Data processing method and device, equipment, medium and product

Abstract

The invention discloses a data processing method, device and equipment, a medium and a product, relates to the field of data processing and artificial intelligence, and can be applied to the field of financial science and technology. The method comprises the steps of obtaining target transaction data of a target transaction; wherein the target transaction is a cross-regional transaction; performing risk monitoring on the target transaction data based on a risk monitoring mode to obtain a risk monitoring result; wherein the risk monitoring mode comprises at least one of a geographical knowledge graph, a risk processing rule base and a risk path monitoring model; and generating a risk monitoring report for the target transaction according to the risk monitoring result. According to the technical scheme, the accuracy of transaction risk monitoring can be improved.

Description

Technical Field

[0001] This invention relates to the fields of data processing and artificial intelligence, and can be used in the field of financial technology. In particular, it relates to a data processing method, apparatus, device, medium, and product. Background Technology

[0002] With the continuous development of financial markets, transaction methods and channels are becoming increasingly diversified, leading to a wide variety and complexity of risks faced by banks and their customers. On the one hand, financial transaction data is massive, diverse in type, and low in value density, exhibiting high time sensitivity and encompassing real-time dynamics such as market price changes, transaction volume, and information on buyers and sellers. This makes traditional risk monitoring methods relying on manual experience and historical data analysis insufficient. On the other hand, various regulatory documents have set forth clear requirements for risk prevention by financial institutions. In particular, current cross-regional inspections, such as those in cross-border transactions, face bottlenecks including: limitations of static rules leading to misjudgments or omissions of compliance risks; a lack of automated analysis capabilities for external knowledge data such as real-time news and geopolitical events; inability to identify situations where cross-border remittances may circumvent scrutiny by using intermediary countries or agent banks; and the inefficiency of manual review for abnormal transactions, making it difficult to handle high-frequency trading scenarios. Therefore, an effective method for monitoring and identifying transaction risks is urgently needed. Summary of the Invention

[0003] This invention provides a data processing method, apparatus, device, medium, and product to improve the accuracy of transaction risk monitoring.

[0004] According to one aspect of the present invention, a data processing method is provided, comprising:

[0005] Obtain the target transaction data; where the target transaction is a cross-regional transaction;

[0006] Risk monitoring is performed on the target transaction data based on risk monitoring methods to obtain risk monitoring results; wherein, the risk monitoring methods include at least one of geopolitical knowledge graph, risk processing rule base and risk path monitoring model;

[0007] A risk monitoring report is generated for the target transaction based on the risk monitoring results.

[0008] According to another aspect of the present invention, a data processing apparatus is provided, comprising:

[0009] The transaction data acquisition module is used to acquire the target transaction data of the target transaction; wherein the target transaction is a cross-regional transaction.

[0010] The risk monitoring module is used to perform risk monitoring on the target transaction data based on risk monitoring methods and obtain risk monitoring results; wherein, the risk monitoring methods include at least one of geopolitical knowledge graph, risk processing rule base and risk path monitoring model;

[0011] The report generation module is used to generate a risk monitoring report for the target transaction based on the risk monitoring results.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] At least one processor; and

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data processing method according to any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the data processing method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the data processing method according to any embodiment of the present invention.

[0018] The technical solution of this invention involves acquiring target transaction data for a cross-regional transaction; performing risk monitoring on the target transaction data based on risk monitoring methods to obtain risk monitoring results; wherein the risk monitoring methods include at least one of geopolitical knowledge graphs, risk handling rule bases, and risk path monitoring models; and generating a risk monitoring report for the target transaction based on the risk monitoring results. Compared to the inefficiency caused by manual review and risk identification of cross-regional transactions in existing methods, this invention improves the accuracy of risk identification, enhances the efficiency of cross-regional transaction review, and reduces manual workload by automatically monitoring the risk of cross-regional transaction data.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a data processing method provided according to an embodiment of the present invention;

[0022] Figure 2 This is a flowchart of a data processing method provided according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a data processing device according to an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the data processing method of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, such as target transaction data, historical transaction data, external knowledge data, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, and do not violate public order and good morals.

[0028] Figure 1 This is a flowchart illustrating a data processing method according to an embodiment of the present invention. This embodiment is applicable to situations involving risk monitoring of transactions, particularly cross-regional transactions such as cross-border remittances. The method can be executed by a data processing device, which can be implemented in hardware and / or software and can be configured in an electronic device that carries data processing functions; for example, a server. Figure 1 As shown, the method includes:

[0029] S110, Obtain the target transaction data of the target transaction.

