Cross-border payment fund routing optimization method and system

Abstract

The invention relates to the technical field of cross-border payment optimization, and discloses a cross-border payment fund routing optimization method and system, and the method comprises the steps: receiving a cross-border payment transaction request, analyzing the transaction parameters of the cross-border payment transaction request, and querying and obtaining an initial available payment channel set and corresponding channel information according to the transaction parameters; according to the transaction parameters, identifying payment routing capability applicable to the transaction and matching a special routing rule of the transaction, and according to the payment routing capability and the special routing rule relationship, performing three-level routing rule filtering processing on the available payment channel set to obtain a remaining available payment channel set; three layers of historical performance index data are obtained, a three-layer performance index model is constructed by performing weighted fusion on the three layers of performance index data, performance indexes of all dimensions are predicted, comprehensive scores of all channels are calculated in combination with customer levels, and the optimal payment channel is selected for each cross-border transaction through the method. The transaction success rate is improved, the cost is reduced, and the user experience is optimized.

Description

Technical Field

[0001] The present invention relates to the field of cross-border payment optimization technology, and in particular to a cross-border payment funds routing optimization method and system. Background Art

[0002] With the rapid development of globalization and the digital economy, the scale of cross-border transactions continues to surge, while payment methods are experiencing unprecedented fragmentation and diversification. This places demands on payment systems to handle transactions in a real-time, intelligent, and reliable manner. Efficient and low-cost payment routing is crucial for optimizing user experience, controlling operating expenses, and improving capital turnover efficiency. However, existing payment channel evaluation and selection technologies are insufficient to address this complex landscape, suffering from numerous fundamental flaws, including high fees, slow transaction times, a wide range of channel specificities, and complex compliance procedures.

[0003] First, key dimensions that influence payment routing decisions, such as cost (reflected in payment channel fees), transaction processing speed (usually measured by the time required to complete), and transaction success rate, are often evaluated using isolated and single calculation models. There is no multi-dimensional analysis of cross-border payment routing choices, and no optimization based on compliance, intelligence, ease of maintenance, and business applicability. Cost assessment relies too much on static historical global averages, failing to effectively capture and adapt to the specific behavioral patterns of different customer groups and their differences in sensitivity to rates; predictions of speed indicators often ignore the significant fluctuations brought about by transaction periods, such as the doubling of processing time during peak hours; more importantly, these dimensions themselves are calculated in different ways, and their scoring scales are not unified, making it difficult to form an objective and consistent quantitative basis for comprehensively comparing the pros and cons of different payment channels; secondly, at the data utilization level, existing methods have significant shortcomings. For newly connected customers or newly activated payment channels, due to the lack of sufficient historical transaction data (i.e., the cold start problem), it is impossible to provide reliable assessments. At the same time, it fails to effectively establish a correlation model between customer group portrait characteristics (such as the industry to which they belong and historical transaction habits) and real-time transaction environment characteristics, making it difficult to capture the payment channel performance patterns of specific groups in different scenarios.

[0004] Therefore, there is an urgent need for an intelligent payment channel comprehensive evaluation system that can intelligently and uniformly process multi-dimensional evaluations, adaptively optimize core parameters, effectively overcome various data anomalies, and have the ability to model hierarchical data, in order to cope with the increasingly complex payment ecological environment challenges, maximize transaction efficiency and optimize user experience. Summary of the Invention

[0005] The present invention aims to address the shortcomings of the aforementioned prior art by providing a method and system for optimizing cross-border payment routing. This method integrates resources across multiple access channels to optimize digital currency transactions using routing algorithms. Cross-border payment channel routing optimization involves using an intelligent decision-making system to select the optimal payment channel for each cross-border transaction, thereby increasing transaction success rates, reducing costs, and optimizing the user experience.

