Patent Application
20240202822
This technology generally relates to methods and systems for detecting abnormal and/or anomalous rejections of requests for quotes for foreign exchange transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue.
Financial institutions, such as banks, facilitate voluminous numbers of transactions for their clients on a daily basis, as part of the regular conduct of commercial business activities. Many such transactions involve foreign exchange.
Foreign exchange transactions typically are initiated by a request for quote that is received from a customer or potential customer. The number of such requests for quote may be on the order of tens or hundreds of thousands per day. As a result, many banks use conventional pricing algorithms to determine whether such requests for quote are to be accepted or rejected.
Typically, a conventional pricing algorithm determines that a small but significant percentage of requests for quotes with respect to foreign exchange transactions are to be rejected. For example, approximately 10%-15% of such requests may be rejected. In addition, when such a request is rejected, the conventional pricing algorithm does not provide an explanation as to a reason for the rejection.
In this regard, in many situations, there may be a circumstance that has caused the algorithm to reject the request that would be resolvable if a specialist was aware of the circumstance, but because of the volume of requests, it is typically not feasible to make specialists aware of such situations. As a result, there is no opportunity to resolve an issue that has caused the rejection, and in addition, the customer for whom the request has been rejected may complain and may shift its business to another bank. This, in turn, causes an irreplaceable loss of revenue.
Accordingly, there is a need for a method for detecting abnormal and/or anomalous rejections of requests for quotes for foreign exchange transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue.
The present disclosure, through one or more of its various aspects, embodiments, and/or specific features or sub-components, provides, inter alia, various systems, servers, devices, methods, media, programs, and platforms for detecting abnormal and/or anomalous rejections of requests for quotes for foreign exchange transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue.
According to an aspect of the present disclosure, a method for detecting anomalous rejections of requests for quotes for foreign exchange transactions is provided. The method is implemented by at least one processor. The method includes: receiving, by the at least one processor from a first entity, a first request for quote (RFQ) that relates to a proposed foreign exchange (FX) transaction; retrieving, by the at least one processor from a memory, first information that relates to a financial capability of the first entity to conduct the proposed FX transaction; applying, by the at least one processor, a first algorithm that is designed to make an initial determination as to whether the first RFQ is acceptable, based on the first RFQ and the first information; when the initial determination indicates that the first RFQ is not acceptable, applying, by the at least one processor, a second algorithm that uses an artificial intelligence (AI) technique to determine a potential reason that the initial determination has indicated that the first RFQ is not acceptable; and generating a report that includes information that relates to the potential reason that the initial determination has indicated that the first RFQ is not acceptable.
The potential reason may include at least one from among a first reason that is based on an abnormal behavior of the first entity within a predetermined time interval, a second reason that is based on a product type that relates to the proposed FX transaction, a third reason that is based on a currency that relates to the proposed FX transaction, and a fourth reason that is based on a size of the proposed FX transaction.
The predetermined time interval may include one from among a 24-hour interval, a three-day interval, a seven-day interval, a 30-day interval, a six-month interval, and a twelve-month interval.
The second algorithm may use historical information that relates to previous transactions that have been conducted by the first entity as an input.
The second algorithm may use historical information that relates to previous transactions that have been conducted by a plurality of second entities that are similar to the first entity as an additional input.
The second algorithm may use historical information that relates to previous transactions that have been conducted by a plurality of third entities that are not similar to the first entity as another additional input.
The method may further include determining whether a particular entity is similar to the first entity by: identifying a plurality of features that relates to the first entity; identifying, for each respective entity from among a global set of entities, a corresponding set of features that relates to the respective entity; determining, for each respective pairing of a respective entity with the first entity, a respective distance between the plurality of features that relates to the first entity and the corresponding set of features that relates to the respective entity; and determining, for each respective pairing, whether the corresponding entity is similar to the first entity based on the determined distance.
Each entity included in the global set of entities that is not determined as being similar to the first entity may be determined as being included in the plurality of third entities.
The method may further include transmitting the report to a predetermined destination and displaying at least a portion of the report on a display via a graphical user interface (GUI).
