Multi-currency card payment methods and devices

Abstract

This invention discloses a payment method and apparatus for multi-currency cards, relating to the field of blockchain technology. The method includes: obtaining multiple customer categories based on bank customer classification; constructing a risk space, where each dimension of the risk space corresponds one-to-one with the obtained customer categories; determining the coordinates of each currency in the risk space based on transaction data for each currency; determining the coordinates of each transaction scenario in the risk space based on transaction data for each transaction scenario; and for each scenario, determining multiple supported currencies corresponding to that scenario based on the coordinates of each currency and the scenario itself in the risk space. This invention can reduce transaction risks for bank customers.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and in particular to a payment method and apparatus for multi-currency cards. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] When making payment transactions, customers can choose accounts in different currencies. Different currencies correspond to different banking systems, and the transaction processing procedures and risk control capabilities of different banking systems vary. In other words, customers face different transaction risks when choosing different currencies. Summary of the Invention

[0004] This invention provides a payment method for multi-currency cards, the method comprising:

[0005] Based on the bank's customer classification, multiple customer categories are obtained;

[0006] Construct a risk space, where each dimension of the risk space corresponds one-to-one with the acquired customer category;

[0007] Based on the transaction data of each currency, determine the coordinates of that currency in the risk space;

[0008] Based on the transaction data of each transaction scenario, determine the coordinates of that scenario in the risk space;

[0009] For each scenario, based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space, multiple supported currencies are determined for that scenario.

[0010] This invention also provides a payment device for a multi-currency card, the device comprising:

[0011] The customer classification module is used for bank-based customer classification, obtaining multiple customer categories;

[0012] The risk space construction module is used to build a risk space, where each dimension of the risk space corresponds one-to-one with the acquired customer category.

[0013] The module for determining the coordinates of a currency in the risk space is used to determine the coordinates of that currency in the risk space based on the transaction data of each currency.

[0014] The module for determining the coordinates of a scenario in the risk space is used to determine the coordinates of that scenario in the risk space based on the transaction data of each transaction scenario.

[0015] The module supports currency determination, which, for each scenario, determines multiple supported currencies based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space.

[0016] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described multi-currency card payment method.

[0017] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-currency card payment method.

[0018] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described multi-currency card payment method.

[0019] In this embodiment of the invention, compared to existing technologies where different currencies correspond to different banking systems, and these systems have different transaction processing procedures and risk control capabilities, meaning customers face different transaction risks when choosing different currencies, this invention achieves digital banking risk control by classifying customers based on banks to obtain multiple customer categories; constructing a risk space where each dimension corresponds one-to-one with the obtained customer categories; determining the coordinates of each currency in the risk space based on transaction data for each currency; determining the coordinates of each transaction scenario in the risk space based on transaction data for each scenario; and for each scenario, determining multiple supported currencies based on the coordinates of each currency and the scenario in the risk space. This approach of determining different transaction currencies for different transaction scenarios effectively reduces customer risk during transactions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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. In the drawings:

[0021] Figure 1 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 1 ;

[0022] Figure 2 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 2 ;

[0023] Figure 3 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 3 ;

[0024] Figure 4 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 4 ;

[0025] Figure 5 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 5 ;

[0026] Figure 6 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 6 ;

[0027] Figure 7 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 7 ;

[0028] Figure 8 The following is a flowchart of the multi-currency card payment method in an embodiment of the present invention. Figure 8 ;

[0029] Figure 9 This is a block diagram of the payment device structure for a multi-currency card in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0031] Based on the problems existing in the prior art, this invention proposes a multi-currency card payment method for use in banks. The specific method flow is as follows: Figure 1 As shown, the method includes:

[0032] Step 101: Obtain multiple customer categories based on the bank's customer classification;

[0033] Step 102: Construct a risk space, where each dimension of the risk space corresponds one-to-one with the acquired customer category;

[0034] Step 103: Based on the transaction data of each currency, determine the coordinates of that currency in the risk space;

[0035] Step 104: Based on the transaction data of each transaction scenario, determine the coordinates of that scenario in the risk space;

[0036] Step 105: For each scenario, based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space, determine the multiple supported currencies corresponding to the scenario.

[0037] Specifically, different scenarios can include shopping, payment scenarios, hospitals, ETC transactions, private transfers, etc.