[0006] In one aspect, a method for optimizing cross-border payment fund routing is provided, comprising the following steps: S1: Receive a cross-border payment transaction request, parse its transaction parameters, and query the global channel operation platform based on the transaction parameters to obtain an initial set of available payment channels and their corresponding channel information; S2: Invoke the payment routing service, identify the payment routing capability applicable to the transaction based on the transaction parameters, match the special routing rules of the transaction, perform three-level routing rule filtering on the available payment channel set based on the relationship between the payment routing capability and the special routing rules, and obtain the remaining available payment channel set; S3: Obtain historical performance indicator data at the customer-specific layer, customer group layer, and global layer, construct a three-layer performance indicator model by weighted fusion of the three-layer performance indicator data, predict the performance indicators of each dimension through the three-layer performance indicator model, and calculate the comprehensive score of each channel based on the application of differentiated routing strategies in combination with customer levels to obtain the optimal payment channel.

[0007] Furthermore, in step S2, the three-level routing rule filtering process specifically includes: According to the matching rules of the customer ID, payment card number BIN code, and payment currency combination included in the transaction parameters, the designated payment channel is obtained. If a designated payment channel exists, all the extracted designated payment channels are used as a new available channel set. If no designated payment channel exists, the next level of filtering is performed; Identify the business region provided by the transaction parameters, select and verify payment channels that support the business characteristics of the business region, where the business characteristics include a special business scenario tag, business type, and business function. The business scenario tag includes an anti-money laundering level, trade type code, and business region code. Screen the lowest-cost and most successful payment channels based on the financial cost matrix, compare the paying bank and the beneficiary bank using the first four digits of the SWIFT code, match the available channels with those in the same industry as the current agency issuing bank, and remove channels that only support the same industry.

[0008] Furthermore, in step S3, obtaining historical performance indicator data at the customer-specific layer, customer group layer, and global layer includes: Extracting historical transaction data of the current customer on the target payment channel to obtain the customer-specific performance indicator; The grouping is obtained based on the industry classification and transaction scale of the current customers, and the historical transaction data of the group on the target payment channel is calculated to obtain the customer group-level performance indicators; Aggregate historical transaction data of all platform channels to obtain the global performance indicators.

[0009] Furthermore, in step S3, the weighted fusion of the three-layer performance indicator data includes: The weight coefficients of the three-layer performance indicators are calculated separately through a dynamic weight calculation model, and the three-layer performance indicators are integrated into a comprehensive performance indicator for each payment channel according to the weight coefficients. The comprehensive performance indicator is the predicted value output by the model.

[0010] Preferably, the dynamic weight calculation model includes: The calculation formulas for the weight coefficients of the customer-specific layer performance indicators and the customer group layer performance indicators are as follows: in, is the level identifier, represents the customer specific layer, Indicates the customer segmentation layer, represents the global layer, Indicates the number of historical transactions at this level. Indicates the number of days since today the latest data of this level. Indicates the maximum weight upper limit factor of this level, which is used to limit the maximum influence of this level. Indicates the normalization factor of the transaction volume at this level, Indicates the minimum attenuation coefficient of this level, Indicates the age decay period of the data at this level; For the global layer performance index, its weight coefficient , Represents the global layer baseline weight.

[0011] More preferably, the three-layer performance indicators are integrated into a comprehensive performance indicator for each payment channel according to the weight coefficient, including: The indicator values ​​of the same dimension in the three-layer performance indicators are weighted averaged according to their weight coefficients to obtain the comprehensive indicator value of the dimension. The formula for constructing the three-component performance indicator model through the weighted fusion formula is as follows: in, is the dimension identifier, Represents each dimension The comprehensive indicators include overall success rate, average processing time, average handling fee rate, success rate by country / region, and success rate by amount range. , represents the dimension validity indicator function, Indicates the performance indicator of this dimension at each level.

[0012] Furthermore, in step S3, the customer-level application differentiated routing strategy includes: Parsing the customer identification information in the transaction request, obtaining the grade classification identifier corresponding to the customer's signed service agreement through the customer profiling system, and loading a preset benchmark weight allocation scheme from the policy library based on the grade classification identifier. The benchmark weight allocation scheme defines the priority relationship of various dimensions, including cost, timeliness, and success rate, in routing decisions; Receive preference instructions sent by the customer configuration terminal, adjust the baseline weight distribution plan in real time through the weight correction factor, and generate a customer-specific routing strategy; Establish a transaction feature perception mechanism to dynamically adjust the weight ratio of each dimension based on the key attributes of the current transaction, where the key attributes of the transaction include at least the transaction amount, the target country risk level, and the business scenario code; The finalized dimension weight combination is applied to perform payment channel comprehensive score calculation and complete the optimal payment channel selection decision.

