Block chain-based cross-border financial transaction risk real-time prevention and control method and system

Abstract

The invention relates to the technical field of cross-border financial transaction security and block chain application, and discloses a block chain-based cross-border financial transaction risk real-time prevention and control method, which comprises the following operation steps: S1, establishing a multi-chain composite distributed account book architecture, deploying a PBFT + PoA hybrid consensus mechanism, constructing a transaction main chain, a privacy data channel and a supervision side chain, and constructing a block chain; basic operation of transaction metadata storage, sensitive data encryption transmission and supervision data capture channels is guaranteed; s2, establishing a cross-chain identity hub and data fusion system; according to the cross-border financial transaction risk real-time prevention and control method and system based on the block chain, a multi-chain composite distributed account book architecture is established, a PBFT + PoA hybrid consensus mechanism is deployed, and a transaction main chain, a privacy data channel and a supervision side chain are separated, so that the traceability of transaction metadata is ensured, encrypted transmission of sensitive data is realized, and the risk of the cross-border financial transaction is prevented and controlled in real time. And meanwhile, an exclusive data capturing channel is provided for a supervision mechanism.

Description

Technical Field

[0001] This invention relates to the field of cross-border financial transaction security and blockchain application technology, specifically to a method and system for real-time risk prevention and control of cross-border financial transactions based on blockchain. Background Technology

[0002] With the deep integration of economic globalization and digital finance, the scale of cross-border financial transactions continues to expand, and transaction scenarios are becoming increasingly complex, covering diverse business forms such as cross-border trade settlement, cross-border investment, and cross-border payments. However, the risk control challenges faced by cross-border financial transactions are becoming increasingly prominent: On the one hand, cross-border transactions involve multiple countries / regions, multiple financial institutions, and multiple regulatory systems, making the identity verification process for transaction entities cumbersome. The fragmentation of on-chain and off-chain data forms "data silos," resulting in low efficiency and insufficient credibility of identity authentication. On the other hand, there are differences in regulatory rules in different jurisdictions (such as foreign exchange control policies, GDPR privacy regulations, and anti-money laundering (AML) requirements). Traditional prevention and control systems are unable to achieve real-time adaptation and dynamic compliance verification of multiple rules, which can easily lead to compliance risk vulnerabilities.

[0003] Existing cross-border financial risk prevention and control measures have significant shortcomings: risk identification relies on manual screening and static rule configuration, which lags behind in identifying hidden and suspicious transaction patterns such as splitting transactions and circular transfers, making it difficult to achieve real-time early warning; transaction data is stored in scattered internal systems of various institutions, making cross-institutional and cross-chain data sharing difficult, and there are risks of leakage and tampering during data transmission, which not only affects the comprehensiveness of risk analysis but also restricts the realization of penetrating supervision; the handling of disputes over high-risk transactions lacks standardized collaborative mechanisms, multi-entity joint investigations are inefficient, and the process of generating regulatory reports is cumbersome and prone to data inconsistencies. In addition, traditional centralized risk control systems also have inherent defects such as single points of failure and insufficient data credibility, which cannot meet the multiple requirements of cross-border financial transactions for security, efficiency, and compliance. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a blockchain-based method and system for real-time risk prevention and control in cross-border financial transactions. It boasts advantages such as efficient multi-chain collaborative operation, universally applicable identity authentication, precise and flexible compliance adaptation, real-time intelligent risk identification, controllable data security, convenient regulatory penetration, and efficient collaborative dispute resolution. This invention solves core problems in existing cross-border financial transaction risk prevention and control systems, including complex and inefficient identity verification, fragmented on-chain and off-chain data, difficulties in compliance adaptation across multiple legal jurisdictions, delayed and inaccurate risk identification, imbalance between data sharing and privacy security, insufficient cross-institutional collaborative handling, and untimely synchronization of regulatory data.

