System and method for controlling cross-border payment

Abstract

The present disclosure relates to a cross-border payment system and a method for controlling the same. A cross-border payment system according to the present disclosure comprises a communication module, a memory storing at least one process for performing domain change and payment operations based on the current location of a mobile terminal, and a processor that performs domain change and payment operations based on the current location of the mobile terminal based on the at least one process. The processor can identify the current location of a mobile terminal communicating through the communication module, change a service domain based on the current location of the mobile terminal, link with a language and payment system associated with the changed service domain, and perform payment through the linked payment system.

Description

Technology Field

[0001] The present disclosure relates to a cross-border payment system and a method for controlling the same. More specifically, the present disclosure relates to a cross-border payment system and a method for controlling the same that supports easy integration with POS systems and various local payment systems in each country when expanding overseas, and enables payment in local currency without exchange fees by providing a user location-based automatic domain change and an optimized payment environment. Background Technology

[0002] Cross-border trading (or CBT) refers to the trading of goods, services, technology, etc., across national borders. It includes the delivery of products ordered by overseas customers via online or mobile platforms for B2C (Business-to-Consumer) or B2B (Business-to-Business) transactions. Cross-border trading encompasses both direct overseas purchases and reverse direct overseas purchases, and is taking place across various industries.

[0003] Recently, with the rise of e-commerce and an increase in cases where overseas consumers directly purchase goods online, the demand for cross-border trading is also increasing. Consequently, there is a need for cross-border trading systems that can reduce shipping costs and enhance logistics efficiency. Prior art literature

[0004] Republic of Korea Published Patent Application No. 10-2018-0124299 (Published Nov. 21, 2018) The problem to be solved

[0005] One objective of the present disclosure is to provide a cross-border payment system and a method for controlling the same that can support interoperability with country-specific payment systems and local payment systems in an optimized manner and provide a current location-based domain change and payment environment.

[0006] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0007] A cross-border payment system according to the present disclosure for achieving the aforementioned technical objectives comprises a communication module, a memory storing at least one process for performing domain change and payment operations based on the current location of a mobile terminal, and a processor that performs domain change and payment operations based on the current location of the mobile terminal based on the at least one process. The processor can identify the current location of a mobile terminal communicating through the communication module, change a service domain based on the current location of the mobile terminal, link with a language and payment system linked to the changed service domain, and perform payment through the linked payment system.

[0008] In an embodiment, the processor may include a pre-trained artificial intelligence model that takes at least one of country-specific payment system information, local transaction regulation requirements, payment method security requirements, and weighting information based on market penetration rate as an input value, and outputs as an output value a protocol used for payment between domains of countries including the current location of the mobile terminal.

[0009] In an embodiment, the previously trained artificial intelligence model may be a supervised artificial intelligence model that takes at least one of country-specific payment system information, local transaction regulation requirements, payment method security requirements, and weight information based on market penetration rate as an input value, and takes a correct answer protocol as an output value.

[0010] In an embodiment, the processor may include an intelligent compliance module that takes country-specific financial regulatory data, transaction information, and country information as input values ​​and outputs a compliance score as an output value.

[0011] In an embodiment, the intelligent compliance module can monitor and parse the regulatory data, analyze the results of the monitoring and parsing, and apply them to the compliance logic.

[0012] In an embodiment, the processor may include a payment path optimization module that takes at least one of user location information, payment amount, and context information as an input value and one type of payment method as an output value.

[0013] In an embodiment, the payment path optimization module may use a supervised artificial intelligence model that takes at least one of the user's location information, payment amount, and context information as an input value and the correct payment method as an output value.

[0014] In an embodiment, the processor detects abnormal transactions using transaction information, user profile, and local context information, and can output abnormal transaction information when an abnormal transaction is detected.

[0015] A control method for a cross-border payment system according to the present disclosure may include the steps of determining the current location of a mobile terminal, changing a service domain based on the current location of the mobile terminal and linking with a language and payment system linked to the changed service domain, and performing a payment through the linked payment system.

[0016] In addition to this, a computer program stored on a computer-readable recording medium for implementing the present disclosure may be further provided.

