WO2024112256A1 - Procédé et système d'attribution d'un paramètre de transaction - Google Patents

Procédé et système d'attribution d'un paramètre de transaction Download PDF

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Publication number
WO2024112256A1
WO2024112256A1 PCT/SG2022/050862 SG2022050862W WO2024112256A1 WO 2024112256 A1 WO2024112256 A1 WO 2024112256A1 SG 2022050862 W SG2022050862 W SG 2022050862W WO 2024112256 A1 WO2024112256 A1 WO 2024112256A1
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WIPO (PCT)
Prior art keywords
transaction
data
transaction data
queue
value
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Application number
PCT/SG2022/050862
Other languages
English (en)
Inventor
Xiaokai REN
Original Assignee
Tenpay Global Pte. Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tenpay Global Pte. Ltd. filed Critical Tenpay Global Pte. Ltd.
Priority to PCT/SG2022/050862 priority Critical patent/WO2024112256A1/fr
Publication of WO2024112256A1 publication Critical patent/WO2024112256A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0831—Overseas transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q2220/00—Business processing using cryptography

Definitions

  • Various aspects relate to methods, devices, and non-transitory computer-readable medium for attributing transaction parameters.
  • FIG. 4 is a block diagram showing a comparing step of an example method according to an embodiment of the present disclosure.
  • FIG. 5 is a graph showing example transaction data of the preceding closed exposure transaction data and the present open exposure transaction data.
  • exemplary may be used herein to mean “serving as an example, instance, or illustration”. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs.
  • the phrase “at least one of’ with regard to a group of elements may be used herein to mean a selection of: one of the listed elements, a plurality of one of the listed elements, a plurality of individual listed elements, or a plurality of a multiple of listed elements.
  • any phrases explicitly invoking the aforementioned words expressly refer to more than one of the said objects.
  • data may be understood to include information in any suitable analog or digital form, e.g., provided as a file, a portion of a file, a set of files, a signal or stream, a portion of a signal or stream, a set of signals or streams, and the like. Further, the term “data” may also be used to mean a reference to information, e.g., in form of a pointer. The term “data”, however, is not limited to the aforementioned examples and may take various forms and represent any information as understood in the art. Any type of information, as described herein, may be handled for example via one or more processors in a suitable way, e.g. as data.
  • transaction data refers to data that are associated with selling and/or buying one or more goods and/or services. Such goods and/or services may include one or more currencies. In some embodiments, transaction data may suitably be secured or encrypted. In some embodiments, a transaction data may be a fulfilled or unfulfilled order for a good or service transaction at a particular currency.
  • memory may be understood to include any suitable type of memory or memory device, e.g., a hard disk drive (HDD), a solid-state drive (SSD), a flash memory, etc.
  • HDD hard disk drive
  • SSD solid-state drive
  • flash memory etc.
  • chants refers to business (B-end) users who have signed contracts after being authenticated by the third-party payment platform, and supply transaction resources (goods, services, etc.) for ordinary users.
  • fair rate(s) may be understood as the foreign exchange rates published by banks or financial institutions or liquidity providers, including reference prices for buying and selling foreign currencies. This foreign exchange rate may not be traded directly and entail no trading volume, and the updated frequency thereof may lag.
  • the term “internalized profit” detailed herein may be understood as when the third-party payment receives two opposite orders (for example, a buy order and a sell order) with respect to the same currency, the third-party payment may choose to offset the two orders, without exchanging the currency with the bank.
  • the profit and loss generated by this procedure is called internalized profit.
  • platform rates details herein refers to rates that the platform generates from fair rates by marking up a profit margin and offers to the users.
  • An institution may divide the profit and loss of orders (e.g. transaction data) into realized profit and loss and unrealized profit and loss under risk control.
  • orders e.g. transaction data
  • the profit and loss caused by fluctuations of exchange rates may not be considered as profit or loss.
  • the institution closes transaction with the bank the profit and loss brought by this order may be considered to have been realized.
  • Current profit and loss, unrealized profit and loss may return to a value of zero (0).
  • the platform may manage risks of the foreign currency exchange by taking the accounts of a pair of currency exchange as the smallest unit for analysis and may not track the profit and loss brought by each transaction data. It may be difficult for third-party payment services to make refined assessment of foreign currency exchange services, for example, the longer holding time may cause greater fluctuation in profit and loss of the position, the unreasonable internalization strategy may cause less internalized profit, or a sudden increase in closing cost may cause the cost deviating from the quoted price.
  • Realized profit and loss and unrealized profit and loss according to whether the position is closed or not may reflect the profit and loss caused by market fluctuations.
  • the unrealized profit and loss may be equivalent to the position profit and loss.
  • this part of the profit and loss may be mixed with other factors to become the realized profit and loss.
  • the third-party payment services may not determine the profit and loss brought by the holding time of the position to this order afterwards.
  • the proposed method may include assigning at least one transaction data to at least one data group based on a type of currency exchange; generating an exposure transaction data of the at least one data group, the exposure transaction data being a summary of transaction data having the type of currency exchange obtained within a time period; arranging transaction data in the data group according to a first transaction type in a first data queue, and arranging transaction data according to a second transaction type in a second data queue, the second transaction type being different to the first transaction type; comparing, at a same queue position of the first data queue and the second data queue, an arranged transaction data value in the first data queue with an arranged transaction data value in the second data queue, to obtain a matched value and a remainder; and attributing the transaction parameter based on a function of the matched value.
