Urban financial service full-link risk prevention and control management system

Abstract

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of risk prevention and control, and particularly relates to a city financial business full-link risk prevention and control management system. BACKGROUND

[0002] The current city financial business full-link risk prevention and control fuses front-line technologies such as federated learning, graph neural network, blockchain and deep reinforcement learning, realizes the full-process closed-loop management of risk identification, conduction analysis and cross-scene linkage through multi-source heterogeneous data integration, dynamic risk portrait construction, real-time monitoring and early warning and intelligent disposal, and has high precision, high real-time performance and compliance privacy protection capability.

[0003] However, the existing city financial business full-link risk prevention and control management system still has certain defects. The existing model has high dependency. The graph neural network and the multi-dimensional scoring model need high-quality data support. If the input data has noise or is missing, the risk score may be distorted. The real-time performance and the computing power are contradictory. The stream computing and the deep reinforcement learning need high computing power support, which may cause delay in extreme scenarios. The automation boundary is fuzzy. The smart contract only handles low-risk events. The medium and high risks depend on the expert system, but the historical case coverage is limited, and the new risks are easily misjudged. The cross-scene analysis deviation is biased. The risk infection simulation depends on the definition of the transaction correlation strength. If the correlation relationship modeling is not accurate, the conduction probability may be overestimated or underestimated. The blockchain storage guarantees transparency, but its expansibility limits the efficiency of large-scale real-time disposal. Therefore, the city financial business full-link risk prevention and control management system is proposed. SUMMARY

[0004] The purpose of the present application is to provide a city financial business full-link risk prevention and control management system to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a city financial business full-link risk prevention and control management system, comprising a multi-source heterogeneous data acquisition and governance module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system self-adaptive optimization module.

[0006] The multi-source heterogeneous data acquisition and governance module integrates and standardizes the city financial business data through a federated learning framework and a time series database.

[0007] The dynamic risk portrait construction module dynamically depicts the user and enterprise risk level in real time through a graph neural network and a multi-dimensional scoring model according to multi-source heterogeneous data.

[0008] The real-time risk monitoring and early warning module is based on a dynamic risk portrait and a streaming computing framework, and performs real-time identification and multi-level early warning triggering of abnormal transaction patterns through a deep reinforcement learning algorithm.

[0009] The risk disposal and decision support module performs automatic execution and manual collaborative decision of a risk disposal scheme according to a real-time early warning result through a blockchain smart contract and an expert knowledge base.

[0010] The cross-scenario risk linkage analysis module performs quantitative analysis of risk transmission paths between credit and payment scenarios through a risk infection simulation algorithm in combination with a dynamic risk portrait and real-time monitoring data.

[0011] The compliance and privacy protection module performs privacy protection of data sharing and recording of compliance audit logs relying on homomorphic encryption and blockchain storage technology.

[0012] The system adaptive optimization module performs continuous parameter optimization and strategy iteration of a risk identification model and a disposal strategy through historical risk event data and a meta-learning algorithm.

[0013] Preferably, the multi-source heterogeneous data collection and governance module integrates and standardizes urban financial business data through a federated learning framework and a time series database; collects transaction records, behavior logs and dynamic indicator data from multi-source systems such as banks, enterprises and Internet of Things devices; performs data cleaning, de-identification processing and format unification to build a standardized data lake; realizes cross-institutional data collaborative modeling based on a federated learning framework to ensure that data does not leave the domain; uses a time series database to efficiently store and quickly retrieve dynamic behavior data, forming a unified data foundation supporting multi-scenario analysis.

[0014] Preferably, the dynamic risk portrait construction module performs real-time risk dynamic characterization of users and enterprises by integrating multi-source heterogeneous data and combining a graph neural network and a multi-dimensional scoring model; uses a graph neural network to model structured and unstructured data, mines implicit associations between users and enterprises, and forms a complex heterogeneous graph.

[0015] Preferably, the dynamic risk portrait construction module constructs a multi-dimensional scoring index system covering key dimensions such as credit history, transaction behavior and business stability, and generates a comprehensive risk score through weighted fusion, with the formula being:

[0016]

[0017] In the formula, R(u, e) represents the comprehensive risk score of user u and enterprise e; GNN(U∪E) represents the joint modeling result of the graph neural network for the user set U and the enterprise set E. This represents the weighted summation result of the multi-dimensional scoring model, encompassing the explicit risk characteristics of users and enterprises; ω k S represents the weight of the k-th risk dimension; k (u,e) represents the scoring function for the k-th risk dimension; m represents the total number of risk scoring dimensions;

[0018] Based on real-time updated transaction flows, behavior logs, and IoT data, the model parameters are dynamically adjusted to generate multi-level risk labels for users and enterprises.