[0038] The dimensions of the risk space correspond one-to-one with the customer categories acquired. The specific dimension value is the risk probability of each customer category acquired corresponding to the currency or scenario.

[0039] In one embodiment, based on the bank's customer classification, multiple customer categories are obtained, including:

[0040] Obtain customer attribute data and transaction data of bank customers. Customer attributes may include risk level, income level, and payment transaction amount, while transaction data includes the number of each transaction category (such as deposit, withdrawal, and inquiry) for each customer (such as the number of withdrawals and inquiries).

[0041] Based on customer transaction data, a customer distance function is determined, whereby the distance function is used to determine the distance between any two customers, for example, the distance between two customers is determined as the square root of the sum of the squares of the differences between the quantities of each of the two customers' corresponding transactions;

[0042] The bank classifies its customers based on customer attribute data, resulting in multiple major customer categories. The customer attribute data of customers in each major customer category are roughly the same.

[0043] For each major customer category, cluster all customers in that category based on the customer distance function to obtain multiple customer categories.

[0044] To obtain more accurate classification results, the following steps are performed for the multiple customer categories obtained above:

[0045] Determine the primary transaction channels for each customer in each customer category, and determine the maximum value of the proportion of customers corresponding to each primary transaction channel in that customer category (the ratio of customers corresponding to each primary transaction channel to all customers in that customer category).

[0046] Repeat the following steps until the maximum percentage of customers from each major transaction channel across all customer categories is greater than or equal to a set threshold:

[0047] Select the customer category whose maximum value is less than the set threshold;

[0048] Based on the customer distance function, cluster all customers of the selected customer category and replace the selected customer category with the resulting multiple new customer categories.

[0049] In embodiments of the present invention, such as Figure 2 As shown, it also includes:

[0050] Step 201: When a customer is making a transaction, based on the transaction scenario and the multiple supported currencies corresponding to the scenario, determine the multiple supported currencies for this transaction and recommend the multiple supported currencies for this transaction to the customer.

[0051] Step 202: Receive digitally signed transaction data sent by the customer's counterparty, wherein the digitally signed transaction data is the transaction data in which the customer selects currency accounts based on multiple supported currencies corresponding to this transaction and conducts transactions with counterparties, and sends the transaction confirmation data to the counterparty after digitally signing it;

[0052] Step 203: Based on the exchange rate sent by the blockchain and the amount corresponding to the transaction currency in the transaction data, determine the amount of the supported currency selected by the customer, and perform accounting processing on the account corresponding to the supported currency selected by the customer based on the determined amount. In this process, the blockchain receives the digitally signed transaction data sent by the counterparty and determines the exchange rate corresponding to the transaction time based on the transaction time in the transaction data.

[0053] It should be noted that when mobile terminal information is poor (currency conversion cannot be performed in a timely manner), the foreign currency amount of the transaction needs to be determined later (the corresponding amount for the customer at the time of the transaction is in RMB). This can be achieved by the counterparty sending the information to the bank's server and the blockchain, thus addressing scenarios where the customer's mobile terminal has poor network information.

[0054] Transaction confirmation data can include the amount in RMB, the foreign currency, the transaction time, and the counterparty.

[0055] In embodiments of the present invention, such as Figure 3 As shown, step 103, based on the transaction data of each currency, determines the coordinates of the corresponding currency in the risk space, including:

[0056] Step 301: For each customer category, based on the transaction data of that customer category for that currency, determine the risk probability of that currency for that customer category;

[0057] Step 302: Determine the coordinates of the currency in the risk space. The coordinate value of each dimension is determined as the risk probability of the currency for the corresponding customer category in that dimension.

[0058] In one embodiment, for each customer category, based on the transaction data of that customer category for that currency, the risk probability of that currency corresponding to that customer category is determined, including:

[0059] The transaction data of this customer category for this currency is divided into multiple sample transaction data subsets. The transaction time ranges corresponding to any two sample transaction data subsets do not overlap. The union of all sample transaction data subsets is the transaction data of this customer category for this currency, and the number of transactions contained in each sample transaction data subset is greater than the quantity threshold.

[0060] (Step t) Take the proportion of risk-related transactions in all transactions contained in each sample transaction data subset as the risk sample for that currency and customer category;

[0061] The risk probability of a currency for a given customer category is determined as the mean of all risk samples for that currency for that customer category.