  • the method may include creating transaction data that closes positions with the transaction data in the at least one data group, for example, at a fixed timing (e.g. 0630 am UTC8) every day. That may mean that an artificial exposure transaction data of the created transaction data (e.g. artificial transaction data) and the transaction data in the at least one data group for which the exposure transaction data is generated is zero (e.g. closed to zero).
  • the created transaction data may be compared with the transaction data in the at least data group for which the exposure transaction data is generated to obtain matched values such that all the transaction data (e.g.
  • transaction parameters e.g. the profit and loss
  • transaction parameters may be obtained (e.g. calculated, labelled, assigned, attributed) from the matched values. That may mean transaction parameters (e.g. the profit and loss) may be obtained with respect to each transaction data by creating the created transaction data (e.g. artificial transaction data).
  • aspects of the systems and techniques described here provide technical improvements and advantages over existing approaches.
  • the proposed systems and methods may provide a technical solution to attribute (e.g. calculate, label, assign) a transaction parameter of a group of transaction data taking into account every transaction data.
  • attribute e.g. calculate, label, assign
  • This is an improvement over the prior art where the attribution of transaction parameters is based on a time period regardless of the number of transaction data, and may not be accurate.
  • the use of data queues and arrangement of transaction data based on at least two transaction types for purpose of comparison and matching provides an efficient way of data management for deriving the transaction parameter.
  • the present disclosure provides a time-series-based method for attributing profit and loss in foreign currency exchange transactions.
  • the present disclosure provides a method of attributing the position profit and loss timely, in spite of positions (e.g. exposure) being closed or not, the profit and loss of the positions may be obtained by creating artificial transaction data. Accordingly, the profit and loss of each transaction data is provided and factors affecting the profit and loss may be analyzed, thereby providing ways for the third-party payment service to improve their business profit.
  • the proposed method may be used to analyze the profit and loss brought by the transaction with different banks (e.g. fair rates) and different time periods (e.g. transaction times) by attributing the closing cost. According to the principles of the closing cost, a closing strategy of different time periods (e.g. transaction times) may be designed.
  • the present disclosure distinguishes between trading parties by classifying the transaction data (e.g. orders) amount hedging between merchants and merchants as internalized profit.
  • Third-party payment institutions may give merchants competitive foreign exchange services to grow internalized profits, for example, by providing competitive platform rates to merchants.
  • Example 1 is a computer-implemented method for attributing a transaction parameter, the method executed by at least one processor, and including: assigning at least one transaction data to at least one data group based on a type of currency exchange; generating an exposure transaction data of the at least one data group, the exposure transaction data being a summary of transaction data having the type of currency exchange obtained within a time period; arranging transaction data in the data group according to a first transaction type in a first data queue, and arranging transaction data according to a second transaction type in a second data queue, the second transaction type being different to the first transaction type; comparing, at a same queue position of the first data queue and the second data queue, an arranged transaction data value in the first data queue with an arranged transaction data value in the second data queue, to obtain a matched value and a remainder; and attributing the transaction parameter based on a function of the matched value.
  • Example 2 the subject matter of Example 1 may optionally include that the transaction parameter is a profit and loss indicator of each arranged transaction data value.
  • Example 3 the subject matter of Example 1 may optionally include that the first and second data queues are arranged according to a chronological order, wherein the same queue position is the first queue position of the first data queue and the second data queue.
  • Example 4 the subject matter of Example 1 may optionally include that the at least one transaction data comprise a first transaction data based on a first currency exchange comprising a first currency exchanged to a second currency and a second transaction data based on a second currency exchange comprising the second currency exchanged to the first currency, wherein the type of currency exchange comprises the first currency exchange and the second currency exchange.
  • Example 5 the subject matter of Example 1 may optionally include that the arranged transaction data value in the first data queue is obtained at a first transaction time and the arranged transaction data value in the second data queue is obtained at a second transaction time later than the first transaction time, wherein the function of the matched value for the transaction parameter of the arranged transaction data value in the first data queue comprises multiplying the matched value, the first transaction type and a difference between a second fair rate at the second time and a first fair rate at the first transaction time.
  • the subject matter of Example 5 may optionally include that the transaction parameter of the arranged transaction data value in the second data queue is assigned a zero value.
  • Example 7 the subject matter of Example 1 may optionally include that the arranged transaction data value in the first data queue is obtained at a first transaction time and the arranged transaction data value in the second data queue is obtained at a second transaction time later than the first transaction time, wherein attributing the transaction parameter of the arranged transaction data value in the first/second data queue comprises: calculating a first difference of between a first fair rate at the first transaction time and a first platform rate at the transaction first time; calculating a second difference of between a second fair rate at the second time and a second platform rate at the second time; and multiplying by a predetermined constant, the matched value, the first transaction type and a summation of the first difference and the second difference.
  • Example 8 the subject matter of Example 1 may optionally include that assigning the at least one transaction data to the at least one data group further comprises assigning the at least one transaction data to the at least one data group by delivery dates.
  • Example 9 the subject matter of Example 8 may optionally include that the exposure transaction data of the at least one data group comprises transaction data that was closed in a preceding delivery date and transaction data that was opened in a present delivery date, wherein if a delivery date is equal to or less than today, the delivery date is updated to a succeeding delivery date.
