Economic risk early warning management system based on financial intelligent big data analysis

Abstract

The invention provides an economic risk early warning management system based on financial intelligent big data analysis, and the system comprises a financial data collection module which is used for collecting and preprocessing structured and unstructured financial data; the model construction optimization module is used for constructing a transaction risk scoring and classification model, a time sequence prediction model and an anomaly detection model; the risk monitoring and early warning module is used for constructing an early warning response mechanism and rapidly triggering the early warning mechanism if an abnormal signal is detected; the risk decision support module is used for dynamically adjusting a risk control strategy according to the data for triggering the early warning; the data security module is used for performing encryption processing and authority management on data generated in the system; and the interaction and visualization module is used for feeding back the data generated by each module to a user for interaction in the form of a visualization platform. According to the system, Flink + Spark + XGBoost + LSTM is combined, efficient, accurate and safe financial risk early warning is achieved, and intelligent decision support is provided for financial institutions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of financial early warning, and particularly relates to an economic risk early warning management system based on financial intelligent big data analysis. BACKGROUND

[0002] At present, there are many economic risk early warning systems based on big data and artificial intelligence, such as RiskManager of RiskMetricsGroup, EconomicRiskManagement Solution of SAS, and WatsonFinancialServicesforRiskManagement of IBM. These systems have many limitations. RiskManager mainly integrates market data, credit ratings and macroeconomic indicators for risk analysis, and is widely used in financial institutions. However, its data sources are relatively single, and it lacks in-depth mining of unstructured data (such as news and social media information), resulting in limited coverage of early warning. EconomicRiskManagement Solution of SAS uses advanced statistical modeling and machine learning techniques to comprehensively assess various types of economic risks and has strong data processing capabilities. However, the system still needs to be improved in real-time analysis and adaptive learning, especially in response speed when facing sudden market events.

[0003] WatsonFinancialServicesforRiskManagement of IBM relies on the strong artificial intelligence technology support of IBM Watson, and has strong natural language processing and cognitive computing capabilities. The system can extract useful information from a large amount of text data for risk identification and early warning. However, the Watson system relies more on expert experience in model optimization and parameter tuning, and the degree of automation is not high, and the high cost also limits the widespread application of small and medium-sized enterprises. In addition, a research team from MIT proposed an economic risk early warning model based on deep learning, which successfully predicted the occurrence of several financial crises by analyzing historical economic data and news texts. However, the model faces problems such as data privacy protection and computing resource consumption in practical application, and has not yet realized large-scale commercial deployment.

[0004] In general, existing economic risk early warning management systems have made significant progress in some aspects, but still have common problems such as single data source, outdated analysis method, insufficient real-time response capability, and lack of adaptive learning capability. SUMMARY

[0005] The application aims to provide an economic risk early warning management system based on financial intelligent big data analysis, combine Flink+Spark+XGBoost+LSTM, realize efficient, accurate and safe financial risk early warning, and provide intelligent decision support for financial institutions.

[0006] To achieve the above-mentioned application purposes, the application provides an economic risk early warning management system based on financial intelligent big data analysis, which comprises:

[0007] A financial data acquisition module is used for acquiring structured and unstructured financial data and preprocessing, and transmitting the processed financial data to a model construction optimization module;

[0008] A model construction optimization module is used for constructing transaction risk scoring and classification models, time series prediction models and anomaly detection models according to the processed financial data, and performing hyperparameter optimization on the transaction risk scoring and classification models, time series prediction models and anomaly detection models;

[0009] A risk monitoring and early warning module is used for constructing an early warning response mechanism, monitoring the data output by the transaction risk scoring and classification models, time series prediction models and anomaly detection models in real time, and triggering the early warning mechanism rapidly if an abnormal signal is detected;

[0010] A risk decision support module is used for dynamically adjusting the risk control strategy according to the data triggering the early warning.

[0011] Further, the financial data acquisition module comprises:

[0012] A structured data processing unit is used for data cleaning, de-duplication, missing value processing, abnormal value processing, data conversion and data integration processing on the acquired structured financial data, and storing the processed structured financial data in a relational database;

[0013] An unstructured data processing unit is used for data preprocessing according to the type of unstructured financial data, storing the processed unstructured financial data in a distributed database, and converting the data into a structured form.

[0014] Further, the transaction risk scoring and classification model is constructed, specifically comprising:

[0015] The processed structured and unstructured financial data are integrated, a financial data set is constructed, and the integrated financial data are cleaned, missing values and abnormal values are processed;

[0016] Features related to transaction risk are extracted based on the financial data set, and the extracted features are time-sliced and automatically cross-operated through feature derivation method to obtain a risk feature combination;

[0017] The risk feature set is divided into a training set and a test set, the training set and the test set are adjusted for positive samples, and then input into a model for training to construct a transaction risk scoring and classification model.