[0017] In addition to this, a computer-readable recording medium for recording a computer program for implementing the present disclosure may be further provided. Effects of the invention

[0018] According to the means for solving the problem described above in the present disclosure, the present disclosure can provide an optimized cross-border payment system by providing an intelligent localization function that automatically changes the service domain based on the user's current location and provides a language and payment system optimized for the country.

[0019] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Brief explanation of the drawing

[0020] FIG. 1 is a conceptual diagram showing the overall system of the present disclosure. Figure 2 is a block diagram showing a cross-border payment system. FIG. 3 is a flowchart illustrating a typical control method of a cross-border payment system according to the present disclosure. FIGS. 4, 5, 6, 7, 8, and 9 are conceptual diagrams for explaining the control method examined in FIG. 3 in more detail. Specific details for implementing the invention

[0021] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components.

[0022] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.

[0023] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0024] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0025] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0026] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0027] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0028] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.

[0029] In this specification, the term "device according to the present disclosure" includes all various devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in the form of any one of these.

[0030] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0031] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0032] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0033] Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0034] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined rules of operation or artificial intelligence models configured to perform a desired characteristic (or objective) are created by a basic artificial intelligence model being trained using a number of training data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0035] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0036] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that enables a machine to learn by mimicking human biological neurons. Methodologies of artificial intelligence can be classified according to the learning method into supervised learning, where input and output data are provided together as training data and the solution (output data) to the problem (input data) is predetermined; unsupervised learning, where only input data is provided without output data and the solution (output data) to the problem (input data) is not predetermined; and reinforcement learning, where a reward is given from an external environment whenever an action is taken from the current state, and learning proceeds in a direction that maximizes such reward. In addition, artificial intelligence methodologies can be classified according to the architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be classified into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, and Generative Adversarial Networks (GAN).

[0037] The device and system may include an artificial intelligence model. The artificial intelligence model may be a single model or may be implemented as multiple models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a model that possesses problem-solving capabilities by having artificial neurons (nodes) that form a network through synaptic connections and change the strength of synaptic connections through learning. The neurons of a neural network may include combinations of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a result (output) to be predicted from an arbitrary input by changing the weights of the neurons through learning.

[0038] The processor can create neural networks, train or learn neural networks, perform computations based on received input data, generate information signals based on the results of the computation, or retrain neural networks. Neural network models may include, but are not limited to, various types of models such as Convolutional Neural Networks (CNN), Region with Convolutional Neural Networks (R-CNN), Region Proposal Networks (RPN), Recurrent Neural Networks (RNN), Stacking-based Deep Neural Networks (S-DNN), State-Space Dynamic Neural Networks (S-SDNN), Deconvolution Networks, Deep Belief Networks (DBN), Restructured Boltzmann Machines (RBM), Fully Convolutional Networks, Long Short-Term Memory Networks (LSTM), and Classification Networks, such as GoogleNet, AlexNet, and VGG Network. The processor may include one or more processors to perform computations according to neural network models. For example, a neural network is a deep neural network It may include a (Deep Neural Network).

[0039] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning Machine), ESN (Echo It will be understood by a person skilled in the art that any neural network may be included, but is not limited to, State Network, Deep Residual Network, Differential Neural Computer, Neural Turning Machine, Capsule Network, Kohonen Network, and Attention Network.

[0040] According to an exemplary embodiment of the present disclosure, the processor comprises a Convolutional Neural Network (CNN) such as GoogleNet, AlexNet, VGG Network, Region with Convolutional Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based Deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restructured Boltzmann Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, Time-Series Forecasting, Optimization for ResNet Data Intelligence, Various artificial intelligence structures and algorithms, such as recommendation and data creation, may be used, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0041] FIG. 1 is a conceptual diagram showing the overall system of the present disclosure.

[0042] A system according to the present disclosure may include a mobile terminal (200), a plurality of country domains (300), and a cross-border payment system (100).

[0043] The mobile terminal (200) may refer to a customer's mobile terminal and may include all kinds of devices.

[0044] Multiple country domains (300) refer to domains that operate in multiple countries and may include various web pages, internet addresses, payment systems, POS (Point of Sale) terminals, etc. that provide online e-commerce. The domain (300) may include multiple different domains (300a, 300b, 300c), and each domain may be operated in a different country or linked to a different country's language or a payment system that is commonly used in that country.