[0030] The target transaction refers to a money transfer transaction in a financial scenario; optionally, the target transaction is a cross-regional transaction, such as a cross-border money transfer transaction. Target transaction data refers to the specific data of the target transaction, including but not limited to the unique transaction identifier (i.e., unique message identifier), the identifier of the remitting bank's institution (e.g., the sender's bank code), the identifier of the receiving bank's institution (e.g., the receiving bank's bank code), the remitting bank's customer remittance account, the receiving bank's customer remittance account, the transfer amount, currency, transaction time, etc.; it can be transaction message data, in JSON / XML format.

[0031] Specifically, it involves acquiring the transaction data of the target transaction in real time.

[0032] S120. Conduct risk monitoring on the target transaction data based on risk monitoring methods to obtain risk monitoring results.

[0033] In this embodiment, the risk monitoring method includes at least one of the following: a geopolitical knowledge graph, a risk processing rule base, and a risk path monitoring model. The geopolitical knowledge graph refers to a knowledge graph used for regional risk monitoring of target transaction data. The risk processing rule base contains rules for determining whether target transaction data poses a specific risk, including but not limited to static and dynamic rules. Static rules may include regional blacklists, institutional blacklists, etc.; dynamic rules may include whether transaction frequency and transaction amount meet set requirements, etc.

[0034] The risk path monitoring model is used to identify transaction paths in target transaction data, such as a detour transaction path from region A to region B to region C; optionally, the risk path monitoring model can be obtained by training a graph neural network based on historical transaction data.

[0035] The so-called risk monitoring results refer to the monitoring results of whether the target transaction is compliant.

[0036] One optional approach is to perform static or dynamic monitoring of target transaction data based on risk handling rules in a risk handling rule base to obtain risk monitoring results. For example, static monitoring of target transactions can be performed based on static rules. For instance, target transactions with transaction amounts exceeding a set threshold are designated as risky transactions, blocked, and transferred to manual processing. Another example is the automatic blocking of target transactions involving regional blacklists, which are then transferred to manual review. Alternatively, dynamic monitoring of target transactions can be performed based on dynamic rules. For instance, for dynamic rules on transaction frequency, such as "number of transactions with the same payee > μ + 3σ", where μ / σ is updated hourly, target transactions that meet the dynamic rules on transaction frequency are automatically processed by the system, while those that do not are transferred to manual processing.

[0037] Another optional approach is to perform risk monitoring on the target transaction data based on risk monitoring methods to obtain risk monitoring results, including: determining at least one target processing rule from the risk processing rule base based on the target transaction data; and performing risk monitoring on the target transaction data using at least one target processing rule based on the processing priority of the target processing rule to obtain risk monitoring results.

[0038] Specifically, at least one target processing rule is selected from the risk rule library based on the transaction type of the target transaction data. Then, based on the processing priority of the target processing rules, the target transaction data is monitored for risk in sequence using the target processing rules to obtain the risk monitoring results.

[0039] It is understandable that by selecting the target processing rule corresponding to the target transaction data from the risk rule base and processing the target transaction data based on priority, the risk monitoring rules are made orderly, thereby improving the efficiency of risk monitoring.

[0040] Optionally, the data processing rules in the risk processing rule base may be optimized based on at least one of the following methods;

[0041] If a risk handling rule in the risk handling rule base is not triggered within a preset time period or the false alarm rate of the corresponding rule meets the false alarm rate threshold, the rule weight of the risk handling rule will be adjusted.

[0042] The new risk handling rules are tested in pilot operations to obtain test results. After the test results are verified by indicators, they are added to the risk handling rule library.

[0043] The risk handling rules in the risk handling rule base are monitored by indicators to obtain the monitoring data corresponding to the monitoring indicators, and the risk handling rules are updated based on the monitoring data.

[0044] Specifically, if a risk handling rule in the risk handling rule base is not triggered within a preset time period or the false alarm rate of the corresponding rule meets the false alarm rate threshold, the rule weight of the risk handling rule will be adjusted. For example, if a rule is not triggered for 7 consecutive days, the rule can be moved to the observation pool; or if the false alarm rate of a rule is >20% for 3 consecutive days, the rule weight will be automatically reduced to 50%.

[0045] The new risk handling rules are tested in pilot operations to obtain test results. After the test results are verified by indicators, they are added to the risk handling rule library. Specifically, for a new risk handling rule, the rule is tested in a pilot operation, such as a 5% traffic environment. The test results are verified by characteristic indicators. After the verification is passed, the new risk handling rule is added to the risk handling rule library.