[0019] Preferably, the real-time risk monitoring and early warning module is based on a dynamic risk profile and streaming computing framework. It uses a deep reinforcement learning algorithm to identify abnormal transaction patterns in real time and trigger multi-level early warnings. It accesses real-time transaction flow and behavior log data and uses the streaming computing framework to perform data sharding and feature extraction. It inputs user behavior patterns and enterprise operating indicators from the dynamic risk profile into the deep reinforcement learning model to train rules for identifying abnormal transactions. It classifies early warning levels based on the risk confidence level output by the model and triggers corresponding handling procedures.

[0020] Preferably, the risk management and decision support module, based on real-time early warning results, uses blockchain smart contracts and an expert knowledge base to automate the execution of risk management plans and facilitate collaborative decision-making with human intervention; it receives early warning levels and management suggestions from the real-time early warning module; and it automatically triggers smart contracts to execute preset actions for low-risk events; the implementation formula is as follows:

[0021]

[0022] In the formula, P auto θ represents the probability of automatic handling of low-risk events; x represents the feature vector output by the real-time early warning module; θ represents the weight parameter vector preset by the smart contract; b represents the bias term, which is a constant term preset by the smart contract and used to adjust the global threshold of the model; β represents the decision sensitivity coefficient, which controls the sensitivity of the model to input features.

[0023] For medium- and high-risk events, the system pushes them to the expert system, which generates a response plan by combining historical cases and rules in the knowledge base; the response actions are manually reviewed and confirmed, and the operation logs are recorded through blockchain.

[0024] Preferably, the cross-scenario risk linkage analysis module combines dynamic risk profiles with real-time monitoring data, and uses a risk contagion simulation algorithm to conduct quantitative analysis of the risk transmission path between credit and payment scenarios; integrates user behavior characteristics and enterprise operating indicators in the dynamic risk profile to construct a risk network graph containing nodes of credit and payment scenarios; and extracts transaction correlations based on real-time monitoring data to define the edge weights of risk propagation.

[0025] Preferably, the cross-scenario risk linkage analysis module uses high-risk events as the initial source of infection, and iteratively calculates the probability and scope of risk transmission between scenarios through a risk transmission simulation algorithm; it outputs quantitative analysis results to assist in formulating cross-scenario risk isolation strategies, as shown in the formula:

[0026]

[0027] In the formula, P ij (t) represents the probability of risk propagation from scenario i to scenario j at time t; T ik (t) represents the transaction association strength between scenario i and intermediate node k at time t; T kj (t) represents the transaction association strength between intermediate node k and scenario j at time t; ω k T represents the weight of the intermediate node k; il (t) represents the sum of the transaction association strengths between scenario i and all intermediate nodes l at time t; n represents the total number of intermediate nodes.

[0028] Preferably, the compliance and privacy protection module relies on homomorphic encryption and blockchain notarization technology to record privacy protection and compliance audit logs for data sharing; it connects to standardized data output by the multi-source heterogeneous data acquisition module and encrypts sensitive fields using homomorphic encryption algorithms; it constructs a distributed notarization chain through blockchain to store data access requests, sharing operations, and handling actions on the chain using hashes; it combines a dynamic authorization mechanism to verify the legality of access based on the data usage permissions of users and enterprises; it synchronizes the encrypted data and operation logs to the risk profiling, early warning, and handling modules through cross-scenario risk, and generates tamper-proof compliance audit records on the blockchain to ensure traceability of the entire data flow.

[0029] Preferably, the system adaptive optimization module continuously optimizes the parameters and iterates the strategies of the risk identification model and handling strategy through historical risk event data and meta-learning algorithms; integrates historical risk event data and handling results output by the real-time early warning, handling and cross-scenario analysis modules to extract key features and strategy feedback; dynamically adjusts the weights and rules of the risk identification model and handling strategy based on meta-learning algorithms; verifies the applicability of the optimized model and strategy through simulation tests, and generates an iterative version.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. This invention combines graph neural networks and multi-dimensional scoring models to uncover implicit connections between users and enterprises, generating dynamic risk scores. Graph neural networks process structured and unstructured data, constructing heterogeneous graphs to capture complex relationships. The multi-dimensional scoring system integrates key dimensions such as credit history and transaction behavior, generating real-time risk labels through weighted fusion. The module dynamically adjusts model parameters to ensure that risk profiles are updated synchronously with real-time transaction flows, providing accurate basis for early warning and response.