[0045] The cross-border payment system (100) of the present disclosure can support, when a purchase request is made through a domain of a country from a mobile terminal (200), translating the language of the domain where the purchase request is made into the language of the country where the mobile terminal is currently located, and directly linking with the local payment system of the country where the purchase request is made to perform the payment.

[0046] For example, the cross-border payment system (100) can support payment in the local currency without exchange fees through direct linkage with an overseas local payment system, and can provide a convenient user environment through automatic domain change based on the user's location.

[0047] In addition, the cross-border payment system (100) can provide a function that enables overseas users to conveniently make payments using their own payment methods by supporting integration with local payment systems widely used in each country through multinational payment system API real-time standardization technology, hybrid location detection technology, regulatory requirement automatic parsing algorithm, secure data conversion between heterogeneous payment systems, and a country-specific authentication method integration framework, and can support overseas users in making payments in their own currency without a currency exchange process when making payments in Korea or when Korean users make payments overseas.

[0048] Figure 2 is a block diagram showing a cross-border payment system.

[0049] Referring to FIG. 2, the cross-border payment system (100) may include a communication module (100), a user input module (130), an interface module (140), a memory (170), and a processor (180).

[0050] The components illustrated in FIG. 2 are not essential for implementing the cross-border payment system (100) according to the present disclosure, so the cross-border payment system (100) described in this specification may have more or fewer components than the components listed above.

[0051] The communication module (110) may include one or more components that enable communication with an external device, for example, at least one of a broadcast receiving module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0052] In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, the wireless communication module may include wireless communication modules that support various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, and 6G.

[0053] The location information module is a module for obtaining the location (or current location) of the device according to the present disclosure, and representative examples thereof include a Global Positioning System (GPS) module or a Wireless Fidelity (WiFi) module. For example, if a GPS module is utilized, the location of the device can be obtained using signals sent from GPS satellites. As another example, if a Wi-Fi module is utilized, the location of the device can be obtained based on information from a Wireless Access Point (AP) that transmits or receives wireless signals from the Wi-Fi module. If necessary, the location information module may perform any of the functions of other modules of the communication unit to obtain data regarding the location of the device, either substituted or additionally. The location information module is a module used to obtain the location (or current location) of the device, and is not limited to a module that directly calculates or obtains the location of the device.

[0054] The user input module (130) is for receiving information from a user, and when information is input through the user input module, the processor can control the operation of the device to correspond to the input information. Such a user input module may include a hardware physical key (e.g., a button, dome switch, jog wheel, jog switch, etc. located on at least one of the front, rear, and side of the device) and a software touch key. As an example, the touch key may be composed of a virtual key, soft key, or visual key displayed on a touchscreen-type display unit through software processing, or may be composed of a touch key placed on a part other than the touchscreen. Meanwhile, the virtual key or visual key may be displayed on the touchscreen in various forms, and may be composed of, for example, a graphic, text, an icon, a video, or a combination thereof.

[0055] The interface module (140) serves as a passage for various types of external devices connected to the device. This interface module may include at least one of a wired / wireless headset port, an external charger port, a wired / wireless data port, a memory card port, a port for connecting a device equipped with an identification module (SIM), an audio I / O (Input / Output) port, a video I / O (Input / Output) port, and an earphone port. The device can perform appropriate control related to the external device connected to the interface module.

[0056] The memory (170) can store data supporting various functions of the device and programs for the operation of the processor, and can store input / output data (e.g., music files, still images, videos, etc.), and can store a number of application programs (or applications) running on the device, data for the operation of the device, and instructions. At least some of these application programs can be downloaded from an external server via wireless communication.

[0057] Additionally, the memory (170) may store at least one process (or task, operation, function, control method, process, data, algorithm, program, etc.) or processor for performing the method according to the present disclosure. Such at least one process may be performed under the control of the processor (180) and may refer to information used by the processor (180) to implement the method according to the present disclosure.

[0058] Such memory may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory, magnetic disk, and optical disk. Additionally, the memory may be a database that is separate from the device but connected via wired or wireless connection.

[0059] The processor (180) may be implemented with a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of components within the device, and at least one processor (not shown) that performs the aforementioned operation using the data stored in the memory. In this case, the memory and the processor may each be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

[0060] The processor (180) may include an artificial intelligence model (181), an adaptive payment protocol conversion engine (182), a transaction settlement module (183), an intelligent regulatory compliance module (184), a multinational payment authentication module (185), an exchange rate optimization module (186), a payment path optimization module (187), a POS integration interface module (188), an abnormal transaction detection module (189), a dynamic language optimization engine (190), and a privacy preservation module (191).

