to effectively learn complex risk patterns. The ReLU activation function has advantages such as fewer gradient vanishing problems and high computational efficiency, enabling the deep neural network to be stably trained on a large data scale. To optimize the model parameters to accurately fit the risk patterns in the historical data, the batch gradient descent algorithm is used, and the mean squared error is used as the loss function for optimization, enabling the model to effectively minimize the error between the predicted risk and the actual risk. The batch gradient descent algorithm calculates the gradient information of each small batch of data and gradually updates the network weights, thereby continuously optimizing the model parameters during the training process to obtain the initial model parameter set. Based on the initial model parameter set, a dynamic adjustment layer containing a time series analysis module, an environmental factor response module, and an anomaly detection module is constructed to enhance the model's adaptability to risk changes. Among them, the time series analysis module uses the LSTM network structure to process the time dependence of the data, enabling the model to capture the dynamic changes of transaction data, user behavior data, and external environmental factors in the time dimension and improving the prediction ability for short-term and long-term risk trends. The LSTM network effectively retains long-term dependencies through the gating mechanism and reduces the gradient vanishing problem at the same time, enabling more accurate risk assessment. The environmental factor response module is used to analyze the impact of external economic environment, policy changes, etc. on the risk level, while the anomaly detection module is used to identify abnormal patterns such as abnormal transactions and fraud behaviors, and adaptively adjust the model when significant risk changes are detected. Through the coordinated action of these three modules, the dynamic adjustment layer can real-time update the risk assessment results under different environmental and time conditions to ensure the accuracy and adaptability of the risk control model. The weight initialization of the parameter matrix of the dynamic adjustment layer is carried out to ensure the stability of the optimization process. The process of weight initialization involves assigning initial values to the parameter matrices of the LSTM network, the environmental response module, and the anomaly detection module, and adopting appropriate initialization strategies, such as uniform distribution initialization or Gaussian distribution initialization, to avoid gradient vanishing or gradient explosion problems during the training process.Through reasonable weight initialization, ensure that the dynamic adjustment layer can efficiently learn data features in the early stage of model training, thereby accelerating model convergence and improving the real-time performance of risk assessment. Based on the initialized dynamic adjustment layer, construct a multi-objective optimization framework as the decision execution layer to integrate risk assessment and resource optimization strategies. This optimization framework integrates a risk minimization objective function and a resource consumption minimization objective function, enabling the risk control system to minimize the usage cost of computing resources while providing accurate assessment. To ensure the rationality of the optimization framework, use the method of historical data analysis to determine the weight parameters of different objective functions for dynamic adjustment according to actual business needs. The risk minimization objective function is used to reduce the misjudgment rate of the system in risk assessment, while the resource consumption minimization objective function is used to optimize the computing cost and reduce the model computing latency, thereby improving the operation efficiency of the system while ensuring the risk control effect. Through the optimization solution of the comprehensive objective function, a reasonable trade-off is achieved between risk control and computing resources. Integrate and store the parameter set of the risk assessment benchmark model, the parameter matrix of the dynamic adjustment layer, and the weight parameters of the comprehensive objective function in the parameter server to ensure the unified management and efficient invocation of model parameters. The parameter server is used to store and manage all key parameters of the model and provide real-time parameter update support during the operation of the risk control system. To ensure the traceability of model versions, establish a model version control mechanism to record the historical track of parameter updates, enabling the system to trace back to historical versions and compare model versions to ensure a quick recovery to a stable version in case of anomalies. Through the parameter management and version control mechanisms, the risk control system can achieve continuous optimization and dynamic adjustment of the model, thus ensuring high stability and accuracy in a complex risk environment and forming a risk control model parameter set.

[0015] S3. Use the risk control model parameter set and the standardized multi-dimensional data matrix to perform risk factor sensitivity analysis to obtain the dynamic weight vector; It should be noted that the latest preprocessed data matrix is extracted from the data warehouse and compared with the historical data matrix for calculation to obtain the data offset. During the comparison calculation process, measurement methods such as Euclidean distance, cosine similarity, or Mahalanobis distance are used to calculate the change trend between the new data and the historical data, and the data offset of each risk factor is obtained, reflecting the changes in risk factors between different periods. Perform risk factor sensitivity analysis on the data offset to evaluate the impact degree of different risk factors on the overall risk level. The core of risk factor sensitivity analysis lies in determining the contribution of each factor to the comprehensive risk score, and methods such as regression analysis are used to calculate the impact degree of each risk factor on the overall risk assessment. For numerical variables, the partial derivative method or the contribution analysis based on feature importance is used, while for categorical variables, the difference in probability distribution between categories is used to measure its impact degree. Through this analysis method, accurately identify which risk factors play a key role in the overall risk assessment result, and then construct a risk factor importance matrix, which reflects the relative importance of each risk factor. Perform singular value decomposition on the risk factor importance matrix to obtain a set of eigenvectors. Singular value decomposition is a matrix decomposition technique that maps the original data to a low-dimensional feature space and extracts the most important risk factor influence patterns. Decompose the risk factor importance matrix M into , where U is the left singular vector matrix, Σ is the singular value diagonal matrix, and V is the right singular vector matrix. In this process, the magnitude of the singular value reflects the importance of different risk factors in the data. By intercepting the first few larger singular values, the most critical set of eigenvectors is selected, and the low-contribution risk factors corresponding to the smaller singular values are ignored to improve the calculation efficiency and reduce the interference of redundant information. Based on the set of eigenvectors, calculate the dynamic weight of each risk factor to obtain the final dynamic weight vector. Use the normalization method to process the eigenvectors to ensure the rationality of weight allocation, and combine the historical data weight trend, using the weighted moving average or exponential weighted smoothing method to make the dynamic weight vector smoothly adapt to the changes in the market environment.