[0032] 2. This invention achieves real-time identification and multi-level early warning of abnormal transactions by using a streaming computing framework and deep reinforcement learning algorithms; streaming computing processes real-time transaction streams and behavior logs in segments to extract user behavior patterns from dynamic risk profiles; the deep reinforcement learning model dynamically optimizes anomaly detection rules, classifies early warning levels, and triggers handling procedures; through the multi-level early warning mechanism, the system significantly shortens risk response time and reduces potential losses.

[0033] 3. This invention achieves automation and human collaboration in risk management through blockchain smart contracts and expert knowledge bases; low-risk events are automatically executed through formulas to improve management efficiency; medium- and high-risk events are pushed to the expert system, and management plans are generated by combining historical cases; the blockchain records operation logs to ensure transparency and traceability; the module balances the boundaries between machine decision-making and human review to reduce the risk of misjudgment, and forms a closed-loop feedback mechanism through the linkage of smart contracts and knowledge bases.

[0034] 4. This invention quantifies the transmission path between credit and payment scenarios through a risk contagion simulation algorithm, and constructs a risk network graph that includes user behavior and enterprise operation indicators; taking high-risk events as the initial source of contagion, iteratively calculates the propagation probability and impact range, and outputs quantitative analysis results to assist in the formulation of cross-scenario isolation strategies; effectively identifies risk diffusion paths, avoids local problems from evolving into systemic risks, and improves the stability of the urban financial ecosystem. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of the urban financial business full-chain risk prevention and control management system of the present invention;

[0036] Figure 2 The operation flow of the urban financial business end-to-end risk prevention and control management system of the present invention Figure One ;

[0037] Figure 3 The operation flow of the urban financial business end-to-end risk prevention and control management system of the present invention Figure Two ;

[0038] Figure 4 The operation flow of the urban financial business end-to-end risk prevention and control management system of the present invention Figure Three . Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example

[0041] Please see Figures 1-4 As shown, the present invention provides a technical solution including a multi-source heterogeneous data acquisition and governance module, a dynamic risk profile construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scenario risk linkage analysis module, a compliance and privacy protection module, and a system adaptive optimization module;

[0042] The multi-source heterogeneous data acquisition and governance module integrates and standardizes urban financial business data through a federated learning framework and a time-series database.

[0043] The dynamic risk profile construction module uses graph neural networks and multi-dimensional scoring models to dynamically characterize the risk levels of users and enterprises in real time based on multi-source heterogeneous data.

[0044] The real-time risk monitoring and early warning module is based on dynamic risk profiling and streaming computing framework. It uses deep reinforcement learning algorithms to identify abnormal trading patterns in real time and trigger multi-level early warnings.

[0045] The risk management and decision support module, based on real-time early warning results, uses blockchain smart contracts and an expert knowledge base to automate the execution of risk management plans and facilitate collaborative decision-making with human intervention.

[0046] The cross-scenario risk linkage analysis module combines dynamic risk profiles with real-time monitoring data and uses a risk contagion simulation algorithm to conduct quantitative analysis of the risk transmission path between credit and payment scenarios.

[0047] The compliance and privacy protection module relies on homomorphic encryption and blockchain notarization technology to record data sharing privacy protection and compliance audit logs;

[0048] The system's adaptive optimization module continuously optimizes the parameters and iterates the strategies of the risk identification model and response strategy by using historical risk event data and meta-learning algorithms.

[0049] Preferably, the multi-source heterogeneous data acquisition and governance module integrates and standardizes urban financial business data through a federated learning framework and a time-series database; it collects transaction records, behavior logs, and dynamic indicator data from multiple sources, including banks, enterprises, and IoT devices; it constructs a standardized data lake through data cleaning, de-identification processing, and format unification; it achieves cross-institutional data collaborative modeling based on the federated learning framework to ensure that data does not leave the domain; and it uses a time-series database to efficiently store and quickly retrieve dynamic behavioral data, forming a unified data foundation that supports multi-scenario analysis.

[0050] Preferably, the dynamic risk profile construction module integrates multi-source heterogeneous data, combines graph neural networks and multi-dimensional scoring models to dynamically characterize the real-time risks of users and enterprises; it uses graph neural networks to model structured and unstructured data, mines the implicit relationships between users and enterprises, and forms a complex heterogeneous graph.