[0061] The artificial intelligence model (181), adaptive payment protocol conversion engine (182), transaction settlement module (183), intelligent regulatory compliance module (184), multinational payment authentication module (185), exchange rate optimization module (186), payment path optimization module (187), POS integration interface module (188), abnormal transaction detection module (189), dynamic language optimization engine (190), and privacy preservation module (191) may be implemented as separate chips (or hardware modules) or may be componentized in software and implemented in a block form within the processor (180).

[0062] The operation / function / control method performed by each of the artificial intelligence model (181), adaptive payment protocol conversion engine (182), transaction settlement module (183), intelligent regulatory compliance module (184), multinational payment authentication module (185), exchange rate optimization module (186), payment path optimization module (187), POS integration interface module (188), abnormal transaction detection module (189), dynamic language optimization engine (190), and privacy preservation module (191) can be applied by analogy as if it were performed by the processor (180) or by the processor (180).

[0063] FIG. 3 is a flowchart illustrating a representative control method of a cross-border payment system according to the present disclosure, and FIGS. 4, 5, 6, 7, 8, and 9 are conceptual diagrams for explaining the control method examined in FIG. 3 in more detail.

[0064] A cross-border payment system according to the present disclosure may include a communication module (110), a memory (170) storing at least one process for performing domain change and payment operations according to the current location of a mobile terminal, and a processor (180) that performs domain change and payment operations according to the current location of the mobile terminal based on the at least one process.

[0065] Referring to FIG. 3, the processor (180) can determine (determine, detect, judge) the current location of the mobile terminal (200) communicating through the communication module (100) (S310).

[0066] Afterward, the processor (180) can change the service domain based on the current location of the mobile terminal and link with the language and payment system linked to the changed service domain (S320). The processor (180) can link with the language and payment system linked to the service domain (one of 300a, 300b, and 300c) provided at the current location of the mobile terminal.

[0067] Afterward, the processor (180) can perform payment through the linked payment system (S330). Specifically, the processor (180) can automatically perform POS system linkage, user location-based domain change processing, and language translation.

[0068] For example, the processor (180) may include a pre-trained artificial intelligence model (181) that takes at least one of country-specific payment system information, local transaction regulation requirements, payment method security requirements, and weighting information based on market penetration rate as an input value, and outputs a protocol used for payment between domains of countries including the current location of the mobile terminal as an output value.

[0069] The above-mentioned learned artificial intelligence model (181) may be the artificial intelligence model described in this specification.

[0070] The above-mentioned artificial intelligence model (181) may be a supervised artificial intelligence model that takes at least one of the following as input values: country-specific payment system information, local transaction regulation requirements, payment method security requirements, and weight information based on market penetration rate, and takes the correct answer protocol as output value.

[0071] Referring to FIG. 4, the processor (180) may include an Adaptive Payment Protocol Conversion Engine (182) to solve the problem of having to implement individual interfaces to interact with payment systems in each country.

[0072] The processor (180) (or adaptive payment protocol conversion engine (182)) can act as an automatic translator that enables various payment systems of different countries to communicate with each other. For example, the adaptive payment protocol conversion engine (182) can enable a Korean payment system to easily connect with a Japanese payment system or a Thai payment system.

[0073] According to the present disclosure, even if a new payment system appears, the adaptive payment protocol conversion engine (182) can automatically adapt and perform the connection without the need to redevelop everything from scratch.

[0074] The optimized protocol (P(c,r)) for country c and payment method r can be defined as follows.

[0075] P(c, r) = f(T(c), S(r), M(c,r))

[0076] Here, T(c): local transaction regulatory requirements of country c, S(r): security requirements of payment method r, M(c,r): weights based on the market penetration rate of payment method r in country c.

[0077] The adaptive payment protocol conversion engine (182) can dynamically adapt and continue to operate with minimal code modifications even if a new payment system emerges or the protocol of an existing system changes.

[0078] Meanwhile, the processor (180) may include a distributed ledger-based cross-border transaction settlement module (183).