According to another exemplary embodiment, a computing apparatus for detecting anomalous rejections of requests for quotes for foreign exchange transactions is provided. The computing apparatus includes a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display. The processor is configured to: receive, via the communication interface from a first entity, a first request for quote (RFQ) that relates to a proposed foreign exchange (FX) transaction; retrieve, from the memory, first information that relates to a financial capability of the first entity to conduct the proposed FX transaction; apply a first algorithm that is designed to make an initial determination as to whether the first RFQ is acceptable, based on the first RFQ and the first information; when the initial determination indicates that the first RFQ is not acceptable, apply a second algorithm that uses an artificial intelligence (AI) technique to determine a potential reason that the initial determination has indicated that the first RFQ is not acceptable; and generate a report that includes information that relates to the potential reason that the initial determination has indicated that the first RFQ is not acceptable.
The potential reason may include at least one from among a first reason that is based on an abnormal behavior of the first entity within a predetermined time interval, a second reason that is based on a product type that relates to the proposed FX transaction, a third reason that is based on a currency that relates to the proposed FX transaction, and a fourth reason that is based on a size of the proposed FX transaction.
The predetermined time interval may include one from among a 24-hour interval, a three-day interval, a seven-day interval, a 30-day interval, a six-month interval, and a twelve-month interval.
The second algorithm may use historical information that relates to previous transactions that have been conducted by the first entity as an input.
The second algorithm may use historical information that relates to previous transactions that have been conducted by a plurality of second entities that are similar to the first entity as an additional input.
The second algorithm may use historical information that relates to previous transactions that have been conducted by a plurality of third entities that are not similar to the first entity as another additional input.
The processor may be further configured to determine whether a particular entity is similar to the first entity by: identifying a plurality of features that relates to the first entity; identifying, for each respective entity from among a global set of entities, a corresponding set of features that relates to the respective entity; determining, for each respective pairing of a respective entity with the first entity, a respective distance between the plurality of features that relates to the first entity and the corresponding set of features that relates to the respective entity; and determining, for each respective pairing, whether the corresponding entity is similar to the first entity based on the determined distance.
Each entity included in the global set of entities that is not determined as being similar to the first entity may be determined as being included in the plurality of third entities.
The processor may be further configured to transmit, via the communication interface, the report to a predetermined destination and to cause the display to display at least a portion of the report via a graphical user interface (GUI).
According to yet another exemplary embodiment, a non-transitory computer readable storage medium storing instructions for detecting anomalous rejections of requests for quotes for foreign exchange transactions is provided. The storage medium includes executable code which, when executed by a processor, causes the processor to: receive, from a first entity, a first request for quote (RFQ) that relates to a proposed foreign exchange (FX) transaction; retrieve, from a memory, first information that relates to a financial capability of the first entity to conduct the proposed FX transaction; apply a first algorithm that is designed to make an initial determination as to whether the first RFQ is acceptable, based on the first RFQ and the first information; when the initial determination indicates that the first RFQ is not acceptable, apply a second algorithm that uses an artificial intelligence (AI) technique to determine a potential reason that the initial determination has indicated that the first RFQ is not acceptable; and generate a report that includes information that relates to the potential reason that the initial determination has indicated that the first RFQ is not acceptable.
The potential reason may include at least one from among a first reason that is based on an abnormal behavior of the first entity within a predetermined time interval, a second reason that is based on a product type that relates to the proposed FX transaction, a third reason that is based on a currency that relates to the proposed FX transaction, and a fourth reason that is based on a size of the proposed FX transaction.
The present disclosure is further described in the detailed description which follows, in reference to the noted plurality of drawings, by way of non-limiting examples of preferred embodiments of the present disclosure, in which like characters represent like elements throughout the several views of the drawings.
Through one or more of its various aspects, embodiments and/or specific features or sub-components of the present disclosure, are intended to bring out one or more of the advantages as specifically described above and noted below.
The examples may also be embodied as one or more non-transitory computer readable media having instructions stored thereon for one or more aspects of the present technology as described and illustrated by way of the examples herein. The instructions in some examples include executable code that, when executed by one or more processors, cause the processors to carry out steps necessary to implement the methods of the examples of this technology that are described and illustrated herein.
The computer system 102 may include a set of instructions that can be executed to cause the computer system 102 to perform any one or more of the methods or computer-based functions disclosed herein, either alone or in combination with the other described devices. The computer system 102 may operate as a standalone device or may be connected to other systems or peripheral devices. For example, the computer system 102 may include, or be included within, any one or more computers, servers, systems, communication networks or cloud environment. Even further, the instructions may be operative in such cloud-based computing environment.