[0013] Preferably, the calculation of the comprehensive score of the payment channel further includes: The scores of each dimension of each payment channel are calculated according to the routing strategy. The calculation formula is as follows: in, is obtained through the three-layer performance indicator model The predicted value of the dimension, Indicates the dimension offset factor to prevent calculation anomalies; Apply differentiated routing policy configuration based on the customer class The weight of each dimension in routing decision Scores for each dimension Calculate the comprehensive score using the following formula: The available payment channels are arranged in descending order according to the comprehensive scores, and the payment channel with the highest comprehensive score is selected as the optimal payment channel to execute the cross-border payment transaction.

[0014] Furthermore, in step S1, parsing the transaction parameters includes: Establish a structured transaction data warehouse to store complete parameter information of each transaction, including customer information, submission channel time, processing completion time, transaction amount, currency, sender country code, recipient country code, payment channel used, handling fee, and failure reason.

[0015] Furthermore, in step S2, obtaining the remaining available payment channel set further includes: Monitor the status of each payment channel in real time, update the channel status regularly, and exclude unavailable channels when making routing decisions. Monitoring indicators include channel position balance, channel service availability, channel average response time, and channel failure rate. If the average response time of a channel exceeds a preset threshold or the channel failure rate exceeds a preset upper limit, the channel is marked as unavailable.

[0016] In another aspect, a cross-border payment funds routing optimization system is provided, comprising: A transaction data acquisition module is used to receive cross-border payment transaction requests, parse their transaction parameters, and query the global channel operation platform based on the transaction parameters to obtain an initial set of available payment channels and their corresponding channel information; a routing matching and filtering module, configured to call a payment routing service, identify the payment routing capability applicable to the transaction based on the transaction parameters, match the special routing rules of the transaction, perform a three-level routing rule filtering process on the available payment channel set based on the relationship between the payment routing capability and the special routing rules, and obtain the remaining available payment channel set; The routing optimization module is used to obtain historical performance indicator data at the customer-specific layer, customer group layer, and global layer, and to construct a three-layer performance indicator model by weighted fusion of the three-layer performance indicator data. The performance indicators of each dimension are predicted by the three-layer performance indicator model, and differentiated routing strategies are applied in combination with customer levels to calculate the comprehensive score of each channel to obtain the optimal payment channel.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention builds a three-layer performance indicator model (global layer, customer layer, and channel layer) and its data association rules. When the target customer data sample size falls below a preset threshold, the system automatically performs group profile matching and data inheritance operations. It prioritizes calling group-layer historical data as a compensatory data source, ensuring evaluation continuity while avoiding evaluation failures for new customers / new channels due to data sparsity, significantly improving system coverage and robustness. This invention dynamically generates parameters through a transaction feature analysis mechanism (such as analyzing historical customer transaction records), monitors real-time feedback data through weight optimization, and dynamically adjusts the weights of different levels of data based on data volume and timeliness, eliminating the subjective bias of manually preset weights and ensuring that the system continuously adapts to changes in the business environment. This invention applies the above parameters to performance indicator evaluation through a multi-dimensional intelligent decision-making system, adopts differentiated routing strategies for different levels of customers (premium, business, standard), and dynamically adjusts routing decisions based on customer characteristics and historical performance. This allows the selection of the optimal payment channel for each cross-border transaction, thereby improving transaction success rates, reducing costs, and optimizing user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a cross-border payment funds routing optimization method according to the present invention; Figure 2 This is a schematic diagram of a payment routing rule logic subdivision process of the present invention; Figure 3 This is a schematic diagram of the main framework of a cross-border payment funds routing optimization system of the present invention. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The present invention establishes a unified dimensional evaluation framework to normalize the cost, speed, and success rate of payment channels to form a standard comparable score, constructs a hierarchical data architecture including the global layer, customer layer, and channel layer, and automatically triggers the group portrait matching mechanism to call the group layer data when the data is insufficient. At the same time, it dynamically generates parameters based on transaction feature analysis, and combines the weight optimization mechanism with real-time feedback to realize adaptive adjustment of the evaluation strategy, ultimately forming a multi-dimensional integrated, abnormality-immune, and continuously evolving intelligent evaluation closed loop.