[0018] Further, a time series prediction model is constructed, specifically including:

[0019] Extracting time series data in the transaction risk scoring and classification model output data, processing missing values in the time series data;

[0020] The processed time series data is constructed into supervised learning data through a sliding window and input into an LSTM network for training to construct a time series prediction model.

[0021] Further, an anomaly detection model is constructed, specifically including:

[0022] Anomaly feature screening is performed on the data output by the transaction risk scoring and classification model and the time series prediction model, and missing values and abnormal values of the anomaly features are processed;

[0023] Randomly extracting a subsample from the processed anomaly features, and constructing an anomaly detection model through an isolation forest algorithm.

[0024] Further, the anomaly detection model is specifically used to perform:

[0025] Random feature selection is performed on the subsample, and a split value corresponding to the random feature is randomly selected, the subsample is split by the split value, and the above steps are repeated until a preset tree depth limit is reached to complete the construction of an isolation tree;

[0026] Multiple isolation trees are integrated to form an isolation forest, and the path length of each anomaly feature corresponding to the subsample in the isolation tree is calculated;

[0027] An anomaly score is calculated according to the path length, and a quantile number is dynamically selected as an anomaly threshold according to the distribution of the anomaly score.

[0028] Further, a warning mechanism is constructed, specifically including:

[0029] The financial data collection module is connected to Kafka to collect structured and unstructured financial data in real time, and Kafka is set based on a topic partitioning strategy through business dimensions and Avro serialization;

[0030] The risk monitoring and warning module is connected to Flink to perform dynamic data stream processing on the financial data collected by Kafka in real time;

[0031] The abnormal features exceeding the abnormal threshold judged by Flink are subjected to real-time fraud and fund chain risk detection, abnormal behavior sequence monitoring is carried out on real-time fraud, if an abnormal sequence is detected, a pre-warning response is triggered, upstream and downstream conduction real-time monitoring is carried out on fund chain risk, if abnormal fluctuation occurs, a pre-warning response is triggered;

[0032] The pre-warning response is a mild pre-warning and a severe pre-warning, the mild pre-warning triggers an artificial auditing queue, delays loan and sends a short message verification, the severe pre-warning real-time intercepts transactions and freezes an account, and synchronously notifies a risk control personnel.

[0033] Further, a risk control strategy is dynamically adjusted, and specifically includes:

[0034] Structured data and unstructured data are integrated, a full-link risk portrait is constructed, a response scheme is formulated in combination with a pre-warning response level, and a response scheme is formulated;

[0035] An optimal execution scheme is recommended for the abnormal features triggering the pre-warning response through a linear programming algorithm, the execution process is subjected to real-time monitoring, and the optimal execution scheme is dynamically adjusted;

[0036] The optimal execution scheme is obtained by combining a plurality of response schemes through a linear programming algorithm.

[0037] Further, the system further includes a data security module for encrypting and managing the rights of the data generated in the system, and is specifically used for performing the following operations:

[0038] Sensitive data distribution in system data is identified through a scanning tool, and data sensitivity in the sensitive data distribution is identified using a metadata label;

[0039] According to the sensitive data distribution and the data sensitivity, encryption keys are assigned to different data types through dynamic encryption technology;

[0040] Based on the data sensitivity, user attributes and environment attributes are combined to dynamically adjust access rights.

[0041] Further, the system further includes an interaction and visualization module for feeding back the data generated by the financial data acquisition module, the model construction and optimization module, the risk monitoring and pre-warning module, the risk decision support module and the data security module to the user in the form of a visualization platform, and the user interacts through the visualization platform.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] The system integrates structured and unstructured financial data, uses Flink+Kafka to realize real-time data collection and Spark batch processing analysis, and uses distributed storage technology to efficiently manage massive data. On this basis, multiple models are built to accurately identify risks, and millisecond-level detection and instant warning are realized by relying on the Flink real-time computing framework and the Kafka streaming data pipeline. The system also has an adaptive learning mechanism, uses incremental learning algorithms to ensure that the model can be continuously optimized, and uses different levels of encryption to encrypt different data to protect data security and privacy. In addition, it provides a friendly interactive interface and multiple visualization tools to intuitively display data to users. The application builds a multi-dimensional and multi-level comprehensive risk assessment framework, reveals the internal relationship between economic variables and the risk transmission path, and provides strong support for the development of financial data risk management strategies. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0045] Figure 1 The economic risk early warning management system based on financial intelligent big data analysis provided by the embodiment of the present application is shown in the structural schematic diagram. DETAILED DESCRIPTION

[0046] The present application will be further described in detail below in combination with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, and not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, not all the structures.

[0047] Reference Figure 1 The embodiment provides an economic risk early warning management system based on financial intelligent big data analysis.