[0079] Referring to FIG. 5, conventional international remittances involve multiple banks, taking a long time and incurring high fees. However, the cross-border system of the present disclosure can directly record and verify transactions using a digital ledger shared by banks in multiple countries. This can have the effect of banks in multiple countries (300a, 300b, 300c) sitting at one table to verify transactions in real time, thereby reducing intermediate steps and enabling the rapid exchange of money.

[0080] The processor (180) (or transaction settlement module (183)) of the present disclosure can implement a settlement system utilizing distributed ledger technology to solve the delay and cost issues of cross-border payment settlement.

[0081] Settlement Time can be defined as follows.

[0082] SettlementTime(Tx) = BaseTime + ∑(Delay(n)) - AccelerationFactor(Tx)

[0083] Here, BaseTime is the base settlement time, Delay(n) is the delay time caused by the nth intermediary financial institution, and AccelerationFactor(Tx) is the acceleration factor based on transaction characteristics.

[0084] According to the transaction settlement module (183), it is possible to enable faster and cheaper settlement than existing SWIFT or international interbank settlement systems, and can provide transaction traceability and transparency.

[0085] Meanwhile, the processor (180) can provide an intelligent regulatory compliance automation system.

[0086] To this end, referring to FIG. 6, the processor (180) may include an intelligent regulatory compliance module (184) that takes regulatory data, transaction information, and country information related to financial by country as input values ​​and outputs a regulatory compliance score as an output value.

[0087] The intelligent regulatory compliance module (184) can monitor and parse the regulatory data, analyze the results of the monitoring and parsing, and apply them to the regulatory compliance logic.

[0088] Financial regulations differ from country to country and change frequently, but the intelligent regulatory compliance module (184) can automatically check and comply with such regulations. For example, in the United States, transactions exceeding a certain amount must be reported, and in Europe, personal information protection is stricter; the intelligent regulatory compliance module (184) can automatically recognize these country-specific regulations and apply them to transactions / payments. This can have the effect of providing real-time advice, much like a legal expert from each country.

[0089] The present disclosure can monitor in real time and automatically comply with the complex and continuously changing payment regulations of each country.

[0090] The compliance score can be defined as follows.

[0091] ComplianceScore(Tx, c) = ∑[w_i * Rule_i(Tx, c)]

[0092] Here, Tx is a transaction, c is a country, w_i is the weight of the i-th regulation rule, and Rule_i is the compliance score function for the i-th regulation rule.

[0093] The intelligent regulatory compliance module (184) can use machine learning to automatically parse payment regulation documents of each country and detect regulatory changes and reflect them in the system.

[0094] Meanwhile, the processor (180) can provide a hybrid biometric authentication-based multinational payment authentication framework (or multinational payment authentication module (185)).

[0095] The authentication method for payment varies from country to country. Some countries use fingerprints, while others may use (prefer) facial recognition. The multinational payment authentication module (185) can securely perform identity authentication in the manner preferred (or used) in the country where the user is located, or allow the authentication method used by the user to be used in other countries. For example, when a Korean user makes a payment in Japan, they can authenticate using the method used in Korea, while simultaneously satisfying Japan's security requirements.

[0096] Referring to FIG. 7, the processor (180) (or multinational payment authentication module (185)) can implement a framework that integrates payment security requirements and biometric authentication methods that differ by country.

[0097] The AuthScore may be a score used to determine the authentication method and can be defined as follows.

[0098] AuthScore = α * BiometricMatch + β * GeoConsistency + γ * BehavioralPattern

[0099] Here, α, β, and γ are the weights of each element (dynamically adjusted according to country-specific regulations), BiometricMatch is the biometric match, GeoConsistency is location information consistency, and BehavioralPattern is the user behavior pattern match.

[0100] The multinational payment authentication module (185) can provide a consistent experience to the user while satisfying the security requirements of each country, and can provide authentication success / failure and authentication level information as output values.

[0101] Meanwhile, the processor (180) may include an exchange rate optimization module (186) that provides a real-time exchange rate optimization algorithm.

[0102] The exchange rate optimization module (186) can implement a real-time exchange rate optimization algorithm to minimize exchange fees.