[0016] S4. Perform iterative risk assessment based on the dynamic weight vector to obtain the comprehensive risk score; Specifically, a risk state space is defined based on the feature vector of the current evaluation object and the environmental state vector, enabling risk assessment to adapt to different environmental conditions and accurately depict the dynamic evolution of risks. The construction of the risk state space requires comprehensive consideration of multiple factors, including transaction behaviors, user credit records, market volatility, and policy changes, etc., and these factors are quantitatively represented in the form of state vectors. On this basis, a risk assessment operation space containing six types of local evaluation operators is constructed, enabling the system to evaluate the risk level from different perspectives. These six types of evaluation operators cover various methods such as statistical analysis, rule engines, machine learning prediction, historical backtracking, anomaly detection, and external expert system suggestions, to ensure the comprehensiveness and robustness of the evaluation. At the same time, in order to optimize the selection strategy of the evaluation operators, the weighted combination of the accuracy of risk assessment and the consumption of computing resources is defined as the reward function, enabling the system to balance the relationship between accuracy and computing cost when performing the evaluation task, forming an adaptive Q-learning based framework. According to the Q-learning based framework, a Q-value matrix is constructed and the Q-value matrix is initialized as a zero matrix, which is used as the decision basis for the selection of evaluation operators. The Q-value matrix is used to store the values of different state-action pairs. Initially, the Q-values of all state-action pairs are zero, indicating that no learning has been carried out. In subsequent iterative processes, the Q-values will be continuously updated as learning progresses, to reflect the actual performance of different evaluation operators in specific states. In each evaluation cycle, in order to explore new evaluation operators while ensuring the utilization of excellent strategies, the ε-greedy strategy is adopted, that is, an evaluation operator is randomly selected with probability ε to increase exploration, thereby avoiding the dilemma of local optimality. At the same time, an operator with the largest Q-value in the current state is selected with probability 1 - ε, to ensure that the model can select as many evaluation methods with proven better effects as possible. In this way, while ensuring a certain degree of exploration, the system is more inclined to select operators with better historical performance, thereby improving the evaluation accuracy and reducing the waste of computing resources, and obtaining the current optimal evaluation operator. The current optimal evaluation operator and the dynamic weight vector are used to perform the risk assessment operation, and the Q-value matrix is updated based on the evaluation results, to continuously optimize the decision-making ability of the model. The Q-value update follows the update formula of Q-learning, that is, by weighted calculation of the current state, the selected operator, the obtained reward, and the maximum Q-value of the next state, so as to adjust the Q-value of the current state-action pair to better approximate the optimal strategy. To improve the stability and generalization ability of the model, an experience pool D is maintained based on the updated Q-value matrix, and the transition quadruple, that is, (current state, selected operator, reward value, next state), is stored, and then batch updates are performed from the experience pool to reduce the variance problem caused by single-step updates. At the same time, a double Q-network architecture is adopted, and the instability of learning is reduced through a soft update mechanism. The design of the double Q-network can effectively reduce the overestimation problem and improve the stability and convergence speed of the learning process. After obtaining the optimized Q-learning model, in order to improve the accuracy and exploration ability of risk assessment, cross-evaluation and mutation mechanisms are introduced.Cross-evaluation fuses different evaluation results through a crossover operator to reduce the limitations of a single evaluation operator, and at the same time utilizes the complementarity of multiple evaluation methods to improve the overall prediction accuracy. The mutation operator enhances the exploration ability of unknown risks by introducing random perturbations, enabling the model to still have strong adaptability when facing new types of risks. Through multiple rounds of iteration, the evaluation strategy and risk identification ability are continuously optimized, and finally a comprehensive risk score is generated.

[0017] S5. Construct a multi-layer threshold structure based on the comprehensive risk score and perform dynamic adjustment to obtain personalized risk thresholds; Among them, the historical risk score data is grouped by risk level, and the k-means clustering algorithm is used to perform clustering analysis on each group of data to extract representative risk thresholds from a large amount of historical data. Through k-means clustering, the historical risk scores are effectively classified according to different risk characteristics, and k clustering centers are determined as the initial threshold set to form the basic threshold layer. Based on the comprehensive risk score, the risk score deviation is calculated to measure the degree of change of the current risk score relative to the historical data, and the threshold adjustment amount is calculated. The calculation of the risk score deviation adopts the sliding window method. By comparing the deviation degree between the current risk score and the historical mean or median, it is judged whether there is an abnormal fluctuation in the current risk situation. According to the size of the risk score deviation, the threshold adjustment amount is calculated. The size of the adjustment amount depends on the severity of the risk change. If the deviation between the current score and the historical mean is large, the adjustment amount will increase accordingly to respond to the risk change in a timely manner; if the deviation is small, the adjustment range will be relatively small to maintain the stability of the risk control model. Adjustment is performed according to the threshold adjustment amount and the basic threshold layer to obtain the adjusted threshold layer. The adjustment method adopts the weighted moving average method or the exponential smoothing method to keep the threshold stable while dynamically changing, so as to avoid over-adjustment due to short-term fluctuations. And combined with factors such as the market environment and user behavior characteristics, the adjustment process is further optimized. For example, when the market environment fluctuates greatly, the threshold adjustment range is appropriately relaxed to improve the adaptability of the risk control system, and when the market is stable, a relatively stable adjustment strategy is maintained to ensure the reliability of the risk control strategy. Based on the adjusted threshold layer, the risk change rate is monitored to ensure that the threshold can adapt to sudden risk changes. When the system detects that the risk score change rate exceeds the preset threshold, an emergency threshold calculation is triggered to generate an emergency threshold layer. The calculation of the emergency threshold layer adopts dynamic prediction methods, such as the autoregressive model (ARIMA) based on time series analysis or the deep learning method (LSTM), to quickly predict the future risk change trend, actively increase the risk control intensity when the risk rises sharply to reduce potential losses, and appropriately relax the control when the risk falls to avoid misjudging normal operations. The performance indicators of the adjusted threshold layer and the emergency threshold layer are analyzed to optimize the self-learning mechanism and generate self-learning optimized thresholds. The core goal of performance analysis is to ensure that the adjusted threshold can minimize the impact on normal users while reducing risks. Therefore, multi-dimensional evaluation indicators, such as misjudgment rate, missed judgment rate, and risk control coverage rate, are used to evaluate the effectiveness of different threshold layers. The reinforcement learning method is adopted to enable the system to automatically adjust the threshold during long-term operation to adapt to the changing market environment, thereby improving the self-adaptability of the risk control system.After completing the calculation of the self-learning optimized threshold, the threshold is optimized in combination with the business scenario. Therefore, by monitoring changes in indicators such as transaction frequency and amount distribution, the current business scenario type is automatically identified, and the most matching threshold configuration is selected from the pre-trained scenario-threshold mapping library. The scenario recognition method uses supervised learning or unsupervised learning methods. Through cluster analysis or classification modeling of historical data, the system automatically matches the most suitable risk threshold configuration according to real-time transaction behavior. The selected threshold configuration is fused with the self-learning optimized threshold, and historical benchmarks, real-time status, and emergency requirements are comprehensively considered to finally determine the personalized risk threshold. In this way, the system can provide accurate risk control strategies in different business scenarios and make adaptive adjustments when the risk environment changes, thereby enhancing the flexibility and stability of the risk control system and ensuring its effective operation under different market conditions.

[0018] S6. Trigger risk control measures based on the comprehensive risk score and personalized risk threshold and execute the feedback mechanism to obtain the risk control execution result.