[0005] (II) Technical Solution To achieve the aforementioned goals of efficient multi-chain collaborative operation, universally applicable identity authentication, precise and flexible compliance adaptation, real-time intelligent risk identification, controllable data security, convenient regulatory penetration, and efficient collaborative dispute resolution, this invention provides the following technical solution: a blockchain-based method for real-time risk prevention and control in cross-border financial transactions, with the following operation steps: Step S1: Build a multi-chain composite distributed ledger architecture, deploy a PBFT+PoA hybrid consensus mechanism, and construct a transaction main chain, a privacy data channel, and a regulatory side chain to ensure the basic operation of transaction metadata storage, encrypted transmission of sensitive data, and regulatory data capture channels; Step S2: Establish a cross-chain identity hub and data fusion system. Connect to external authoritative data sources through decentralized oracles to complete verification and on-chain verification. Construct decentralized identity (DID) based on verifiable credentials (VC) to achieve on-chain and off-chain data association and one-time identity authentication that is universal across the entire network. Step S3: Initiate the initialization of the multi-dimensional risk smart contract, deploy the KYC / AML contract, transaction behavior analysis contract, and compliance consistency contract, and configure the basic parameters to adapt to the regulatory rules of multiple jurisdictions; Step S4: Perform pre-transaction identity and compliance verification. Verify the identity information and blacklist status of the transacting parties through KYC / AML contracts, and use compliance consistency contracts to match local regulatory rules (such as foreign exchange controls, GDPR, etc.) to complete the initial compliance screening. Step S5: Collect transaction data in real time and conduct risk analysis. Identify suspicious patterns such as splitting transactions and circular transfers by analyzing transaction behavior contracts. Combine real-time transaction data, historical credit and external intelligence, and generate a dynamic risk score of 0-100 by the dynamic risk scoring engine. Step S6: Trigger a tiered response based on the risk score. Blue level (prompt) risk only pushes a warning message, yellow level (warning) initiates key monitoring, orange level (blocked pending confirmation) suspends the transaction and routes it to the compliance officer's workbench, and red level (automatic rejection) directly blocks the transaction. Step S7: Handle high-risk transaction disputes, conduct joint investigations, synchronize cross-chain data to the internal chains of financial institutions and global regulatory chains through cross-chain bridge technology, and use privacy computing technologies such as homomorphic encryption to ensure data security. At the same time, the alliance governance committee will coordinate the handling of high-risk transaction disputes and conduct joint investigations. Step S8: Risk control results and regulatory report. Output the risk control results and regulatory report, push the global risk view and early warning information to the risk control dashboard of financial institutions, synchronize penetrating regulatory data to the regulatory panoramic window, automatically generate standardized regulatory reports (SAR) to directly report to the regulatory sidechain, and provide interactive services such as transaction query and risk warning to the client.

[0006] A blockchain-based real-time risk prevention and control system for cross-border financial transactions includes a distributed ledger and consensus layer, a data fusion and identity layer, an intelligent risk control and response layer, a cross-chain collaboration and data security layer, a governance and incentive layer, and an application and interaction layer.

[0007] Furthermore, the distributed ledger and consensus layer includes a multi-chain composite architecture and a hybrid consensus mechanism.

[0008] Furthermore, the data fusion and identity layer includes on-chain and off-chain data fusion and a cross-chain identity hub.

[0009] Furthermore, the intelligent risk control and response layer includes multi-dimensional risk smart contracts, a dynamic risk scoring engine, and a collaborative handling mechanism.

[0010] Furthermore, the cross-chain collaboration and data security layer includes cross-chain data synchronization and end-to-end data security.

[0011] Furthermore, the governance and incentive layer includes an alliance governance committee, fine-grained permission management, and a token incentive system.

[0012] Furthermore, the application and interaction layer includes a risk control dashboard for financial institutions, a panoramic regulatory view, client applications, and developer support.

[0013] Furthermore, the intelligent risk control and response layer also includes an adaptive stress testing and scenario simulation engine. This engine utilizes historical data and external intelligence to automatically generate virtual stress test scenarios periodically or when abnormal market fluctuations are detected (such as sudden financial controls in a country or systemic risks in an industry).

[0014] Furthermore, the blockchain-based real-time risk control system for cross-border financial transactions also includes an anonymous credit aggregation layer based on zero-knowledge proofs. When a trading entity (such as a small or medium-sized enterprise) needs to transact with different financial institutions, it does not need to expose its complete transaction history to each institution. Instead, this layer allows the entity to use zero-knowledge proof technology to prove specific facts such as "my credit score is higher than XX points" or "I have not defaulted in the past year" to verification nodes on different chains, without revealing the specific counterparty, amount, and time of the transaction.

[0015] (III) Beneficial Effects Compared with existing technologies, this invention provides a method and system for real-time risk prevention and control of cross-border financial transactions based on blockchain, which has the following beneficial effects: 1. This blockchain-based method and system for real-time risk prevention and control of cross-border financial transactions, by building a multi-chain composite distributed ledger architecture and deploying a PBFT+PoA hybrid consensus mechanism, separates the main transaction chain, privacy data channel and regulatory side chain, which not only ensures the traceability of transaction metadata, but also realizes the encrypted transmission of sensitive data, and provides a dedicated data capture channel for regulatory agencies.

[0016] 2. This blockchain-based method and system for real-time risk prevention and control in cross-border financial transactions establishes a cross-chain identity hub and data fusion system. By combining decentralized oracles and decentralized identity (DID) technology, it achieves accurate correlation between on-chain and off-chain data and one-time identity authentication that is universally applicable across the entire network. This significantly simplifies the identity verification process for multiple entities and platforms in cross-border transactions, solves the problem of "data silos," and enhances the credibility of identity authentication and data fusion, providing a solid foundation for risk prevention and control.

[0017] 3. This blockchain-based method and system for real-time risk prevention and control in cross-border financial transactions, through the deployment of multi-dimensional risk smart contracts and a dynamic risk scoring engine, achieves automatic compliance screening before transactions, real-time identification of suspicious patterns during transactions, and dynamic risk quantification assessment from 0 to 100 points, triggering a tiered response mechanism. Compared with traditional manual screening and static rule-based prevention and control, it significantly improves the accuracy and timeliness of risk identification, effectively prevents hidden risks such as splitting transactions and illegal cross-border transfers, and reduces compliance operation costs.