  • Example 10 the subject matter of Example 3 may optionally include that comparing an arranged transaction data value in the first data queue with an arranged transaction data value in the second data queue to obtain a matched value and a remainder comprises: (i) matching the arranged transaction data value in the first data queue with the arranged transaction data value in the second data queue to obtain the matched value and the remainder; (ii) returning the remainder to the first queue position of the respective data queue as an arranged transaction data value; and (iii) repeating steps in (i) and (ii) until all transaction data are matched.
  • Example 11 the subject matter of Example 10 may optionally include consolidating the transaction parameters of each matched value generated by a transaction data.
  • Example 12 the subject matter of Example 1 may optionally include obtaining fair rates and platform rates in the market in accordance with transaction times.
  • Example 13 the subject matter of Example 1 may optionally include assigning an attribute to each transaction data.
  • Example 14 the subject matter of Example 13 may optionally include the attribute is selected from a first attribute of a client characteristic and a second attribute of a liquidity provider characteristic.
  • Example 15 the subject matter of Example 1 may optionally include filtering the at least one transaction data to remove sensitive data.
  • Example 16 is a system for attributing a transaction parameter of a plurality of transactions based on different currency exchange rates, the system including: a processor configured to assign at least one transaction data to at least one data group based on a type of currency exchange; generate an exposure transaction data of the at least one data group, the exposure transaction data being a summary of transaction data having the type of currency exchange obtained within a time period; arrange transaction data in the data group according to a first transaction type in a first data queue, and arrange transaction data in the group according to a second transaction type in a second data queue, the second transaction type being different to the first transaction type; compare, at a same queue position of the first data queue and the second data queue, an arranged transaction data value in the first data queue with an arranged transaction data value in the second data queue, to obtain a matched value and a remainder; computing a difference between the arranged data value at the same queue position of the first data queue and the second data queue; and attribute the transaction parameter based on a function of the matched value.
  • Example 16 the subject matter of Example 16 may optionally include at least one terminal device registered with the processor, the at least one terminal device configured to send the at least one transaction data to the input module.
  • Example 18 the subject matter of Example 17 may optionally include that the registration includes generating a unique identifier for each terminal device.
  • Example 19 the subject matter of Example 17 may optionally include whitelisting specific IP addresses associated with the at least one terminal device.
  • Example 20 the subject matter of Example 18 may optionally include the at least one terminal device is a mobile computer device.
  • Example 21 is a non-transitory computer-readable medium storing computer executable code comprising instructions for attributing a transaction parameter according to the method of examples 1 to 15.
  • Example 23 is a computer-implemented method for attributing a transaction parameter, the method executed by at least one processor, and including: assigning at least one transaction data to at least one data group based on a type of currency exchange; generating an exposure transaction data of the at least one data group, the exposure transaction data being a summary of transaction data having the type of currency exchange obtained within a time period; creating transaction data that closes positions with the transaction data in the at least one data group; comparing the transaction data in the at least one data group and the created transaction data to obtain a matched value and a remainder; and attributing the transaction parameter based on a function of the matched value.
  • FIG. 1 is a block diagram showing an example electronic device 100, according to an embodiment of the present disclosure.
  • the electronic device 100 may be a laptop computer, a desktop computer, a tablet computer, an automobile computer, a smart phone, a personal digital assistant, a server, or other electronic devices capable of running computer applications.
  • the electronic device 100 includes a processor 102, an input/output (I/O) module 104, memory 106, a power unit 108, and one or more network interfaces 110.
  • the electronic device 100 can include additional components.
  • the processor 102, input/output (I/O) module 104, memory 106, power unit 108, and the network interface(s) 110 are housed together in a common housing or other assembly.
  • the processor 102 can execute instructions, for example, to generate output data based on data inputs.
  • the instructions can include programs, codes, scripts, modules, or other types of data stored in memory (e.g., memory 106). Additionally or alternatively, the instructions can be encoded as pre-programmed or re-programmable logic circuits, logic gates, or other types of hardware or firmware components or modules.
  • the processor 102 may be, or may include, a multicore processor having a plurality of cores, and each such core may have an independent power domain and can be configured to enter and exit different operating or performance states based on workload.
  • the processor 102 may be, or may include, a general-purpose microprocessor, as a specialized co-processor or another type of data processing apparatus. In some cases, the processor 102 performs high-level operation of the electronic device 100. For example, the processor 102 may be configured to execute or interpret software, scripts, programs, functions, executables, or other instructions stored in the memory 106.
  • the example I/O module 104 may include a mouse, keypad, touch screen, scanner, optical reader, and/or stylus (or other input device(s)) through which a user of the electronic device 100 may provide input to the electronic device 100, and may also include one or more audio speakers for providing audio output and a video display device for providing textual, audiovisual, and/or graphical output.
  • the example memory 106 may include computer-readable storage media, for example, a volatile memory device, a non-volatile memory device, or both.
  • the memory 106 may include one or more read-only memory devices, random-access memory devices, buffer memory devices, or a combination of these and other types of memory devices. In some instances, one or more components of the memory can be integrated or otherwise associated with another component of the electronic device 100.
  • the memory 106 may store instructions that are executable by the processor 102. In some examples, the memory 106 may store instructions for an operating system 112 and for application programs 114.
  • the memory 106 may also store a database 116.
  • the example power unit 108 provides power to the other components of the electronic device 100.
  • the other components may operate based on electrical power provided by the power unit 108 through a voltage bus or other connection.
  • the power unit 108 includes a battery or a battery system, for example, a rechargeable battery.
