Financial risk monitoring system and monitoring method based on big data

Abstract

The invention discloses a financial risk monitoring system based on big data, and the system comprises a data collection module which is used for collecting financial transaction data, market information data, customer behavior data and social media data from a plurality of data sources in real time; the data processing and analyzing module is used for performing real-time stream processing and batch analysis on the acquired data; the block chain recording module is used for recording and storing all transaction data through a block chain technology and carrying out data verification; the intelligent contract module is used for automatically executing a financial transaction protocol and triggering a risk monitoring operation through a preset condition, and the intelligent contract module automatically executes a predetermined risk control term according to a transaction behavior; the risk monitoring and early warning module is used for generating real-time risk early warning based on the data analysis result; according to the system, the efficiency, transparency, accuracy and compliance of a financial risk monitoring system can be effectively improved, and a more advanced and reliable risk monitoring and early warning solution is provided for the financial industry.

Description

Technical Field

[0001] The present invention relates to a financial risk monitoring system and monitoring method based on big data. Background Art

[0002] With the rapid development of the financial industry and continuous progress of technology, the scale and complexity of financial transactions have been increasing. The prevention and monitoring of various risks such as market risk, credit risk, and operational risk have become major challenges faced by financial institutions and regulatory authorities. Traditional financial risk management methods mainly rely on manual monitoring, rule-based systems, and single data sources. When facing massive, complex, and changing financial data, these methods often appear inadequate and are easily affected by human factors and technical limitations.

[0003] Traditional financial risk monitoring systems usually rely on multiple data sources, such as transaction data, market quotation data, customer behavior data, etc. However, due to the variety and different sources of these data sources, there are often certain delays and accuracy problems in data collection, integration, and analysis. With the real-time changes in the financial market, it is crucial to capture and analyze data in a timely manner for effectively identifying potential risks. However, the existing technologies cannot meet the requirements of high-speed, high-frequency, and high-capacity data processing. Traditional data processing methods can often only perform batch analysis and are difficult to meet the needs of real-time risk monitoring.

[0004] In traditional financial transaction records, the transparency and security of data often rely on centralized database systems, which makes the data vulnerable to external attacks, internal tampering, or misoperations, threatening the authenticity and integrity of transaction data. Especially in some complex financial transactions and cross-border transactions, how to ensure the transparency, immutability, and traceability of transaction data has become an urgent problem to be solved.

[0005] Most of the existing risk monitoring systems rely on manual review and traditional rule engines, often having problems such as slow response speed and poor recognition accuracy. Manual review not only takes time but is also easily affected by subjective factors and cannot achieve rapid real-time analysis and risk judgment of massive financial data. The existing early warning mechanisms mainly rely on simple rule detection, lacking flexibility and precision, resulting in many potential risks not being discovered in time or missing the best opportunity for prevention.

[0006] In financial transactions, it is crucial to ensure that each transaction complies with relevant regulations and compliance requirements. However, traditional compliance inspection methods usually rely on manual review and static compliance inspection standards, prone to problems such as low efficiency and high error rate. With the complexity of the financial market and the diversification of transaction methods, manual intervention in compliance review cannot effectively cope with the rapidly changing market environment and complex transaction structures and often cannot detect compliance risks in time.

[0007] As important breakthroughs in fintech in recent years, big data technology and blockchain technology have gradually been applied to financial risk management. However, there are still many technical problems in the combined application of these two technologies, such as the real-time processing ability of big data and the scalability problem of blockchain. In addition, there is a certain technical conflict between the efficiency of big data analysis and the decentralized storage of blockchain. How to improve the data processing efficiency while ensuring data security and transparency has become a key bottleneck in the development of technology. Summary of the Invention

[0008] The purpose of the present invention is to provide a financial risk monitoring system and monitoring method based on big data, which can effectively improve the efficiency, transparency, accuracy and compliance of the financial risk monitoring system, and provide a more advanced and reliable risk monitoring and early warning solution for the financial industry.