[0103] The exchange rate optimization module (186) can compare exchange rates of various banks and exchange offices in real time to find the best exchange rate (meaning the cheapest exchange rate that is most beneficial to the user). For example, it may be more advantageous to exchange Korean Won → Dollar → Baht than to directly exchange Korean Won for Thai Baht, and the exchange rate optimization module (186) can automatically perform such complex calculations to provide the user with the lowest cost exchange method.

[0104] The optimal rate path can be defined as follows.

[0105] OptimalRate(source, target, t) = MarketRate(t) * [1 - OptimizationFactor(source, target, volume, t)]

[0106] Here, source and target are the won currency and target currency, t is the time of the transaction, volume is the transaction size, and OptimizationFactor is the optimization factor (considering liquidity, market volatility, etc.).

[0107] The exchange rate optimization module (186) can compare exchange rates of several financial institutions and cryptocurrency exchanges in real time and select the most advantageous path.

[0108] Meanwhile, the processor (180) may include a context-aware payment routing engine (or payment routing optimization module (187)).

[0109] The payment path optimization module (187) can implement an engine that dynamically determines the optimal payment path by considering the location of the user (mobile terminal), payment amount, preference, network status, etc.

[0110] Referring to FIG. 8, the processor (180) may include a payment path optimization module (187) that takes at least one of the user's location information, payment amount, and context information as an input value and one type of payment method as an output value.

[0111] The above payment path optimization module (187) may be a supervised artificial intelligence model that takes at least one of the user's location information, payment amount, and context information as an input value and the correct payment method as an output value. The above artificial intelligence model may be the artificial intelligence model described in the present disclosure.

[0112] The payment path optimization module (187) can recommend the best payment method tailored to the user's situation. For example, the payment path optimization module (187) can suggest a payment method that works offline in areas with unstable internet connections, and can recommend a method with enhanced security for large transactions. Additionally, the payment path optimization module (187) can remember the payment method frequently used by the user and suggest it so that it can be easily used again in the future.

[0113] The payment optimization route (OptimalRoute) can be defined as follows.

[0114] OptimalRoute(u, m, c) = argmax_r { Utility(r, u, m, c)}

[0115] Here, u is the user (or mobile terminal), m is the payment amount, c is the context (location, time, network condition, etc.), r is the possible payment path, and Utility is the utility function of the payment path under given conditions.

[0116] The payment path optimization module (187) can propose an optimal payment path by comprehensively considering availability, fees, speed, security, etc.

[0117] Meanwhile, the processor (180) may provide a cross-platform POS integration interface (or POS integration interface module (188)).

[0118] The POS integrated interface module (188) can provide a standardized interface for integration with various hardware and software POS systems.

[0119] Stores around the world use various types of payment terminals (POS). The POS integrated interface module (188) can serve as a universal adapter that allows any type of terminal to be easily connected to the cross-border payment system (100). This is compatible with various POS devices, much like a multi-adapter that can be used anywhere in the world, and can have the effect of enabling stores to accept global payments without the need for separate equipment.

[0120] Interface functions (I) can be defined as follows.

[0121] I(pos) = { D(pos), C(pos), P(pos), S(pos)}

[0122] Here, D(pos) is the data model transformation function, C(pos) is the communication protocol adaptation function, P(pos) is the payment processing workflow mapping function, and S(pos) is the security requirement satisfaction function.

[0123] The POS integrated interface module (188) minimizes hardware dependency and can dynamically adapt to the characteristics of the POS system.

[0124] Meanwhile, the processor (180) may include a cross-border AI system (or abnormal transaction detection module (189)) for detecting abnormal transactions.

[0125] The abnormal transaction detection module (189) can implement an AI system that detects abnormal transactions that may occur in cross-border payments in real time.

[0126] Referring to FIG. 9, the processor (180) (or abnormal transaction detection module (189)) can detect abnormal transactions using transaction information, user profile and local context information, and output abnormal transaction information when an abnormal transaction is detected.

[0127] The abnormal transaction detection module (189) can function as an international surveillance camera to detect fraud or illegal transactions. The abnormal transaction detection module (189) learns the user's usual transaction patterns and can mark as suspicious activity if an abnormally large amount is suddenly traded in another country. The abnormal transaction detection module (189) also learns common fraud patterns for each country and can detect types of fraud that frequently occur in specific countries in advance.

[0128] To this end, the abnormal transaction detection module may include a data collection module, a feature extraction module, an artificial intelligence module, and a country-specific fraud pattern database.