In a networked deployment, the computer system 102 may operate in the capacity of a server or as a client user computer in a server-client user network environment, a client user computer in a cloud computing environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system 102, or portions thereof, may be implemented as, or incorporated into, various devices, such as a personal computer, a tablet computer, a set-top box, a personal digital assistant, a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless smart phone, a personal trusted device, a wearable device, a global positioning satellite (GPS) device, a web appliance, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single computer system 102 is illustrated, additional embodiments may include any collection of systems or sub-systems that individually or jointly execute instructions or perform functions. The term “system” shall be taken throughout the present disclosure to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.
As illustrated in
The processor 104 may also be a microprocessor, a microcomputer, a processor chip, a controller, a microcontroller, a digital signal processor (DSP), a state machine, or a programmable logic device. The processor 104 may also be a logical circuit, including a programmable gate array (PGA) such as a field programmable gate array (FPGA), or another type of circuit that includes discrete gate and/or transistor logic. The processor 104 may be a central processing unit (CPU), a graphics processing unit (GPU), or both. Additionally, any processor described herein may include multiple processors, parallel processors, or both. Multiple processors may be included in, or coupled to, a single device or multiple devices.
The computer system 102 may also include a computer memory 106. The computer memory 106 may include a static memory, a dynamic memory, or both in communication. Memories described herein are tangible storage mediums that can store data as well as executable instructions and are non-transitory during the time instructions are stored therein. Again, as used herein, the term “non-transitory” is to be interpreted not as an eternal characteristic of a state, but as a characteristic of a state that will last for a period of time. The term “non-transitory” specifically disavows fleeting characteristics such as characteristics of a particular carrier wave or signal or other forms that exist only transitorily in any place at any time. The memories are an article of manufacture and/or machine component. Memories described herein are computer-readable mediums from which data and executable instructions can be read by a computer. Memories as described herein may be random access memory (RAM), read only memory (ROM), flash memory, electrically programmable read only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a cache, a removable disk, tape, compact disk read only memory (CD-ROM), digital versatile disk (DVD), floppy disk, blu-ray disk, or any other form of storage medium known in the art. Memories may be volatile or non-volatile, secure and/or encrypted, unsecure and/or unencrypted. Of course, the computer memory 106 may comprise any combination of memories or a single storage.
The computer system 102 may further include a display 108, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid state display, a cathode ray tube (CRT), a plasma display, or any other type of display, examples of which are well known to skilled persons.
The computer system 102 may also include at least one input device 110, such as a keyboard, a touch-sensitive input screen or pad, a speech input, a mouse, a remote control device having a wireless keypad, a microphone coupled to a speech recognition engine, a camera such as a video camera or still camera, a cursor control device, a global positioning system (GPS) device, an altimeter, a gyroscope, an accelerometer, a proximity sensor, or any combination thereof. Those skilled in the art appreciate that various embodiments of the computer system 102 may include multiple input devices 110. Moreover, those skilled in the art further appreciate that the above-listed, exemplary input devices 110 are not meant to be exhaustive and that the computer system 102 may include any additional, or alternative, input devices 110.
The computer system 102 may also include a medium reader 112 which is configured to read any one or more sets of instructions, e.g. software, from any of the memories described herein. The instructions, when executed by a processor, can be used to perform one or more of the methods and processes as described herein. In a particular embodiment, the instructions may reside completely, or at least partially, within the memory 106, the medium reader 112, and/or the processor 110 during execution by the computer system 102.
Furthermore, the computer system 102 may include any additional devices, components, parts, peripherals, hardware, software or any combination thereof which are commonly known and understood as being included with or within a computer system, such as, but not limited to, a network interface 114 and an output device 116. The output device 116 may be, but is not limited to, a speaker, an audio out, a video out, a remote-control output, a printer, or any combination thereof.
Each of the components of the computer system 102 may be interconnected and communicate via a bus 118 or other communication link. As illustrated in
The computer system 102 may be in communication with one or more additional computer devices 120 via a network 122. The network 122 may be, but is not limited to, a local area network, a wide area network, the Internet, a telephony network, a short-range network, or any other network commonly known and understood in the art. The short-range network may include, for example, Bluetooth, Zigbee, infrared, near field communication, ultraband, or any combination thereof. Those skilled in the art appreciate that additional networks 122 which are known and understood may additionally or alternatively be used and that the exemplary networks 122 are not limiting or exhaustive. Also, while the network 122 is illustrated in
The additional computer device 120 is illustrated in
Of course, those skilled in the art appreciate that the above-listed components of the computer system 102 are merely meant to be exemplary and are not intended to be exhaustive and/or inclusive. Furthermore, the examples of the components listed above are also meant to be exemplary and similarly are not meant to be exhaustive and/or inclusive.