[0046] The risk handling rules in the risk handling rule base are monitored by indicators to obtain the monitoring data corresponding to the monitoring indicators, and the risk handling rules are updated according to the monitoring data. Specifically, if the precision rate in the monitoring data drops below a set value, the corresponding data handling rule is rolled back to its previous version; if the recall rate in the monitoring data meets the set conditions, an alarm is triggered and the use of the corresponding data handling rule is suspended.

[0047] Understandably, optimizing and updating the risk handling rule base can ensure the accuracy of transaction processing, while also reducing the workload of manual strategy configuration and shortening the time for reviewing abnormal transactions.

[0048] Another option is to use a risk path monitoring model to perform path reasoning on the target transaction data to obtain the target transaction path. The risk path monitoring model is a graph neural network model. Based on the target transaction path, the risk monitoring results of the target transaction are identified. Specifically, the target transaction data can be input into the risk path monitoring model, which outputs the target transaction path. This allows for the determination of whether the target transaction path involves transaction risks, such as monitoring detours like "Country A → Country B (outside the blacklist) → Country C (within the blacklist)".

[0049] Understandably, using a path reasoning model to implement transaction path reasoning can accurately identify detours in cross-border transfer transactions and improve the accuracy of risk identification.

[0050] S130. Generate a risk monitoring report for the target transaction based on the risk monitoring results.

[0051] Specifically, it can add annotations and explanations to target transactions based on risk monitoring results, that is, indicate the reasons for each target transaction being approved or not approved, and generate a report with specific annotations and notes including "risk path", "source of policy basis" and "matching degree of similar cases", to assist manual review, and can save the report for subsequent regulatory and audit review of the specific reasons for interception.

[0052] Optionally, target transactions can be intercepted in real time based on risk monitoring results; or, a risk heat map can be generated based on risk monitoring results.

[0053] Specifically, risky target transactions identified in the risk monitoring results are intercepted in real time, triggering manual review and freezing of the transactions. A risk heatmap can also be generated for all transactions based on the risk monitoring results.

[0054] It is understandable that visualizing the risk monitoring results facilitates a comprehensive understanding of the transaction risk situation across the entire region.

[0055] The technical solution of this invention involves acquiring target transaction data for a cross-regional transaction; performing risk monitoring on the target transaction data based on risk monitoring methods to obtain risk monitoring results; wherein the risk monitoring methods include at least one of geopolitical knowledge graphs, risk handling rule bases, and risk path monitoring models; and generating a risk monitoring report for the target transaction based on the risk monitoring results. Compared to the inefficiency caused by manual review and risk identification of cross-regional transactions in existing methods, this invention improves the accuracy of risk identification, enhances the efficiency of cross-regional transaction review, and reduces manual workload by automatically monitoring the risk of cross-regional transaction data.

[0056] Figure 2 This is a flowchart of a data processing method provided according to an embodiment of the present invention. Based on the above embodiments, this embodiment further optimizes the process of "performing risk monitoring on target transaction data based on a risk monitoring method to obtain risk monitoring results," providing an optional implementation scheme. For example... Figure 2 As shown, the method includes:

[0057] S210. Obtain the target transaction data of the target transaction; wherein, the target transaction is a cross-regional transaction.

[0058] S220. Risk monitoring is conducted on the target transaction data based on risk monitoring methods to obtain risk monitoring results.

[0059] The risk monitoring methods include at least one of the following: geopolitical knowledge graph, risk handling rule base, and risk path monitoring model.

[0060] S230. Generate a risk monitoring report for the target transaction based on the risk monitoring results.

[0061] An alternative approach involves conducting risk monitoring on target transaction data based on risk monitoring methods to obtain risk monitoring results, including: extracting keywords from the target transaction data to obtain target keywords; and searching for risk monitoring results from a geopolitical knowledge graph based on the target keywords.

[0062] Specifically, keywords are extracted from the target transaction data to obtain target keywords, and risk monitoring results are obtained by searching the geopolitical knowledge graph using the target keywords as indexes.

[0063] Understandably, this can improve the efficiency of risk checks on fund transfers, enhance the efficiency of compliance checks on the clearing side, and ensure the real-time, legal, and secure nature of business processing.