[0018] 4. This blockchain-based method and system for real-time risk prevention and control of cross-border financial transactions, through the combination of cross-chain collaborative technology, privacy computing technology and alliance governance mechanism, not only achieves secure synchronization and joint investigation of transaction data across institutions and regions, ensuring privacy and security during data sharing, but also establishes an efficient collaborative handling and regulatory docking channel through standardized automatic reporting of regulatory reports and synchronization of penetrating regulatory data, improving the efficiency of handling high-risk transaction disputes, while meeting the regulatory compliance requirements of multiple jurisdictions, thus achieving the dual goals of risk prevention and control and regulatory penetration.

[0019] 5. This blockchain-based method and system for real-time risk prevention and control in cross-border financial transactions, through adaptive stress testing and scenario simulation engines, enables the system not only to identify known risks but also to proactively quantify potential systemic shocks, making risk control decisions more robust. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the cross-border financial transaction risk real-time prevention and control method of the present invention; Figure 2 This is a schematic diagram of the real-time risk prevention and control system for cross-border financial transactions of the present invention; Figure 3This is a flowchart illustrating the operation of the real-time risk prevention and control system for cross-border financial transactions according to the present invention. Detailed Implementation

[0021] 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.

[0022] Please see Figure 1-3 A blockchain-based method for real-time risk prevention and control in cross-border financial transactions, with the following steps: Step S1: Build a multi-chain composite distributed ledger architecture, deploy a PBFT+PoA hybrid consensus mechanism, and construct a transaction main chain, a privacy data channel, and a regulatory side chain to ensure the basic operation of transaction metadata storage, encrypted transmission of sensitive data, and regulatory data capture channels; Step S2: Establish a cross-chain identity hub and data fusion system. Connect to external authoritative data sources through decentralized oracles to complete verification and on-chain verification. Construct decentralized identity (DID) based on verifiable credentials (VC) to achieve on-chain and off-chain data association and one-time identity authentication that is universal across the entire network. Step S3: Initiate the initialization of the multi-dimensional risk smart contract, deploy the KYC / AML contract, transaction behavior analysis contract, and compliance consistency contract, and configure the basic parameters to adapt to the regulatory rules of multiple jurisdictions; Step S4: Perform pre-transaction identity and compliance verification. Verify the identity information and blacklist status of the transacting parties through KYC / AML contracts, and use compliance consistency contracts to match local regulatory rules (such as foreign exchange controls, GDPR, etc.) to complete the initial compliance screening. Step S5: Collect transaction data in real time and conduct risk analysis. Identify suspicious patterns such as splitting transactions and circular transfers by analyzing transaction behavior contracts. Combine real-time transaction data, historical credit and external intelligence, and generate a dynamic risk score of 0-100 by the dynamic risk scoring engine. Step S6: Trigger a tiered response based on the risk score. Blue level (prompt) risk only pushes a warning message, yellow level (warning) initiates key monitoring, orange level (blocked pending confirmation) suspends the transaction and routes it to the compliance officer's workbench, and red level (automatic rejection) directly blocks the transaction. Step S7: Handle high-risk transaction disputes, conduct joint investigations, synchronize cross-chain data to the internal chains of financial institutions and global regulatory chains through cross-chain bridge technology, and use privacy computing technologies such as homomorphic encryption to ensure data security. At the same time, the alliance governance committee will coordinate the handling of high-risk transaction disputes and conduct joint investigations. Step S8: Risk control results and regulatory report. Output the risk control results and regulatory report, push the global risk view and early warning information to the risk control dashboard of financial institutions, synchronize penetrating regulatory data to the regulatory panoramic window, automatically generate standardized regulatory reports (SAR) to directly report to the regulatory sidechain, and provide interactive services such as transaction query and risk warning to the client.

[0023] A blockchain-based real-time risk prevention and control system for cross-border financial transactions includes a distributed ledger and consensus layer, a data fusion and identity layer, an intelligent risk control and response layer, a cross-chain collaboration and data security layer, a governance and incentive layer, and an application and interaction layer.

[0024] In the implementation of this case, the distributed ledger and consensus layer includes a multi-chain composite architecture and a hybrid consensus mechanism; The multi-chain composite architecture specifically constructs three core links: the transaction main chain, the privacy data channel, and the regulatory side chain. The transaction main chain is responsible for storing the core metadata of cross-border financial transactions, ensuring the public traceability of transaction records; the privacy data channel adopts an encrypted transmission protocol to specifically carry sensitive information in the transaction process and prevent data leakage; the regulatory side chain reserves a dedicated interface to support regulatory agencies in efficiently capturing the data required for compliance, achieving parallel operation of regulation and transactions without conflict. The three links work together to form a stable multi-chain operating system. The hybrid consensus mechanism adopts a PBFT+PoA fusion model. PBFT (Practical Byzantine Fault Tolerance) ensures the fault tolerance and consistency of the consensus process, and can maintain the stable operation of the system in the event of some node anomalies, meeting the high reliability requirements of cross-border transactions. PoA (Proof of Authority) significantly improves transaction confirmation efficiency by authorizing trusted nodes to participate in the consensus, balancing the dual needs of security and timeliness in cross-border financial scenarios.