[0021] The specific implementation of the present invention is described below with reference to the accompanying drawings and embodiments.

[0022] Example 1 See also Figure 1, a cross-border payment fund routing optimization method provided in this embodiment, the technical solution includes the following steps: S1: Receive a cross-border payment transaction request, parse its transaction parameters, and query the global channel operation platform based on the transaction parameters to obtain an initial set of available payment channels and their corresponding channel information; S2: Invoke the payment routing service, identify the payment routing capability applicable to the transaction based on the transaction parameters, match the special routing rules of the transaction, perform three-level routing rule filtering on the available payment channel set based on the relationship between the payment routing capability and the special routing rules, and obtain the remaining available payment channel set; S3: Obtain historical performance indicator data at the customer-specific layer, customer group layer, and global layer, construct a three-layer performance indicator model by weighted fusion of the three-layer performance indicator data, predict the performance indicators of each dimension through the three-layer performance indicator model, and calculate the comprehensive score of each channel based on the application of differentiated routing strategies in combination with customer levels to obtain the optimal payment channel.

[0023] In step S1, the global channel operation platform of this embodiment is the GCOP system, and the parsing of its transaction parameters further includes: Establish a structured transaction data warehouse to store complete parameter information of each transaction, including customer information, submission channel time, processing completion time, transaction amount, currency, sender country code, recipient country code, payment channel used, handling fee, and failure reason.

[0024] In step S2, the three-level routing rules include customer-specified routing rules, business support routing rules, and cost rules. The filtering process according to the three-level routing rules specifically includes: First, the customer-specified routing rules are processed: the designated payment channel is obtained based on the matching rules of the customer ID, payment card number BIN code, and payment currency combination included in the transaction parameters. If a designated payment channel exists, all the extracted designated payment channels are used as the new available channel set. If no designated payment channel exists, the next level of filtering is performed; Then, the business support routing rules are processed: foreign currency business channels are queried, the business region provided by the transaction parameters is identified, and payment channels that support the business characteristics of the business region are screened and verified. The business characteristics include special business scenario tags, business types, and business functions. The business scenario tags include anti-money laundering levels, trade type codes, and business region codes. Finally, the cost rules are processed: the lowest-cost and most successful payment channels are screened based on the financial cost matrix, the paying bank and the beneficiary bank are compared using the first four digits of the SWIFT code, and the channels in the same industry as the current agency issuing bank are matched among the available channels. If the match fails and there are channels that only support the same industry, the channels that only support the same industry are removed.

[0025] In addition, the obtaining of the remaining available payment channel set in step S2 further includes: Monitor the status of each payment channel in real time, update the channel status regularly, and exclude unavailable channels when making routing decisions. Monitoring indicators include channel position balance, channel service availability, channel average response time, and channel failure rate. If the average response time of a channel exceeds a preset threshold or the channel failure rate exceeds a preset upper limit, the channel is marked as unavailable.

[0026] In this example, we build a channel health dashboard to monitor the following indicators: The channel position balance alarm threshold is less than the estimated daily trading volume × 120%, the average response time circuit breaker threshold is greater than 5 seconds, and the failure rate circuit breaker threshold is greater than 8%. The available channel set is dynamically adjusted: When a channel triggers a circuit breaker threshold, it is automatically removed from the available set. After maintenance is restored, it must pass a success rate test (simulated transaction success rate > 95%) to be reactivated.