[0062] In one embodiment, the method further includes:

[0063] After (step t) taking the proportion of risky transactions in all transactions contained in each sample transaction data subset as the risk sample for that currency corresponding to that customer category, the variance and the number of risk samples corresponding to the risk probability of that currency corresponding to that customer category are determined based on all risk samples (multiple samples from the same distribution) for that currency corresponding to that customer category.

[0064] The square of the variance of the risk probability corresponding to the customer category for that currency, divided by the number of risk samples, is taken as the convergence error corresponding to the risk probability of the customer category for that currency.

[0065] In one embodiment, after step 302 determines the coordinates of the currency in the risk space, and the coordinate value of each dimension is determined as the risk probability of the customer category corresponding to that dimension, the method further includes:

[0066] The convergence error of the coordinate values ​​of the currency in each dimension of the risk space is defined as the convergence error corresponding to the risk probability of the customer category corresponding to that dimension.

[0067] In embodiments of the present invention, such as Figure 4 As shown, step 104 determines the coordinates of the corresponding scenario in the risk space based on the transaction data of each transaction scenario, including:

[0068] Step 401: For each customer category, based on the transaction data of customers in that customer category in that transaction scenario, determine the risk probability of that customer category for that transaction scenario;

[0069] Step 402: Determine the coordinates of the transaction scenario in the risk space. The coordinate value of each dimension is determined as the risk probability of the corresponding customer category for that transaction scenario.

[0070] It should be noted that step 401 can refer to the method in step 301 to determine the risk probability of the customer category for the corresponding transaction scenario.

[0071] In embodiments of the present invention, such as Figure 5 As shown, in step 105, for each scenario, based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space, multiple supported currencies for that scenario are determined, including:

[0072] Step 501: Determine the partial order of each currency based on its coordinates in the risk space. For any two currencies, the partial order can be used to determine whether the first currency is superior to the second currency.

[0073] Step 502: Based on the partial order corresponding to the currency, determine multiple maximal currencies of the partial order, wherein the maximal currency is the maximal element of the partial order;

[0074] Step 503: For each scenario, based on the scenario's coordinates in the risk space and the multiple maximum currencies in the partial order, determine the multiple supported currencies corresponding to the scenario.

[0075] In embodiments of the present invention, such as Figure 6 As shown, step 501, for each scenario, determines the partial order corresponding to each currency based on its coordinates in the risk space, including:

[0076] Step 601: For any two currencies, if for each dimension of the risk space, the coordinate value of the first currency in that dimension is less than or equal to the coordinate value of the second currency in that dimension, and the coordinate value of the first currency in each dimension is less than a set value, then the first currency is determined to be superior to the second currency.

[0077] Another method for determining the partial order corresponding to a currency is, in this embodiment of the invention, step 501, for each scenario, determining the partial order corresponding to the currency based on the coordinates of each currency in the risk space, including:

[0078] Step 601: For any two currencies, if for each dimension of the risk space, the coordinate value of the first currency in that dimension is less than or equal to the coordinate value of the second currency in that dimension, and the coordinate values ​​of the first currency in each dimension are all less than a set value, and the convergence error of the coordinate values ​​of the first currency in each dimension of the risk space is less than the convergence threshold, then the first currency is determined to be superior to the second currency.

[0079] The convergence threshold can be determined as follows: This represents the maximum acceptable risk probability error, and ψ represents the acceptable risk probability error greater than this maximum risk probability error. The probability of;

[0080] The convergence threshold is determined as follows:

[0081] In one embodiment, based on the partial order corresponding to the currency, multiple maximal currencies of the partial order are determined, including:

[0082] For each currency, determine the partial order relation between that currency and every other currency except that currency. If no other currency is superior to that currency, then determine that the currency is the maximal currency of the partial order.

[0083] In embodiments of the present invention, such as Figure 7 As shown, step 503, for each scenario, determines multiple supported currencies corresponding to that scenario based on the scenario's coordinates in the risk space and the multiple maximum currencies in the partial order, including:

[0084] Step 701: For each maximum currency in the partial order, if for each dimension, the coordinate value of the maximum currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, then the maximum currency is identified as one of the multiple supported currencies corresponding to the scenario.

[0085] In embodiments of the present invention, such as Figure 8 As shown, when there is no extremely large currency, satisfying that for each dimension, the coordinate value of the extremely large currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, it also includes:

[0086] Step 801: For each extremely large coin, determine the relationship between the coordinate values ​​of the extremely large coin in each dimension of the risk space and the coordinate values ​​of the scenario in each dimension of the risk space.