[0051] Preferably, the dynamic risk profile construction module constructs a multi-dimensional scoring indicator system, covering key dimensions such as credit history, transaction behavior, and operational stability, and generates a comprehensive risk score through weighted fusion, as shown in the formula:

[0052]

[0053] In the formula, R(u,e) represents the combined risk score of user u and enterprise e; GNN(U∪E) represents the joint modeling result of the graph neural network on the user set U and the enterprise set E; This represents the weighted summation result of the multi-dimensional scoring model, encompassing the explicit risk characteristics of users and enterprises; ω k S represents the weight of the k-th risk dimension; k (u,e) represents the scoring function for the k-th risk dimension; m represents the total number of risk scoring dimensions;

[0054] Based on real-time updated transaction flows, behavior logs, and IoT data, the model parameters are dynamically adjusted to generate multi-level risk labels for users and enterprises.

[0055] Preferably, the real-time risk monitoring and early warning module is based on a dynamic risk profile and streaming computing framework. It uses a deep reinforcement learning algorithm to identify abnormal transaction patterns in real time and trigger multi-level early warnings. It accesses real-time transaction flow and behavior log data and uses the streaming computing framework to perform data sharding and feature extraction. It inputs user behavior patterns and enterprise operating indicators from the dynamic risk profile into the deep reinforcement learning model to train rules for identifying abnormal transactions. It classifies early warning levels based on the risk confidence level output by the model and triggers corresponding handling procedures.

[0056] Preferably, the risk management and decision support module, based on real-time early warning results, uses blockchain smart contracts and an expert knowledge base to automate the execution of risk management plans and facilitate collaborative decision-making with human intervention; it receives early warning levels and management suggestions from the real-time early warning module; and it automatically triggers smart contracts to execute preset actions for low-risk events; the implementation formula is as follows:

[0057]

[0058] In the formula, P auto θ represents the probability of automatic handling of low-risk events; x represents the feature vector output by the real-time early warning module; θ represents the weight parameter vector preset by the smart contract; b represents the bias term, which is a constant term preset by the smart contract and used to adjust the global threshold of the model; β represents the decision sensitivity coefficient, which controls the sensitivity of the model to input features.

[0059] For medium- and high-risk events, the system pushes them to the expert system, which generates a response plan by combining historical cases and rules in the knowledge base; the response actions are manually reviewed and confirmed, and the operation logs are recorded through blockchain.

[0060] Preferably, the cross-scenario risk linkage analysis module combines dynamic risk profiles with real-time monitoring data, and uses a risk contagion simulation algorithm to conduct quantitative analysis of the risk transmission path between credit and payment scenarios; integrates user behavior characteristics and enterprise operating indicators in the dynamic risk profile to construct a risk network graph containing nodes of credit and payment scenarios; and extracts transaction correlations based on real-time monitoring data to define the edge weights of risk propagation.

[0061] Preferably, the cross-scenario risk linkage analysis module uses high-risk events as the initial source of infection, and iteratively calculates the probability and scope of risk transmission between scenarios through a risk transmission simulation algorithm; it outputs quantitative analysis results to assist in formulating cross-scenario risk isolation strategies, as shown in the formula:

[0062]

[0063] In the formula, P ij (t) represents the probability of risk propagation from scenario i to scenario j at time t; T ik (t) represents the transaction association strength between scenario i and intermediate node k at time t; T kj (t) represents the transaction association strength between intermediate node k and scenario j at time t; ω k T represents the weight of the intermediate node k; il (t) represents the sum of the transaction association strengths between scenario i and all intermediate nodes l at time t; n represents the total number of intermediate nodes.

[0064] Preferably, the compliance and privacy protection module relies on homomorphic encryption and blockchain notarization technology to record privacy protection and compliance audit logs for data sharing; it connects to standardized data output by the multi-source heterogeneous data acquisition module and encrypts sensitive fields using homomorphic encryption algorithms; it constructs a distributed notarization chain through blockchain to store data access requests, sharing operations, and handling actions on the chain using hashes; it combines a dynamic authorization mechanism to verify the legality of access based on the data usage permissions of users and enterprises; it synchronizes the encrypted data and operation logs to the risk profiling, early warning, and handling modules through cross-scenario risk, and generates tamper-proof compliance audit records on the blockchain to ensure traceability of the entire data flow.

[0065] Preferably, the system adaptive optimization module continuously optimizes the parameters and iterates the strategies of the risk identification model and handling strategy through historical risk event data and meta-learning algorithms; integrates historical risk event data and handling results output by the real-time early warning, handling and cross-scenario analysis modules to extract key features and strategy feedback; dynamically adjusts the weights and rules of the risk identification model and handling strategy based on meta-learning algorithms; verifies the applicability of the optimized model and strategy through simulation tests, and generates an iterative version.