[0019] Specifically, the comprehensive risk score is compared with the personalized risk threshold to determine whether the current risk level exceeds the acceptable range, which serves as the basis for risk control decisions. The comprehensive risk score represents the assessment result of the current risk, while the personalized risk threshold is dynamically set based on factors such as historical data, scenario analysis, and emergency adjustments. Comparing the two effectively determines whether intervention measures are needed for the current risk and determines the intensity of risk control according to the degree of deviation. When the comprehensive risk score exceeds the personalized threshold, the system triggers corresponding control strategies to avoid losses caused by potential risks. When the score is close to the threshold but does not exceed it, the system takes mild monitoring measures to ensure that the risk is controllable. Based on the basis of risk control decisions, a corresponding set of control strategies is selected from a predefined control measure library to form a candidate set of control strategies. The control measure library contains a series of executable risk control means, including transaction freezing, enhanced identity verification, limit restrictions, risk control warning prompts, account monitoring upgrades, etc. The selection of these measures depends on the specific risk type and severity. By matching the basis of risk control decisions with the control measure library, the system efficiently screens out a set of candidate control strategies that are most suitable for the current risk situation to ensure the rationality and accuracy of control measures. Apply a resource optimization algorithm to the candidate set of control strategies and solve the optimal control scheme through the Lagrange multiplier method to balance the effectiveness of risk control measures and the utilization efficiency of system resources. Since risk control execution involves multiple factors such as computing resources, manual review costs, and user experience, while ensuring the effectiveness of risk control, the computing overhead and operating costs are minimized as much as possible. The Lagrange multiplier method can effectively handle multiple-constraint optimization problems. By constructing an objective function and introducing Lagrange multipliers, on the premise of ensuring that system resource constraints are not violated, the optimal combination of control strategies is calculated, minimizing resource consumption while reducing risks, thereby improving the overall efficiency of the risk control system. After calculating the optimal control execution plan, the control execution module converts this plan into specific system operations and records relevant execution data to ensure the traceability of control measures. The control execution module transforms risk control decisions into actual system behaviors, such as adjusting account permissions, sending risk notifications, triggering manual review processes, etc. At the same time, the execution timestamp, operation parameters, and execution status are recorded during the execution process for subsequent analysis and optimization. The risk control execution record is used to monitor the execution process and provide data support for the system's feedback mechanism, enabling the risk control model to be adjusted and improved according to the actual execution effect. After the risk control measures are executed, a real-time monitoring mechanism is started to collect control effect data and evaluate the actual effect of the control strategies. The real-time monitoring mechanism analyzes whether the control measures have achieved the expected risk reduction effect through multi-dimensional data such as user behavior changes, transaction success rates, and customer feedback, while avoiding excessive interference with normal users. Through continuous monitoring of the control effect data, the system dynamically adjusts the risk control strategies to ensure their adaptation to the changing business environment and risk patterns.After obtaining the control effect data, calculate the control effect score to quantify the effectiveness of the control measures, and feedback this score to the risk control model parameter set for update, so as to continuously optimize the decision-making ability of the risk control model. The calculation of the control effect score is based on a series of key indicators, such as the risk reduction rate, misjudgment rate, user satisfaction, execution cost, etc., and a weighted calculation method is adopted to comprehensively measure the overall performance of the control measures. This score is used to update the parameters of the risk control model, enabling the model to make more accurate adjustments in future risk assessments and control strategy selections. To improve the transparency and interpretability of the system, generate a complete risk control execution result, which includes the risk assessment process, the basis for selecting control measures, and the final execution effect, for subsequent auditing and optimization. Through this feedback mechanism, the system continuously self-optimizes, enabling the risk control strategy to adapt to different market environments and business requirements, and ultimately achieving precise and efficient risk control.

[0020] In one example, multi-source data is collected and preprocessed to obtain a standardized multi-dimensional data matrix, including: Connect the data interface module to the structured transaction database, unstructured text database, real-time data stream, and external public database through a standardized API interface protocol to establish a data transmission channel; Collect multi-source original data streams including transaction amount, transaction frequency, transaction timestamp, and transaction object identifier based on the data transmission channel; Execute a data verification program on the multi-source original data streams to obtain a cleaned data set, and convert the cleaned data set into a unified data structure to obtain a standardized data set; Extract features from the standardized data set to obtain a feature vector set, and perform dimensionality reduction processing on the feature vector set to obtain a standardized multi-dimensional data matrix.

[0021] In this example, through a standardized API interface protocol, the data interface module is connected to multiple heterogeneous data sources, including structured transaction databases, unstructured text databases, real-time data streams, and external public databases, to build a stable and efficient data transmission channel. The structured transaction database mainly stores information such as user transaction records, account balances, credit scores, etc., while the unstructured text database contains user risk assessment reports, complaint information, social media comments, etc. The real-time data stream provides information such as users' latest transaction behaviors, device usage, login records, etc., and the external public database includes external factors affecting financial transactions such as government regulatory data, industry risk indices, exchange rate fluctuations, etc. Through the standardized API interface protocol, it is ensured that the data formats and transmission protocols between various data sources are consistent, enabling data to flow smoothly between different systems, thus avoiding data loss or information island problems caused by incompatible formats. Based on the data transmission channel, a multi-source raw data stream containing transaction amount, transaction frequency, transaction timestamp, and transaction object identifier is collected. Among them, the transaction amount is used to measure the economic value of a single transaction, the transaction frequency reflects the user's trading activity, the transaction timestamp is used to analyze the time pattern of trading behaviors, and the transaction object identifier is used to identify the identities of both parties to the transaction, in order to construct a user transaction network. During the data collection process, a unified data parsing strategy is adopted to convert raw data in different formats into a processable standard format to ensure the smooth progress of subsequent data processing. A data verification program is executed on the raw data to ensure the accuracy and consistency of the data. Data verification usually includes multiple steps. For example, integrity checks are used to detect whether data is missing, duplicate data deduplication ensures that data is not calculated multiple times, and outlier detection identifies abnormal changes in transaction amounts through statistical methods (such as Z-score detection) or machine learning-based methods (such as the isolation forest algorithm), thereby reducing the risk of fraud or system false alarms. During the data verification process, a time series analysis method is adopted to perform logical checks on the transaction timestamps to ensure that the data conforms to reasonable time patterns, such as detecting whether transactions occur during non-business hours, or whether there are a large number of high-frequency trading behaviors in a short period of time, etc. After data verification and cleaning, a high-quality data set is obtained and converted into a unified data structure to construct a standardized data set. Feature extraction is performed on the standardized data set to extract core information from the original transaction data that is helpful for risk assessment.The process of feature extraction involves multiple methods. For example, for transaction amounts, statistical features such as mean, standard deviation, and median are calculated to reflect the user's transaction pattern; for transaction frequencies, their time distribution is calculated, such as the number of transactions during peak hours and the proportion of night-time transactions; for transaction timestamps, a time series model is constructed to analyze whether there are periodic changes in the user's transaction behavior; for transaction object identifiers, by constructing a user transaction network, it is analyzed whether the user's transaction relationships have abnormal patterns. For example, trading with multiple unfamiliar accounts in a short period indicates the risk of fraud. Through natural language processing methods, keywords related to user credit assessment are extracted from unstructured text data. For example, high-risk words such as "fraud" and "frozen account" are identified in complaint records and converted into numerical features for subsequent risk assessment. The feature vector set is processed for dimensionality reduction to reduce the data dimension, improve computational efficiency, and avoid overfitting problems caused by high-dimensional data. During the dimensionality reduction process, methods such as principal component analysis or manifold learning are used to select the most representative features from the original feature set, while reducing data redundancy and improving the generalization ability of the model. For example, assume that in the transaction data, there is... dimensional feature vectors , where some of the features are redundant, and the goal of dimensionality reduction is to find a new low-dimensional feature space such that the main information of the data is retained. Through the eigenvalue decomposition of the feature covariance matrix : where, is the feature covariance matrix, is the corresponding eigenvector, is the eigenvalue. By selecting the eigenvectors corresponding to the larger eigenvalues, a low-dimensional representation is constructed, where is the first matrix composed of the largest eigenvectors