[0087] Step 802: For each dimension, if the coordinate value of the extremely large currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, then the difference between the coordinate value of the scenario in that dimension of the risk space and the coordinate value of the extremely large currency in that dimension of the risk space is determined as the first coordinate difference corresponding to the extremely large currency.

[0088] Step 803: For each dimension, if the coordinate value of the extremely large currency in that dimension of the risk space is greater than the coordinate value of the scenario in that dimension of the risk space, then the difference between the coordinate value of the extremely large currency in that dimension of the risk space and the coordinate value of the scenario in that dimension of the risk space is determined as the second coordinate difference corresponding to the extremely large currency.

[0089] Step 804: Determine the priority of the maximum currency based on the corresponding first coordinate difference and the corresponding second coordinate difference.

[0090] In one embodiment, determining the priority of the largest currency based on the corresponding first coordinate difference and the corresponding second coordinate difference includes:

[0091] For each extremely large currency, the sum of the first coordinate differences corresponding to that extremely large currency is taken as the first sum value corresponding to that extremely large currency, and the sum of the second coordinate differences corresponding to that extremely large currency is taken as the second sum value corresponding to that extremely large currency.

[0092] Based on the corresponding first and second sum values, the priority of the maximal currency is determined; whereby the priority of the i-th maximal currency is determined as follows: and Let g be the first and second sums corresponding to the j-th maximum currency, respectively. g is a bivariate real-valued function, monotonically increasing for the first independent variable and monotonically decreasing for the second independent variable, with a range greater than 0 (e.g., ...). ).

[0093] In one embodiment, when no extremely large currency exists, and the coordinate value of the extremely large currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space for each dimension, and after determining the priority of each extremely large currency, the extremely large currency is recommended to the customer in that scenario based on the priority. Specifically:

[0094] Sort the largest currencies; and choose a probability density function f;

[0095] For each maximum coin, the cumulative priority corresponding to that maximum coin is determined by the sum of the priority of the maximum coin and the priorities of all maximum coins preceding it in the sorting.

[0096] For each maximum currency, the endpoint value t corresponding to that maximum currency is determined based on its cumulative priority r, where... m is the minimum value of the domain of function f;

[0097] Generate a random number s based on function f;

[0098] Select the smallest endpoint value greater than s from the endpoint values ​​corresponding to the largest currencies;

[0099] Recommend the largest currency whose endpoint value is equal to the minimum endpoint value to customers in this scenario.

[0100] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0101] This invention also provides a multi-currency card payment device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the multi-currency card payment method, the implementation of this device can refer to the implementation of the multi-currency card payment method; repeated details will not be elaborated further.

[0102] Figure 9 This is a structural block diagram of a multi-currency card payment device in an embodiment of the present invention, such as... Figure 9 As shown, the device includes:

[0103] Customer classification module 02 is used for bank-based customer classification to obtain multiple customer categories;

[0104] Risk Space Construction Module 04 is used to construct the risk space, where each dimension of the risk space corresponds one-to-one with the acquired customer category.

[0105] The risk space coordinate determination module 06 is used to determine the coordinates of each currency in the risk space based on the transaction data of each currency.

[0106] The scenario coordinate determination module 08 is used to determine the coordinates of the scenario in the risk space based on the transaction data of each transaction scenario.

[0107] The currency determination module 10 is used to determine multiple supported currencies for each scenario based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space.

[0108] In this embodiment of the invention, it further includes:

[0109] When a customer is making a transaction, based on the transaction scenario and the multiple supported currencies corresponding to the scenario, the multiple supported currencies for this transaction are determined and recommended to the customer.

[0110] The system receives digitally signed transaction data from the client's counterparty. The digitally signed transaction data is generated when the client selects a currency account from multiple supported currencies to conduct a transaction with the counterparty, and then sends the digitally signed transaction confirmation data to the counterparty.

[0111] The amount of the supported currency selected by the customer is determined based on the exchange rate sent by the blockchain and the amount corresponding to the transaction currency in the transaction data. Based on the determined amount, the accounting is processed for the account corresponding to the supported currency selected by the customer. In this process, the blockchain receives the digitally signed transaction data sent by the counterparty and determines the exchange rate corresponding to the transaction time based on the transaction time in the transaction data.