  • the power unit 108 includes an adapter (e.g., an AC adapter) that receives an external power signal (from an external source) and coverts the external power signal to an internal power signal conditioned for a component of the electronic device 100.
  • the power unit 108 may include other components or operate in another manner.
  • the electronic device 100 may be configured to operate in a wireless, wired, or cloud network environment (or a combination thereof).
  • the electronic device 100 can access the network using the network interface(s) 110.
  • the network interface(s) 110 can include one or more adapters, modems, connectors, sockets, terminals, ports, slots, and the like.
  • the wireless network that the electronic device 100 accesses may operate, for example, according to a wireless network standard or another type of wireless communication protocol.
  • the wireless network may be configured to operate as a Wireless Local Area Network (WLAN), a Personal Area Network (PAN), a metropolitan area network (MAN), or another type of wireless network.
  • WLAN Wireless Local Area Network
  • PAN Personal Area Network
  • MAN metropolitan area network
  • FIG. 2 is a block diagram showing an example system 204 according to an embodiment of the present disclosure.
  • the system 204 may be implemented in a platform providing foreign currency exchange services, for example, a third-party payment service.
  • third-party payment services may provide merchants operating on the platform with quotations of different foreign currency exchange rates (e.g. platform rates), for example, in cross-border payment scenarios, whereby users (e.g. user 202) who buy from the merchants may pay in foreign currencies.
  • third-party payment services may buy currencies from and/or sell currencies to banks (e.g. bank 206).
  • a merchant may supply products in RMB (e.g. local currency), a user may purchase a product in a specific foreign currency (e.g. US dollars) through the platform (e.g. the third-party payment service).
  • the platform e.g. the third-party payment service
  • the platform may buy the specific foreign currency (e.g. US dollars) from the user and sell RMB to the user for payment to the merchant.
  • the specific foreign currency (e.g. US dollars) and RMB may be considered as a pair of currency exchange.
  • the system 204 may receive a first transaction data from the user 202 to the platform (e.g. the third-party payment service), e.g.
  • the first transaction data may include a pair of currency exchange, for example, the system 204 may be buying a first currency (e.g. US dollars) from the user 202 at a first exchange rate (e.g. a first platform rate) that is prevailing at the first transaction time and sell a second currency (e.g. RMB) to the user 202 at a second exchange rate (e.g. a second platform rate) that is prevailing at the first transaction time.
  • a first currency e.g. US dollars
  • RMB second currency
  • a merchant may supply products in a specific foreign currency (e.g. US dollars), a user may purchase a product in RMB (e.g. local currency) through the platform (e.g. the third-party payment service).
  • the platform e.g. the third-party payment service
  • the platform may buy RMB from the user and sell the specific foreign currency (e.g. US dollars) to the user for payment to the merchant.
  • the system 204 may receive a second transaction data from the user 202 to the platform (e.g. the third-party payment service), e.g. the user buying the product from the merchant operated on the platform that the system 204 is implemented.
  • the second transaction data may include a pair of currency exchange, for example, the system 204 may be buying the second currency (e.g. RMB) from the user 202 at a third exchange rate (e.g. a third platform rate) that is prevailing at a second transaction time and sell the first currency (e.g. US dollars) to the user 202 at a fourth exchange rate (e.g. a fourth platform rate) that is prevailing at the second transaction time.
  • a third exchange rate e.g. a third platform rate
  • the first currency e.g. US dollars
  • the first transaction data and the second transaction data as described above may be compared as described herein to attribute a transaction parameter (e.g. internalized profits and losses), wherein the exchange rates that are prevailing at the first and second transaction times are factored.
  • a transaction parameter e.g. internalized profits and losses
  • the platform may buy a specific foreign currency (e.g. US dollars) from a liquidity provider (e.g. bank 206) using RMB (e.g. local currency).
  • the system 204 may receive a third transaction data from the platform (e.g. the third-party payment service) to the bank 206, e.g. the platform (e.g. the third-party payment service) buying the first currency (e.g. US dollars) from the bank 206.
  • the third transaction data may include a pair of currency exchange, for example, the system 204 may be buying the first currency (e.g. US dollars) from the bank 206 at a fifth exchange rate (e.g. a fifth platform rate) that is prevailing at a third transaction time and sell the second currency (e.g. RMB) to the bank 206 at a sixth exchange rate (e.g. a sixth platform rate) that is prevailing at the third transaction time.
  • the platform may sell a specific foreign currency (e.g. US dollars) from the bank (e.g. bank 206) to get RMB (e.g. local currency).
  • the system 204 may receive a fourth transaction data from the platform (e.g. the third-party payment service) to the bank 206, e.g. the platform (e.g. the third-party payment service) selling the first currency (e.g. US dollars) to the bank 206.
  • the fourth transaction data may include a pair of currency exchange, for example, the system 204 may buying the second currency (e.g. RMB) from the bank 206 at a seventh exchange rate (e.g. a seven platform rate) that is prevailing at a fourth transaction time and sell the first currency (e.g. US dollars) to the bank 206 at an eighth exchange rate (e.g. an eighth platform rate) that is prevailing at the fourth transaction time.
  • the third transaction data and the fourth transaction data as described above may be compared as described herein to attribute a transaction parameter (e.g. warehousing profits and losses), wherein the exchange rates that are prevailing at the third and fourth transaction times are factored.