[0022] In an example, a dynamic risk control model is constructed based on the standardized multi-dimensional data matrix, and a set of risk control model parameters is calculated, including: A basic risk assessment layer is constructed through a deep neural network, taking the standardized multi-dimensional data matrix as the input data, and setting a network structure matching the data feature dimension to obtain a risk assessment benchmark model; The ReLU activation function is used to perform non-linear mapping processing on the risk assessment benchmark model, and the parameter training is performed through the batch gradient descent algorithm with the mean square error as the loss function to obtain an initial model parameter set; Construct a dynamic adjustment layer that includes a time series analysis module, an environmental factor response module, and an anomaly detection module based on the initial model parameter set. Among them, the time series analysis module uses an LSTM network structure to process the data time dependence and obtain the parameter matrix of the dynamic adjustment layer; Perform weight initialization on the parameter matrix of the dynamic adjustment layer to obtain the initialized dynamic adjustment layer; Construct a multi-objective optimization framework as the decision execution layer based on the initialized dynamic adjustment layer, integrate the risk minimization objective function and the resource consumption minimization objective function, and determine the weight parameters through historical data analysis to obtain the comprehensive objective function; Integrate and store the parameter set of the risk assessment benchmark model, the parameter matrix of the dynamic adjustment layer, and the weight parameters of the comprehensive objective function in the parameter server, and establish a model version control mechanism to record the historical trajectory of parameter updates to obtain the risk control model parameter set.

[0023] In this example, the standardized multi-dimensional data matrix is used as the input data, and a neural network structure matching the data feature dimension is designed to construct the basic risk assessment layer. Since the risk control system involves various heterogeneous data, including transaction amount, transaction frequency, user credit score, social behavior, etc., the input layer of the network needs to be able to adapt to high-dimensional data and extract complex risk patterns through a multi-layer neural network. In this process, a fully connected neural network is used as the basic risk assessment layer, and the structure of this layer is matched according to the data feature dimension to ensure that it can effectively capture the influence patterns of various risk factors. In the basic risk assessment layer, the output of each layer of neurons undergoes a non-linear mapping to enhance the expression ability of the model. The ReLU (Rectified Linear Unit) is used as the activation function, enabling the neural network to efficiently learn complex non-linear risk patterns. The advantage of ReLU is that it can effectively avoid the gradient vanishing problem and accelerate the training process. The training of the model uses the batch gradient descent algorithm and the mean square error as the loss function to ensure that the model can effectively fit the historical risk data. During the gradient descent process, the direction of parameter update is determined by the gradient of the loss function, and the update formula is expressed as: Among them, represents the current model parameters, is the learning rate, represents the loss function Gradient with respect to parameters. Through continuous iterative optimization, the model can gradually converge to the optimal parameter configuration and finally obtain the initial model parameter set, which is used to calculate the risk score for the input standardized data matrix. After obtaining the initial model parameter set, a dynamic adjustment layer is constructed to improve the model's adaptability to time dynamics and environmental factors. The dynamic adjustment layer consists of a time series analysis module, an environmental factor response module, and an anomaly detection module. The time series analysis module adopts a long short-term memory (LSTM) network structure to capture the time dependence of the data and analyze the long-term trends of user behavior. The core of the LSTM lies in its gating mechanism, including the input gate, forget gate, and output gate. These gating units can retain long time series information during training and effectively suppress the vanishing gradient, enabling the risk control system to model long-term risk patterns. The environmental factor response module is used to analyze the impact of external factors such as market environment and policy changes on risks, while the anomaly detection module uses self-supervised learning or unsupervised learning methods, such as autoencoders or isolation forest algorithms, to identify abnormal patterns in transaction data. When constructing the dynamic adjustment layer, its parameter matrix is initialized with weights to ensure stable training of the model. The weight initialization method uses uniform distribution initialization or Xavier initialization to avoid problems of gradient explosion or gradient vanishing. The initialized dynamic adjustment layer is combined with the basic risk assessment layer to provide more accurate risk prediction capabilities. Based on the dynamic adjustment layer, a multi-objective optimization framework is constructed as the decision execution layer to achieve a balance between risk control and computational resource consumption. The decision execution layer adopts an optimization framework that includes a risk minimization objective function and a resource consumption minimization objective function to ensure that while reducing risks, the usage cost of computational resources is minimized as much as possible. This optimization problem is solved by the Lagrangian optimization method, and its objective function is expressed as: where represents the comprehensive optimization objective function, represents the risk minimization objective function, represents the resource consumption minimization objective function, and It is a weight parameter, representing the importance weights of the two in the overall optimization. The weight parameter is adjusted through historical data analysis to ensure the adaptability of the model in different business scenarios. After completing multi-objective optimization, all key parameters of the risk control model, including the parameter set of the risk assessment benchmark model, the parameter matrix of the dynamic adjustment layer, and the weight parameters of the comprehensive objective function, are integrated and stored in the parameter server to achieve centralized management and efficient invocation of the model. The parameter server can support distributed training and model updates, and provides a version control mechanism to record the historical track of parameter updates, enabling the system to trace back to historical versions and conduct parameter comparison analysis when it is necessary to trace back model changes or adjust risk control strategies. Through the version control mechanism, it is ensured that the risk control model has flexible adjustment capabilities in the face of different market environments, and by continuously optimizing model parameters, the adaptability and accuracy of the risk control system are improved.

[0024] In one example, a risk factor sensitivity analysis is performed using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector, including: Extract the latest preprocessed data matrix from the data warehouse and compare it with the historical data matrix to calculate the data offset; Perform a risk factor sensitivity analysis on the data offset to obtain the influence degree of each factor, and construct a risk factor importance matrix based on the factor influence degree to obtain a risk factor importance measurement standard; Perform a singular value decomposition on the risk factor importance matrix to obtain a set of eigenvectors, and calculate the dynamic weight of each risk factor based on the set of eigenvectors to obtain a dynamic weight vector.