[0048] The system of the embodiment adopts a microservice architecture, combines Lambda architecture for data processing, and supports real-time risk early warning and offline data analysis. Data collection collects transaction data and market data in real time through Kafka+FlinkCDC; data storage stores structured data through PostgreSQL, stores logs and retrieves data through Elasticsearch, and stores long-term data through S3; data processing performs stream computing and anomaly detection through Flink, and performs batch processing and historical risk analysis on financial data through Spark; machine learning constructs a transaction risk scoring and classification model using XGBoost, constructs a time series prediction model using an LSTM network, and constructs an anomaly detection model using IsolationForest; model deployment provides high-performance inference services through TritonInferenceServer; API services are built based on FastAPI; visualization is performed through interactive chart display by ECharts and real-time monitoring by Grafana; security is controlled through OAuth2+RBAC, and data is encrypted through AES-256.

[0049] The system comprises:

[0050] A financial data collection module for collecting structured and unstructured financial data and preprocessing the financial data, and transmitting the preprocessed financial data to a model construction and optimization module. The financial data collection module comprises:

[0051] A structured data processing unit for performing data cleaning, deduplication, missing value processing, outlier processing, data conversion, and data integration processing on the acquired structured financial data, and storing the processed structured financial data in a relational database.

[0052] An unstructured data processing unit for performing data preprocessing on the unstructured financial data according to the type of the unstructured financial data, storing the processed unstructured financial data in a distributed database, and converting the processed unstructured financial data into a structured form.

[0053] In the embodiment, in the financial data collection and preprocessing, the processing procedures of structured and unstructured financial data are significantly different. For the processing of structured financial data, the structured data is directly acquired through database query (SQL), API interface call, ETL tool, etc., and the real-time data (such as transaction records) captured and collected in real time are processed through stream collection technology (such as Kafka and Flume). The structured financial data preprocessing involves data cleaning, deduplication, missing value processing, outlier processing, data conversion, standardization, normalization, discretization, data integration, etc. The processed financial data is stored in a relational database.

[0054] For the processing of unstructured financial data, data collection uses Apache POI to extract Word / Excel content, crawls web page pictures / videos through a crawler, or calls an API to obtain social media content; real-time collection of logs and sensor data is performed through tools such as Flume and Logstash. The collected data is preprocessed according to the type of the data, and labels are added for machine learning model training when the data is processed (such as image classification annotation and text sentiment analysis labels). For text data, stop words are removed through NLP technology, and part-of-speech tagging is performed; for image / video data, image / video features are extracted and format conversion is performed (such as JPEG to PNG); for audio data, speech-to-text conversion and noise reduction processing are performed through ASR technology. After the structured data is processed, large files are stored using the HDFS distributed file system, and are then converted into an analyzable structured form. For text summaries, keywords are extracted and structured metadata (such as author and title) are generated; for feature engineering, numerical features are generated through word vectors (Word2Vec) and image embeddings (CNN features). The converted data is indexed to build an inverted index or a vector index for fast retrieval in subsequent steps.

[0055] The model construction optimization module is configured to construct a transaction risk scoring and classification model, a time series prediction model, and an anomaly detection model according to the processed financial data, and to perform hyperparameter tuning on the transaction risk scoring and classification model, the time series prediction model, and the anomaly detection model.

[0056] In this embodiment, the transaction risk scoring and classification model, the time series prediction model, and the anomaly detection model are hyperparameter tuned. First, the hyperparameter range is determined, then a Gaussian process or a random forest is selected as a surrogate model to fit the relationship between the hyperparameters and the objective function (such as the validation set AUC), then a function selection is performed, the expected improvement (EI) or the upper confidence bound (UCB) is used to select the next candidate parameter, and finally the model is trained and the target value is calculated, the surrogate model is updated until the maximum number of iterations is reached, and the optimal parameter combination and the corresponding model performance are returned.

[0057] The transaction risk scoring and classification model is constructed, specifically including:

[0058] The processed structured and unstructured financial data is integrated to construct a financial data set, and the integrated financial data is cleaned to handle missing values and outliers.

[0059] Features related to transaction risk are extracted based on the financial data set, and the extracted features are time-sliced and automatically cross-operated through feature derivation to obtain a risk feature combination.

[0060] The risk feature combination is divided into a training set and a test set, and after adjusting the positive samples in the training set and the test set, the model is input for training to construct a transaction risk scoring and classification model.

[0061] In this embodiment, the transaction risk scoring and classification model is constructed by using XGBoost. On the basis of the financial data collection and preprocessing in the previous step, the processed structured and unstructured financial data is integrated to construct a financial data set, and the core feature dimension and time series feature of the financial data set are determined. The core feature dimension includes but is not limited to user basic information (such as age, gender, occupation stability, etc.), financial behavior (such as income / debt ratio, historical overdue days, number of multi-lending platforms), and time series feature involves dynamic statistical window (such as average and variance of consumption amount in the past 3 / 6 / 12 months), cross-time comparison (such as the ratio of consumption amount in the last month to the historical average).