  • a transaction parameter e.g. warehousing profits and losses
  • the first transaction data and the fourth transaction data as described above may be compared as described herein to attribute a transaction parameter (e.g. warehousing/hedging profits and losses), wherein the exchange rates that are prevailing at the first and fourth transaction times are factored.
  • a transaction parameter e.g. warehousing/hedging profits and losses
  • FIG. 3 is a flow chart showing an example method 300 according to an embodiment of the present disclosure.
  • the method 300 may include putting together information within a time period regarding exchanges rate, transaction data from a first party to a second party and transaction data from the second party to a third party, prior to the step 302.
  • the exchange rates may include fair rates in accordance with transaction times and may be obtained periodically from the various information sources/providers (e.g. Bloomberg, Reuters).
  • the exchange rates may also include platform rates in accordance with transaction times and may be obtained based on the fair rates, for example, a mark-up on top of the fair rates by the platform.
  • the fair rate may vary according to transaction time and accordingly the platform rates may vary according to transaction time.
  • the first party may include clients (e.g. users who purchase products from the platform), the second party may include platforms (e.g. third-party payment services) and the third party may include financial instructions (e.g. banks).
  • the method 300 may include assigning an attribute to each transaction data.
  • the attribute may be selected from a first attribute of a client characteristic and a second attribute of a bank (or financial institution) characteristic.
  • transaction data from the first party (e.g. clients) to the second party (e.g. platforms) may be assigned an attribute of a client characteristic and the transaction data from the second party (e.g. platforms) to the third party (e.g. banks) may be assigned an attribute of a bank (or financial institution) characteristic.
  • the method 300 may include assigning (step 302) at least one transaction data to at least one data group based on a type of currency exchange.
  • the at least one transaction data may include one or more of the first transaction data, the second transaction data, the third transaction data and/or the fourth transaction data as described above with reference to FIG. 2.
  • the type of currency exchange may comprise a first currency exchange and a second currency exchange wherein the first currency exchange may comprise a first currency exchanged to a second currency and the second currency exchange comprises the second currency exchanged to the first currency.
  • the type of currency exchange may include exchange of a pair of currency, e.g. buying/selling either one of the pair of currency using/for the other currency of the pair of currency.
  • the type of currency exchange may include exchange of RMB and US dollars, e.g. buying/selling RMB for US dollars and buying/selling US dollars for RMB.
  • transaction data involving exchange of a pair of currency may be assigned to at least one data group. That may mean the first, second, third and fourth transaction data as described above with reference with FIG. 2 may be assigned to the at least one data group.
  • the method 300 may also include assigning the at least one transaction data to the at least one data group by delivery dates. If a delivery date of transaction data is equal to or less than today, the delivery date may be updated to the succeeding delivery date.
  • the method 300 may include (step 303) generating an exposure transaction data of the at least one data group.
  • the exposure transaction data may be a summary of transaction data having the type of currency exchange obtained within a time period. Accordingly, the exposure transaction data may indicate an open or close position of a currency for the time period. For example, in the examples described above with reference to FIG. 2, the exposure transaction data may indicate a difference between buying and selling of the specific currency (e.g. US dollars).
  • the exposure transaction data may not zero out a currency's exposure (e.g. the difference between buying and selling of a specific currency (e.g. US dollars)) within the time period (e.g. daily) as the platform may not close positions with banks within the time period (on a daily basis).
  • the time period may include a period of 24 hours. Therefore, the profit and loss of an exposure transaction data may be the profit and loss when the platform makes a closing position with banks.
  • the exposure transaction data may accumulate a preceding closed exposure transaction data and a present open exposure transaction data.
  • the preceding closed exposure transaction data may be a summary of transaction data having the type of currency exchange obtained within a preceding time period (e.g. a closed time period). That is, the preceding closed exposure transaction data may indicate an open or close position of a currency for the preceding time period.
  • the present open exposure transaction data may be a summary of transaction data having the type of currency exchange obtained within the (present) time period. That is, the present open exposure transaction data may indicate an open or close position of a currency for the (present) time period.
  • the preceding closed exposure transaction data may be considered as the earliest transaction data of the (present) time period.
  • the exposure transaction data of the at least one data group may include transaction data that was closed in a preceding delivery date and transaction data that was opened in a present delivery date, wherein if a delivery date is equal to or less than today, the delivery date may be updated to a succeeding delivery date.
  • Transaction data summarized in the preceding closed exposure transaction data may have the same attribute of a client/liquidity provider characteristic as transaction data first received in the succeeding time period and having the same value.
  • Transaction data summarized in the present open exposure transaction data may have the same attribute of a client/liquidity provider characteristic as the transaction data that were not successfully matched in the preceding time period.
  • the method may further include creating transaction data so as to close the positions of the exposure transaction data.
  • the transaction data in the data group and the created transaction data may be summarized to a closed position (e.g. the resulted exposure data is zero).
  • the created transaction data may be assigned an attribute of a client/liquidity provider characteristic.
  • the liquidity provider characteristic may be a bank characteristic, which will be described in more details with reference to FIG. 5.
  • the created transaction data may be combined with the transaction data in the data group for which the exposure transaction data is generated, and the combined transaction data may be processed by the steps 304, 305, 306 as transaction data in the data group.
  • the method 300 may also include searching for fair rates and platform rates according to transaction times.
  • the method 300 may include (step 304) arranging transaction data in the data group according to a first transaction type in a first data queue, and arranging transaction data according to a second transaction type in a second data queue.