[0066] Working Principle: The system achieves end-to-end risk management through multi-module collaboration. The multi-source heterogeneous data acquisition and governance module utilizes federated learning and time-series databases to integrate data from banks, enterprises, and the Internet of Things, constructing a standardized data lake and ensuring cross-institutional data privacy. The dynamic risk profiling module combines graph neural networks and multi-dimensional scoring models to uncover implicit connections between users and enterprises, generating real-time risk scores. The real-time monitoring and early warning module, based on streaming computing and deep reinforcement learning, identifies abnormal transactions, classifies warning levels, and triggers handling procedures. The risk handling module automatically processes low-risk events through blockchain smart contracts, while medium- and high-risk events are collaboratively decided by an expert system and recorded on the blockchain. The cross-scenario linkage analysis module quantifies the risk transmission paths in credit and payment scenarios, assisting in the formulation of isolation strategies. The compliance and privacy module relies on homomorphic encryption and blockchain notarization to ensure data sharing compliance. The adaptive optimization module iterates model parameters through meta-learning algorithms to continuously improve system efficiency. Overall, the system achieves closed-loop management from data governance to risk handling, balancing real-time performance, accuracy, and compliance.

[0067] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0068] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A city financial business end-to-end risk prevention and control management system, characterized by: It includes modules for multi-source heterogeneous data collection and governance, dynamic risk profile construction, real-time risk monitoring and early warning, risk disposal and decision support, cross-scenario risk linkage analysis, compliance and privacy protection, and system adaptive optimization. The multi-source heterogeneous data acquisition and governance module integrates and standardizes urban financial business data through a federated learning framework and a time-series database. The dynamic risk profile construction module uses graph neural networks and multi-dimensional scoring models to dynamically characterize the risk levels of users and enterprises in real time based on multi-source heterogeneous data. The real-time risk monitoring and early warning module is based on dynamic risk profiling and streaming computing framework. It uses deep reinforcement learning algorithms to identify abnormal trading patterns in real time and trigger multi-level early warnings. The risk management and decision support module, based on real-time early warning results, uses blockchain smart contracts and an expert knowledge base to automate the execution of risk management plans and facilitate collaborative decision-making with human intervention. The cross-scenario risk linkage analysis module combines dynamic risk profiles with real-time monitoring data and uses a risk contagion simulation algorithm to conduct quantitative analysis of the risk transmission path between credit and payment scenarios. The compliance and privacy protection module relies on homomorphic encryption and blockchain notarization technology to record data sharing privacy protection and compliance audit logs; The system's adaptive optimization module continuously optimizes the parameters and iterates the strategies of the risk identification model and response strategy by using historical risk event data and meta-learning algorithms.

2. The urban financial business full-chain risk prevention and control management system according to claim 1, characterized in that: The multi-source heterogeneous data acquisition and governance module integrates and standardizes urban financial business data through a federated learning framework and a time-series database. Transaction records, behavior logs, and dynamic indicator data are collected from multiple sources including banks, enterprises, and IoT devices; a standardized data lake is constructed through data cleaning, de-identification, and format unification; and cross-institutional collaborative data modeling is achieved based on a federated learning framework. By leveraging time-series databases, dynamic behavioral data can be efficiently stored and quickly retrieved, forming a unified data foundation that supports multi-scenario analysis.

3. The urban financial business end-to-end risk prevention and control management system according to claim 1, characterized in that: The dynamic risk profile construction module integrates multi-source heterogeneous data and combines graph neural networks with multi-dimensional scoring models to dynamically depict the real-time risks of users and enterprises. It uses graph neural networks to model structured and unstructured data, mines the implicit relationships between users and enterprises, and forms a complex heterogeneous graph.

4. The urban financial business full-chain risk prevention and control management system according to claim 3, characterized in that: The dynamic risk profile construction module builds a multi-dimensional scoring indicator system, covering key dimensions such as credit history, transaction behavior, and operational stability. It then generates a comprehensive risk score through weighted fusion, as shown in the formula: In the formula, R(u,e) represents the combined risk score of user u and enterprise e; GNN(U∪E) represents the joint modeling result of the graph neural network on the user set U and the enterprise set E; This represents the weighted summation result of a multi-dimensional scoring model, covering the explicit risk characteristics of users and enterprises; ω k S represents the weight of the k-th risk dimension; k (u,e) represents the scoring function for the k-th risk dimension; m represents the total number of risk scoring dimensions; Based on real-time updated transaction flows, behavior logs, and IoT data, the model parameters are dynamically adjusted to generate multi-level risk labels for users and enterprises.