[0027] In step S3, obtaining historical performance indicator data at the customer-specific layer, customer group layer, and global layer further includes: Extracting historical transaction data of the current customer on the target payment channel to obtain the customer-specific performance indicator; The grouping is obtained based on the industry classification and transaction scale of the current customers, and the historical transaction data of the group on the target payment channel is calculated to obtain the customer group-level performance indicators; The global performance indicators are obtained by aggregating historical transaction data of all platform channels.

[0028] Then, the three-layer performance indicator data is weightedly fused, including: The weight coefficients of the three-layer performance indicators are calculated separately through a dynamic weight calculation model, and the three-layer performance indicators are integrated into a comprehensive performance indicator for each payment channel according to the weight coefficients. The comprehensive performance indicator is the predicted value output by the model.

[0029] Specifically, the dynamic weight calculation model includes: The calculation formulas for the weight coefficients of the customer-specific layer performance indicators and the customer group layer performance indicators are as follows: in, is the level identifier, represents the customer specific layer, Indicates the customer segmentation layer, represents the global layer, Indicates the number of historical transactions at this level. Indicates the number of days since today the latest data of this level. Indicates the maximum weight upper limit factor of this level, which is used to limit the maximum influence of this level. Indicates the normalization factor of the transaction volume at this level, Indicates the minimum attenuation coefficient of this level, Indicates the age decay period of the data at this level; For the global layer performance index, its weight coefficient , Represents the global layer baseline weight.

[0030] In this embodiment, the customer layer weight coefficient is: Clustering layer weight coefficient: Global layer weight coefficient: .

[0031] Then, the three-layer performance indicators are integrated into a comprehensive performance indicator for each payment channel according to the weight coefficient, including: The indicator values ​​of the same dimension in the three-layer performance indicators are weighted averaged according to their weight coefficients to obtain the comprehensive indicator value of the dimension. The formula for constructing the three-component performance indicator model through the weighted fusion formula is as follows: in, is the dimension identifier, Represents each dimension The comprehensive indicators include overall success rate, average processing time, average handling fee rate, success rate by country / region, and success rate by amount range. , represents the dimension validity indicator function, Indicates the performance indicator of this dimension at each level.

[0032] In addition, the customer-level application differentiated routing strategy described in step S3 includes: Parsing the customer identification information in the transaction request, obtaining the grade classification identifier corresponding to the customer's signed service agreement through the customer profiling system, and loading a preset benchmark weight allocation scheme from the policy library based on the grade classification identifier. The benchmark weight allocation scheme defines the priority relationship between the cost dimension, timeliness dimension, and success rate dimension in routing decisions; Receive preference instructions sent by the customer configuration terminal, adjust the baseline weight distribution plan in real time through the weight correction factor, and generate a customer-specific routing strategy; Establish a transaction feature perception mechanism to dynamically adjust the weight ratio of each dimension based on the key attributes of the current transaction, where the key attributes of the transaction include at least the transaction amount, the target country risk level, and the business scenario code; The finalized dimension weight combination is applied to perform payment channel comprehensive score calculation and complete the optimal payment channel selection decision.

[0033] Specifically, in this embodiment, the differentiated routing policy configuration based on customer level application is as follows: Advanced customer strategy: Activate a scoring matrix with a default weight of 20% for cost, 40% for speed, and 40% for success rate, prioritizing transaction speed and success rate. This can be adjusted based on the customer's explicit preferences. Commercial customers: A balanced matrix with a weighting of 30% on cost, 35% on speed, and 35% on success rate is used to balance various indicators. This can be adjusted based on the customer's stated preferences. Standard customers: Enable a cost priority matrix with a weight of 50% for cost, 25% for speed, and 25% for success rate, focusing more on cost-effectiveness. This matrix can be adjusted based on the customer's stated preferences.

[0034] The calculation of the comprehensive score of the payment channel further includes: The scores of each dimension of each payment channel are calculated according to the routing strategy. The calculation formula is as follows: in, is obtained through the three-layer performance indicator model The predicted value of the dimension, Indicates the dimension offset factor to prevent calculation anomalies; Apply differentiated routing policy configuration based on the customer class The weight of each dimension in routing decision Scores for each dimension Calculate the comprehensive score using the following formula: The available payment channels are arranged in descending order according to the comprehensive scores, and the payment channel with the highest comprehensive score is selected as the optimal payment channel to execute the cross-border payment transaction.