[0064] Optionally, the geopolitical knowledge graph is determined as follows: entities are extracted from historical transaction data and external knowledge data to obtain entities; where entities include regions, institutions, accounts, trading parties, transaction paths, and risk tags; relationships between different entities are constructed; entities are used as nodes and relationships are used as edges to construct the geopolitical knowledge graph.

[0065] The external knowledge data includes the SWIFT / BIC database, the International Bank Account Number (IBAN) structure database, and relevant financial event policy data. The IBAN structure database includes the institution name, country, branch code, and compliance status. It also includes the country code, check digit algorithm, and bank code mapping table.

[0066] Specifically, entities can be extracted from historical transaction data and external knowledge data using Natural Language Processing (NLP) technology. These entities include regions, institutions, accounts, trading parties, transaction paths, and risk tags. For example, rule-based regular expression matching and NLP entity recognition can be used to extract entities from historical transaction data and external knowledge data. Then, a rule engine is used to construct relationships between different entities. For instance, a rule engine might use (SWIFT:BANK_X) - [located in] -> (COUNTRY: Germany); (IBAN:DEXX...) - [belongs to] -> (BANK: Deutsche Bank). Entities are then used as nodes, and relationships as edges to construct and store a geospatial knowledge graph. Risk tags are used as node attributes.

[0067] Understandably, building a geopolitical knowledge graph using external knowledge data and historical transaction data is more comprehensive, thereby ensuring compliance checks on target transfer transactions.

[0068] The technical solution of this invention involves acquiring target transaction data for a cross-regional transaction; performing risk monitoring on the target transaction data based on risk monitoring methods to obtain risk monitoring results; wherein the risk monitoring methods include at least one of geopolitical knowledge graphs, risk handling rule bases, and risk path monitoring models; and generating a risk monitoring report for the target transaction based on the risk monitoring results. Compared to the inefficiency caused by manual review and risk identification of cross-regional transactions in existing methods, this invention improves the accuracy of risk identification, enhances the efficiency of cross-regional transaction review, and reduces manual workload by automatically monitoring the risk of cross-regional transaction data.

[0069] Figure 3 This is a schematic diagram of a data processing apparatus according to an embodiment of the present invention. This embodiment is applicable to situations involving risk monitoring of transactions, particularly cross-regional transactions such as cross-border remittances. The data processing apparatus can be implemented in hardware and / or software and can be configured within an electronic device that carries data processing functions, such as a server. Figure 3 As shown, the device includes:

[0070] The transaction data acquisition module 310 is used to acquire the target transaction data of the target transaction; wherein the target transaction is a cross-regional transaction.

[0071] The risk monitoring module 320 is used to perform risk monitoring on target transaction data based on risk monitoring methods and obtain risk monitoring results; wherein, the risk monitoring methods include at least one of geo-knowledge graph, risk processing rule base and risk path monitoring model;

[0072] The report generation module 330 is used to generate a risk monitoring report for the target transaction based on the risk monitoring results.

[0073] The technical solution of this invention involves acquiring target transaction data for a cross-regional transaction; performing risk monitoring on the target transaction data based on risk monitoring methods to obtain risk monitoring results; wherein the risk monitoring methods include at least one of geopolitical knowledge graphs, risk handling rule bases, and risk path monitoring models; and generating a risk monitoring report for the target transaction based on the risk monitoring results. Compared to the inefficiency caused by manual review and risk identification of cross-regional transactions in existing methods, this invention improves the accuracy of risk identification, enhances the efficiency of cross-regional transaction review, and reduces manual workload by automatically monitoring the risk of cross-regional transaction data.

[0074] Optionally, the risk monitoring module 320 is used for:

[0075] Extract keywords from the target transaction data to obtain target keywords;

[0076] Risk monitoring results are obtained by searching the geopolitical knowledge graph based on target keywords.

[0077] Optionally, the device also includes a geopolitical knowledge graph determination module for:

[0078] Entities are extracted from historical transaction data and external knowledge data; these entities include regions, institutions, accounts, trading parties, transaction paths, and risk labels.

[0079] Establish relationships between different entities;

[0080] By using entities as nodes and relationships as edges, a geopolitical knowledge graph is constructed.

[0081] Optionally, the risk monitoring module 320 is used for:

[0082] The target transaction path is obtained by performing path reasoning on the target transaction data based on the risk path monitoring model; wherein, the risk path monitoring model is a graph neural network model.

[0083] Identify the risk monitoring results of the target transaction based on the target transaction path.