  • the second transaction type may be different from the first transaction type.
  • the first and second data queues may be arranged according to a chronological order. That may mean the first and second data queues are arranged according to an ascending or descending chronological order. That is, the transaction data in the first and second data queues may start from the latest transaction data to the earliest transaction data or from the earliest transaction data to the latest transaction data.
  • the transaction type may be classified according to currency transaction with respect to a pair of currencies.
  • the first transaction type may include buying a first currency and selling a second currency
  • the second transaction type may include buying the second currency and selling the first currency.
  • the first transaction type may refer to an opposite transaction of the second transaction type with respect to the pair of currencies.
  • the first transaction type may be assigned as a “plus (+)” computation and the second transaction type may be assigned as a “minus (-)” computation.
  • the method 300 may include (step 305) comparing, at a same queue position of the first data queue and the second data queue, an arranged transaction data value in the first data queue with an arranged transaction data value in the second data queue, to obtain a matched value and a remainder.
  • An arranged transaction data value may be the full value (the original value) or a partial value of a transaction data that is arranged at a queue position.
  • full value it is intended to mean the original value at which the transaction data was transacted, e.g. the price of the product or the amount of money at the transacted currency that the user paid for the product.
  • partial value it is intended to mean a remainder of the full value after comparing to obtain the matched value. More details will be described with reference to FIG. 4.
  • the same queue position may include the first queue position of the first data queue and the second data queue. That may mean the arranged transaction data value is generated from the latest transaction data or the earliest transaction data according to the descending or ascending chronological order in which the transaction data is arranged.
  • the method 300 may include (step 306) attributing the transaction parameter based on a function of the matched value.
  • the step of attributing the transaction parameter may include calculating, labeling and assigning the transaction parameter.
  • the transaction parameter may be a profit and loss indicator of each arranged transaction data value.
  • the method 300 may further include filtering the at least one transaction data to remove sensitive data. This may include filtering identification information such as personal details, account details and/or financial details. This may also include associating the at least one transaction data with a tag (e.g. encryption) that is only readable by a system (e.g. system 204) in which the method 300 is implemented.
  • a tag e.g. encryption
  • the arranged transaction data value in the first data queue may be obtained at a first transaction time and the arranged transaction data value in the second data queue may be obtained at a second transaction time.
  • the second transaction time may be later than the first transaction time, e.g. occurred in a later time.
  • the transaction data from which the arranged transaction data value is generated in the first data queue may occurs first, and the transaction data from which the arranged transaction data value is generated in the second data queue may occur subsequently.
  • the function of the matched value for the transaction parameter of the arranged transaction data value in the first data queue may comprise multiplying the matched value, the first transaction type and a difference between a second fair rate at the second transaction time and a first fair rate at the first transaction time.
  • the transaction parameter of the arranged transaction data value in the second data queue may be assigned a zero value.
  • the function of the matched value for the transaction parameter of the arranged transaction data value in the second data queue may comprise multiplying the matched value, the second transaction type and a difference between a first fair rate at the first transaction time and a second fair rate at the second transaction time.
  • the transaction parameter of the arranged transaction data value in the first data queue may be assigned a zero value.
  • the profit and loss is attributed to the transaction data that was transacted at an earlier time, and the profit and loss is assigned to a zero value for the transaction data that was transacted at a later time.
  • the profit and loss of a holding position may be calculated, and factors including fair rates and platform rates at different transaction times may be considered.
  • one of the transaction data may be assigned an attribute of a client characteristic, and the other transaction data may be assigned an attribute of a bank characteristic.
  • the arranged transaction data value in the first data queue may be obtained at a first transaction time and the arranged transaction data value in the second data queue may be obtained at a second transaction time.
  • the second transaction time may be later than the first transaction time.
  • a first difference of between a first fair rate at the first transaction time and a first platform rate at the first transaction time, and a second difference of between a second fair rate at the second transaction time and a second platform rate at the second transaction time may be calculated.
  • Attributing the transaction parameter of the arranged transaction data value in the first/second data queue may comprise multiplying a predetermined constant, the matched value, the first transaction type and a summation of the first difference and the second difference.
  • Attributing the transaction parameter of the arranged transaction data value in the first/second data queue may comprise multiplying a predetermined constant, the matched value, the second transaction type and a summation of the first difference and the second difference.
  • the predetermined constant may be half (i.e. 0.5). That may mean that the profit and loss is equally attributed to the transaction data that was transacted at an earlier time and the transaction data that was transacted at a later time. In this manner, the profit and loss of a holding position may be shared by the two transaction data, and factors including fair rates and platform rates at different transaction times may be considered. It shall be appreciated that the predetermined constant may be a fraction/percentage other than 0.5, but include any possible fraction/percentage that is considered reasonable or practical in attributing the transaction parameter (e.g. the profit and loss), such as 0.4, 0.6, or 0.45. In such an embodiment, both transaction data may be assigned an attribute of a client characteristic, or both transaction data may be assigned an attribute of a bank characteristic.
  • FIG. 4 is a block diagram showing a comparing step 400 of an example method according to various embodiments.
  • the comparing step 400 may be similar to the step 305 of the method 300.
  • the comparing step 400 may include comparing, at a same queue position of the first data queue and the second data queue, an arranged transaction data value in the first data queue with an arranged transaction data value in the second data queue, to obtain a matched value and a remainder.