[0009] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0010] A financial risk monitoring system based on big data, comprising:

[0011] A data acquisition module, used for real-time acquisition of financial transaction data, market quotation data, customer behavior data and social media data from multiple data sources;

[0012] A data processing and analysis module, used for real-time stream processing and batch analysis of the acquired data;

[0013] A blockchain recording module, used for recording and storing all transaction data through blockchain technology to ensure the transparency, immutability and traceability of transaction data. The blockchain module uses an encrypted hash function to generate a hash value of the transaction data for data verification;

[0014] A smart contract module, used for automatically executing financial transaction protocols to ensure transaction compliance, and triggering risk monitoring operations through preset conditions. The smart contract module automatically executes predetermined risk control clauses according to transaction behaviors;

[0015] A risk monitoring and early warning module, used for generating real-time risk warnings based on data analysis results.

[0016] Preferably, the data acquisition module includes:

[0017] Historical and real-time transaction data of financial exchanges;

[0018] Customer account behavior data, including fund flow, transaction frequency and customer identity data;

[0019] Market sentiment data from social media and news platforms, used to assist in market risk assessment.

[0020] Preferably, the data processing and analysis module includes:

[0021] A real-time data stream processing module for high-speed processing of large-scale financial transaction data;

[0022] An anomaly detection module based on machine learning and statistical methods, capable of real-time marking and warning of potential market anomalies;

[0023] A market trend prediction module for analyzing and predicting possible market fluctuations.

[0024] Preferably, the smart contract module automatically executes transaction compliance checks and risk control measures through a decentralized smart contract execution mechanism, and the execution conditions include:

[0025] When the transaction amount exceeds the preset threshold, the transaction is automatically suspended and subject to manual review;

[0026] When detecting abnormal behavior, automatically trigger a risk warning and execute corresponding compliance measures.

[0027] Preferably, the risk monitoring and warning module generates a risk warning report in real time through a comprehensive risk assessment model, and adjusts transaction behavior according to the risk level. The report includes:

[0028] The current market risk status;

[0029] Details and analysis results of abnormal transactions;

[0030] Risk warning level and corresponding suggestions.

[0031] Another technical problem to be solved by the present invention is to provide a big data-based financial risk monitoring method, including:

[0032] Real-time collection of financial transaction data, market quotation data, customer behavior data, and social media data. The data includes:

[0033] {d1, d2,..., d n}(n is the number of data items)

[0034] Performing real-time stream processing and batch data analysis on the data, and using machine learning models for anomaly detection and market risk identification. The specific analysis steps include:

[0035] Using the Z-score algorithm based on statistical methods to detect abnormal transaction behaviors:

[0036]

[0037] Where X is the value of the current transaction, μ is the mean of historical data, and σ is the standard deviation. When ∣Z∣>3, mark the transaction as abnormal;

[0038] Group the market data through cluster analysis to identify potential manipulation behaviors, using the K-means clustering algorithm:

[0039]

[0040] Where J is the objective function of clustering, xj is the jth data point, Ci is the ith cluster, and μi is the center point of cluster i;

[0041] Record all transaction data through blockchain technology to ensure the transparency, immutability, and traceability of transactions, using cryptographic hash functions:

[0042] H = Hash(T)

[0043] Where T is the transaction data and H is the hash value of the transaction data, and this hash value is stored on the blockchain;

[0044] Automatically execute the protocol terms of financial transactions through smart contracts;

[0045] Based on data analysis and the execution results of smart contracts, monitor market dynamics in real time, generate risk warning reports, identify potential risks in a timely manner, and trigger the warning mechanism. The risk assessment formula is:

[0046]

[0047] Where Wi is the weight of risk factor i, Si is the risk score of the corresponding factor, and Rrisk is the overall risk value. When Rrisk exceeds the predetermined threshold, trigger a risk warning.

[0048] Preferably, the data collection step includes the following data sources:

[0049] Historical and real-time transaction records of financial exchanges;

[0050] Account behavior data, fund flows, and transaction frequencies of customers;

[0051] Market sentiment data in social media and news;

[0052] Real-time transaction data of the blockchain network.

[0053] Preferably, the data processing and analysis step performs real-time data analysis through a real-time stream processing platform. The anomaly detection algorithm specifically analyzes market behaviors through the Z-score algorithm to mark abnormal transaction behaviors.

[0054] Another technical problem to be solved by the invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the financial risk monitoring method based on big data as described in any one of the above items is implemented.

[0055] Another technical problem to be solved by the invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the financial risk monitoring method based on big data as described in any of the above items is implemented.