In accordance with various embodiments of the present disclosure, the methods described herein may be implemented using a hardware computer system that executes software programs. Further, in an exemplary, non-limited embodiment, implementations can include distributed processing, component/object distributed processing, and parallel processing. Virtual computer system processing can be constructed to implement one or more of the methods or functionalities as described herein, and a processor described herein may be used to support a virtual processing environment.
As described herein, various embodiments provide optimized methods and systems for detecting abnormal and/or anomalous rejections of requests for quotes (RFQs) for foreign exchange (FX) transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue.
Referring to
The method for detecting abnormal and/or anomalous rejections of RFQs for FX transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue may be implemented by an Anomalous Rejection of Request For Quote for Foreign exchange Transaction (ARRFQFXT) device 202. The ARRFQFXT device 202 may be the same or similar to the computer system 102 as described with respect to
Even further, the application(s) may be operative in a cloud-based computing environment. The application(s) may be executed within or as virtual machine(s) or virtual server(s) that may be managed in a cloud-based computing environment. Also, the application(s), and even the ARRFQFXT device 202 itself, may be located in virtual server(s) running in a cloud-based computing environment rather than being tied to one or more specific physical network computing devices. Also, the application(s) may be running in one or more virtual machines (VMs) executing on the ARRFQFXT device 202. Additionally, in one or more embodiments of this technology, virtual machine(s) running on the ARRFQFXT device 202 may be managed or supervised by a hypervisor.
In the network environment 200 of
The communication network(s) 210 may be the same or similar to the network 122 as described with respect to
By way of example only, the communication network(s) 210 may include local area network(s) (LAN(s)) or wide area network(s) (WAN(s)), and can use TCP/IP over Ethernet and industry-standard protocols, although other types and/or numbers of protocols and/or communication networks may be used. The communication network(s) 210 in this example may employ any suitable interface mechanisms and network communication technologies including, for example, teletraffic in any suitable form (e.g., voice, modem, and the like), Public Switched Telephone Network (PSTNs), Ethernet-based Packet Data Networks (PDNs), combinations thereof, and the like.
The ARRFQFXT device 202 may be a standalone device or integrated with one or more other devices or apparatuses, such as one or more of the server devices 204(1)-204(n), for example. In one particular example, the ARRFQFXT device 202 may include or be hosted by one of the server devices 204(1)-204(n), and other arrangements are also possible. Moreover, one or more of the devices of the ARRFQFXT device 202 may be in a same or a different communication network including one or more public, private, or cloud networks, for example.
The plurality of server devices 204(1)-204(n) may be the same or similar to the computer system 102 or the computer device 120 as described with respect to
The server devices 204(1)-204(n) may be hardware or software or may represent a system with multiple servers in a pool, which may include internal or external networks. The server devices 204(1)-204(n) hosts the databases 206(1)-206(n) that are configured to store customer-specific information that relates to financial accounts and data that relates to criteria for conducting foreign exchange transactions.
Although the server devices 204(1)-204(n) are illustrated as single devices, one or more actions of each of the server devices 204(1)-204(n) may be distributed across one or more distinct network computing devices that together comprise one or more of the server devices 204(1)-204(n). Moreover, the server devices 204(1)-204(n) are not limited to a particular configuration. Thus, the server devices 204(1)-204(n) may contain a plurality of network computing devices that operate using a master/slave approach, whereby one of the network computing devices of the server devices 204(1)-204(n) operates to manage and/or otherwise coordinate operations of the other network computing devices.
The server devices 204(1)-204(n) may operate as a plurality of network computing devices within a cluster architecture, a peer-to peer architecture, virtual machines, or within a cloud architecture, for example. Thus, the technology disclosed herein is not to be construed as being limited to a single environment and other configurations and architectures are also envisaged.
The plurality of client devices 208(1)-208(n) may also be the same or similar to the computer system 102 or the computer device 120 as described with respect to
The client devices 208(1)-208(n) may run interface applications, such as standard web browsers or standalone client applications, which may provide an interface to communicate with the ARRFQFXT device 202 via the communication network(s) 210 in order to communicate user requests and information. The client devices 208(1)-208(n) may further include, among other features, a display device, such as a display screen or touchscreen, and/or an input device, such as a keyboard, for example.
