Machine learning-based data full-process risk assessment method and system
By using machine learning-based multi-source data acquisition, dynamic preprocessing, and feature engineering, combined with dynamic risk assessment and feedback optimization, the problem of insufficient accuracy and timeliness in risk assessment in traditional methods is solved, achieving efficient and accurate risk identification and assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2025-09-23
- Publication Date
- 2026-07-10
AI Technical Summary
Traditional data end-to-end risk assessment methods rely on static rules and human experience, which cannot dynamically perceive abnormal patterns in the data flow. This results in insufficient accuracy and timeliness of risk assessment results, especially when facing unknown risk types and covert attacks.
By employing a machine learning-based approach, through multi-source data acquisition, dynamic preprocessing, feature engineering, machine learning model training, and dynamic risk assessment, combined with a feedback optimization mechanism, real-time risk assessment of data streams is achieved.
It improves the accuracy and reliability of risk assessment, can adaptively identify risk patterns in complex environments, respond to environmental changes in real time, output comprehensive risk reports, and continuously improve the system's practicality and robustness through iterative model optimization.
Smart Images
Figure CN122364686A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of artificial intelligence technology, specifically to a data-driven risk assessment method and system based on machine learning. Background Technology
[0002] Artificial intelligence is a new technological science that studies, develops, and applies theories, methods, technologies, and application systems to simulate, extend, and expand human intelligence. It is a branch of computer science that attempts to understand the nature of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems.
[0003] Currently, due to the fact that the entire data process involves multiple stages and the data forms are diverse, traditional methods often rely on static rules and human experience when conducting real-time risk assessments. They cannot dynamically perceive abnormal patterns in the data flow. When unknown risk types and covert attacks occur, it can lead to large deviations in risk assessments, making it difficult to guarantee the accuracy and timeliness of the assessment results.
[0004] Therefore, a data-driven risk assessment method and system based on machine learning is proposed to solve the above problems. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides a data end-to-end risk assessment method and system based on machine learning, which solves the problems mentioned in the background section.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a data end-to-end risk assessment method and system based on machine learning, the method comprising the following steps:
[0007] S1. Obtain the raw data stream, including structured and unstructured data, through the multi-source data acquisition unit, and generate an initial dataset;
[0008] S2. Perform data preprocessing operations based on the initial dataset, including missing value imputation, outlier detection, and data standardization, to generate a cleaned dataset;
[0009] S3. Use feature engineering algorithms to extract and select features from the cleaned dataset, including correlation analysis and principal component analysis, to generate a feature-optimized dataset;
[0010] S4. Input the feature optimization dataset into the machine learning prediction model for risk assessment training. The machine learning prediction model includes an ensemble learning algorithm and a deep learning network to generate risk prediction result data.
[0011] S5. Perform dynamic risk assessment calculations based on the risk prediction results data, and output a risk assessment report in combination with the risk probability threshold.
[0012] Preferably, the multi-source data acquisition unit in S1 includes an API interface module, a sensor data integration module, and a log file parsing module, which generates an initial dataset through real-time data stream processing technology.
[0013] Preferably, the data preprocessing operation in S2 uses automated scripts and a rule engine, missing value imputation is based on the K-nearest neighbor algorithm, and outlier detection is based on the isolated forest model.
[0014] Preferably, the feature engineering algorithm in S3 includes feature importance ranking and dimensionality reduction, the correlation analysis uses the Pearson correlation coefficient, and the principal component analysis retains principal components with a variance contribution rate greater than 95%.
[0015] Preferably, the machine learning prediction model in S4 is the XGBoost ensemble algorithm, the risk assessment training includes cross-validation and hyperparameter tuning, and the generated risk prediction result data includes risk scores and classification labels.
[0016] Preferably, in S5, the dynamic risk assessment calculation is combined with Monte Carlo simulation, the risk probability threshold is dynamically adjusted according to historical data, and the output risk assessment report includes a risk level visualization chart.
[0017] Preferably, feedback optimization is performed based on the risk assessment report, and the machine learning prediction model is updated through the model retraining unit to achieve iterative risk control.
[0018] Preferably, the system includes:
[0019] The data acquisition and input module uses a multi-source data acquisition unit to acquire raw data streams, generates an initial dataset through a data format conversion unit, and outputs preprocessed input data through a data caching unit.