[0056] The beneficial effects of the present invention are:

[0057] Through the design of primary and backup network paths and the lossless switching mechanism (synchronization mechanism of the main switching board and the backup switching board), it is ensured that business traffic can be switched seamlessly and data loss can be avoided when a failure occurs; through the priority-based traffic scheduling strategy, different traffic is reasonably allocated to ensure that high-priority services can be processed first, and the traffic scheduling strategy is adjusted in real time according to the network load, effectively avoiding traffic conflicts and congestion.

[0058] By adopting enhanced traffic models and congestion prediction technology based on traffic size, congestion level, and flow path, it can perceive network traffic in real time and predict and alleviate congestion in advance, thereby effectively avoiding data loss and delay caused by congestion; through the multi-path load balancing module, combined with the flow identifier and the real-time health status of the link, the path selection of the data flow is dynamically adjusted to avoid overloading of a single link while ensuring uniform distribution of traffic.

[0059] Through the 5G edge computing gateway device module, the front-end data is sliced, encapsulated, and encrypted, and multi-path transmission technology is adopted to ensure the efficient transmission of data between different links, and it can be dynamically adjusted according to the health of the link, thereby improving the security and reliability of data transmission; through the integrated management platform module, machine learning and artificial intelligence technologies are introduced to monitor and dynamically optimize network traffic, server load, link health, etc. in real time, improve the intelligence level of the system, and realize automated fault prediction, load balancing and resource scheduling; through the background application platform module, the decrypted and unsealed data transmitted by the aggregation server is processed, and the final application service is provided to ensure high efficiency, low latency and lossless data transmission and processing. Specific implementation methods

[0061] The principles and features of the present invention are described below, and the examples are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described more specifically by way of example in the following paragraphs. According to the following description and claims, the advantages and features of the present invention will become clearer.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the specification of this invention are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. Embodiment

[0063] A financial risk monitoring system based on big data, comprising:

[0064] A data acquisition module, configured to collect in real time financial transaction data, market quotation data, customer behavior data, and social media data from multiple data sources;

[0065] A data processing and analysis module, configured to perform real-time stream processing and batch analysis on the collected data;

[0066] A blockchain recording module, configured to record and store all transaction data through blockchain technology, ensuring the transparency, immutability, and traceability of transaction data. The blockchain module uses a cryptographic hash function to generate a hash value of the transaction data for data verification;

[0067] A smart contract module, configured to automatically execute financial transaction protocols, ensure the compliance of transactions, and trigger risk monitoring operations through preset conditions. The smart contract module automatically executes predetermined risk control terms according to transaction behaviors;

[0068] A risk monitoring and early warning module, configured to generate real-time risk early warnings based on data analysis results.

[0069] The data acquisition module can collect information in real time from multiple data sources (such as financial transaction data, market quotation data, customer behavior data, and social media data). Through multi-source data integration, the system can comprehensively and real-time understand market dynamics, transaction behaviors, and customer sentiment, thereby better capturing potential risk signals and improving the accuracy and timeliness of risk identification. The data processing and analysis module combines real-time stream processing and batch analysis technologies to ensure efficient processing of large-scale financial data streams. In terms of real-time monitoring, the system can quickly respond to market fluctuations and changes in transaction behaviors and issue early warnings in a timely manner. In terms of batch analysis, it can deeply explore long-term trends in the data and identify potential systemic risks.

[0070] The blockchain record module uses blockchain technology for data storage to ensure the transparency, immutability and traceability of transaction data. All transaction records are verified and stored through encrypted hash functions, which greatly improves the security and reliability of data. Even when subjected to external attacks or internal tampering, the data can still maintain integrity and credibility, prevent data loss and tampering, and ensure the transparency of financial transactions;

[0071] The smart contract module can automatically execute financial transaction agreements and ensure the compliance of transactions. By triggering risk monitoring operations through preset conditions, smart contracts can automatically execute predetermined risk control clauses based on transaction behaviors. This can not only reduce human intervention and improve execution efficiency, but also reduce the error rate and deviation of human operations, thereby ensuring the compliance and stability of financial transactions.