[0025] Among them, the main transaction chain is responsible for storing the core metadata of cross-border financial transactions, ensuring the public traceability of transaction records; The privacy data channel uses an encrypted transmission protocol and is specifically designed to carry sensitive information during the transaction process to prevent data leakage. The regulatory sidechain reserves a dedicated interface to support regulatory agencies in efficiently capturing the data required for compliance, enabling regulation and transactions to proceed in parallel without conflict. The three-layer link works together to form a stable multi-chain operation system. The hybrid consensus mechanism adopts a PBFT+PoA fusion model, in which: PBFT (Practical Byzantine Fault Tolerance) ensures the fault tolerance and consistency of the consensus process, and can maintain the stable operation of the system in the event of some node failures, meeting the high reliability requirements of cross-border transactions. PoA (Proof of Authority) significantly improves transaction confirmation efficiency by authorizing trusted nodes to participate in consensus, balancing the dual requirements of security and timeliness in cross-border financial scenarios. By organically combining the aforementioned multi-chain architecture and consensus mechanism, the advantages of data layering and consensus mechanism are complemented, ensuring the traceability and transparency of transaction records, as well as the secure transmission of sensitive information and the efficient acquisition of regulatory data. At the same time, by integrating the consensus mechanism, a balance is achieved between fault tolerance and processing efficiency, providing a stable, secure, and efficient technical foundation for real-time risk prevention and control of cross-border financial transactions.

[0026] In the implementation of the case, data fusion and identity layer include on-chain and off-chain data fusion and cross-chain identity hub; The integration of on-chain and off-chain data connects to authoritative external data sources (such as customs trade data, central bank foreign exchange data, international anti-money laundering databases, etc.) through decentralized oracles. After verifying the external data, it is uploaded to the chain, realizing the accurate correlation between on-chain transaction data and off-chain real-world scenario data, breaking down data silos and providing comprehensive data support for risk analysis. The cross-chain identity hub builds decentralized identity (DID) based on verifiable credentials (VC), assigning a unique and tamper-proof identity identifier to each transaction entity, enabling one-time identity authentication to be universally applicable across the network, solving the problem of identity verification for multiple entities and across platforms in cross-border transactions, while ensuring the privacy and compliance of identity information.

[0027] Among them, the on-chain and off-chain data fusion connects with external authoritative data sources through decentralized oracles, verifies the external data and completes the on-chain data, realizes the accurate correlation between on-chain transaction data and off-chain real-world scenario data, breaks down data silos, and provides comprehensive data support for risk analysis; Specifically, it involves accessing external trusted data sources such as customs trade data, central bank foreign exchange data, and international anti-money laundering databases through decentralized oracles to verify the authenticity and integrity of the data, and writing the verification results into on-chain smart contracts to achieve reliable mapping and real-time synchronization of on-chain and off-chain data. The cross-chain identity hub builds decentralized identity (DID) based on verifiable credentials (VC), assigning a unique and tamper-proof identity identifier to each transaction entity, enabling one-time identity authentication to be universally applicable across the entire network, and solving the problem of identity verification for multiple entities and across platforms in cross-border transactions; Specifically, it involves issuing digital identity credentials to transaction entities through a W3C-standard verifiable credential (VC) system that are certified by cryptographic signatures and timestamps. These credentials are then uniformly registered and resolved in a cross-chain identity hub, enabling mutual recognition and trusted transfer of identities across different chains and systems after a single authentication, thus ensuring the privacy, security, and compliance of identity information. Through the collaborative mechanism of on-chain and off-chain data fusion and cross-chain identity hub, the comprehensive integration of transaction data and the reliable flow of identity information have been achieved. This not only solves the pain points of data fragmentation and duplicate identity authentication in cross-border transactions, but also improves the authenticity of data sources and the interoperability of identity systems through decentralized oracles and standard identity protocols, providing a unified and reliable data and identity foundation for subsequent intelligent risk control and collaborative supervision.

[0028] In the implementation of the case, the intelligent risk control and response layer includes multi-dimensional risk smart contracts, a dynamic risk scoring engine, and a collaborative handling mechanism; The multidimensional risk smart contract includes a KYC / AML contract, a transaction behavior analysis contract, and a compliance consistency contract. The KYC / AML contract is used to verify the authenticity of the transacting parties' identities and their blacklist status. The transaction behavior analysis contract is specifically designed to identify suspicious transaction patterns such as splitting transactions and circular transfers. The compliance consistency contract has a built-in multi-jurisdictional regulatory rule library and automatically matches localized compliance requirements such as foreign exchange controls and GDPR. The three types of contracts work together to complete the execution of risk control logic throughout the entire process. The dynamic risk scoring engine integrates real-time transaction data, historical credit records of trading entities, and external risk intelligence. It uses a preset algorithm model to quantitatively assess transaction risks and generate a dynamic risk score of 0-100. The score is updated in real time as the transaction data and external environment change, providing a precise basis for risk response. The collaborative handling mechanism triggers tiered responses based on dynamic risk scores. Blue-level (warning) risks only push warning information to relevant parties and do not affect transaction execution; yellow-level (warning) risks activate key monitoring mode and continuously track the subsequent dynamics of the transaction; orange-level (interception pending confirmation) risks suspend the transaction and route it to the compliance officer's workbench, pending manual review to decide whether to release it; red-level (automatic rejection) risks directly block the transaction, forming a closed-loop process covering different risk levels.