[0035] In this embodiment, we calculate the cost score, speed score and success rate score of each payment channel according to the routing strategy weight configuration, where: Cost Score The calculation formula is: ; Speed ​​score The calculation formula is: ; Success rate score To predict the success rate; The comprehensive rating is: = in, 、 、 They are cost weight, speed weight and success rate weight respectively.

[0036] In summary, the entire payment routing rule logic subdivision process of the present invention is as follows: Figure 2 shown.

[0037] In addition, the method of the present invention also includes a cache update mechanism, including: Regular full update: scheduled tasks update the performance indicators of all caches; Real-time incremental updates: relevant indicators are updated immediately after the transaction is completed; Intelligent caching strategy: Highly accessed data is cached first to improve system response speed.

[0038] In this embodiment, we adopt a three-level cache architecture for the customer-specific layer, customer group layer and global layer, where: Customer-specific layer: LRU cache pool, Key = customer ID + channel ID, TTL = 2 hours, update trigger condition is real-time update upon transaction completion, triggering cache invalidation; Customer grouping layer: distributed cache, key = industry code + channel ID, TTL = 4 hours, update trigger condition is to batch update the incremental data of the previous 24 hours at 02:00 every day; Global layer: memory-mapped files, full loading, daily updates, and the update trigger condition is a full cache reconstruction at 04:00 every day.

[0039] The framework used in the optimization method of this embodiment is as follows Figure 3 Shown, including: The system input source, specifically the "cross-border payment transaction" business flow, is the trigger entry for the payment routing service; The payment routing service is the core functional body and consists of five parallel modules: the payment routing engine, which serves as the core scheduling hub and coordinates the operation of various modules; routing strategy capability management, which is used to store and maintain the routing strategy logic library; business routing configuration management, which is responsible for configuring rules for business parameters (such as currency and transaction type); special routing rule management, which is used to handle exception rules for specific scenarios (such as blacklist channels and emergency routes); and payment routing decision, which is the final output module and generates channel selection instructions. External data interaction system. The above-mentioned payment routing service relies on two types of external systems to provide decision-making basis, including the GCOP system and the cost accounting system. The GCOP system is used to obtain the transaction parameter data described in step S1, and the cost accounting system is used to provide channel transaction cost data. The core function is trial calculation (pre-calculation of channel fees for payment transactions).

[0040] Its service operation process performs the following steps in sequence: 1. Payment Routing Service Receives Foreign Currency Remittance Transaction Request 2. Payment routing engine initiates collaboration: Request the GCOP system to obtain available channels based on payment parameters: 3. Acquired data is collaboratively screened through routing policy capability management (policy rules), business routing configuration management (business parameter adaptation), and special routing rule management (exception filtering); 4. The payment routing decision module integrates all inputs and outputs the optimal channel (the illustrated example includes the preferred results among candidate channels such as Channel 1, Channel 2, and Channel 3).

[0041] Based on this framework, this embodiment also provides a cross-border payment funds routing optimization system, including: A transaction data acquisition module is used to receive cross-border payment transaction requests, parse their transaction parameters, and query the global channel operation platform based on the transaction parameters to obtain an initial set of available payment channels and their corresponding channel information; a routing matching and filtering module, configured to call a payment routing service, identify the payment routing capability applicable to the transaction based on the transaction parameters, match the special routing rules of the transaction, perform a three-level routing rule filtering process on the available payment channel set based on the relationship between the payment routing capability and the special routing rules, and obtain the remaining available payment channel set; The routing optimization module is used to obtain historical performance indicator data at the customer-specific layer, customer group layer, and global layer, and to construct a three-layer performance indicator model by weighted fusion of the three-layer performance indicator data. The performance indicators of each dimension are predicted by the three-layer performance indicator model, and differentiated routing strategies are applied in combination with customer levels to calculate the comprehensive score of each channel to obtain the optimal payment channel.