[0084] Optionally, the risk monitoring module 320 is used for:

[0085] At least one target processing rule is determined from the risk processing rule base based on the target transaction data;

[0086] Based on the processing priority of the target processing rules, at least one target processing rule is used to perform risk monitoring on the target transaction data to obtain the risk monitoring results.

[0087] Optionally, the device also includes a rule optimization module for:

[0088] The data processing rules in the risk processing rule base are optimized based on at least one of the following methods;

[0089] If a risk handling rule in the risk handling rule base is not triggered within a preset time period or the false alarm rate of the corresponding rule meets the false alarm rate threshold, the rule weight of the risk handling rule will be adjusted.

[0090] The new risk handling rules are tested in pilot operations to obtain test results. After the test results are verified by indicators, they are added to the risk handling rule library.

[0091] The risk handling rules in the risk handling rule base are monitored by indicators to obtain the monitoring data corresponding to the monitoring indicators, and the risk handling rules are updated based on the monitoring data.

[0092] Optionally, the device also includes a post-processing module for:

[0093] Target transactions are intercepted in real time based on risk monitoring results;

[0094] Alternatively, a risk heatmap can be generated based on the risk monitoring results.

[0095] The data processing apparatus provided in the embodiments of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0096] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0097] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the data processing method of the present invention. Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0098] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0099] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0100] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data processing methods.

[0101] In some embodiments, the data processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).

[0102] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0106] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0107] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0108] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0109] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data processing method, characterized in that, include: Obtain the target transaction data; where the target transaction is a cross-regional transaction; Risk monitoring is performed on the target transaction data based on risk monitoring methods to obtain risk monitoring results; wherein, the risk monitoring methods include at least one of geopolitical knowledge graph, risk processing rule base and risk path monitoring model; A risk monitoring report is generated for the target transaction based on the risk monitoring results.

2. The method according to claim 1, characterized in that, Risk monitoring is performed on the target transaction data based on risk monitoring methods to obtain risk monitoring results, including: Keyword extraction is performed on the target transaction data to obtain target keywords; Risk monitoring results are obtained by searching the geopolitical knowledge graph based on the target keywords.

3. The method according to claim 1 or 2, characterized in that, The geopolitical knowledge graph is determined in the following way: Entities are extracted from historical transaction data and external knowledge data; wherein, the entities include regions, institutions, accounts, trading parties, transaction paths, and risk tags. Establish relationships between different entities; By using entities as nodes and relationships as edges, a geopolitical knowledge graph is constructed.

4. The method according to claim 1, characterized in that, Risk monitoring is performed on the target transaction data based on risk monitoring methods to obtain risk monitoring results, including: The target transaction path is obtained by performing path reasoning on the target transaction data based on the risk path monitoring model; wherein, the risk path monitoring model is a graph neural network model. Based on the target transaction path, identify the risk monitoring results of the target transaction.

5. The method according to claim 1, characterized in that, Risk monitoring is performed on the target transaction data based on risk monitoring methods to obtain risk monitoring results, including: At least one target processing rule is determined from the risk processing rule base based on the target transaction data; Based on the processing priority of the target processing rules, at least one target processing rule is used to perform risk monitoring on the target transaction data to obtain risk monitoring results.

6. The method according to claim 1 or 5, characterized in that, Also includes: The data processing rules in the risk processing rule base are optimized based on at least one of the following methods; If a risk handling rule in the risk handling rule base is not triggered within a preset time period or the false alarm rate of the corresponding rule meets the false alarm rate threshold, the rule weight of the risk handling rule will be adjusted. The new risk handling rules are tested in pilot operations to obtain test results. After the test results are verified by indicators, they are added to the risk handling rule library. The risk handling rules in the risk handling rule base are monitored by indicators to obtain the monitoring data corresponding to the monitoring indicators, and the risk handling rules are updated according to the monitoring data.

7. The method according to any one of claims 1-6, characterized in that, Also includes: The target transaction is intercepted in real time based on the risk monitoring results. Alternatively, a risk heatmap can be generated based on the risk monitoring results.

8. A data processing apparatus, characterized in that, include: The transaction data acquisition module is used to acquire the target transaction data of the target transaction; wherein the target transaction is a cross-regional transaction. The risk monitoring module is used to perform risk monitoring on the target transaction data based on risk monitoring methods and obtain risk monitoring results; wherein, the risk monitoring methods include at least one of geopolitical knowledge graph, risk processing rule base and risk path monitoring model; The report generation module is used to generate a risk monitoring report for the target transaction based on the risk monitoring results.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the data processing method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the data processing method according to any one of claims 1-7.

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