[0029] Among them, the multi-dimensional risk smart contract includes KYC / AML contract, transaction behavior analysis contract and compliance consistency contract. The KYC / AML contract is used to verify the authenticity of the transaction parties' identities and blacklist status. The transaction behavior analysis contract is specifically designed to identify suspicious transaction patterns such as splitting transactions and circular transfers. The compliance consistency contract has a built-in multi-jurisdictional regulatory rule library and automatically matches localized compliance requirements such as foreign exchange control and GDPR. The three types of contracts work together to complete the execution of risk control logic throughout the entire process. Specifically, by using smart contracts to automatically perform pre-transaction identity verification, in-transaction behavior pattern recognition, and compliance rule matching, the traditional risk control process, which involves manual participation and decentralized rules, is automated and systematized, enabling the coded deployment and trusted on-chain execution of risk control logic; The dynamic risk scoring engine integrates real-time transaction data, historical credit records of trading entities, and external risk intelligence. It uses a preset algorithm model to quantitatively assess transaction risks and generate a dynamic risk score of 0 to 100. The score is updated in real time as the transaction data and external environment change, providing a precise basis for risk response. Specifically, by combining machine learning models with a rule engine, the system analyzes transaction behavior characteristics, historical records, and external intelligence in real time, dynamically calculates risk scores, and feeds the scoring results back to the smart contract in real time, supporting immediate response and continuous tracking of risk status. The collaborative handling mechanism triggers tiered responses based on dynamic risk scores. Blue-level (warning) risks only push warning information to relevant parties and do not affect transaction execution; yellow-level (warning) risks activate key monitoring mode and continuously track subsequent transaction dynamics; orange-level (interception pending confirmation) risks suspend transactions and route them to the compliance workbench, pending manual review to decide whether to release them; red-level (automatic rejection) risks directly block transactions, forming a closed-loop process covering different risk levels. Specifically, it uses a preset response rule engine to automatically trigger different levels of handling processes based on dynamic risk scores, and supports manual intervention and multi-entity collaborative handling to achieve automated and manual collaborative risk control closed-loop management. By organically integrating multi-dimensional smart contracts, dynamic scoring engines, and tiered response mechanisms, a fully automated risk control system has been achieved, encompassing risk identification, quantitative assessment, and tiered disposal. This system not only enhances the real-time performance and accuracy of risk control but also retains necessary space for manual review and collaborative processing, providing intelligent and flexible operational support for risk prevention and control in cross-border financial transactions.

[0030] In the implementation of the case, the cross-chain collaboration and data security layer includes cross-chain data synchronization and end-to-end data security; Cross-chain data synchronization establishes communication links between different blockchain networks through cross-chain bridge technology, enabling real-time synchronization of transaction data to the internal chains of financial institutions and global regulatory chains. This ensures that internal risk control analysis of financial institutions and supervision and inspection by regulatory agencies can obtain complete and consistent data, supporting collaborative risk control across institutions and regions. End-to-end data security employs privacy-preserving computing technologies such as homomorphic encryption and encrypted signatures to encrypt and protect transaction data throughout its entire lifecycle, from collection and transmission to storage and use, preventing unauthorized theft or tampering while ensuring data availability. Simultaneously, a data access control system is established, granting access to view and operate data only to authorized entities, ensuring data security and compliant use.

[0031] Among them, cross-chain data synchronization establishes communication links between different blockchain networks through cross-chain bridge technology, enabling real-time synchronization of transaction data to the internal chains of financial institutions and global regulatory chains. This ensures that internal risk analysis of financial institutions and supervision and inspection by regulatory agencies can obtain complete and consistent data, supporting collaborative risk control across institutions and regions. Specifically, by building a cross-chain bridging system based on a relay chain or side chain architecture, it supports data mapping and state synchronization between the transaction main chain, privacy channel and regulatory side chain, realizes the cross-chain trusted transmission of transaction metadata, compliance tags and risk scores, and adopts atomic swap and state verification mechanisms to ensure the consistency and integrity of cross-chain transactions. End-to-end data security employs privacy-preserving computing technologies such as homomorphic encryption and encrypted signatures to encrypt and protect transaction data throughout its entire lifecycle, from collection and transmission to storage and use, preventing unauthorized theft or tampering while ensuring data availability. Simultaneously, a data access control system is established, granting access to view and operate data only to authorized entities to ensure data security and compliant use. Specifically, it involves: implementing risk calculation and compliance comparison in ciphertext state with the support of homomorphic encryption; verifying data authenticity without disclosing the original content by combining zero-knowledge proof technology; and building a fine-grained access control mechanism based on attribute-based encryption and smart contracts to achieve hierarchical authorization and auditing of data according to role, scenario, and sensitivity. By deeply integrating cross-chain data synchronization mechanisms with end-to-end privacy protection technologies, efficient collaboration and consistency of data in a multi-chain environment are achieved. Advanced cryptographic methods ensure the confidentiality and integrity of data during sharing, computation, and storage, thus building a secure, reliable, and efficient collaborative data infrastructure for risk prevention and compliance cooperation in cross-border financial transactions.