Although the exemplary network environment 200 with the ARRFQFXT device 202, the server devices 204(1)-204(n), the client devices 208(1)-208(n), and the communication network(s) 210 are described and illustrated herein, other types and/or numbers of systems, devices, components, and/or elements in other topologies may be used. It is to be understood that the systems of the examples described herein are for exemplary purposes, as many variations of the specific hardware and software used to implement the examples are possible, as will be appreciated by those skilled in the relevant art(s).
One or more of the devices depicted in the network environment 200, such as the ARRFQFXT device 202, the server devices 204(1)-204(n), or the client devices 208(1)-208(n), for example, may be configured to operate as virtual instances on the same physical machine. In other words, one or more of the ARRFQFXT device 202, the server devices 204(1)-204(n), or the client devices 208(1)-208(n) may operate on the same physical device rather than as separate devices communicating through communication network(s) 210. Additionally, there may be more or fewer ARRFQFXT devices 202, server devices 204(1)-204(n), or client devices 208(1)-208(n) than illustrated in
In addition, two or more computing systems or devices may be substituted for any one of the systems or devices in any example. Accordingly, principles and advantages of distributed processing, such as redundancy and replication also may be implemented, as desired, to increase the robustness and performance of the devices and systems of the examples. The examples may also be implemented on computer system(s) that extend across any suitable network using any suitable interface mechanisms and traffic technologies, including by way of example only teletraffic in any suitable form (e.g., voice and modem), wireless traffic networks, cellular traffic networks, Packet Data Networks (PDNs), the Internet, intranets, and combinations thereof.
The ARRFQFXT device 202 is described and illustrated in
An exemplary process 300 for implementing a mechanism for detecting abnormal and/or anomalous rejections of RFQs for FX transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue by utilizing the network environment of
Further, ARRFQFXT device 202 is illustrated as being able to access a customer-specific account data repository 206(1) and a foreign exchange transaction criteria database 206(2). The anomalous rejections of FX RFQs module 302 may be configured to access these databases for implementing a method for detecting abnormal and/or anomalous rejections of RFQs for FX transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue.
The first client device 208(1) may be, for example, a smart phone. Of course, the first client device 208(1) may be any additional device described herein. The second client device 208(2) may be, for example, a personal computer (PC). Of course, the second client device 208(2) may also be any additional device described herein.
The process may be executed via the communication network(s) 210, which may comprise plural networks as described above. For example, in an exemplary embodiment, either or both of the first client device 208(1) and the second client device 208(2) may communicate with the ARRFQFXT device 202 via broadband or cellular communication. Of course, these embodiments are merely exemplary and are not limiting or exhaustive.
Upon being started, the anomalous rejections of FX RFQs module 302 executes a process for detecting abnormal and/or anomalous rejections of RFQs for FX transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue. An exemplary process for detecting abnormal and/or anomalous rejections of RFQs for FX transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue is generally indicated at flowchart 400 in
In process 400 of
At step S404, the anomalous rejections of FX RFQs module 302 retrieves information that relates to the requester from a memory, such as, for example, customer-specific account data repository 206(1). The information that is retrieved relates to a financial capability of the requester to conduct the proposed FX transaction.
At step S406, the anomalous rejections of FX RFQs module 302 applies a first algorithm that is designed to make an initial determination as to whether the RFQ received in step S402 is acceptable. In many instances, the RFQ is deemed as being acceptable, and in this scenario, the process 400 ends, and the proposed FX transaction proceeds in a normal manner.
However, when the RFQ is initially determined in step S406 as not being acceptable, then at step S408, the anomalous rejections of FX RFQs module 302 applies a second algorithm that uses an artificial intelligence (AI) technique to detect whether the rejection is abnormal or anomalous and to determine a potential reason that the initial determination made in step S406 has indicated that the RFQ is not acceptable. In an exemplary embodiment, the potential reason may include any one of more of a first reason that is based on an abnormal behavior of the requester within a predetermined time interval, a second reason that is based on a product type that relates to the proposed FX transaction, a third reason that is based on a currency that relates to the proposed FX transaction, and a fourth reason that is based on a size of the proposed FX transaction. The predetermined time interval may include, for example, any one of a 24-hour interval, a three-day interval, a seven-day (i.e., one week) interval, a thirty-day or one-month interval, a six-month interval, a twelve-month interval, or any other suitable interval.