[0062] The continuous variables in the core feature dimension and the time series feature are processed by equal frequency / equal distance bucketing, and are converted into weight evidence values to improve the non-linear capture ability and interpretability of the model; the missing values and abnormal values are processed, the missing values are automatically processed by using XGBoost, and the abnormal values are removed by using 3-Sigma method. The volatility of user behavior in the core feature dimension is calculated by using stability index; after time slicing and automatic cross of the above-processed data by using feature derivation method, the rolling statistical features (such as historical 12-month slice of credit utilization rate) are generated by dividing according to time window (such as 30 days), and then the risk feature combination is screened by using the built-in feature importance evaluation of XGBoost.

[0063] The obtained risk feature combination is input into the pre-trained transaction risk scoring and classification model for training and optimization. First, the core parameters are configured, the low learning rate needs to be combined with more trees (n_estimators) to prevent underfitting, and the learning rate is usually set to 0.01-0.3, and then the grid search and early stopping method are used for optimization. Second, sample processing and verification design are performed, the risk feature combination is divided into a training set and a test set by stratified sampling, the positive and negative sample ratios in the training / verification / test sets are ensured to be consistent, the time window verification is performed on the training set and the test set, the test set needs to be set apart from the training set in time (such as taking the data of the last 30 days), and time leakage is avoided. Finally, the positive sample weight is adjusted to alleviate the problem of sparse default samples.

[0064] The trained transaction risk scoring and classification model is verified and deployed. First, performance evaluation is performed, appropriate indicators are selected, and the task is classified, precision-recall rate, etc. After that, the feature stability is monitored by PSI (Population Stability Index) and SHAP value analysis (quantifying the contribution of features to a single prediction to assist in risk policy formulation) is performed. Second, production deployment is performed. Through real-time scoring, the trained transaction risk scoring and classification model is packaged as an API. The feature library is updated regularly (such as adding new credit data fields). The effect of the transaction risk scoring and classification model is verified through A / B testing. Finally, the transaction risk scoring and classification model supports millisecond-level risk prediction (such as anti-fraud interception).

[0065] The time series prediction model is constructed, specifically including:

[0066] The time series data in the transaction risk scoring and classification model output data is extracted, and the missing values in the time series data are processed.

[0067] The processed time series data is constructed into supervised learning data through a sliding window, and is input into an LSTM network for training to construct a time series prediction model.

[0068] In this embodiment, the time series prediction model is constructed mainly for stock market volatility and interest rate change prediction based on transaction risk scoring and classification model output data. First, the time series data is extracted, and the extracted time series data is processed for missing values, standardization, and sliding window construction. The time series data is converted into a supervised learning format. After processing the processed time series data through a designed input layer, LSTM layer, and output layer, a loss function and time series cross-validation (TimeSeriesSplit) are defined to avoid future information leakage. The time series prediction model is trained and verified. The prediction results output by the time series prediction model are restored to the original data scale, and a comparison chart of true value and predicted value is drawn for visualization.

[0069] The anomaly detection model is constructed, specifically including:

[0070] The data output by the transaction risk scoring and classification model and the time series prediction model is screened for abnormal features, and the missing values and abnormal values of the abnormal features are processed.

[0071] Subsamples are randomly extracted from the processed abnormal features, and an anomaly detection model is constructed through an isolation forest algorithm. The anomaly detection model is specifically used to perform:

[0072] Random feature selection is performed on the subsamples, and a split value corresponding to the random feature is randomly selected. The subsamples are split by the split value. The above steps are repeated until a preset tree depth limit is reached to complete the construction of an isolation tree.

[0073] The multiple isolated trees are integrated to form an isolated forest, and the path length of each abnormal feature corresponding to the sub-sample in the isolated tree is calculated.

[0074] An abnormal score is calculated according to the path length, and a quantile is dynamically selected as an abnormal threshold according to the distribution of the abnormal score.

[0075] In this embodiment, according to the business requirements and domain knowledge, the features related to anomalies in the data output by the transaction risk scoring and classification model and the time series prediction model are screened to ensure that potential abnormal behaviors can be effectively reflected, such as transaction amount, frequency, and IP geographic location. First, remove duplicate, incorrect, or inconsistent data records, handle missing values and outliers. For missing values, you can use padding, interpolation, or deletion methods; for outliers, you can use statistical methods or machine learning algorithms for detection and processing. Standardize continuous features to have zero mean and unit variance to avoid bias in distance calculation. Randomly sample sub-samples from the processed abnormal features, usually using the max_samples parameter to control the size of the sub-samples, and sub-sampling helps improve the generalization ability and training efficiency of the model.