[0129] The Anomaly Score can be defined as follows.

[0130] AnomalyScore(Tx) = ∑[w_i * f_i(Tx, UserProfile, GeoContext)]

[0131] Here, f_i is the i-th anomaly detection feature function, w_i is the weight of each feature, UserProfile is the user's past transaction patterns, and GeoContext is the transaction patterns and risk level by region.

[0132] The abnormal transaction detection module (189) can apply a region-specific abnormal transaction detection model by utilizing a country-specific fraud pattern database. The abnormal transaction detection module (189) can provide abnormal transaction information and flag information as output values.

[0133] Meanwhile, the processor (180) may include a dynamic language and culture optimization engine (or a dynamic language optimization engine (190)).

[0134] The dynamic language optimization engine (190) may be an engine that optimizes not only the language of the payment environment but also cultural elements (payment method preference, UI arrangement, etc.) according to the user's location and preference.

[0135] The dynamic language optimization engine (190) can not only translate the language but also match the payment method and screen layout preferred by people of that country.

[0136] For example, Americans prefer credit card payments and like simple interfaces, whereas Chinese people may prefer QR code payments and screens displaying more information. The dynamic language optimization engine (190) can recognize cultural differences for each country and provide a payment environment in the way most familiar (most commonly used) to users of each country.

[0137] The culture vector can be defined as follows.

[0138] CultureVector(u, c) = { L(u,c), UI(u,c), P(u,c), F(u,c)}

[0139] Here, L is language preference, UI is interface layout preference, P is payment method preference, and F is gesture and interaction method.

[0140] The dynamic language optimization engine (190) can provide an overall user experience optimized for each culture beyond simple language translation.

[0141] Meanwhile, the processor (180) may include a privacy-preserving cross-border data sharing framework (or privacy-preserving module (191)).

[0142] The privacy preservation module (191) can provide a framework that allows necessary payment information to be shared securely while complying with national data protection laws.

[0143] Personal information protection laws vary from country to country. The privacy preservation module (191) can safely share only the information necessary for payment while complying with the laws of each country. For example, the privacy preservation module (191) can encrypt and transmit only the minimum information necessary for payment approval and not store important personal information. This can have the effect of enabling cross-border payments by appropriately sharing only the necessary information, much like a diplomat safely delivering secret information.

[0144] Data sharing can be defined as follows.

[0145] DataSharing(d, s, r) = Encrypt(Minimize(d, r), K(s,r))

[0146] Here, d is the data to be shared, s is the source entity (origin country), r is the receiving entity (destination country), Minimize is a function that selects only the minimum necessary data, and K is the security key between the source and receiving entities.

[0147] This framework (privacy preservation module (191)) can enable necessary verification while preserving privacy by utilizing advanced cryptographic techniques such as homomorphic encryption and zero-knowledge proof.

[0148] According to the means for solving the problem described above in the present disclosure, the present disclosure can provide an optimized cross-border payment system by providing an intelligent localization function that automatically changes the service domain based on the user's current location and provides a language and payment system optimized for the country.

[0149] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0150] In the present disclosure, the operation / function / control method performed by the artificial intelligence model (181) can also be understood as being performed by the processor (180).

[0151] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0152] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0153] As described above, the disclosed embodiments have been explained with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted restrictively.