[0025] In this example, the latest preprocessed data matrix is extracted from the data warehouse and compared with the historical data matrix to calculate the data offset. In the risk control system, the data warehouse stores structured and semi-structured data from multiple sources, including transaction records, user behavior, market environment data, etc. When extracting data, it is necessary to ensure the consistency and integrity of the data to avoid calculation errors caused by data missing or format mismatch. Assume that the latest data matrix at the current time point is represented as while the historical data matrix is represented as The data offset is calculated through matrix difference, that is where represents the change of the current data compared with the historical data, and each element reflects the change amplitude of the th data sample on the th feature. For example, if the trading frequency, trading amount, or credit score of a certain user fluctuates violently in a short period of time, then the corresponding will be relatively large, indicating that this risk factor has an important impact on the overall risk assessment. By calculating the data offset, risk factors with relatively large changes are initially screened out. Perform a sensitivity analysis of risk factors on the data offset to evaluate the impact degree of different risk factors on the overall risk level. The core of the risk factor sensitivity analysis lies in measuring the impact of each factor on the comprehensive risk score. Therefore, the impact of each factor is quantified through regression analysis, information gain calculation, or feature contribution analysis methods based on the Shapley value. For example, in a multiple linear regression model, the risk score and each risk factor The relationship between them is: Among them, represents the th regression coefficient of the risk factor, is the error term. The larger the regression coefficient , the higher the impact degree of this factor on the risk score. Therefore, it is one of the important indicators of the risk factor. In addition to regression analysis, the information gain method is used to calculate the information contribution degree of each risk factor to the target variable, so as to measure the influence of the factor. Through this series of analyses, a risk factor importance matrix is constructed, where represents the rd sample at the th factor's importance weight. This matrix is used for subsequent feature selection and weight calculation. Perform dimensionality reduction processing on the risk factor importance matrix to extract the core feature patterns and eliminate redundant information. Perform singular value decomposition on the risk factor importance matrix to obtain the main feature vector set of the data. The basic form of singular value decomposition is: Among them, is the left singular vector matrix, is the diagonal singular value matrix, is the right singular vector matrix. The magnitude of the singular value reflects the contribution degree of different feature patterns. Therefore, intercept the first largest singular value corresponding feature vectors to form the dimensionality-reduced feature vector set . The goal of this process is to remove low-influence features and only retain the most representative risk factors, thereby improving the calculation efficiency and reducing the impact of noise. Calculate the dynamic weight of each risk factor based on the feature vector set to obtain the dynamic weight vector. The calculation of the dynamic weight is based on the linear combination of feature vectors, where the weight values are adjusted through a normalization method to ensure that the sum of the weights of all factors is 1. For example, if the th column vector in represents the risk factor The projection in the low-dimensional space, then the final dynamic weight of this factor is expressed as: Wherein, represents the dynamic weight of the th risk factor, is an element in the eigenvector matrix , represents the selected number of principal features, is the total number of original features.

[0026] This embodiment further includes: performing scenario intelligent recognition and risk control strategy adaptive adjustment according to the dynamic weight vector and the standardized multi-dimensional data matrix to obtain risk control parameters for scenario optimization, including: applying an unsupervised clustering algorithm to the standardized multi-dimensional data matrix, including an integrated method of K-means, DBSCAN, and Gaussian mixture model, grouping the transaction behavior patterns, and evaluating the optimal number of clusters based on the silhouette coefficient and the Davies-Bouldin index to obtain an initial scenario division result; constructing a decision tree model based on the initial scenario division result, extracting the key distinguishing features of each scenario, and calculating the information gain ratio to evaluate the feature importance to obtain a scenario feature vector; performing a tensor fusion operation on the scenario feature vector and the dynamic weight vector to construct a scenario-risk association matrix M, where the matrix element M_ij represents the influence degree of the jth risk factor in the ith scenario, to obtain a scenario-based risk mapping; based on the scenario-based risk mapping, retrieving the historical effective policy set from the risk control policy library, and selecting the most matching policy combination through weighted Jaccard similarity calculation to obtain a candidate policy set; performing Monte Carlo tree search on the candidate policy set, simulating the execution effect of N random policy combinations in the policy space to obtain an optimized policy combination; performing an adaptability test on the optimized policy combination and the current business state vector B, triggering a conflict resolution mechanism, and finding the Pareto optimal solution through a multi-objective genetic algorithm to obtain a finally conflict-resolved policy set; based on the finally conflict-resolved policy set, performing weight calibration on the dynamic weight vector and performing targeted adjustment on the key parameters of the risk control model, including core parameters such as the Q learning rate, discount factor, and exploration probability, and at the same time establishing a scenario-policy effect index table to store the mapping relationship between the current scenario and the policy effect to obtain risk control parameters for scenario optimization.

[0027] In one example, iterative risk assessment is performed based on the dynamic weight vector to obtain a comprehensive risk score, including: Defining a risk state space based on the feature vector and environmental state vector of the current evaluation object, constructing a risk assessment operation space including six types of local evaluation operators, and defining a reward function by weighted combination of risk assessment accuracy and computing resource consumption to obtain a Q learning basic framework; Construct a Q-value matrix based on the Q-learning basic framework and initialize the Q-value matrix as a zero matrix, which serves as the decision basis for the selection of evaluation operators, and obtain the initialized Q-value matrix; In each evaluation period, according to the ε-greedy strategy, randomly select an evaluation operator with probability ε, and select the operator with the maximum Q-value in the current state with probability 1-ε to obtain the current optimal evaluation operator; Perform a risk assessment operation using the current optimal evaluation operator and the dynamic weight vector to obtain the updated Q-value matrix; Maintain an experience pool D to store transition quadruples based on the updated Q-value matrix, and perform batch updates from the experience pool. At the same time, implement a double Q-network architecture to reduce learning instability through a soft update mechanism to obtain an optimized Q-learning model; Introduce a cross-evaluation and mutation mechanism into the optimized Q-learning model. The cross operator fuses different evaluation results, and the mutation operator introduces random perturbations to enhance the exploration ability of unknown risks, and generate a comprehensive risk score after multiple iterations.