[0032] In the implementation of this case, the governance and incentive layer includes the alliance governance committee, fine-grained permission management, and a token incentive system; The alliance governance committee is composed of core participants such as financial institutions, regulatory agencies, and technology service providers. It is responsible for formulating system operation rules, handling high-risk transaction disputes, conducting joint investigations, coordinating the interests of all parties, and ensuring the fairness and compliance of the system operation. Fine-grained access control divides different access levels according to the roles of participants, business scenarios, and data sensitivity, and configures precise operation permissions for each entity, clearly defining the permission boundaries for operations such as data access, transaction approval, and rule modification, and preventing risks caused by unauthorized operations; The token incentive system sets up a compliance contribution incentive mechanism, which rewards entities that actively participate in system maintenance, promptly report risks and hidden dangers, and optimize regulatory rules with tokens. At the same time, the tokens are linked to system usage rights and decision-making participation rights to incentivize participants to actively comply with system rules and improve the overall operational efficiency and compliance level of the system.

[0033] The alliance governance committee is composed of core participants such as financial institutions, regulatory agencies, and technology service providers. It is responsible for formulating system operation rules, handling high-risk transaction disputes, conducting joint investigations, coordinating the interests of all parties, and ensuring the fairness and compliance of the system operation. Specifically, by establishing a multi-party alliance governance committee, a transparent and open decision-making process and dispute resolution mechanism are established. Committee members formulate and revise system rules through a combination of on-chain voting and off-chain meetings, and conduct joint review and handling of high-risk transactions to ensure that the governance mechanism is fair, efficient and auditable. Fine-grained access control divides different access levels according to the roles of participants, business scenarios, and data sensitivity, and configures precise operation permissions for each entity, clearly defining the permission boundaries for operations such as data access, transaction approval, and rule modification, and preventing risks caused by unauthorized operations; Specifically, by using role-based access control and attribute encryption technology, differentiated data viewing and operation permissions are set for different participants (such as financial institutions, compliance officers, regulatory agencies, and ordinary users), and permission changes and access behaviors are recorded on the blockchain to achieve dynamic and adjustable permission management and full traceability. The token incentive system sets up a compliance contribution incentive mechanism, which rewards entities that actively participate in system maintenance, promptly report risks and hidden dangers, and optimize regulatory rules with tokens; at the same time, the tokens are linked to system usage rights and decision-making participation rights, which incentivizes participants to actively comply with system rules and improve the overall operational efficiency and compliance level of the system. Specifically, token rewards are automatically distributed through smart contracts. Rewards are given for activities such as node operation and maintenance, reporting of risk events, and submission of rule suggestions. Tokens can be used to pay system service fees, participate in governance voting, and obtain priority services, forming a virtuous cycle mechanism of "contribution-incentive-governance". By organically combining alliance governance, access control, and token incentives, a governance system with multi-party participation, clear rights and responsibilities, and compatible incentives has been constructed. This system not only ensures the compliant operation and controllable risks of the system, but also stimulates the enthusiasm and willingness to cooperate of all participants, providing institutional guarantees for the long-term stability and continuous optimization of the system.

[0034] In the implementation of the case, the application and interaction layer includes the risk control dashboard of financial institutions, the regulatory panoramic window, client applications, and developer support; The financial institution risk control dashboard presents a global risk view through a visual interface, integrating various early warning information, transaction risk scores, suspicious transaction statistics and other core data, enabling financial institution risk control personnel to monitor the risk status of cross-border transactions in real time, quickly locate high-risk links and take appropriate measures. The regulatory panoramic view provides synchronized and penetrating regulatory data, enabling regulatory agencies to view the entire process of cross-border transactions in real time. The system automatically generates standardized regulatory reports (SAR) and reports directly to the regulatory sidechain, simplifying the regulatory process and improving regulatory efficiency and accuracy. The client application provides interactive services such as transaction inquiry, risk warning, and compliance consultation for trading entities. Users can view transaction progress, risk level and relevant compliance requirements in real time, improving transaction transparency and user experience. Developer support provides comprehensive technical documentation, API interfaces, and development toolkits, lowering the technical barriers for third-party developers to participate in system function expansion and application adaptation, supporting continuous iterative optimization of system functions, and enhancing the system's scalability and adaptability.