Claims

Claim 1 Communication module; memory storing at least one process for performing domain change and payment operations based on the current location of the mobile terminal; The system includes a processor that performs domain change and payment operations based on the current location of the mobile terminal according to at least one process, wherein the processor identifies the current location of the mobile terminal communicating through the communication module, changes the service domain based on the current location of the mobile terminal, links with a language and payment system linked to the changed service domain, and performs payment through the linked payment system, wherein the processor takes at least one of country-specific payment system information, local transaction regulatory requirements, payment method security requirements, and weighting information based on market penetration rate as an input value through an adaptive payment protocol conversion engine, and outputs as an output value a protocol used for payment between domains of the country including the current location of the mobile terminal, and the protocol (P(c, r)) used for payment is determined according to the following Equation 1, [Equation 1] P(c, r) = f(T(c), S(r), M(c,r)) where, T(c): local transaction regulatory requirements of country c, S(r): security requirements of payment method r, M(c,r): market of payment method r in country c It is a weight based on the penetration rate, and the processor takes country-specific financial regulatory data, transaction information, and country information as input values ​​through an intelligent regulatory compliance module, and outputs a regulatory compliance score as an output value, and the regulatory compliance score (ComplianceScore) is calculated according to the following mathematical formula 2, [Mathematical Formula 2] ComplianceScore = ∑[w_j * Rule_j(Tx, c)] where Tx is a transaction, c is a country, w_j is the weight of the j-th regulatory rule, and Rule_j is a compliance score function for the j-th regulatory rule, and the processor detects abnormal transactions based on user transaction patterns, country-specific transaction patterns, and an abnormal transaction score reflecting risk levels through an abnormal transaction detection module, andThe above AnomalyScore is calculated according to the following Equation 3, [Equation 3] AnomalyScore = ∑[w_i * f_i(Tx, UserProfile, GeoContext)] where Tx is a transaction, f_i is the i-th anomaly detection feature function, w_i is the weight of the i-th feature, UserProfile is the user transaction pattern, and GeoContext is the country-specific transaction pattern and risk level, cross-border payment system. Claim 2 delete Claim 3 A cross-border payment system according to claim 1, wherein the adaptive payment protocol conversion engine is a supervised artificial intelligence model that takes at least one of country-specific payment system information, local transaction regulatory requirements, payment method security requirements, and weight information based on market penetration rate as an input value, and takes a correct answer protocol as an output value. Claim 4 delete Claim 5 In claim 1, the intelligent compliance module monitors and parses the regulatory data, analyzes the results of the monitoring and parsing, and applies them to the compliance logic, in a cross-border payment system. Claim 6 A cross-border payment system according to claim 1, wherein the processor includes a payment path optimization module that takes at least one of user location information, payment amount, and context information as an input value and takes one type of payment method as an output value. Claim 7 In claim 6, the payment path optimization module uses a supervised artificial intelligence model that takes at least one of user location information, payment amount, and context information as an input value and the correct payment method as an output value, in a cross-border payment system. Claim 8 delete Claim 9 A step of determining the current location of a mobile terminal; a step of changing a service domain based on the current location of the mobile terminal, and linking with a language and payment system associated with the changed service domain;The method includes a step of performing a payment through the linked payment system, wherein the linking step takes at least one of country-specific payment system information, local transaction regulatory requirements, payment method security requirements, and weighting information based on market penetration rate as input values ​​through an adaptive payment protocol conversion engine, and outputs a protocol used for payment between domains of countries including the current location of the mobile terminal as an output value, and the protocol used for payment (P(c, r)) is determined according to the following Equation 1, [Equation 1] P(c, r) = f(T(c), S(r), M(c,r)) where T(c): local transaction regulatory requirements of country c, S(r): security requirements of payment method r, M(c,r): weighting based on the market penetration rate of payment method r in country c, and the step of performing the payment takes country-specific financial regulatory data, transaction information, and country information as input values ​​through an intelligent regulatory compliance module, and outputs a regulatory compliance score as an output value, and the regulatory compliance score (ComplianceScore) is determined according to the following Equation 2 A control method for a cross-border payment system, wherein the above Anomaly Score is calculated [Equation 2] ComplianceScore = ∑[w_j * Rule_j(Tx, c)], where Tx is a transaction, c is a country, w_j is the weight of the j-th regulatory rule, and Rule_j is a compliance score function for the j-th regulatory rule; further comprising a step of detecting anomalies based on an Anomaly Score reflecting user transaction patterns, country-specific transaction patterns, and risk levels through an anomaly detection module; wherein the above Anomaly Score is calculated according to the following Equation 3 [Equation 3] AnomalyScore = ∑[w_i * f_i(Tx, UserProfile, GeoContext)], where Tx is a transaction, f_i is the i-th anomaly detection feature function, w_i is the weight of the i-th feature, UserProfile is the user transaction pattern, and GeoContext is the country-specific transaction pattern and risk level.; Claim 10 In claim 9, the adaptive payment protocol conversion engine is a supervised artificial intelligence model that takes at least one of country-specific payment system information, local transaction regulatory requirements, payment method security requirements, and weight information based on market penetration rate as an input value, and takes a correct answer protocol as an output value, a method for controlling a cross-border payment system.

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