[0076] After the above data processing, an anomaly detection model is constructed using the isolated forest (iForest) algorithm. Isolated forest divides data into isolated sub-trees by recursively randomly selecting feature and split value of sub-sample. The construction process of each isolated tree is as follows: randomly select a feature; randomly select a split value of the feature, and divide the data into two parts on the selected feature. Repeat the above steps until the sub-sample is isolated (i.e. there is only one sample in the sub-tree) or the preset tree depth limit is reached. The multiple isolated trees formed are integrated into an isolated forest, and the abnormal degree of each sample is independently evaluated by each tree. Finally, use appropriate evaluation indicators to quantify the performance of the anomaly detection model, and according to the verification results, further adjust and optimize the anomaly detection model.

[0077] The anomaly detection model determines the abnormal score of the sample by aggregating the evaluation results of all trees. For each sub-sample, calculate its path length in each isolated tree, which is the number of edges from the root node to the leaf node where the sample is located. Sub-samples usually have shorter path lengths because they are more easily divided into isolated sub-trees. Calculate the abnormal score according to the path length, usually convert the path length to a score between 0 and 1, and the closer the score is to 1, the more abnormal it is. Set the abnormal threshold according to business requirements and data distribution, use the contamination parameter to adjust the abnormal proportion to represent the proportion of abnormal samples in the expected data; or dynamically select a quantile as a threshold according to the distribution of the abnormal score, for example, select the top 5% of high abnormal score samples as anomalies.

[0078] In order to make the user more intuitive to understand the identified anomaly, the anomaly point distribution diagram is drawn according to the identified abnormal sample, such as the scatter diagram under the two-dimensional feature space, and the normal sample and the abnormal sample should have obvious distribution difference in the diagram, and the rationality of the anomaly detection result can be verified intuitively through visualization. The detected abnormal sample is manually audited to confirm whether it is truly abnormal, manual review can evaluate the accuracy and recall rate of the model, and at the same time help to find the possible misjudgment of the model.

[0079] The risk monitoring and early warning module is used to build an early warning response mechanism to monitor the data output by the transaction risk scoring and classification model, the time series prediction model and the anomaly detection model in real time, and quickly trigger the early warning mechanism if an abnormal signal is detected. The early warning mechanism is built, specifically including:

[0080] The financial data collection module is connected to Kafka to collect structured and unstructured financial data in real time, and Kafka is set based on topic partition strategy through business dimension and Avro serialization.

[0081] The risk monitoring and early warning module is connected to Flink to perform dynamic data stream processing on the financial data collected by Kafka in real time.

[0082] The abnormal features that exceed the abnormal threshold value judged by Flink are detected for real-time fraud and fund chain risk, the real-time fraud is monitored for abnormal behavior sequence, and if an abnormal sequence is detected, the early warning response is triggered, and the fund chain risk is monitored for upstream and downstream transmission, and if an abnormal fluctuation occurs, the early warning response is triggered.

[0083] The early warning response is mild early warning and severe early warning, the mild early warning triggers manual review queue, delays loan and sends SMS verification, and the severe early warning real-time intercepts transactions and freezes accounts, and synchronously notifies risk control personnel.

[0084] The early warning mechanism built in this embodiment, through the combination of streaming computing, state management and dynamic rule engine technologies, realizes the full-process automation and high efficiency from data collection, real-time processing, risk detection, early warning to dynamic adjustment, ensures the timely discovery and response of risks, and at the same time, through performance optimization and fault tolerance mechanism, guarantees the stable operation of the system, finally based on the risk decision support module dynamically adjusts the risk control strategy, forms a closed-loop risk management system, and improves the overall risk prevention and control ability.

[0085] The implementation path and workflow of the above early warning mechanism are: Kafka collects data → Flink real-time processing (including CEP, state management, window calculation) → real-time monitoring and early warning → dynamic adjustment of rules → combination of offline analysis optimization model to build model in optimization module. Specifically, the financial data collection module is connected to Kafka, and through KafkaConnect or ProducerAPI, transaction logs, user behavior data, device sensors and other heterogeneous data sources are connected, supporting writing of millions of events per second. According to the business dimension, KafkaTopic (such as risk_transaction, user_behavior) is divided, combined with partition key (such as user ID) to ensure the order of the same user events, avoid out-of-order risk, use Avro serialization to reduce network transmission overhead and improve throughput.