[0112] In this embodiment of the invention, the currency risk space coordinate determination module 06 is specifically used for:

[0113] For each customer category, the risk probability of that currency for that customer category is determined based on the transaction data of that customer category for that currency.

[0114] Determine the coordinates of the currency in the risk space, and the coordinate value of each dimension is determined as the risk probability of the currency for the corresponding customer category in that dimension.

[0115] In this embodiment of the invention, the scenario coordinate determination module 08 is specifically used for:

[0116] For each customer category, the risk probability of that customer category for that transaction scenario is determined based on the transaction data of customers in that customer category in that transaction scenario;

[0117] Determine the coordinates of the transaction scenario in the risk space, and the coordinate value of each dimension is determined as the risk probability of the corresponding customer category for that transaction scenario.

[0118] In this embodiment of the invention, the currency determination module 10 is specifically used for:

[0119] Based on the coordinates of each currency in the risk space, the partial order corresponding to the currency is determined. For any two currencies, the partial order can be used to determine whether the first currency is superior to the second currency.

[0120] Based on the partial order corresponding to the currency, determine multiple maximal currencies of the partial order, where the maximal currency is the maximal element of the partial order;

[0121] For each scenario, based on the scenario's coordinates in the risk space and the multiple maximum currencies in the partial order, the multiple supported currencies corresponding to that scenario are determined.

[0122] In this embodiment of the invention, the currency determination module 10 is specifically used for:

[0123] For any two currencies, if for each dimension of the risk space, the coordinate value of the first currency in that dimension is less than or equal to the coordinate value of the second currency in that dimension, and the coordinate value of the first currency in each dimension is less than a set value, then the first currency is determined to be superior to the second currency.

[0124] In this embodiment of the invention, the currency determination module 10 is specifically used for:

[0125] For each maximal currency in the partial order, if for each dimension, the coordinate value of the maximal currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, then the maximal currency is identified as one of the multiple supported currencies for that scenario.

[0126] In this embodiment of the invention, the currency determination module 10 is specifically used for:

[0127] When there is no extremely large currency, and the coordinate value of the extremely large currency in each dimension of the risk space is less than the coordinate value of the scenario in each dimension of the risk space for each extremely large currency, determine the relationship between the coordinate values ​​of the extremely large currency in each dimension of the risk space and the coordinate values ​​of the scenario in each dimension of the risk space for each extremely large currency.

[0128] For each dimension, if the coordinate value of the extremely large currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, then the difference between the coordinate value of the scenario in that dimension of the risk space and the coordinate value of the extremely large currency in that dimension of the risk space is determined as the first coordinate difference corresponding to the extremely large currency.

[0129] For each dimension, if the coordinate value of the largest currency in that dimension of the risk space is greater than the coordinate value of the scenario in that dimension of the risk space, then the difference between the coordinate value of the largest currency in that dimension of the risk space and the coordinate value of the scenario in that dimension of the risk space is determined as the second coordinate difference corresponding to the largest currency.

[0130] The priority of the largest currency is determined based on the corresponding first coordinate difference and the corresponding second coordinate difference.

[0131] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described multi-currency card payment method.

[0132] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-currency card payment method.

[0133] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described multi-currency card payment method.

[0134] In this embodiment of the invention, compared to existing technologies where different currencies correspond to different banking systems, and these systems have different transaction processing procedures and risk control capabilities, meaning customers face different transaction risks when choosing different currencies, this invention achieves digital banking risk control by classifying customers based on banks to obtain multiple customer categories; constructing a risk space where each dimension corresponds one-to-one with the obtained customer categories; determining the coordinates of each currency in the risk space based on transaction data for each currency; determining the coordinates of each transaction scenario in the risk space based on transaction data for each scenario; and for each scenario, determining multiple supported currencies based on the coordinates of each currency and the scenario in the risk space. This approach of determining different transaction currencies for different transaction scenarios effectively reduces customer risk during transactions.