[0020] The preprocessing and feature engineering module receives the preprocessing input data, performs missing value imputation and outlier detection using the data cleaning unit, generates a feature optimization dataset through the feature extraction unit, and outputs the model input data through the feature selection unit.
[0021] The machine learning evaluation module receives the input data of the model, trains the risk assessment model through an ensemble learning engine, generates risk prediction results using deep learning network units, and outputs intermediate evaluation data through a risk calculation unit.
[0022] The dynamic risk analysis module receives the intermediate assessment data, sets the dynamic risk boundary through the probability threshold adjustment unit, outputs the risk assessment result through the Monte Carlo simulation unit, and optimizes the model parameters using the feedback loop unit.
[0023] The report generation and output module integrates the risk assessment results, generates an interactive risk report through the visualization engine unit, and outputs the final risk assessment report through the multi-dimensional fusion unit.
[0024] The adaptive optimization module receives the risk assessment results output by the dynamic risk analysis module, analyzes the prediction error through the model performance monitoring unit, updates the machine learning model through the parameter tuning unit, and generates optimization instructions through the complexity control unit.
[0025] Preferably, in the data acquisition and input module, the multi-source data acquisition unit supports real-time API data streams and batch file import, and the data format conversion unit uses JSON and Parquet formats.
[0026] Preferably, the adaptive optimization module further includes a risk profile construction unit, which is used to analyze user risk preference data and generate customized risk control strategies through the weakness identification unit.
[0027] (III) Beneficial Effects
[0028] Compared with existing technologies, this invention provides a data end-to-end risk assessment method and system based on machine learning, which has the following beneficial effects:
[0029] 1. In this invention, when conducting risk assessment of the entire data process, by establishing a multi-source data collection and standardized processing mechanism, heterogeneous data sources can be integrated and high-quality datasets can be automatically generated. This effectively solves the assessment bias problem caused by inconsistent data quality in traditional methods. At the same time, dynamic preprocessing technology is used to detect and correct data anomalies in real time, which improves the accuracy and reliability of subsequent risk assessments and provides a stable and reliable data foundation for machine learning models.
[0030] 2. In this invention, when performing data feature extraction and model training, by introducing an automated feature engineering and multi-algorithm fusion machine learning evaluation model, key features can be adaptively selected and model parameters optimized. This overcomes the limitations of feature redundancy and dimension loss in traditional evaluation, enabling the system to accurately identify risk patterns in complex data environments and improve the accuracy and generalization ability of risk prediction.
[0031] 3. In this invention, when generating risk assessment results, by combining a dynamic risk threshold adjustment mechanism with a multi-dimensional result fusion method, it can respond to environmental changes in real time and output a comprehensive risk assessment report, avoiding the lag and one-sidedness of traditional static assessment methods. At the same time, relying on the adaptive optimization module to realize continuous model iteration and feedback control, it further enhances the practicality and robustness of the system in real scenarios. Attached Figure Description
[0032] Figure 1 This is a flowchart of a data end-to-end risk assessment method based on machine learning according to the present invention;
[0033] Figure 2 This is an architecture diagram of a data end-to-end risk assessment system based on machine learning, as described in this invention. Detailed Implementation
[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0035] Please see Figure 1 This invention relates to a data-driven end-to-end risk assessment method and system based on machine learning. The method includes the following steps:
[0036] S1. Obtain raw data streams, including structured and unstructured data, through the multi-source data acquisition unit, and generate an initial dataset. The multi-source data acquisition unit refers to a system component used to collect data from multiple sources, supporting real-time streaming processing and ensuring comprehensive data coverage. Structured data refers to data with a predefined format, which is easy to analyze directly. Unstructured data refers to data without a fixed format, which needs to be converted and processed before analysis. The initial dataset refers to the raw data set formed after multi-source data acquisition, which serves as preprocessing input.
[0037] S2. Perform data preprocessing operations based on the initial dataset, including missing value imputation, outlier detection, and data standardization, to generate a cleaned dataset. Data preprocessing operations refer to the data cleaning process, including missing value imputation, outlier detection, and data standardization, to ensure data quality.
[0038] S3. Use feature engineering algorithms to extract and select features from the cleaned dataset, including correlation analysis and principal component analysis, to generate a feature-optimized dataset. Feature engineering algorithms refer to a set of algorithms used to extract and select key features from data to improve the quality of model input.