[0035] Among them, the financial institution risk control dashboard presents a global risk view with a visual interface, integrates various early warning information, transaction risk scores, suspicious transaction statistics and other core data, and supports financial institution risk control personnel to monitor the risk status of cross-border transactions in real time, quickly locate high-risk links and take disposal measures. Specifically, by developing a web-based visual dashboard platform, connecting to the real-time data interface of the intelligent risk control system, the platform dynamically displays risk distribution, abnormal transaction trends, and early warning events in the form of charts, dashboards, heat maps, etc., supporting risk control personnel to perform multi-dimensional screening, drill-down analysis, and one-click processing. The regulatory panoramic view provides synchronized and penetrating regulatory data, enabling regulatory agencies to view the entire process of cross-border transactions in real time. The system automatically generates standardized regulatory reports (SAR) and reports directly to the regulatory sidechain, simplifying the regulatory process and improving regulatory efficiency and accuracy. Specifically, by providing regulatory agencies with dedicated Web and API access channels, the system enables penetrating queries of transaction data across the entire chain (including identity, behavior, compliance status, etc.). The system automatically generates SAR reports based on preset templates and achieves encrypted on-chain reporting and real-time delivery of the reports through the regulatory side's connection interface. The client application provides interactive services such as transaction inquiry, risk warning, and compliance consultation for trading entities. Users can view transaction progress, risk level and relevant compliance requirements in real time, improving transaction transparency and user experience. Specifically, by developing mobile and web client applications, integrating functions such as DID identity login, transaction status query, risk warning push, and compliance knowledge base, we provide users with one-stop cross-border transaction services and risk self-management capabilities. Developer support provides comprehensive technical documentation, API interfaces, and development toolkits to lower the technical barriers for third-party developers to participate in system function expansion and application adaptation, support continuous iterative optimization of system functions, and enhance the system's scalability and adaptability. Specifically, by open-sourcing some core components and interface protocols, we provide detailed development guidelines, SDKs, sandbox environments, and test chain support to encourage ecosystem developers to develop customized risk control modules, data service applications, or industry solutions based on the system architecture. By constructing a multi-layered collaborative application and interaction system, it has achieved comprehensive coverage from institutional risk control and regulatory penetration to user services and ecosystem expansion. This not only meets the differentiated functional needs of different participants, but also promotes the continuous evolution of the system and the co-construction of the ecosystem through open interfaces and developer support, forming a technically feasible, business-usable, and ecosystem-sustainable cross-border financial risk control service platform.

[0036] In the implementation of the case, the adaptive stress testing and scenario simulation engine will use historical data and external intelligence to automatically generate virtual stress test scenarios on a regular basis or when abnormal market fluctuations are detected (such as a country suddenly imposing financial regulations or a systemic risk in an industry).

[0037] For example, when the system detects a surge in transaction volume related to Country A, the engine automatically simulates a scenario where "Country A suddenly tightens foreign exchange controls," and uses historical credit and transaction pattern data to conduct a thorough stress test on all in-transit and pending transactions involving Country A. The test results are added to the dynamic risk score in the form of a "stress coefficient," serving as another important basis for tiered response. This allows the system not only to identify known risks but also to proactively quantify potential systemic shocks, making risk control decisions more robust.

[0038] In the implementation case, the anonymous credit aggregation layer based on zero-knowledge proofs operates as a separate logical layer. When a trading entity (such as a small or medium-sized enterprise) needs to transact with different financial institutions, it does not need to expose its complete transaction history to each institution. Instead, this layer allows the entity to use zero-knowledge proof technology to prove specific facts such as "my credit score is higher than XX points" or "I have not defaulted in the past year" to verification nodes on different chains, without revealing the specific counterparty, amount, or time of the transaction.

[0039] This layer aggregates encrypted credit data authorized by users from different chains (transaction main chains, internal chains of financial institutions) to form a global, anonymous credit view. When scoring, the risk control contract can directly initiate zero-knowledge queries to this aggregation layer to obtain definitive proof of the creditworthiness of the transaction entity. This greatly enhances the robustness of risk control because it is based on more comprehensive "invisible" credit data, while simultaneously elevating privacy protection to a new level.

[0040] In summary, this blockchain-based method and system for real-time risk prevention and control in cross-border financial transactions, by building a multi-chain composite distributed ledger architecture and deploying a PBFT+PoA hybrid consensus mechanism, separates the main transaction chain, privacy data channel, and regulatory sidechain. This ensures the traceability of transaction metadata, achieves encrypted transmission of sensitive data, and provides regulatory agencies with a dedicated data capture channel. By establishing a cross-chain identity hub and data fusion system, combined with decentralized oracles and decentralized identity (DID) technology, it achieves precise correlation between on-chain and off-chain data and one-time identity authentication that is universally applicable across the entire network. This significantly simplifies the identity verification process for multiple entities and platforms in cross-border transactions, solves the "data silo" problem, and enhances the credibility of identity authentication and data fusion, providing a solid foundation for risk prevention and control.

[0041] Furthermore, by deploying multi-dimensional risk smart contracts and a dynamic risk scoring engine, it achieves automatic compliance screening before transactions, real-time identification of suspicious patterns during transactions, and dynamic risk quantification assessment from 0 to 100 points, triggering a tiered response mechanism. Compared to traditional manual screening and static rule-based prevention, this significantly improves the accuracy and timeliness of risk identification, effectively preventing hidden risks such as splitting transactions and illegal cross-border transfers, and reducing compliance operation costs. Through the combination of cross-chain collaboration technology, privacy computing technology, and alliance governance mechanisms, it achieves secure synchronization and joint investigation of transaction data across institutions and regions, ensuring privacy and security during data sharing. It also establishes an efficient collaborative handling and regulatory connection channel through standardized automatic reporting of regulatory reports and transparent regulatory data synchronization, improving the efficiency of handling high-risk transaction disputes. At the same time, it meets the regulatory compliance requirements of multiple jurisdictions, achieving the dual goals of risk prevention and regulatory penetration.