[0028] In this example, define a risk state space, which is composed of the feature vector of the current evaluation object and the environmental state vector. The feature vector is used to describe individual risk factors, including transaction amount, transaction frequency, account balance, credit score, etc., while the environmental state vector is used to represent external influencing factors, such as market volatility, industry credit index, policy changes, etc. By constructing this risk state space, it is ensured that the risk control system can comprehensively consider the interaction between individual behaviors and the external environment, thereby improving the accuracy of risk assessment. On the basis of defining the risk state space, construct a risk assessment operation space, which contains six types of local evaluation operators, and each operator is aimed at specific risk characteristics or evaluation methods. For example, the operator based on statistical analysis can calculate the mean and standard deviation of the transaction amount, the operator based on the rule engine can detect abnormal transaction patterns, the operator based on machine learning can predict individual credit risks, and the operator based on association network analysis can identify suspicious associations between trading objects. In addition, it also includes an operator based on time series analysis to detect trend changes in trading behaviors, and an operator based on external data fusion to introduce industry credit data or macroeconomic indicators. On this basis, in order to optimize the operator selection process, construct a reward function, which combines the risk assessment accuracy and the computational resource consumption in a weighted manner to ensure that the system optimizes the use efficiency of computational resources while ensuring the evaluation accuracy: where, represents the reward obtained after selecting the operator in the state , represents the evaluation accuracy, represents the computational resource consumption, and is a parameter for adjusting the weights of the two. Through this reward function, it is ensured that the selection of the evaluation operator can not only improve the evaluation accuracy but also control the calculation cost, enabling the system to operate efficiently in a large-scale data environment. Based on this Q-learning basic framework, a Q-value matrix is constructed and initialized as a zero matrix, which serves as the decision basis for the selection of the evaluation operator. In the Q-learning algorithm, the Q-value matrix is used to store the historical performance of different operators in different states. Initially, due to the lack of empirical data, all Q-values are set to zero. Subsequently, in each training process, the system updates the Q-values to gradually optimize the selection strategy of the evaluation operator. In each evaluation period, a -epsilon-greedy strategy is used to select the operator, that is, with a probability of randomly select an operator to increase exploration, and with a probability of select the operator with the largest Q-value in the current state to ensure that the operator with the best historical performance is selected as much as possible. Through this step, while ensuring a certain degree of exploration, it avoids falling into local optima and finally obtains the current optimal evaluation operator. Use the optimal evaluation operator and the dynamic weight vector to perform the risk assessment operation, and update the Q-value matrix based on the evaluation results. The update of the Q-value follows the update formula of Q-learning: where, is the learning rate, is the discount factor, is the new state after executing the operator, represents the Q-value of the optimal operator in the new state. Through this update mechanism, the system can continuously optimize the selection of the risk assessment operator, making the evaluation strategy tend to be optimal in the long-term operation. To improve the learning efficiency and stability of the model, an experience pool is maintained to store historical experiences, that is, after each evaluation operation, the transition quadruple is stored in the experience pool and batch updated regularly to reduce the instability during the learning process. The double Q-network architecture is adopted to reduce the overestimation problem when updating the Q-values. In the double Q-network, the system maintains two Q-value matrices, namely the main Q-network and the target Q-network . Each time an update is made, the target Q-network adopts a soft update mechanism, that is: where, is the soft update coefficient, usually taking a relatively small value to ensure a smoother update of the target Q-network and reduce oscillations during training. After optimizing the Q-learning model, a cross-evaluation and mutation mechanism is introduced to enhance the exploration ability for unknown risks. The cross-evaluation mechanism uses a crossover operator to fuse different evaluation results, that is, during the evaluation process, the system simultaneously applies multiple operators and synthesizes their results to reduce the possibility of misjudgment by a single operator. The mutation mechanism enhances the model's adaptability to unknown risk patterns by introducing random perturbations. In actual implementation, the mutation operator randomly adjusts the weights of the evaluation operators or selects non-optimal operators with a certain probability to increase the diversity of evaluation strategies. The role of this mechanism is that the risk assessment model not only needs to rely on historical data for training but also needs to have a certain ability of adaptive exploration to identify risk patterns that have not yet occurred. After multiple iterations of optimization, the system finally generates a stable comprehensive risk score.

[0029] In one example, a multi-layer threshold structure is constructed based on the comprehensive risk score and dynamically adjusted to obtain personalized risk thresholds, including: Group the historical risk score data by risk level, and perform clustering analysis on each group of data using the k-means clustering algorithm to obtain k clustering center points as the initial threshold set, resulting in the basic threshold layer; Calculate the risk score deviation based on the comprehensive risk score and calculate the threshold adjustment amount; Perform adjustment according to the threshold adjustment amount and the basic threshold layer to obtain the adjusted threshold layer; Monitor the risk change rate of the adjusted threshold layer. When the system detects that the risk score change rate exceeds the preset threshold, trigger the calculation of the emergency threshold to obtain the emergency threshold layer; Analyze the performance indicators of the adjusted threshold layer and the emergency threshold layer to obtain the self-learning optimized threshold; Automatically identify the current business scenario type by monitoring changes in transaction frequency and amount distribution indicators, and select the most matching threshold configuration from the pre-trained scenario-threshold mapping library to fuse with the self-learning optimized threshold, taking into account historical benchmarks, real-time status, and emergency requirements to obtain personalized risk thresholds.

[0030] In this example, the historical risk score data is analyzed and grouped according to the risk level to ensure that adaptive initial thresholds are set at different risk levels. To achieve this goal, the k-means clustering algorithm is used to perform clustering analysis on each group of data. By calculating the data distribution of each risk level, representative risk thresholds are automatically extracted. During the k-means clustering process, assume that the historical risk score data set is , and divide it into groups to extract the representative center points of each group as the initial threshold set. The optimization objective of the k-means algorithm is expressed as: Among them, represents the th clustering cluster, is the center point of this cluster, is a data point in the historical risk score, and represents the Euclidean distance between the data point and the clustering center. Through iterative optimization, the data points are made as close as possible to the optimal center point, thereby determining the basic threshold layer corresponding to each risk level. After obtaining the basic threshold layer, the risk score deviation is calculated based on the current comprehensive risk score, and the threshold adjustment amount is calculated therefrom. The risk score deviation is defined as the difference between the current risk score and the historical mean, that is: Among them, represents the current comprehensive risk score, and represents the average risk score of similar users or historical data. When exceeds a certain range, it indicates that the current risk score deviates from the historical average level, and the risk threshold needs to be adjusted. In order to calculate the threshold adjustment amount, analyze the change trend of the risk score, and combine the fluctuation range of the historical risk score to calculate a reasonable threshold correction factor, so that the adjusted threshold can adapt to market changes and avoid excessive fluctuations. After calculating the threshold adjustment amount, combine it with the basic threshold layer for adjustment to obtain the adjusted threshold layer. During the adjustment process, a weighted average method is used so that the adjusted threshold can ensure stability while being able to flexibly respond to changes in the market environment. The adjusted threshold is expressed as: Among them, represents the adjusted risk threshold, is the initial threshold of the basic threshold layer, and 𝜆 is the adjustment coefficient used to control the intensity of the threshold adjustment. If the market environment is relatively stable, then takes a larger value, making the adjustment range smaller; if the market fluctuates greatly, then takes a smaller value to ensure that the threshold can quickly adapt to changes. On the basis of adjusting the threshold layer, monitor its risk change rate to ensure that when the change speed of the risk score exceeds the preset threshold, an emergency threshold calculation can be triggered, so as to provide additional protection measures in extreme cases. The risk change rate is measured by calculating the time derivative of the risk score, that is: Among them, represents the change rate of the risk score, is the risk score at the previous moment, is the time interval. When Exceeding a certain threshold , it indicates that the current market environment has experienced drastic fluctuations. At this time, the system needs to trigger emergency threshold calculation to adjust the threshold strategy to prevent excessive risk accumulation. After completing the emergency threshold calculation, the performance indicators of the adjustment threshold layer and the emergency threshold layer are analyzed to optimize the self-learning mechanism and generate the optimized risk threshold. In the performance analysis process, the performance of the adjusted threshold in historical data is evaluated, including indicators such as misjudgment rate, rejection rate, and risk control effect, and the threshold strategy is automatically adjusted using reinforcement learning or adaptive optimization methods. For example, if a certain adjustment strategy performs well in past data, it will be preferred in similar environments in the future. If a certain strategy has a high misjudgment rate in historical data, its weight should be reduced or further optimized. In order to ensure that the risk threshold can match different business scenarios, the current business scenario type is automatically identified by combining indicators such as transaction frequency and transaction amount distribution, and the most matching threshold configuration is selected from the pre-trained scenario-threshold mapping library to finally generate a personalized risk threshold.