[0039] S4. Input the feature optimization dataset into the machine learning prediction model for risk assessment training. The machine learning prediction model includes ensemble learning algorithms and deep learning networks to generate risk prediction result data. The machine learning prediction model refers to the trained AI model, which is applied to risk assessment and includes ensemble learning algorithms and deep learning networks.
[0040] S5. Perform dynamic risk assessment calculations based on risk prediction results data, and output a risk assessment report by combining risk probability thresholds. Risk prediction results data refers to the risk assessment results output by the model, which serve as input for dynamic analysis. Dynamic risk assessment calculations refer to real-time analysis methods that combine Monte Carlo simulations to simulate changes in risk scenarios. Risk probability thresholds refer to adjustable thresholds that set risk trigger boundaries based on historical data. Risk assessment reports refer to the final output document, which includes visual charts to support decision-making.
[0041] The multi-source data acquisition unit in S1 includes an API interface module, a sensor data integration module, and a log file parsing module, which generates an initial dataset through real-time data stream processing technology.
[0042] The API interface module refers to the application programming interface module, which is used to obtain real-time data streams from external systems. The sensor data integration module refers to the hardware interface component, which is used to collect real-time data from IoT devices. The log file parsing module refers to the software component, which is used to parse system log files and extract structured information. Real-time data stream processing technology refers to the streaming computing framework, which supports continuous data ingestion and processing.
[0043] In S2, data preprocessing operations are performed using automated scripts and a rule engine. Missing value imputation is based on the K-nearest neighbor algorithm, and outlier detection is based on the isolated forest model.
[0044] Automated scripts refer to executable code scripts (automatically execute preprocessing tasks); rule engines refer to software systems that automate decision-making based on predefined rules; K-nearest neighbors algorithm refers to machine learning algorithms used for missing value imputation that estimate missing values based on the values of neighboring data points; and isolated forest model refers to anomaly detection algorithms that quickly identify outliers in data by constructing random trees to isolate outliers.
[0045] The feature engineering algorithms in S3 include feature importance ranking and dimensionality reduction. The correlation analysis uses the Pearson correlation coefficient, and the principal component analysis retains principal components with a variance contribution rate greater than 95%.
[0046] Feature importance ranking refers to the algorithm process of sorting features according to their predictive contribution and selecting key features. Dimensionality reduction refers to data compression techniques to reduce feature dimensions to avoid overfitting. Pearson correlation coefficient is a statistical measure that calculates the linear correlation between features. Principal component analysis refers to the dimensionality reduction algorithm that converts data into orthogonal principal components. Variance contribution rate refers to the proportion of data variation explained by principal components in PCA to ensure minimal information loss.
[0047] In S4, the machine learning prediction model is the XGBoost ensemble algorithm. The risk assessment training includes cross-validation and hyperparameter tuning. The generated risk prediction results data includes risk scores and classification labels.
[0048] XGBoost ensemble refers to the gradient boosting framework, which combines weak learners into a strong model through iterative training to improve prediction accuracy. Cross-validation refers to the model validation technique, which splits the dataset to evaluate generalization ability. Hyperparameter tuning refers to the optimization process, which adjusts model parameters to maximize performance. Risk score index output quantifies the degree of risk. Classification label refers to the discrete output, which is applied to risk category classification.
[0049] S5 combines dynamic risk assessment calculations with Monte Carlo simulations. The risk probability threshold is dynamically adjusted based on historical data, and the output risk assessment report includes a visual chart of the risk level.
[0050] Monte Carlo simulation refers to a random sampling method that simulates a large number of risk scenarios to estimate the probability distribution. Dynamic adjustment of risk probability threshold refers to an adaptive mechanism that updates the threshold based on historical risk events. Risk level visualization charts refer to graphical output that intuitively displays the risk level.
[0051] Based on the risk assessment report, feedback optimization is performed, and the machine learning prediction model is updated through the model retraining unit to achieve iterative risk control;
[0052] The model retraining unit refers to a system component that periodically retrains the model using new data to maintain prediction accuracy. Iterative risk control refers to a closed-loop process that continuously optimizes risk assessment through feedback loops.
[0053] The system includes:
[0054] The data acquisition and input module uses a multi-source data acquisition unit to acquire raw data streams, a data format conversion unit to generate an initial dataset, and a data caching unit to output preprocessed input data.
[0055] The data format conversion unit refers to a software component that converts data into a standard format, while the data caching unit refers to a memory component that temporarily stores data to accelerate subsequent processing.