[0042] It should be noted that the steps in the cross-border payment funds routing optimization method provided in this embodiment can be implemented using corresponding modules in the cross-border payment funds routing optimization system. Those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, that is, the embodiments in the system can be understood as preferred examples for implementing the method, which will not be elaborated here.

[0043] In addition to implementing the system and its various devices provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. by logically programming the method steps. Therefore, the system and its various devices provided by the present invention can be considered a hardware component, and the devices included therein for implementing the various functions can also be considered as structures within the hardware component; the devices for implementing the various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0044] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that do not depart from the principles of the present invention, which are apparent to those skilled in the art, should also be considered within the scope of protection of the present invention.

[0045] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A cross-border payment funds routing optimization method, characterized in that: The steps include: S1: Receive a cross-border payment transaction request, parse its transaction parameters, and query the global channel operation platform based on the transaction parameters to obtain an initial set of available payment channels and their corresponding channel information; S2: Invoke the payment routing service, identify the payment routing capability applicable to the transaction based on the transaction parameters, match the special routing rules of the transaction, perform three-level routing rule filtering on the available payment channel set based on the relationship between the payment routing capability and the special routing rules, and obtain the remaining available payment channel set; S3: Obtain historical performance indicator data at the customer-specific layer, customer group layer, and global layer, construct a three-layer performance indicator model by weighted fusion of the three-layer performance indicator data, predict the performance indicators of each dimension through the three-layer performance indicator model, and calculate the comprehensive score of each channel based on the application of differentiated routing strategies in combination with customer levels to obtain the optimal payment channel.

2. The cross-border payment funds routing optimization method according to claim 1, characterized in that: In step S2, the three-level routing rule filtering process specifically includes: According to the matching rules of the customer ID, payment card number BIN code, and payment currency combination included in the transaction parameters, the designated payment channel is obtained. If a designated payment channel exists, all the extracted designated payment channels are used as a new available channel set. If no designated payment channel exists, the next level of filtering is performed; Identify the business region provided by the transaction parameters, select and verify payment channels that support the business characteristics of the business region, where the business characteristics include a special business scenario tag, business type, and business function. The business scenario tag includes an anti-money laundering level, trade type code, and business region code. Screen the lowest-cost and most successful payment channels based on the financial cost matrix, compare the paying bank and the beneficiary bank using the first four digits of the SWIFT code, match the available channels with those in the same industry as the current agency issuing bank, and remove channels that only support the same industry.

3. The cross-border payment funds routing optimization method according to claim 1, characterized in that: In step S3, obtaining historical performance indicator data at the customer-specific layer, customer group layer, and global layer further includes: Extracting historical transaction data of the current customer on the target payment channel to obtain the customer-specific performance indicator; The grouping is obtained based on the industry classification and transaction scale of the current customers, and the historical transaction data of the group on the target payment channel is calculated to obtain the customer group-level performance indicators; Aggregate historical transaction data of all platform channels to obtain the global performance indicators.

4. The cross-border payment funds routing optimization method according to claim 3, characterized in that: In step S3, the weighted fusion of the three-layer performance indicator data includes: The weight coefficients of the three-layer performance indicators are calculated separately through a dynamic weight calculation model, and the three-layer performance indicators are integrated into a comprehensive performance indicator for each payment channel according to the weight coefficients. The comprehensive performance indicator is the predicted value output by the model.

5. The cross-border payment funds routing optimization method according to claim 4, characterized in that: The dynamic weight calculation model includes: The calculation formulas for the weight coefficients of the customer-specific layer performance indicators and the customer group layer performance indicators are as follows: in, is the level identifier, represents the customer specific layer, Indicates the customer segmentation layer, represents the global layer, Indicates the number of historical transactions at this level. Indicates the number of days since today the latest data of this level. Indicates the maximum weight upper limit factor of this level, which is used to limit the maximum influence of this level. Indicates the normalization factor of the transaction volume at this level, Indicates the minimum attenuation coefficient of this level, Indicates the age decay period of the data at this level; For the global layer performance index, its weight coefficient , Represents the global layer baseline weight.