[0031] In one example, risk control measures are triggered and a feedback mechanism is executed based on the comprehensive risk score and personalized risk threshold to obtain risk control execution results, including: Compare the comprehensive risk score with the personalized risk threshold to obtain the basis for risk control decision-making; Select a corresponding control strategy set from the control measure library based on the risk control decision basis to obtain a candidate control strategy set; Apply resource optimization algorithm to the set of candidate control strategies and solve them under resource constraints by Lagrange multiplier method to obtain the optimal control execution plan; The optimal control execution plan is converted into specific system operations through the control execution module, and the execution timestamp, operation parameters and execution status are recorded to obtain the risk control execution record; Initiate a real-time monitoring mechanism based on risk control execution records to obtain control effect data; The control effect score is calculated based on the control effect data, and the control effect score is fed back to the risk control model parameter set for updating, and a risk control execution result is generated that includes the risk assessment process, the basis for selecting control measures, and the execution effect.

[0032] In this example, the composite risk score is compared to the personalized risk threshold to determine whether the risk level of the current assessment object exceeds the acceptable range. Reflect the current risk status and personalize the risk threshold It is dynamically set by factors such as historical data, scenario analysis and emergency adjustment, and the relationship between the two is calculated, namely: Derive the degree to which the current risk deviates from the preset threshold , if , it indicates that the current risk exceeds the tolerance range and corresponding control measures need to be taken. While if , it means that the current risk is still within the acceptable range and no intervention or only low-level monitoring is required. Based on the risk control decision basis, select the corresponding set of control strategies from the control measure library to construct the candidate control strategy set. The control measure library contains a series of executable risk control strategies, such as strengthening authentication, freezing transactions, reducing credit limits, triggering manual reviews, etc. The selection of these measures depends on the risk level and business scenario. The process of selecting control strategies is represented as an optimization problem. Let be the set of all available control strategies, then the candidate control strategy set is represented as: where is the risk trigger threshold corresponding to each control strategy. Only when exceeds the trigger threshold of a certain strategy, will this strategy be included in the candidate strategy set . After obtaining the candidate control strategy set, optimize the combination of control strategies to solve the optimal control execution plan under resource constraints. Since risk control execution involves multiple factors such as computing resources, manual review costs, and user experience, the optimization problem of control execution is modeled as a constrained optimization problem and solved using the Lagrange multiplier method. Let be the risk reduction benefit function of the control strategy, be the execution cost function. The goal is to find the optimal control strategy combination such that the risk reduction is maximized while satisfying the resource constraint , where is the upper limit of available resources. This optimization problem is represented as: where is the Lagrange multiplier, representing the influence weight of resource consumption on the overall goal. By solving this optimization problem, the optimal control execution plan that satisfies the resource constraint is obtained. Convert the optimal control execution plan into specific system operations through the control execution module and record relevant parameters during the execution process to ensure the traceability of control measures. The control execution module converts the risk control decision into actual operations, such as adjusting transaction limits, triggering additional authentication, or freezing accounts, and records the execution timestamp , operation parameters and execution status , a risk control execution record is formed for subsequent analysis and optimization. After the risk control measures are executed, a real-time monitoring mechanism is initiated to collect control effect data and evaluate the actual effect of the control strategy. The control effect data is evaluated through multi-dimensional data such as user behavior changes, transaction success rates, and customer feedback. For example, in the scenario of transaction risk management, the effectiveness of the control measures is judged by analyzing the subsequent transaction behaviors of the controlled accounts. The calculation of the control effect is defined as: where represents the control effect score, represents the number of risks successfully intercepted, represents the number of normal transactions wrongly rejected, and are the weight coefficients of the two respectively. Through this calculation method, the effectiveness of the control strategy is quantified, and while reducing risks, the impact on normal users is minimized. After obtaining the control effect score, it is fed back to the risk control model parameter set for update to achieve the adaptive optimization of the risk control system. The update process adopts the method of reinforcement learning. By adjusting the model parameters, the future selection of control strategies becomes more accurate. For example, if a certain control strategy performs well in past data, it should be preferentially selected in future similar environments, while if the misjudgment rate of a certain strategy is high, its weight should be reduced or the trigger conditions should be adjusted. To improve the transparency and interpretability of the system, a complete risk control execution result is generated, which should include the risk assessment process, the basis for selecting control measures, and the final execution effect for subsequent auditing and optimization.

[0033] Referring to Figure 2 , this embodiment provides a dynamic risk control device, including: Collection unit 1, used to collect and preprocess multi-source data to obtain a standardized multi-dimensional data matrix; Construction unit 2, used to construct a dynamic risk control model based on the standardized multi-dimensional data matrix and calculate the risk control model parameter set; Analysis unit 3, used to perform risk factor sensitivity analysis using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; Evaluation unit 4, used to perform iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; Adjustment unit 5, used to construct a multi-layer threshold structure based on the comprehensive risk score and perform dynamic adjustment to obtain a personalized risk threshold; Feedback unit 6, used to trigger risk control measures and execute a feedback mechanism according to the comprehensive risk score and the personalized risk threshold to obtain a risk control execution result.

[0034] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be elaborated here.

[0035] Refer to Figure 3 , in an embodiment of the present invention, a computer device is further provided. This computer device may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of this computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0036] Those skilled in the art can understand that Figure 3 the structure shown in

[0037] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0038] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0039] It should be noted that in this article, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, apparatus, article, or method that includes the element.

[0040] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A dynamic risk control method, characterized in that: include: Collect and preprocess multi-source data to obtain a standardized multi-dimensional data matrix; Constructing a dynamic risk control model according to the standardized multidimensional data matrix and calculating a risk control model parameter set; Perform risk factor sensitivity analysis using the risk control model parameter set and the standardized multidimensional data matrix to obtain a dynamic weight vector; Performing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; Building a multi-layer threshold structure based on the comprehensive risk score and performing dynamic adjustment to obtain a personalized risk threshold; The risk control measures are triggered according to the comprehensive risk score and the personalized risk threshold and a feedback mechanism is executed to obtain a risk control execution result.