[0042] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time risk prevention and control of cross-border financial transactions based on blockchain, characterized in that: The operation steps are as follows: Step S1: Build a multi-chain composite distributed ledger architecture, deploy a PBFT+PoA hybrid consensus mechanism, and construct a transaction main chain, a privacy data channel, and a regulatory side chain to ensure the basic operation of transaction metadata storage, encrypted transmission of sensitive data, and regulatory data capture channels; Step S2: Establish a cross-chain identity hub and data fusion system. Connect to external authoritative data sources through decentralized oracles to complete verification and on-chain verification. Construct decentralized identity (DID) based on verifiable credentials (VC) to achieve on-chain and off-chain data association and one-time identity authentication that is universal across the entire network. Step S3: Initiate the initialization of the multi-dimensional risk smart contract, deploy the KYC / AML contract, transaction behavior analysis contract, and compliance consistency contract, and configure the basic parameters to adapt to the regulatory rules of multiple jurisdictions; Step S4: Perform pre-transaction identity and compliance verification. Verify the identity information and blacklist status of the transacting parties through KYC / AML contracts, and use compliance consistency contracts to match local regulatory rules (such as foreign exchange controls, GDPR, etc.) to complete the initial compliance screening. Step S5: Collect transaction data in real time and conduct risk analysis. Identify suspicious patterns such as splitting transactions and circular transfers by analyzing transaction behavior contracts. Combine real-time transaction data, historical credit and external intelligence, and generate a dynamic risk score of 0-100 by the dynamic risk scoring engine. Step S6: Trigger a tiered response based on the risk score. Blue level (prompt) risk only pushes a warning message, yellow level (warning) initiates key monitoring, orange level (blocked pending confirmation) suspends the transaction and routes it to the compliance officer's workbench, and red level (automatic rejection) directly blocks the transaction. Step S7: Handle high-risk transaction disputes, conduct joint investigations, synchronize cross-chain data to the internal chains of financial institutions and global regulatory chains through cross-chain bridge technology, and use privacy computing technologies such as homomorphic encryption to ensure data security. At the same time, the alliance governance committee will coordinate the handling of high-risk transaction disputes and conduct joint investigations. Step S8: Risk control results and regulatory report. Output the risk control results and regulatory report, push the global risk view and early warning information to the risk control dashboard of financial institutions, synchronize penetrating regulatory data to the regulatory panoramic window, automatically generate standardized regulatory reports (SAR) to directly report to the regulatory sidechain, and provide interactive services such as transaction query and risk warning to the client.

2. A blockchain-based real-time risk prevention and control system for cross-border financial transactions, comprising a real-time risk prevention and control system for cross-border financial transactions, characterized in that: The real-time risk prevention and control system for cross-border financial transactions includes a distributed ledger and consensus layer, a data fusion and identity layer, an intelligent risk control and response layer, a cross-chain collaboration and data security layer, a governance and incentive layer, and an application and interaction layer.

3. The real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 2, characterized in that: The distributed ledger and consensus layer includes a multi-chain composite architecture and a hybrid consensus mechanism.

4. The real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 2, characterized in that: The data fusion and identity layer includes on-chain and off-chain data fusion and a cross-chain identity hub.

5. A real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 2, characterized in that: The intelligent risk control and response layer includes multi-dimensional risk smart contracts, a dynamic risk scoring engine, and a collaborative handling mechanism.

6. A real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 2, characterized in that: The cross-chain collaboration and data security layer includes cross-chain data synchronization and end-to-end data security.

7. A real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 2, characterized in that: The governance and incentive layer includes an alliance governance committee, fine-grained permission management, and a token incentive system.

8. A real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 2, characterized in that: The application and interaction layer includes a risk control dashboard for financial institutions, a panoramic regulatory view, client applications, and developer support.

9. A real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 5, characterized in that: The intelligent risk control and response layer also includes an adaptive stress testing and scenario simulation engine. This engine uses historical data and external intelligence to automatically generate virtual stress test scenarios periodically or when abnormal market fluctuations are detected (such as sudden financial controls in a country or systemic risks in an industry).

10. A real-time risk prevention and control system for cross-border financial transactions based on blockchain according to claim 2, characterized in that: It also includes an anonymous credit aggregation layer based on zero-knowledge proofs. When a trading entity (such as a small or medium-sized enterprise) needs to transact with different financial institutions, it does not need to expose its complete transaction history to each institution. Instead, this layer allows the entity to prove specific facts such as "my credit score is higher than XX points" or "I have not defaulted in the past year" to verification nodes on different chains through zero-knowledge proof technology, without revealing the specific counterparty, amount, and time of the transaction.

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