  • the first data queue may include transaction data according to the first transaction type and the second data queue may include transaction data according to the second transaction type.
  • the created transaction data may be combined with the transaction data in the data group for which the exposure transaction data is generated, and the combined transaction data may be processed by the comparing step 400 as transaction data in the data group.
  • the comparing step 400 may include (i) matching the arranged transaction data value in the first data queue with the arranged transaction data value in the second data queue to obtain the matched value and the remainder; (ii) returning the remainder to the first queue position of the respective data queue as an arranged transaction data value; (iii) repeating steps in (i) and (ii) until all transaction data (e.g. the arranged transaction data values generated from transaction data) are matched.
  • the comparing step 400 may further include consolidating the transaction parameters of each matched value generated by a transaction data (e.g. according to a series number of the transaction data).
  • FIG. 4 shows a first data queue 401 and a second data queue 402, wherein the first data queue 401 includes arranged transaction data values (from the first queue position), first queue position storing data value “100”, second queue position storing data value “30”, third queue position storing data value “50”, and the second data queue 402 includes arranged transaction data values (from the first queue position) first queue position storing data value “50”, second queue position storing data value “50”, third queue position storing data value “80”.
  • the first data queue 401 may include transaction data in the data group for which the exposure transaction data is generated and the second data queue 402 may include the created transaction data in accordance with the exposure transaction.
  • the table 403 is arranged for records of matched values, wherein column 410 is for matched values of the first data queue and the column 420 is for matched values of the second data queue.
  • the first arranged transaction data value “100” at the first queue position of the first data queue 401 is compared with the second arranged transaction data value “50” at the first queue position of the second data queue 402 to obtain a matched value “50” and a reminder “50” which is returned to the first queue position of the first data queue 401 (401a). That is, the first arranged transaction data value “100” at the first queue position of the first data queue 401 is split, with a value of “50” allocated into the matched value and a value of “50” allocated to the reminder value, whereas the second arranged transaction data value “50” at the first queue position of the second data queue 402 is matched for the matched value “50”.
  • the greater value e.g. the first arranged transaction data value “100” at the first queue position of the first data queue 401
  • the greater value e.g. the first arranged transaction data value “100” at the first queue position of the first data queue 401
  • a value of “50” allocated into the less value e.g. the second arranged transaction data value “50” at the first queue position of the second data queue 402
  • a remainder value e.g. the difference between the value “ 100” and the value “50”
  • the remainder value is allocated or returned to the first queue position of the respective data queue (e.g. the first data queue 401 (401a)).
  • the matched value may be the less value of the compared arranged transaction data values.
  • the remainder may become the arranged transaction data value that is at the first queue position of the respective data queue.
  • the arranged transaction data value “50” originally at the second queue position in the second data queue 402 is pushed to the first queue position of the second data queue 402a.
  • the matched value “50” are recorded in the first row of the table 403a.
  • the first arranged transaction data value “50” at the first queue position of the first data queue 401a is compared with the second arranged transaction data value “50” at the first queue position of the second data queue 402a to obtain a matched value “50” and a reminder “0” (i.e. perfect match with no remainder or remainder being zero).
  • the first arranged transaction data value “50” at the first queue position of the first data queue 401a is perfectly or fully matched with the second arranged transaction data value “50” at the first queue position of the second data queue 402 to obtain the matched value “50”.
  • the arranged transaction data value “30” originally at the second queue position in the first data queue 401a is pushed to the first queue position of the first data queue 401b, and the arranged transaction data value “80” originally at the second queue position in the second data queue 402a is pushed to the first queue position of the second data queue 402b.
  • the matched value “50” are recorded in the second row of the table 403b.
  • the comparing steps as described above may continue to match the values “30” in the first data queue 401b with “80” in the second data queue 402b. . . until all the arranged transaction data values (e.g. transaction data in the first data queue and the second data queue are matched).
  • the transaction parameters of the matched values may be attributed in accordance with the step 306 of the method 300.
  • the transaction parameter of the transaction data (e.g. with a full value “100” at the first queue position of the first data queue 401) may be obtained by consolidating the transaction parameters of the matched values (e.g. the matched values “50” and “50” as shown in the first and second rows of the table 403b) that are generated by the transaction data (e.g. with a full value “100” at the first queue position of the first data queue 401). Stated differently, the transaction parameter of the transaction data (e.g.
  • a first matched value e.g. with a partial value “50” of the full value “100” allocated to the first queue position of the first data queue 401
  • a second matched value e.g. the arranged transaction data value with a partial value “50” at the first queue position of the first data queue 401a
  • a first transaction parameter of the first matched value e.g. the matched value “50” as shown in the first row of the table 403b
  • a second transaction parameter of the second matched value e.g. the matched value “50” as shown in the second row of the table 403b
  • FIG. 5 is a graph 500 showing example transaction data in a preceding closed exposure transaction data and a present open exposure transaction data.
  • the x-axis 510 of graph 500 represents time.
  • the two-headed arrow 520 divides the time axis 510 into a left side and a right side.
  • the right side of the time axis 510 represents a preceding time period, indicating by the “close” arrow 512 and the left side of the time axis 510 represents a present time period, indicating by the “open” arrow 511.
  • Table 1 shows a new attribute is assigned to each transition data in the preceding closed exposure transaction data (e.g. original attributes are shown as close in the first column of Table 1) and the present open exposure transaction data (e.g. original attributes are shown as open in the first column of Table 1).