[0056] The preprocessing and feature engineering module receives preprocessed input data, performs missing value imputation and outlier detection using the data cleaning unit, generates a feature optimization dataset through the feature extraction unit, and outputs model input data through the feature selection unit. The feature extraction unit refers to the algorithm module, which applies feature engineering to extract key features, and the feature selection unit refers to the decision module, which selects highly important features.
[0057] The machine learning evaluation module receives input data from the model, trains the risk assessment model through the ensemble learning engine, generates risk prediction results using deep learning network units, and outputs intermediate evaluation data through the risk calculation unit. The ensemble learning engine refers to the model training framework that implements the ensemble learning algorithm. The deep learning network unit refers to the hardware / software module that runs the neural network. The risk calculation unit refers to the computing component that performs dynamic risk assessment.
[0058] The dynamic risk analysis module receives intermediate assessment data, sets dynamic risk boundaries through the probability threshold adjustment unit, outputs risk assessment results through the Monte Carlo simulation unit, and optimizes model parameters using the feedback loop unit. The probability threshold adjustment unit refers to the adaptive module that dynamically sets the risk boundaries, the Monte Carlo simulation unit refers to the simulation engine that executes the Monte Carlo method, and the feedback loop unit refers to the closed-loop component that feeds the analysis results back to the model.
[0059] The report generation and output module integrates risk assessment results, generates interactive risk reports through the visualization engine unit, and outputs the final risk assessment report through the multi-dimensional fusion unit. The visualization engine unit refers to the graphics library that generates interactive reports, and the multi-dimensional fusion unit refers to the data processing module that integrates multi-source assessment results.
[0060] The adaptive optimization module receives the risk assessment results output by the dynamic risk analysis module, analyzes the prediction error through the model performance monitoring unit, updates the machine learning model through the parameter tuning unit, and generates optimization instructions through the complexity control unit. The model performance monitoring unit refers to the diagnostic tool that tracks model errors, the parameter tuning unit refers to the optimizer that adjusts the model hyperparameters, and the complexity control unit refers to the decision component that generates optimization instructions based on data complexity.
[0061] In the data acquisition and input module, the multi-source data acquisition unit supports real-time API data streams and batch file imports. The data format conversion unit uses JSON and Parquet formats. Real-time API data streams refer to real-time data transmission protocols that support instant data access. Batch file imports refer to batch processing modes that are applied to historical data analysis. JSON format refers to a lightweight data exchange format that is easy to parse and extend. Parquet format refers to a columnar storage format that optimizes the efficiency of big data queries.
[0062] The adaptive optimization module also includes a risk profile building unit, which is used to analyze user risk preference data and generate customized risk control strategies through the weakness identification unit. The risk profile building unit refers to the analysis module, which creates user risk profiles. User risk preference data refers to user-defined input and personalized assessment criteria. The weakness identification unit refers to diagnostic tools, which identify the risk vulnerabilities of the system and users. The customized risk control strategy refers to dynamic strategies that optimize control for specific scenarios.
[0063] The following is a data-driven risk assessment method and system operation steps based on machine learning:
[0064] 1. Data Acquisition Principles and Steps
[0065] Principle Description: The core of this step lies in integrating heterogeneous data sources through a multi-source data acquisition unit to generate an initial dataset as the input basis for the entire process. Its principle relies on real-time data stream processing technology to ensure that the data is standardized and cached at the source, providing stable input for subsequent processing. The workflow includes real-time API data stream ingestion and batch file import. The data format conversion unit automatically unifies the data format, reducing the complexity of subsequent processing.
[0066] Sub-mechanism explanation: The API interface module, sensor data integration module, and log file parsing module work together. In principle, these modules use a stream processing framework to capture data changes in real time and temporarily store data through a data caching unit to prevent data loss and improve throughput. This solves the latency problem caused by data source isolation in traditional methods and ensures the real-time nature of risk assessment.
[0067] Key innovation: Through a multi-source integration mechanism, the system automatically generates a high-quality initial dataset, avoiding data silos and providing a comprehensive input foundation for machine learning models.
[0068] 2. Preprocessing Principles and Steps
[0069] Principle Description: This step focuses on data cleaning and standardization, transforming the initial dataset into a cleaned dataset. Automated scripts and a rule engine are used to perform missing value imputation, outlier detection, and data standardization. The core principle is dynamic preprocessing technology, which corrects data anomalies in real time, improves data quality, and ensures the accuracy of subsequent risk assessments.