5. The urban financial business end-to-end risk prevention and control management system according to claim 1, characterized in that: The real-time risk monitoring and early warning module is based on dynamic risk profiling and streaming computing framework. It uses deep reinforcement learning algorithms to identify abnormal trading patterns in real time and trigger multi-level early warnings. Access real-time transaction streams and behavior log data, and use a streaming computing framework for data sharding and feature extraction; User behavior patterns and enterprise operating indicators from dynamic risk profiles are input into a deep reinforcement learning model to train rules for identifying abnormal transactions; warning levels are assigned based on the risk confidence level output by the model, triggering corresponding handling procedures.

6. The urban financial business end-to-end risk prevention and control management system according to claim 1, characterized in that: The risk management and decision support module, based on real-time early warning results, uses blockchain smart contracts and an expert knowledge base to automate the execution of risk management plans and facilitate collaborative decision-making with human intervention. Receives warning levels and handling suggestions from the real-time warning module; automatically triggers smart contracts to execute preset actions for low-risk events; the implementation formula is: In the formula, P auto represents the probability of automatic handling of low-risk events; x represents the feature vector output by the real-time early warning module; θ represents the weight parameter vector preset by the smart contract; b represents the bias term, which is a constant term preset by the smart contract and is used to adjust the global threshold of the model. β represents the decision sensitivity coefficient, which controls the model's sensitivity to input features; For medium- and high-risk events, the system pushes them to the expert system, which generates a response plan by combining historical cases and rules in the knowledge base; the response actions are manually reviewed and confirmed, and the operation logs are recorded through blockchain.

7. The urban financial business full-chain risk prevention and control management system according to claim 1, characterized in that: The cross-scenario risk linkage analysis module combines dynamic risk profiles with real-time monitoring data, and uses a risk contagion simulation algorithm to quantitatively analyze the risk transmission path between credit and payment scenarios; it integrates user behavior characteristics and enterprise operating indicators from the dynamic risk profile to construct a risk network graph containing nodes of credit and payment scenarios; and it extracts transaction correlations based on real-time monitoring data to define the edge weights of risk propagation.

8. The urban financial business end-to-end risk prevention and control management system according to claim 7, characterized in that: The cross-scenario risk linkage analysis module uses high-risk events as the initial source of infection, and iteratively calculates the probability and scope of risk transmission between scenarios through a risk contagion simulation algorithm; it outputs quantitative analysis results to assist in formulating cross-scenario risk isolation strategies, as shown in the formula: Where i,j∈{credit, payment} In the formula, P ij (t) represents the probability of risk propagation from scenario i to scenario j at time t; T ik (t) represents the transaction association strength between scenario i and intermediate node k at time t; T kj (t) represents the transaction association strength between intermediate node k and scenario j at time t; ω k T represents the weight of the intermediate node k; il (t) represents the sum of the transaction association strengths between scenario i and all intermediate nodes l at time t; n represents the total number of intermediate nodes.

9. The urban financial business end-to-end risk prevention and control management system according to claim 1, characterized in that: The compliance and privacy protection module relies on homomorphic encryption and blockchain notarization technology to protect the privacy of shared data and record compliance audit logs. It connects to standardized data output from multi-source heterogeneous data acquisition modules, encrypting sensitive fields using homomorphic encryption algorithms. A distributed notarization chain is constructed using blockchain to hash and store data access requests, sharing operations, and handling actions on the chain. Combined with a dynamic authorization mechanism, the module verifies the legality of access based on user and enterprise data usage permissions. The encrypted data and operation logs are synchronized across scenarios to the risk profiling, early warning, and handling modules, generating immutable compliance audit records on the blockchain to ensure traceability of the entire data flow.

10. The urban financial business full-chain risk prevention and control management system according to claim 1, characterized in that: The system's adaptive optimization module continuously optimizes parameters and iterates strategies for risk identification models and response strategies using historical risk event data and meta-learning algorithms; it integrates historical risk event data and response results output by the real-time early warning, response, and cross-scenario analysis modules to extract key features and strategy feedback. The weights and rules of the risk identification model and response strategy are dynamically adjusted based on the meta-learning algorithm; the applicability of the optimized model and strategy is verified through simulation testing, and an iterative version is generated.

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