  • the attributes remain for the transaction data having an attribute of Client/Bank characteristic (e.g. original attributes are the same as the new attributes).
  • the transition data in the preceding closed exposure transaction data are shown at the right side of the time axis 510 indicating by the “close” arrow 512.
  • the transaction data in present open exposure transaction data are shown at the left side of the time axis 510 indicating by the “open” arrow 511.
  • the second, third rows of Table 1 show the transaction data having attributes of Client, Bank characteristic and values of +50, +40, respectively, corresponding to arrows 501, 502 in FIG. 5 showing values of +50, +40.
  • the fourth row of Table 1 shows the transaction data having an original attribute of close and value of -30, corresponding to arrow 503 in FIG. 5 showing a value of -30.
  • the fifth, sixth rows of Table 1 show the transaction data having original attributes of close and values of -20, -40, respectively, corresponding to arrow 504 in FIG. 5 showing a value of -60, that is the arrow 504 showing a summation of the values -20 and -40.
  • a system for attributing a transaction parameter of a plurality of transactions based on different currency exchange rates.
  • the system may include a processor configured to assign at least one transaction data to at least one data group based on a type of currency exchange; generate an exposure transaction data of the at least one data group, the exposure transaction data being a summary of transaction data having the type of currency exchange obtained within a time period; arrange transaction data in the data group according to a first transaction type in a first data queue, and arrange transaction data in the group according to a second transaction type in a second data queue, the second transaction type being different to the first transaction type; compare, at a same queue position of the first data queue and the second data queue, an arranged transaction data value in the first data queue with an arranged transaction data value in the second data queue, to obtain a matched value and a remainder; computing a difference between the arranged data value at the same queue position of the first data queue and the second data queue; and attribute the transaction parameter based on a function of
  • the system may further include at least one terminal device registered with the processor, the at least one terminal device configured to send the at least one transaction data to the input module.
  • the registration may include generating a unique identifier for each terminal device.
  • the system may further include whitelisting specific IP addresses associated with the at least one terminal device.
  • the at least one terminal device may be a mobile computer device.
  • the unique identifier may be associated with a device identity number of the mobile computer device.
  • a non-transitory computer-readable medium storing computer executable code comprising instructions for attributing a transaction parameter is also provided according to the method 300 as described above.
  • the aforementioned instructions may include a computer program element or a computer program product.
  • the proposed method may be made under the assumption that the future transaction data have been received during the attributing of the transaction parameters (e.g. the created transaction data).
  • the proposed method may be used in the real-time attributing of transaction parameters, and the exposure transaction data may be replaced by transaction data having a liquidity provider characteristic. That is, some of the internalized profits and gains (e.g. transaction parameters) may be included in the closing profits and gains with banks.
  • the fair rates e.g. the foreign exchange rates published by banks or financial institutions or liquidity
  • adapted rates from other services subscribed by the platform e.g. the second party
  • the foreign exchanges rates with a marked up or a discount may be used to attribute (e.g. calculate) of the transaction parameters.
  • the matching mechanism as described herein may adopt First-In-First-Out queues, allowing transaction data with an earlier transaction time to be matched earlier.
  • the proposed method may include assigning at least one transaction data to at least one data group based on a transaction type; generating an exposure transaction data of the at least one data group having a first transaction type, wherein the transaction data in the at least one data group having a first transaction type has a first exposure (e.g. a first non-zero exposure). That may mean the at least one data group having a second transaction type does not have an exposure (e.g. a zero exposure).
  • the method may include matching the exposure transaction data of the at least one data group having a first transaction type with transaction data having the second transaction type when transaction data having the second transaction type occurs and attributing transaction parameters corresponding by weight.
  • a computer storage medium can be, or can be included in, a computer-readable storage device, a computer- readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them.
  • a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal.
  • the computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).
  • the term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing.
  • the apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
  • the apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross -platform runtime environment, a virtual machine, or a combination of one or more of them.
  • a computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment.
  • a computer program may, but need not, correspond to a file in a file system.
  • a program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code).
  • a computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
  • Some of the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output.
  • the processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

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Abstract

Selon certains aspects, l'invention concerne un procédé mis en œuvre par ordinateur pour attribuer un paramètre de transaction. Le procédé peut comprendre l'attribution d'au moins une donnée de transaction à au moins un groupe de données sur la base d'un type d'échange de devises ; la génération de données de transaction d'exposition de l'au moins un groupe de données étant un résumé de données de transaction ayant le type d'échange de devises obtenu au cours d'une période de temps ; la disposition des données de transaction dans le groupe de données en fonction d'un premier/deuxième type de transaction dans une première/deuxième file d'attente de données, le deuxième type de transaction étant différent du premier type de transaction ; la comparaison, à une même position de file d'attente des première et deuxième files d'attente de données, d'une valeur de données de transaction disposée dans la première file d'attente de données à une valeur de données de transaction disposée dans la deuxième file d'attente de données, pour obtenir une valeur mise en correspondance et un reste ; et l'attribution du paramètre de transaction sur la base d'une fonction de la valeur mise en correspondance.
PCT/SG2022/050862 2022-11-25 2022-11-25 Procédé et système d'attribution d'un paramètre de transaction WO2024112256A1 (fr)

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CN107833138A (zh) * 2017-11-06 2018-03-23 中国银行股份有限公司 一种基于净额轧差的结售汇交易的处理方法及装置
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