[0070] Sub-mechanism explanation: Missing value imputation is based on the K-nearest neighbor algorithm, which uses the similarity of data point neighbors to estimate missing values and maintain data integrity. Outlier detection is based on the isolated forest model, which isolates outliers by constructing random trees and quickly identifies outliers. At the same time, the rule engine applies predefined rules to automatically perform standardization processing. At the system level, the data cleaning unit receives preprocessed input data and is seamlessly connected with the feature engineering module.
[0071] Key innovations: The dynamic preprocessing mechanism detects and corrects data in real time, overcoming the data drift problem of traditional static cleaning, providing reliable input for feature engineering, and reducing evaluation bias.
[0072] 3. Feature Engineering Principles and Steps
[0073] Principle Description: This step utilizes feature engineering algorithms to extract key features from the cleaned dataset and generate a feature-optimized dataset. The principle is to remove redundant information and retain high-value features through dimensionality reduction and feature selection, thereby optimizing the input to the machine learning model.
[0074] Sub-mechanism explanation: The feature importance ranking algorithm evaluates the contribution of each feature to the risk and selects key variables. Principal component analysis is used for dimensionality reduction to retain principal components with variance contribution rates, ensuring that information loss is minimized. Pearson correlation coefficient is used for correlation analysis. In the system implementation, the feature extraction unit and the feature selection unit work together. The former applies the algorithm to extract features, while the latter outputs the model input data based on importance ranking, reducing the impact of the dimensionality curse.
[0075] Key innovation: The automated feature engineering mechanism adaptively selects features, solving the problem of model overfitting caused by feature redundancy in traditional methods and improving the accuracy of risk pattern recognition.
[0076] 4. Machine Learning Evaluation Principles and Steps
[0077] Principle Description: This step inputs the feature optimization dataset into the machine learning prediction model for risk assessment training, generating risk prediction result data. The principle is based on the fusion of ensemble learning and deep learning networks, optimizing the generalization ability of risk prediction through model training.
[0078] Sub-mechanism explanation: The ensemble learning engine uses the gradient boosting principle to combine multiple weak learners to iteratively train the model. Risk assessment training includes cross-validation and hyperparameter tuning. Meanwhile, deep learning network units handle complex nonlinear patterns. At the system level, the risk calculation unit outputs intermediate evaluation data to prepare for dynamic analysis, which ensures the robustness of the model in changing environments.
[0079] Key innovation: The multi-algorithm fusion mechanism adaptively optimizes parameters, overcoming the low adaptability of traditional single models to new risk types and improving prediction accuracy.
[0080] 5. Principles and Steps of Dynamic Risk Assessment
[0081] Principle Description: This step dynamically calculates risk based on risk prediction data and outputs a risk assessment report by combining the risk probability threshold. The core principle is Monte Carlo simulation and adaptive threshold adjustment, responding in real-time to environmental changes and generating a comprehensive risk report.
[0082] Sub-mechanism explanation: The dynamic risk assessment calculation adopts the Monte Carlo simulation method, which simulates risk scenarios through random sampling, calculates the probability distribution, and dynamically adjusts the risk probability threshold based on historical data. The probability threshold adjustment unit sets the dynamic risk boundary, while the Monte Carlo simulation unit outputs the risk assessment results. In the report generation stage, the visualization engine unit and the multi-dimensional fusion unit integrate the results to generate an interactive report. However, the user requests that no charts be displayed, so only the text principle is described. The report includes risk scores, classification labels, and risk level analysis. The feedback loop unit optimizes the model parameters to form a closed loop.
[0083] Key innovations: Dynamic thresholds and simulation mechanisms enable real-time risk assessment, avoiding the lag of traditional static methods and enhancing timeliness and decision support.
[0084] 6. Adaptive Optimization Principles and Steps
[0085] Principle Description: This step uses a feedback optimization mechanism to update the model based on the risk assessment report, thereby achieving iterative risk control. The principle lies in continuous learning and adjustment to ensure the system's practicality and robustness in real-world scenarios.
[0086] Sub-mechanism explanation: The model performance monitoring unit analyzes prediction errors, the parameter tuning unit updates the machine learning model, the risk profile construction unit parses user risk preference data, the weakness identification unit generates customized risk control strategies, and the complexity control unit generates optimization instructions based on the data scale. At the method level, the model retraining unit implements iterative risk control to ensure the system's adaptive evolution.