[0135] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A payment method using a multi-currency card, characterized in that, include: Based on the bank's customer classification, multiple customer categories are obtained; Construct a risk space, where each dimension of the risk space corresponds one-to-one with the acquired customer category; where the dimension value of the risk space is the risk probability of each acquired customer category corresponding to a currency or scenario; Based on the transaction data of each currency, determine the coordinates of that currency in the risk space; Based on the transaction data of each transaction scenario, determine the coordinates of that scenario in the risk space; For each scenario, based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space, multiple supported currencies for that scenario are determined. Based on the transaction data of each currency, determine the corresponding currency's coordinates in the risk space, including: For each customer category, the risk probability of that currency for that customer category is determined based on the transaction data of that customer category for that currency. Determine the coordinates of the currency in the risk space, and the coordinate value of each dimension is determined as the risk probability of the currency for the corresponding customer category in that dimension; For each customer category, based on the customer category's transaction data for that currency, determine the risk probability of that currency for that customer category, including: The transaction data of this customer category for this currency is divided into multiple sample transaction data subsets, wherein the transaction time ranges corresponding to any two sample transaction data subsets do not overlap, the union of all sample transaction data subsets is the transaction data of this customer category for this currency, and the number of transactions contained in each sample transaction data subset is greater than the number threshold. The percentage of risky transactions in all transactions contained in each subset of sample transaction data is used as the risk sample for that currency and corresponding customer category. The risk probability of a currency for a given customer category is determined as the mean of all risk samples for that currency for that customer category. Based on the transaction data for each transaction scenario, determine the coordinates of the corresponding scenario in the risk space, including: For each customer category, the risk probability of that customer category for that transaction scenario is determined based on the transaction data of customers in that customer category in that transaction scenario; Determine the coordinates of the transaction scenario in the risk space, and the coordinate value of each dimension is determined as the risk probability of the corresponding customer category for that transaction scenario; For each customer category, based on the transaction data of customers in that customer category for that transaction scenario, determine the risk probability of that customer category for that transaction scenario, including: The transaction data of this customer category for this transaction scenario is divided into multiple sample transaction data subsets. The transaction time ranges corresponding to any two sample transaction data subsets do not overlap. The union of all sample transaction data subsets is the transaction data of this customer category for this transaction scenario, and the number of transactions contained in each sample transaction data subset is greater than the number threshold. The proportion of risk-related transactions in all transactions included in each subset of sample transaction data is used as the risk sample for that customer category in that transaction scenario; The risk probability of the transaction scenario corresponding to the corresponding customer category is determined as the mean of all risk samples of the transaction scenario corresponding to the corresponding customer category; For each scenario, based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space, multiple supported currencies are determined for that scenario, including: Based on the coordinates of each currency in the risk space, the partial order corresponding to the currency is determined. For any two currencies, the partial order can be used to determine whether the first currency is superior to the second currency. Based on the partial order corresponding to the currency, determine multiple maximal currencies of the partial order, where the maximal currency is the maximal element of the partial order; For each scenario, based on the scenario's coordinates in the risk space and the multiple maximum currencies in the partial order, the multiple supported currencies corresponding to that scenario are determined.

2. The method as described in claim 1, characterized in that, Also includes: When a customer is making a transaction, based on the transaction scenario and the multiple supported currencies corresponding to the scenario, the multiple supported currencies for this transaction are determined and recommended to the customer. The system receives digitally signed transaction data from the client's counterparty. The digitally signed transaction data is generated when the client selects a currency account from multiple supported currencies to conduct a transaction with the counterparty, and then sends the digitally signed transaction confirmation data to the counterparty. The amount of the supported currency selected by the customer is determined based on the exchange rate sent by the blockchain and the amount corresponding to the transaction currency in the transaction data. Based on the determined amount, the accounting is processed for the account corresponding to the supported currency selected by the customer. In this process, the blockchain receives the digitally signed transaction data sent by the counterparty and determines the exchange rate corresponding to the transaction time based on the transaction time in the transaction data.

3. The method as described in claim 1, characterized in that, For each scenario, based on the coordinates of each currency in the risk space, the corresponding partial order of the currency is determined, including: For any two currencies, if for each dimension of the risk space, the coordinate value of the first currency in that dimension is less than or equal to the coordinate value of the second currency in that dimension, and the coordinate value of the first currency in each dimension is less than a set value, then the first currency is determined to be superior to the second currency.

4. The method as described in claim 1, characterized in that, For each scenario, based on the scenario's coordinates in the risk space and the multiple maximum currencies in the partial order, the corresponding multiple supported currencies are determined, including: For each maximal currency in the partial order, if for each dimension, the coordinate value of the maximal currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, then the maximal currency is identified as one of the multiple supported currencies for that scenario.