2. The dynamic risk control method according to claim 1, characterized in that: The multi-source data is collected and preprocessed to obtain a standardized multi-dimensional data matrix, including: Connect the data interface module to the structured transaction database, unstructured text database, real-time data stream and external public database through the standardized API interface protocol to establish a data transmission channel; Collecting multi-source original data streams including transaction amounts, transaction frequencies, transaction timestamps, and transaction object identifiers based on the data transmission channel; Performing a data verification procedure on the multi-source original data streams to obtain a cleaned data set, and converting the cleaned data set into a unified data structure to obtain a standardized data set; Feature extraction is performed on the standardized data set to obtain a feature vector set, and dimension reduction processing is performed on the feature vector set to obtain a standardized multi-dimensional data matrix.

3. The dynamic risk control method according to claim 1, characterized in that: The step of constructing a dynamic risk control model according to the standardized multidimensional data matrix and calculating a risk control model parameter set includes: A basic risk assessment layer is constructed through a deep neural network, the standardized multidimensional data matrix is ​​used as input data, and a network structure matching the data feature dimension is set to obtain a risk assessment benchmark model; The risk assessment benchmark model is subjected to nonlinear mapping processing by using a ReLU activation function, and parameter training is performed by using a batch gradient descent algorithm with a mean square error as a loss function to obtain an initial model parameter set; Based on the initial model parameter set, a dynamic adjustment layer including a time series analysis module, an environmental factor response module and an anomaly detection module is constructed, wherein the time series analysis module uses an LSTM network structure to process the time dependency of data to obtain a dynamic adjustment layer parameter matrix; Initializing the weights of the dynamic adjustment layer parameter matrix to obtain an initialized dynamic adjustment layer; Based on the initialized dynamic adjustment layer, a multi-objective optimization framework is constructed as a decision execution layer, the risk minimization objective function and the resource consumption minimization objective function are integrated, and the weight parameters are determined by historical data analysis to obtain a comprehensive objective function; The parameter set of the risk assessment benchmark model, the dynamic adjustment layer parameter matrix and the weight parameters of the comprehensive objective function are integrated and stored in the parameter server, and a model version control mechanism is established to record the historical trajectory of parameter updates to obtain the risk control model parameter set.

4. The dynamic risk control method according to claim 1, characterized in that: The method of performing risk factor sensitivity analysis using the risk control model parameter set and the standardized multidimensional data matrix to obtain a dynamic weight vector includes: Extract the latest preprocessed data matrix from the data warehouse and compare it with the historical data matrix to obtain the data offset; Performing risk factor sensitivity analysis on the data offset to obtain the influence degree of each factor, and constructing a risk factor importance matrix according to the influence degree of the factors to obtain a risk factor importance measurement standard; The risk factor importance matrix is ​​subjected to singular value decomposition to obtain a set of eigenvectors, and the dynamic weight of each risk factor is calculated based on the set of eigenvectors to obtain a dynamic weight vector.

5. The dynamic risk control method according to claim 1, characterized in that: The performing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score includes: Based on the feature vector of the current evaluation object and the environmental state vector, the risk state space is defined, and a risk assessment operation space containing six types of local evaluation operators is constructed. The weighted combination of risk assessment accuracy and computing resource consumption is defined as a reward function, and the basic framework of Q learning is obtained. Constructing a Q value matrix according to the Q learning basic framework and initializing the Q value matrix to a zero matrix as a decision basis for selecting an evaluation operator, thereby obtaining an initialized Q value matrix; In each evaluation cycle, the evaluation operator is randomly selected with probability ε according to the ε-greedy strategy, and the operator with the largest Q value in the current state is selected with probability 1-ε to obtain the current optimal evaluation operator; Performing a risk assessment operation using the current optimal assessment operator and the dynamic weight vector to obtain an updated Q value matrix; Based on the updated Q value matrix, an experience pool D is maintained to store the transfer quadruple, and batch updates are performed from the experience pool, while the dual Q network architecture is implemented to reduce learning instability through a soft update mechanism to obtain an optimized Q learning model; A cross-evaluation and mutation mechanism is introduced into the optimized Q-learning model. Different evaluation results are fused through the crossover operator, and the mutation operator introduces random disturbance to enhance the ability to explore unknown risks. A comprehensive risk score is generated after multiple iterations.

6. The dynamic risk control method according to claim 1, characterized in that: The step of constructing a multi-layer threshold structure based on the comprehensive risk score and performing dynamic adjustment to obtain a personalized risk threshold includes: The historical risk score data is grouped according to the risk level, and each group of data is clustered and analyzed using the k-means clustering algorithm to obtain k cluster center points as the initial threshold set to obtain the basic threshold layer; calculating a risk score deviation based on the comprehensive risk score and calculating a threshold adjustment amount; Perform adjustment according to the threshold adjustment amount and the basic threshold layer to obtain an adjusted threshold layer; Monitoring the risk change rate of the adjustment threshold layer, when the system detects that the risk score change rate exceeds a preset threshold, triggering emergency threshold calculation to obtain an emergency threshold layer; Analyze the performance indicators of the adjustment threshold layer and the emergency threshold layer to obtain a self-learning optimization threshold; By monitoring changes in transaction frequency and amount distribution indicators, the current business scenario type is automatically identified, and the most matching threshold configuration is selected from the pre-trained scenario-threshold mapping library and integrated with the self-learning optimization threshold. Personalized risk thresholds are obtained by comprehensively considering historical benchmarks, real-time status and emergency needs.

7. The dynamic risk control method according to claim 1, characterized in that: The triggering of risk control measures according to the comprehensive risk score and the personalized risk threshold and executing the feedback mechanism to obtain the risk control execution result include: Comparing the comprehensive risk score with the personalized risk threshold to obtain a basis for risk control decision-making; Selecting a corresponding control strategy set from a control measure library based on the risk control decision basis to obtain a candidate control strategy set; Applying a resource optimization algorithm to the candidate control strategy set and solving it under resource constraints by Lagrange multiplier method to obtain an optimal control execution plan; The optimal control execution scheme is converted into a specific system operation through a control execution module, and the execution timestamp, operation parameters and execution status are recorded to obtain a risk control execution record; Initiate a real-time monitoring mechanism based on the risk control execution record to obtain control effect data; A control effect score is calculated based on the control effect data, the control effect score is fed back to the risk control model parameter set for updating, and a risk control execution result including a risk assessment process, a basis for selecting control measures, and an execution effect is generated.

8. A dynamic wind control device, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the dynamic wind control device comprises: An acquisition unit, used for acquiring and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; A construction unit, used to construct a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculate a risk control model parameter set; An analysis unit, configured to perform risk factor sensitivity analysis using the risk control model parameter set and the standardized multidimensional data matrix to obtain a dynamic weight vector; an evaluation unit, configured to perform iterative risk evaluation based on the dynamic weight vector to obtain a comprehensive risk score; An adjustment unit, configured to construct a multi-layer threshold structure based on the comprehensive risk score and perform dynamic adjustment to obtain a personalized risk threshold; A feedback unit is used to trigger risk control measures and execute a feedback mechanism according to the comprehensive risk score and the personalized risk threshold to obtain a risk control execution result.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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