[0072] The risk monitoring and early warning module can generate risk warnings in real time based on the analysis results of massive data. The system can immediately trigger early warnings when trading behavior is abnormal, the market fluctuates violently, or other potential risks occur, helping financial institutions to identify and respond to possible risks in advance. Timely risk warnings can help reduce potential losses and ensure the security of financial transactions; through smart contracts, the system automatically executes transaction agreements and triggers risk monitoring operations, and can ensure compliance with relevant regulations and compliance requirements at every stage of the transaction. In addition, the introduction of blockchain technology also makes transaction data and monitoring results traceable, increases regulatory transparency, and helps regulators better conduct market supervision; through the comprehensive use of big data, blockchain and smart contract technology, this system improves data analysis capabilities while increasing the system's ability to respond to various uncertainties and emergencies in the financial market. Whether it is when the market fluctuates violently or when unexpected trading risks occur, the system can respond quickly and automatically implement protective measures through preset risk control clauses, thereby improving the stability and security of financial transactions.

[0073] The data acquisition module comprises:

[0074] Historical and real-time trading data of financial exchanges;

[0075] Customer account behavior data, including fund flows, transaction frequency, and customer identity data;

[0076] Market sentiment data from social media and news platforms is used to assist in market risk assessment.

[0077] The data processing and analysis module includes:

[0078] Real-time data stream processing module, used for high-speed processing of large-scale financial transaction data;

[0079] The anomaly detection module based on machine learning and statistical methods can mark and warn potential market anomalies in real time;

[0080] The market trend prediction module is used to analyze and predict possible market fluctuations.

[0081] The data collection module covers historical and real-time data of financial exchanges, customer behavior data, and market sentiment data from social media and news platforms. Through multi-dimensional data collection, the system can comprehensively evaluate and analyze market dynamics. By combining customer behavior data and market sentiment data, the system can more accurately capture risk signals in the market and provide in-depth market risk assessments. This comprehensive analytical ability can effectively identify potential market fluctuations and trading risks.

[0082] Through the real-time data stream processing module and the anomaly detection module based on machine learning and statistical methods, the system can process a large amount of financial transaction data at high speed and mark and warn abnormal behaviors in the market in real time. This means that the system can identify potential risks in a timely manner before they occur and notify financial institutions through the warning mechanism to help them take corresponding measures quickly. This efficient real-time response ability significantly improves the security and stability of the market and reduces potential losses caused by market anomalies or operational errors.

[0083] The market trend prediction module uses historical data and machine learning methods to analyze and predict market fluctuations, thus providing data support for financial decision-making. Through accurate market trend prediction, financial institutions can understand the potential trends of the market in advance and formulate more reasonable investment decisions and risk management strategies. This not only helps financial institutions improve profitability but also enhances their ability to respond to sudden market changes, thereby optimizing risk management and investment strategies.

[0084] The intelligent contract module automatically executes transaction compliance checks and risk control measures through a decentralized intelligent contract execution mechanism, and the execution conditions include:

[0085] When the transaction amount exceeds the preset threshold, the transaction is automatically suspended and subject to manual review;

[0086] When abnormal behavior is detected, a risk warning is automatically triggered and corresponding compliance measures are executed.

[0087] The risk monitoring and warning module generates risk warning reports in real time through a comprehensive risk assessment model and adjusts transaction behaviors according to the risk levels. The reports include:

[0088] The current market risk status;

[0089] Details and analysis results of abnormal transactions;

[0090] Risk warning levels and corresponding countermeasures.

[0091] The smart contract module realizes the automation of transaction compliance inspection and risk control through a decentralized mechanism. Specifically, when the transaction amount exceeds the preset threshold, the system will automatically suspend the transaction and require manual review to prevent risks or violations that may be caused by large transactions. In addition, when the system detects abnormal behavior, it will automatically trigger a risk warning and take corresponding compliance measures, which can reduce human errors or operation delays and improve the compliance and risk control efficiency of transactions.

[0092] The risk monitoring and warning module generates a risk warning report in real time through a comprehensive risk assessment model, analyzing the current market risk status, abnormal transactions, and risk levels. This real-time monitoring and timely reporting function enables financial institutions to quickly identify and respond to potential market risks, helping institutions make timely adjustments when the market experiences sharp fluctuations or abnormal transactions. By dynamically adjusting trading behaviors and risk management strategies, losses and risks can be effectively reduced.

[0093] Through comprehensive risk assessment, the solution can not only generate a risk warning report but also adjust trading behaviors according to risk levels. In addition, the countermeasures included in the report can help decision-makers take appropriate countermeasures for different levels of risk. In this way, financial institutions can manage and prevent risks more targeted, rather than simply relying on static compliance rules or manual intervention, further enhancing the intelligence and accuracy of decision-making.