[0086] After completing data collection, Flink CEP is used to define risk patterns for stream computing (such as 5 consecutive login failures within 30 seconds), and after combining WITHIN time window constraints event sequence, CEP complex event detection is performed. After real-time data processing, ValueState or ListState is used to store recent user behavior (such as the last 10 transaction records), and state management is implemented through cross-event state association analysis to calculate the total transaction amount and standard deviation within 1 minute, and abnormal fluctuations (such as a 10-fold increase in amount) are identified through rolling window statistics. After identifying abnormal fluctuations, AsyncI / O is used to query HBase user portrait (such as historical default records) asynchronously to realize table association and dynamic data widening. Storage and feedback write real-time calculation results to HBase or Redis, complete storage data to support low-latency queries (such as updating user risk score to HBase for downstream system calls). Finally, risk rule configuration is stored in MySQL, and FlinkBroadcastStream is used to realize dynamic updating of risk rules.

[0087] The above risk monitoring and early warning part is for real-time fraud detection and fund chain risk early warning. Real-time fraud detection monitors abnormal behavior sequences, for example: CEP detects abnormal links such as "multiple device login in a short time → high-frequency password modification → large amount of transfer"; monitor geographic location conflicts, compare the current login IP with the historical commonly used area, and calculate the physical moving speed (such as crossing 3000 kilometers within 10 minutes) combined with time difference. Fund chain risk early warning monitors upstream and downstream transmission, for example: real-time analysis of whether the supplier / customer financial indicators trigger industry chain risk early warning (such as a 50% decrease in accounts receivable turnover rate); build enterprise guarantee relationship graph, dynamically calculate and monitor guarantee chain level risk (such as a 30% increase in the probability of default of a three-level or more guarantee chain).

[0088] In the process of real-time fraud detection and fund chain risk early warning, if there is an anomaly, a multi-level threshold strategy is adopted for hierarchical response, and a dynamic feedback mechanism is established, for example: in the case of mild early warning (such as single transaction anomaly), an artificial review queue is triggered, the loan is delayed, and a short message verification is sent; in the case of severe risk (such as blacklist matching), the transaction is intercepted in real time and the account is frozen, and the risk control personnel are notified synchronously. The change of the interception rate in the dynamic feedback mechanism (such as the sudden drop of the interception rate triggering the rule failure alarm) is calculated in real time by Flink and written into Prometheus. According to the response implementation model, the interception sample is returned to the offline training set, and the model in the optimization module is updated regularly.

[0089] In the last performance optimization and fault tolerance mechanism, the calculation performance is improved by adopting back pressure control and resource isolation. FlinkCredit-Based flow control is enabled to avoid downstream blocking by dynamically adjusting the production rate, which improves the throughput by 20% compared with traditional TCP back pressure. Independent TaskManager is allocated to CPU-intensive tasks such as CEP rule matching to avoid resource competition with IO operations. Kafka transaction writing and FlinkCheckpoint are enabled to ensure that risk event processing is not lost or repeated; RocksDB incremental checkpointing is configured to persist state data to HDFS, and the recovery time is controlled within 60 seconds.

[0090] The risk decision support module is used to dynamically adjust the risk control strategy according to the data triggering the early warning. The dynamic adjustment of the risk control strategy specifically includes:

[0091] Integrate structured and unstructured data to build a full-link risk profile and develop a response plan based on the early warning response level.

[0092] The optimal execution plan is recommended for the abnormal features triggering the early warning response through a linear programming algorithm, and the execution process is monitored in real time to dynamically adjust the optimal execution plan.

[0093] The optimal execution plan is obtained by combining multiple response plans through a linear programming algorithm.

[0094] In this embodiment, the risk monitoring and early warning system integrates structured and unstructured data, builds a full-link risk profile in real time through dynamic sensing, quantitative evaluation and prediction, and develops a differentiated response plan based on the risk level, such as automatically triggering an artificial review process when a specific risk behavior sequence is detected, and recommending an optimal decision-making plan through a linear programming algorithm to provide multi-dimensional decision-making support, covering key decision-making types such as emergency response, strategic adjustment, resource allocation and long-term risk management, predicting fraud probability through transaction risk scoring and classification models, reducing false positive rate, dynamically adjusting risk control strategy, implementing proactive defense, and reducing risk loss.

[0095] The system also includes a data security module for encrypting and managing the rights of data generated in the system, specifically for performing the following operations:

[0096] Identify sensitive data distribution in system data through scanning tools, and use metadata tags to identify data sensitivity in sensitive data distribution;

[0097] According to the sensitive data distribution and data sensitivity, assign encryption keys to different data types through dynamic encryption technology;

[0098] Based on data sensitivity, combined with user attributes and environmental attributes, dynamically adjust access permissions.

[0099] In this embodiment, the financial data generated in the system is encrypted, access controlled and supervised and managed, the database is enabled with transparent encryption, the file system is enabled with encrypted volumes, the device / user identity is continuously verified, and the decryption permissions are dynamically adjusted. Among them, sensitive data such as user passwords, biometric features, and financial records are forcibly encrypted; confidential data such as contracts, patents, and customer privacy are dynamically encrypted (based on access permissions) information; the sensitive data distribution in the financial data is identified by scanning tools (such as AWS Macie), and the data sensitivity of the sensitive data distribution is identified using metadata tags; internal data such as operation reports and log files are selectively encrypted (according to compliance requirements); structured data is encrypted at the database field level; unstructured files are encrypted at the file system level; and real-time streaming data is encrypted at the transmission layer.