6. The cross-border payment funds routing optimization method according to claim 5, characterized in that: The three-layer performance indicators are integrated into the comprehensive performance indicators of each payment channel according to the weight coefficients, including: The indicator values ​​of the same dimension in the three-layer performance indicators are weighted averaged according to their weight coefficients to obtain the comprehensive indicator value of the dimension. The formula for constructing the three-component performance indicator model through the weighted fusion formula is as follows: in, is the dimension identifier, Represents each dimension The comprehensive indicators include overall success rate, average processing time, average handling fee rate, success rate by country / region, and success rate by amount range. , represents the dimension validity indicator function, Indicates the performance indicator of this dimension at each level.

7. The cross-border payment funds routing optimization method according to claim 6, characterized in that: In step S3, the customer-level application differentiated routing strategy includes: Parsing the customer identification information in the transaction request, obtaining the grade classification identifier corresponding to the customer's signed service agreement through the customer profiling system, and loading a preset benchmark weight allocation scheme from the policy library based on the grade classification identifier. The benchmark weight allocation scheme defines the priority relationship of various dimensions, including cost, timeliness, and success rate, in routing decisions; Receive preference instructions sent by the customer configuration terminal, adjust the baseline weight distribution plan in real time through the weight correction factor, and generate a customer-specific routing strategy; Establish a transaction feature perception mechanism to dynamically adjust the weight ratio of each dimension based on the key attributes of the current transaction, where the key attributes of the transaction include at least the transaction amount, the target country risk level, and the business scenario code; The finalized dimension weight combination is applied to perform payment channel comprehensive score calculation and complete the optimal payment channel selection decision.

8. The cross-border payment funds routing optimization method according to claim 7, characterized in that: The calculation of the comprehensive score of the payment channel further includes: The scores of each dimension of each payment channel are calculated according to the routing strategy. The calculation formula is as follows: in, is obtained through the three-layer performance indicator model The predicted value of the dimension, Indicates the dimension offset factor to prevent calculation anomalies; Apply differentiated routing policy configuration based on the customer class The weight of each dimension in routing decision Scores for each dimension Calculate the comprehensive score using the following formula: The available payment channels are arranged in descending order according to the comprehensive scores, and the payment channel with the highest comprehensive score is selected as the optimal payment channel to execute the cross-border payment transaction.

9. The cross-border payment funds routing optimization method according to claim 1, characterized in that: In step S1, the parsing of the transaction parameters further includes: Establish a structured transaction data warehouse to store complete parameter information of each transaction, including customer information, submission channel time, processing completion time, transaction amount, currency, sender country code, recipient country code, payment channel used, handling fee, and failure reason.

10. The cross-border payment funds routing optimization method according to claim 2, characterized in that: In step S2, obtaining the remaining available payment channel set further includes: Monitor the status of each payment channel in real time, update the channel status regularly, and exclude unavailable channels when making routing decisions. Monitoring indicators include channel position balance, channel service availability, channel average response time, and channel failure rate. If the average response time of a channel exceeds a preset threshold or the channel failure rate exceeds a preset upper limit, the channel is marked as unavailable.

11. A cross-border payment funds routing optimization system, characterized in that: include: A transaction data acquisition module is used to receive cross-border payment transaction requests, parse their transaction parameters, and query the global channel operation platform based on the transaction parameters to obtain an initial set of available payment channels and their corresponding channel information; a routing matching and filtering module, configured to call a payment routing service, identify the payment routing capability applicable to the transaction based on the transaction parameters, match the special routing rules of the transaction, perform a three-level routing rule filtering process on the available payment channel set based on the relationship between the payment routing capability and the special routing rules, and obtain the remaining available payment channel set; The routing optimization module is used to obtain historical performance indicator data at the customer-specific layer, customer group layer, and global layer, and to construct a three-layer performance indicator model by weighted fusion of the three-layer performance indicator data. The performance indicators of each dimension are predicted by the three-layer performance indicator model, and differentiated routing strategies are applied in combination with customer levels to calculate the comprehensive score of each channel to obtain the optimal payment channel.

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