[0094] A financial risk monitoring method based on big data, including:

[0095] Real-time collection of financial transaction data, market quotation data, customer behavior data, and social media data, where the data includes:

[0096] {d1, d2,..., d n}(n is the number of data items)

[0097] Performing real-time stream processing and batch data analysis on the data, and using machine learning models for anomaly detection and market risk identification. The specific analysis steps include:

[0098] Using the Z-score algorithm based on statistical methods to detect abnormal trading behaviors:

[0099]

[0100] Among them, X is the value of the current transaction, μ is the mean of historical data, σ is the standard deviation. When |Z| > 3, mark this transaction as abnormal;

[0101] Group market data through cluster analysis to identify potential manipulation behaviors, using the K-means clustering algorithm:

[0102]

[0103] where J is the objective function of clustering, xj is the j-th data point, Ci is the i-th cluster, and μi is the center point of cluster i;

[0104] Record all transaction data through blockchain technology to ensure the transparency, immutability, and traceability of transactions, using cryptographic hash functions:

[0105] H = Hash(T)

[0106] where T is the transaction data, H is the hash value of the transaction data, and this hash value is stored on the blockchain;

[0107] Automatically execute the protocol terms of financial transactions through smart contracts;

[0108] Based on data analysis and the execution results of smart contracts, monitor market dynamics in real time, generate risk warning reports, promptly identify potential risks and trigger the warning mechanism. The risk assessment formula is:

[0109]

[0110] where Wi is the weight of risk factor i, Si is the risk score of the corresponding factor, Rrisk is the overall risk value, and when Rrisk exceeds a predetermined threshold, the risk warning is triggered.

[0111] By collecting financial transaction data, market quotation data, customer behavior data, and social media data in real time, market dynamics and transaction information can be obtained in a timely manner. This enables the system to quickly identify abnormal transaction behaviors, market risks, and potential manipulation behaviors through stream processing and batch data analysis. Combining machine learning models and statistical methods, such as the Z-score algorithm and the K-means clustering algorithm, can accurately and quickly detect abnormal transactions and manipulation behaviors, thereby realizing real-time risk monitoring and warning.

[0112] Adopt blockchain technology to record all transaction data, ensuring the transparency, immutability, and traceability of transaction data. Generate the hash value of the transaction data through a cryptographic hash function and store it on the blockchain, effectively preventing data from being tampered with or forged. This not only increases the trustworthiness of the system but also provides a reliable data audit and tracking path for regulatory authorities to ensure the compliance of transaction activities.

[0113] The protocol terms for smart contracts to automatically execute financial transactions make the financial transaction process more efficient and automated. Through smart contracts, the execution of transactions no longer relies on manual intervention, thus reducing the possibility of human errors and fraud. Smart contracts can ensure that all parties complete transactions according to pre-set rules, increasing the accuracy and compliance of transactions.

[0114] Based on data analysis and the execution results of smart contracts, the system can monitor market changes in real time and generate risk warning reports. These reports not only include the identification of potential risks, but also quantify and evaluate the overall risk level through risk assessment formulas (such as Wi being the weight of risk factors and Si being the risk score). When the overall risk value (Rrisk) exceeds a predetermined threshold, the system will trigger a warning mechanism. This dynamic risk assessment and warning system helps decision-makers timely understand the potential risks in the market and make corresponding adjustments to reduce the accumulation and losses of risks.

[0115] This method integrates information from multiple dimensions such as financial transactions, market conditions, customer behavior, and social media data, providing a more comprehensive perspective for risk monitoring. The incorporation of social media data and customer behavior data enables the system to capture market sentiment and potential unstructured risk factors, while market condition data and financial transaction data provide accurate market trend and trading pattern analysis. This multi-dimensional data fusion can more accurately identify complex market risks and behavior patterns.

[0116] The data collection steps include the following data sources:

[0117] Historical and real-time transaction records of financial exchanges;

[0118] Customer account behavior data, fund flows, and trading frequencies;

[0119] Market sentiment data in social media and news;

[0120] Real-time transaction data of blockchain networks.

[0121] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the financial risk monitoring method based on big data as described above.

[0122] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the financial risk monitoring method based on big data as described above.

[0123] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0124] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0125] The above embodiments of the present invention do not limit the protection scope of the present invention. The implementation manners of the present invention are not limited thereto. All kinds of modifications, substitutions, or changes made to the above structure of the present invention according to the above content of the present invention, in accordance with the common general knowledge and conventional means in the art, without departing from the above basic technical idea of the present invention, shall fall within the protection scope of the present invention.