[0100] Specifically, the encryption key for encrypting the data is dynamically assigned according to the role, and the encryption strength is dynamically selected in combination with user attributes (department, job level) and environmental attributes (IP address, time), and automatically upgraded to TLS1.3+ national encryption algorithm when accessed from external networks. The key is associated with the user attributes, and the user key that meets the conditions can be decrypted. The master key (MasterKey) protects the data encryption key (DEK), and the data encryption key protects the actual data. Regularly detect unencrypted data exposure risks through the tool Nessus, update classification rules, and evaluate the impact of encryption algorithms on business, such as database query delay. Verify whether the encryption strategy complies with GDPR, CCPA and other regulations through third-party audits (ISO27001 certification).

[0101] Through the above hierarchical encryption and dynamic access control technology, enterprises can achieve fine-grained management of data security, while balancing efficiency and compliance. In actual implementation, it is necessary to select a combination of technologies according to the business scenario, and establish a continuous improvement mechanism.

[0102] Specifically, access control adopts OAuth2+RBAC for permission management. The process of permission management is as follows: after the user logs in the authentication center, the client obtains the Access Token, associates the user identity in the Token to the local role, and dynamically injects the permission; add role attribute; the resource server verifies the Token through the Introspection endpoint; record all Token issuance and API access logs, regularly review role permission allocation, and ensure that the user only accesses the necessary resources.

[0103] On the other hand, supervisory management requires that the data generated in the system meet the GDPR / CCPA data privacy requirements. The specific checking method is: identify PII (personal identity information) and sensitive data (such as payment records, health information), establish a data inventory and flow map; perform static data AES-256 encryption, dynamic data TLS1.3 transmission, and enable field-level encryption for the database; regularly conduct employee security awareness training (such as phishing attack simulation); set up a DPO (Data Protection Officer) role to supervise whether the data is compliant.

[0104] The system also includes an interaction and visualization module for feeding back the data generated by the financial data collection module, model construction and optimization module, risk monitoring and early warning module, risk decision support module and data security module to the user in the form of a visualization platform. The user interacts through the visualization platform.

[0105] The risk early warning management system in the embodiment realizes a "perception-analysis-decision-feedback" closed loop through a user interaction mechanism and a visualization design. The core visualization components of the system include real-time monitoring screens, risk maps and dynamic displays of early warning signals, support multi-dimensional data analysis views, cover time, space, business and other dimensions, and users can customize the display content and simulate decision-making impacts. The displayed data includes raw and cleaned data in the basic data layer, aggregated statistical values and model outputs in the derived data layer, and auxiliary decision-making data, thereby supporting risk management decisions from real-time monitoring to strategic adjustment. Users can drag and customize early warning rules, modify thresholds in real time, and the system instantly feeds back prediction results and pushes early warning information through multiple channels, triggers phone alarms in emergencies, and matches response strategies according to risk levels, such as low-risk pop-up prompts and high-risk operation blocking; users can also click on the chart to drill down to detailed data, or dynamically filter data through time axis, geographic fence and other dimensions.

[0106] In summary, the risk early warning management system needs to be continuously optimized due to the dynamic evolution of risks, technical iteration and vulnerability repair, changes in business rules and compliance requirements, etc. The system realizes "data → model → rule → interaction" full-link closed-loop feedback and dynamic enhancement. Through automatic monitoring (using Prometheus + ELK monitoring system for data collection, storage and query, alarm and visualization) and model evaluation (using A / B test to compare the performance of new and old models in actual business scenarios to verify the value of the model), the system realizes continuous optimization from data collection, model evaluation, rule update to user interaction, full-link closed-loop feedback and dynamic enhancement, ensuring the effectiveness of the system's continuous optimization and risk management.