[0087] Key innovation: The feedback control mechanism solves the model aging problem in traditional methods through continuous iteration, and improves the stability of the system in long-term operation.
[0088] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A data-driven end-to-end risk assessment method based on machine learning, characterized in that, The method includes the following steps: S1. Obtain the raw data stream, including structured and unstructured data, through the multi-source data acquisition unit, and generate an initial dataset; S2. Perform data preprocessing operations based on the initial dataset, including missing value imputation, outlier detection, and data standardization, to generate a cleaned dataset; S3. Use feature engineering algorithms to extract and select features from the cleaned dataset, including correlation analysis and principal component analysis, to generate a feature-optimized dataset; S4. Input the feature optimization dataset into the machine learning prediction model for risk assessment training. The machine learning prediction model includes an ensemble learning algorithm and a deep learning network to generate risk prediction result data. S5. Perform dynamic risk assessment calculations based on the risk prediction results data, and output a risk assessment report in combination with the risk probability threshold.
2. The data end-to-end risk assessment method based on machine learning according to claim 1, characterized in that: The multi-source data acquisition unit in S1 includes an API interface module, a sensor data integration module, and a log file parsing module, which generates an initial dataset through real-time data stream processing technology.
3. The data end-to-end risk assessment method based on machine learning according to claim 1, characterized in that: The data preprocessing operation in S2 uses automated scripts and a rule engine. Missing value imputation is based on the K-nearest neighbor algorithm, and outlier detection is based on the isolated forest model.
4. The data end-to-end risk assessment method based on machine learning according to claim 1, characterized in that: The feature engineering algorithm in S3 includes feature importance ranking and dimensionality reduction. The correlation analysis uses the Pearson correlation coefficient, and the principal component analysis retains the principal components with a variance contribution rate greater than 95%.
5. The data end-to-end risk assessment method based on machine learning according to claim 1, characterized in that: The machine learning prediction model in S4 is the XGBoost ensemble algorithm. The risk assessment training includes cross-validation and hyperparameter tuning, and the generated risk prediction result data includes risk scores and classification labels.
6. The data end-to-end risk assessment method based on machine learning according to claim 1, characterized in that: The dynamic risk assessment calculation in S5 combines Monte Carlo simulation, and the risk probability threshold is dynamically adjusted based on historical data. The output risk assessment report includes a visual chart of the risk level.
7. The data end-to-end risk assessment method based on machine learning according to claim 1, characterized in that: Based on the risk assessment report, feedback optimization is performed, and the machine learning prediction model is updated through the model retraining unit to achieve iterative risk control.
8. A data-driven end-to-end risk assessment system based on machine learning, characterized in that, The system includes: The data acquisition and input module uses a multi-source data acquisition unit to acquire raw data streams, generates an initial dataset through a data format conversion unit, and outputs preprocessed input data through a data caching unit. The preprocessing and feature engineering module receives the preprocessing input data, performs missing value imputation and outlier detection using the data cleaning unit, generates a feature optimization dataset through the feature extraction unit, and outputs the model input data through the feature selection unit. The machine learning evaluation module receives the input data of the model, trains the risk assessment model through an ensemble learning engine, generates risk prediction results using deep learning network units, and outputs intermediate evaluation data through a risk calculation unit. The dynamic risk analysis module receives the intermediate assessment data, sets the dynamic risk boundary through the probability threshold adjustment unit, outputs the risk assessment result through the Monte Carlo simulation unit, and optimizes the model parameters using the feedback loop unit. The report generation and output module integrates the risk assessment results, generates an interactive risk report through the visualization engine unit, and outputs the final risk assessment report through the multi-dimensional fusion unit. The adaptive optimization module receives the risk assessment results output by the dynamic risk analysis module, analyzes the prediction error through the model performance monitoring unit, updates the machine learning model through the parameter tuning unit, and generates optimization instructions through the complexity control unit.
9. The data end-to-end risk assessment method and system based on machine learning according to claim 1, characterized in that: In the data acquisition and input module, the multi-source data acquisition unit supports real-time API data streams and batch file imports, and the data format conversion unit uses JSON and Parquet formats.
10. The data end-to-end risk assessment method and system based on machine learning according to claim 1, characterized in that: The adaptive optimization module also includes a risk profile construction unit, which is used to analyze user risk preference data and generate customized risk control strategies through the weakness identification unit.