Claims

1. A financial risk monitoring system based on big data, characterized in that, Including: A data collection module for real-time collection of financial transaction data, market quotation data, customer behavior data, and social media data from multiple data sources; A data processing and analysis module for real-time stream processing and batch analysis of the collected data; A blockchain recording module for recording and storing all transaction data through blockchain technology to ensure the transparency, immutability, and traceability of transaction data. The blockchain module uses an encrypted hash function to generate a hash value for transaction data for data verification; A smart contract module for automatically executing financial transaction protocols to ensure transaction compliance and triggering risk monitoring operations through preset conditions. The smart contract module automatically executes predetermined risk control terms based on transaction behaviors; A risk monitoring and early warning module for generating real-time risk warnings based on data analysis results.

2. The financial risk monitoring system based on big data according to claim 1, wherein The data collection module includes: Historical and real-time transaction data of financial exchanges; Customer account behavior data, including fund flow, transaction frequency, and customer identity data; Market sentiment data from social media and news platforms for assisting in market risk assessment.

3. The financial risk monitoring system based on big data according to claim 1, characterized in that, The data processing and analysis module includes: A real-time data stream processing module for high-speed processing of large-scale financial transaction data; An anomaly detection module based on machine learning and statistical methods capable of real-time marking and warning of potential market anomalies; A market trend prediction module for analyzing and predicting possible market fluctuations.

4. The financial risk monitoring system based on big data according to claim 1, characterized in that, The smart contract module automatically executes transaction compliance checks and risk control measures through a decentralized smart contract execution mechanism. The execution conditions include: When the transaction amount exceeds a preset threshold, automatically suspend the transaction and conduct manual review; When detecting abnormal behaviors, automatically trigger risk warnings and execute corresponding compliance measures.

5. The financial risk monitoring system based on big data according to claim 1, wherein The risk monitoring and early warning module generates real-time risk warning reports through a comprehensive risk assessment model and adjusts transaction behaviors according to the risk level. The reports include: The current market risk status; Detailed information and analysis results of abnormal transactions; Risk warning levels and response suggestions.

6. A financial risk monitoring method based on big data, characterized in that, Including: Real-time collection of financial transaction data, market quotation data, customer behavior data, and social media data. The data includes: {d1, d2,..., d n} (where n is the number of data items) Perform real-time stream processing and batch data analysis on the data, and use machine learning models for anomaly detection and market risk identification. The specific analysis steps include: Use the Z-score algorithm based on statistical methods to detect abnormal transaction behaviors: Where X is the value of the current transaction, μ is the mean of historical data, σ is the standard deviation. When ∣Z∣>3, mark the transaction as abnormal; Group market data through cluster analysis to identify potential manipulation behaviors, using the K-means clustering algorithm: Where J is the objective function of clustering, xj is the jth data point, Ci is the ith cluster, and μi is the center point of cluster i; Record all transaction data through blockchain technology to ensure the transparency, immutability, and traceability of transactions, using an encrypted hash function: H = Hash(T) Where T is the transaction data and H is the hash value of the transaction data. This hash value is stored on the blockchain; Protocol terms for automatically executing financial transactions through smart contracts; Based on data analysis and the execution results of smart contracts, monitor market dynamics in real time, generate risk warning reports, identify potential risks in a timely manner and trigger the warning mechanism. The risk assessment formula is: Where Wi is the weight of risk factor i, Si is the risk score of the corresponding factor, and Rrisk is the overall risk value. When Rrisk exceeds a predetermined threshold, a risk warning is triggered.

7. The financial risk monitoring method based on big data according to claim 6, wherein, The data collection steps include the following data sources: Historical and real-time trading records of financial exchanges; Account behavior data, fund flows, and trading frequencies of customers; Market sentiment data in social media and news; Real-time trading data of blockchain networks.

8. The financial risk monitoring method according to claim 6, wherein The data processing and analysis steps perform real-time data analysis through a real-time stream processing platform. The anomaly detection algorithm specifically analyzes market behavior through the Z-score algorithm and marks abnormal trading behaviors.

9. An electronic device, characterized in that, Comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the big data-based financial risk monitoring method as described in claims 6-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the big data-based financial risk monitoring method as described in claims 6-8.

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