[0107] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An economic risk early warning management system based on financial intelligent big data analysis, characterized in that, The system comprises: a financial data collection module for collecting structured and unstructured financial data and preprocessing, and transmitting the processed financial data to a model construction optimization module; a model construction optimization module for constructing a transaction risk scoring and classification model, a time series prediction model and an anomaly detection model according to the processed financial data, and performing hyperparameter tuning on the transaction risk scoring and classification model, the time series prediction model and the anomaly detection model; a risk monitoring and early warning module for constructing an early warning response mechanism, and monitoring the data output by the transaction risk scoring and classification model, the time series prediction model and the anomaly detection model in real time, and triggering the early warning mechanism rapidly if an abnormal signal is detected; a risk decision support module for dynamically adjusting a risk control strategy according to the data triggering the early warning. 2.The economic risk early warning management system based on financial intelligent big data analysis according to claim 1, characterized in that, The financial data collection module comprises: a structured data processing unit for performing data cleaning, deduplication, missing value processing, abnormal value processing, data conversion and data integration processing on the acquired structured financial data, and storing the processed structured financial data in a relational database; an unstructured data processing unit for performing data preprocessing according to the type of unstructured financial data, storing the processed unstructured financial data in a distributed database, and converting the processed unstructured financial data into a structured form. 3.The economic risk early warning management system based on financial intelligent big data analysis of claim 1, wherein, The transaction risk scoring and classification model is constructed, specifically including: integrating the processed structured and unstructured financial data, constructing a financial data set, and cleaning, processing missing values and abnormal values of the integrated financial data; extracting features related to transaction risk based on the financial data set, and performing time slicing and automatic cross operation on the extracted features through feature derivation to obtain a risk feature combination; dividing the risk feature combination into a training set and a test set, adjusting the positive samples of the training set and the test set, and inputting the training set and the test set into a model for training to construct a transaction risk scoring and classification model. 4.The economic risk early warning management system based on financial intelligent big data analysis of claim 1, wherein, The time series prediction model is constructed, specifically including: extracting time series data in the data output by the transaction risk scoring and classification model, and processing missing values in the time series data; constructing supervised learning data through a sliding window from the processed time series data, and inputting the supervised learning data into an LSTM network for training to construct a time series prediction model. 5.The economic risk early warning management system based on financial intelligent big data analysis according to claim 1, characterized in that, The anomaly detection model is constructed, specifically including: performing abnormal feature screening on the data output by the transaction risk scoring and classification model and the time series prediction model, and processing missing values and abnormal values of the abnormal features; randomly extracting a sub-sample from the processed abnormal features, and constructing an anomaly detection model through an isolation forest algorithm. 6.The economic risk early warning management system based on financial intelligent big data analysis according to claim 5, characterized in that, The anomaly detection model is specifically used to perform: randomly selecting features and splitting values corresponding to the randomly selected features, splitting the sub-sample through the splitting values, and repeating the above steps until a preset tree depth limit is reached to complete the construction of an isolated tree; integrating multiple isolated trees to form an isolation forest, and calculating the path length of each abnormal feature corresponding to the sub-sample in the isolated tree; calculating an anomaly score according to the path length, and dynamically selecting a quantile as an anomaly threshold according to the distribution of the anomaly score. 7.The economic risk early warning management system based on financial intelligent big data analysis according to claim 6, characterized in that, The early warning mechanism comprises the following steps: The financial data collection module is connected to Kafka to collect structured and unstructured financial data in real time, and Kafka is set based on topic partition strategy through business dimension and Avro serialization; The risk monitoring and early warning module is connected to Flink to perform dynamic data stream processing on the financial data collected by Kafka in real time; Real-time fraud and fund chain risk detection are performed on the abnormal features determined by Flink to exceed the abnormal threshold, abnormal behavior sequence monitoring is performed on real-time fraud, and if an abnormal sequence is detected, an early warning response is triggered, and fund chain risk is monitored in real time through upstream and downstream transmission, and if abnormal fluctuations occur, an early warning response is triggered; The early warning response is mild early warning and severe early warning, the mild early warning triggers manual review queue, delays loan and sends SMS verification, and the severe early warning real-time intercepts transactions and freezes accounts, and synchronously notifies risk control personnel. 8.The economic risk early warning management system based on financial intelligent big data analysis according to claim 7, characterized in that, Dynamic adjustment of risk control strategy, comprising the following steps: Integrate structured data and unstructured data, build a full-link risk profile, and develop response plans based on early warning response levels; The abnormal features triggering the early warning response are recommended the optimal execution plan through linear programming algorithm, the execution process is monitored in real time, and the optimal execution plan is dynamically adjusted; The optimal execution plan is obtained by combining multiple response plans through linear programming algorithm. 9.The economic risk early warning management system based on financial intelligent big data analysis of claim 1, wherein, The system further comprises a data security module for encrypting and managing the data generated in the system, specifically for performing the following operations: Sensitive data distribution in system data is identified through a scanning tool, and data sensitivity in sensitive data distribution is identified using metadata tags; According to the sensitive data distribution and data sensitivity, different data types are assigned encryption keys through dynamic encryption technology; Based on data sensitivity, combined with user attributes and environmental attributes, access permissions are dynamically adjusted. 10.The economic risk early warning management system based on financial intelligent big data analysis of claim 9, wherein, The system further comprises an interaction and visualization module for feeding back the data generated by the financial data collection module, model construction and optimization module, risk monitoring and early warning module, risk decision support module and data security module to the user in the form of a visualization platform, and the user interacts through the visualization platform.

Images