5. The method as described in claim 4, characterized in that, When there is no extremely large coin, satisfying that for each dimension, the coordinate value of the extremely large coin in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, it also includes: For each extremely large coin, determine the relationship between the coordinate values ​​of that extremely large coin in each dimension of the risk space and the coordinate values ​​of that scenario in each dimension of the risk space. For each dimension, if the coordinate value of the extremely large currency in that dimension of the risk space is less than the coordinate value of the scenario in that dimension of the risk space, then the difference between the coordinate value of the scenario in that dimension of the risk space and the coordinate value of the extremely large currency in that dimension of the risk space is determined as the first coordinate difference corresponding to the extremely large currency. For each dimension, if the coordinate value of the largest currency in that dimension of the risk space is greater than the coordinate value of the scenario in that dimension of the risk space, then the difference between the coordinate value of the largest currency in that dimension of the risk space and the coordinate value of the scenario in that dimension of the risk space is determined as the second coordinate difference corresponding to the largest currency. The priority of the largest currency is determined based on the corresponding first coordinate difference and the corresponding second coordinate difference.

6. A payment device for a multi-currency card, characterized in that, include: The customer classification module is used for bank-based customer classification, obtaining multiple customer categories; The risk space construction module is used to construct a risk space, in which each dimension of the risk space corresponds one-to-one with the obtained customer category; wherein, the dimension value of the risk space is the risk probability of each obtained customer category corresponding to the currency or scenario; The module for determining the coordinates of a currency in the risk space is used to determine the coordinates of that currency in the risk space based on the transaction data of each currency. The module for determining the coordinates of a scenario in the risk space is used to determine the coordinates of that scenario in the risk space based on the transaction data of each transaction scenario. It supports a currency determination module, which is used to determine multiple supported currencies for each scenario based on the coordinates of each currency in the risk space and the coordinates of the scenario in the risk space. The module for determining the risk space coordinates of a currency is specifically used for: For each customer category, the risk probability of that currency for that customer category is determined based on the transaction data of that customer category for that currency. Determine the coordinates of the currency in the risk space, and the coordinate value of each dimension is determined as the risk probability of the currency for the corresponding customer category in that dimension; The module for determining the risk space coordinates of a currency is specifically used for: The transaction data of this customer category for this currency is divided into multiple sample transaction data subsets, wherein the transaction time ranges corresponding to any two sample transaction data subsets do not overlap, the union of all sample transaction data subsets is the transaction data of this customer category for this currency, and the number of transactions contained in each sample transaction data subset is greater than the number threshold. The percentage of risky transactions in all transactions contained in each subset of sample transaction data is used as the risk sample for that currency and corresponding customer category. The risk probability of a currency for a given customer category is determined as the mean of all risk samples for that currency for that customer category. The module for determining the coordinates of a scene in the risk space is specifically used for: For each customer category, the risk probability of that customer category for that transaction scenario is determined based on the transaction data of customers in that customer category in that transaction scenario; Determine the coordinates of the transaction scenario in the risk space, and the coordinate value of each dimension is determined as the risk probability of the corresponding customer category for that transaction scenario; The module for determining the coordinates of a scene in the risk space is specifically used for: The transaction data of this customer category for this transaction scenario is divided into multiple sample transaction data subsets. The transaction time ranges corresponding to any two sample transaction data subsets do not overlap. The union of all sample transaction data subsets is the transaction data of this customer category for this transaction scenario, and the number of transactions contained in each sample transaction data subset is greater than the number threshold. The proportion of risk-related transactions in all transactions included in each subset of sample transaction data is used as the risk sample for that customer category in that transaction scenario; The risk probability of the transaction scenario corresponding to the corresponding customer category is determined as the mean of all risk samples of the transaction scenario corresponding to the corresponding customer category; It supports a currency determination module, specifically used for: Based on the coordinates of each currency in the risk space, the partial order corresponding to the currency is determined. For any two currencies, the partial order can be used to determine whether the first currency is superior to the second currency. Based on the partial order corresponding to the currency, determine multiple maximal currencies of the partial order, where the maximal currency is the maximal element of the partial order; For each scenario, based on the scenario's coordinates in the risk space and the multiple maximum currencies in the partial order, the multiple supported currencies corresponding to that scenario are determined.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

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