At step S410, the anomalous rejections of FX RFQs module 302 generates a report that includes information that relates to the potential reason for the initial determination of unacceptability of the RFQ as determined by the AI algorithm. Then, at step S412, the anomalous rejections of FX RFQs module 302 transmits the report to a predetermined destination and displays relevant portions of the report via a graphical user interface (GUI). In an exemplary embodiment, the predetermined destination includes a workstation of a person or of a group of persons, such as, for example, an operations team that includes human operators that are well-positioned to review the reason for the initial rejection of the RFQ, and to potentially take action to override the rejection as they deem appropriate.
In an exemplary embodiment, the AI algorithm uses historical information that relates to previous transactions that have been conducted by the requester as an input. In this aspect, the AI algorithm may detect a change in the behavior of the requester and/or changes in any of various aspects of the proposed transaction as compared with analogous aspects of the previous transactions. For example, if the previous transactions exhibit a pattern that indicates the use of particular types of currency and the frequency of rejection by the conventional pricing algorithm is low, and the newly proposed FX transaction would use different types of currency and is more likely to be rejected, then the AI algorithm may detect this as a change that may have triggered an initial determination that the proposed transaction is not acceptable. As another example, if the previous transactions exhibit a pattern that indicates the involvement of particular product types and the newly proposed FX transaction would involve different types of products, then the AI algorithm may detect this as a change that may have triggered an initial determination that the proposed transaction is not acceptable.
In an exemplary embodiment, in addition to the historical information that relates to previous transactions that have been conducted by the requester, the AI algorithm may also use historical information that relates to previous transactions that have been conducted by entities that are deemed as being similar to the requester. In this regard, the AI algorithm may make a determination as to whether a particular entity, such as, for example, another customer, is similar to the requester by 1) identifying a set of features of the requester; 2) identifying a corresponding set of features of the particular entity; and 3) determining a distance between the sets. In an exemplary embodiment, this determination may be made with respect to many entities, i.e., a global set of candidate entities, such as the set of existing customers of the bank; and the determination as to which entities are deemed as being similar to the requester may be made by selecting the entities for which the distances are the closest distances among the candidate entities.
In an exemplary embodiment, in addition to the historical information that relates to previous transactions that have been conducted by the requester and the historical information that relates to previous transactions that have been conducted by entities that are similar to the requester, the AI algorithm may also use historical information that relates to previous transactions that have been conducted by other entities that are not deemed as being similar. In this regard, the AI algorithm may use historical information that relates to the entirety of the global set of candidate entities.
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The feature generation section also receives an input from a reason grouping component that provides data that makes a reason distribution computation feasible. This input is received by a reason features component that analyzes the target client data and the comparing client data in order to determine potential reasons that a particular transaction may be accepted or rejected. The feature generation section also includes an RFQ features component that analyzes the proposed transaction in order to determine potential reasons that this specific proposed transaction may be rejected.
The outputs of the feature generation section are fed to an anomaly detection section that includes a distribution estimation component and a distribution distance component. These components analyze the inputs to make a determination as to whether a rejection of the proposed transaction may be abnormal or anomalous. When a determination is made that the rejection is abnormal, an alerts generation component is notified in order to generate an alert.
Accordingly, with this technology, an optimized process for detecting abnormal and/or anomalous rejections of requests for quotes (RFQs) for foreign exchange (FX) transactions by using artificial intelligence techniques to obtain explanations for the rejections and to facilitate resolutions thereof so as to minimize potential losses of revenue is provided.
Although the invention has been described with reference to several exemplary embodiments, it is understood that the words that have been used are words of description and illustration, rather than words of limitation. Changes may be made within the purview of the appended claims, as presently stated and as amended, without departing from the scope and spirit of the present disclosure in its aspects. Although the invention has been described with reference to particular means, materials and embodiments, the invention is not intended to be limited to the particulars disclosed; rather the invention extends to all functionally equivalent structures, methods, and uses such as are within the scope of the appended claims.
For example, while the computer-readable medium may be described as a single medium, the term “computer-readable medium” includes a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The term “computer-readable medium” shall also include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor or that cause a computer system to perform any one or more of the embodiments disclosed herein.
The computer-readable medium may comprise a non-transitory computer-readable medium or media and/or comprise a transitory computer-readable medium or media. In a particular non-limiting, exemplary embodiment, the computer-readable medium can include a solid-state memory such as a memory card or other package that houses one or more non-volatile read-only memories. Further, the computer-readable medium can be a random-access memory or other volatile re-writable memory. Additionally, the computer-readable medium can include a magneto-optical or optical medium, such as a disk or tapes or other storage device to capture carrier wave signals such as a signal communicated over a transmission medium. Accordingly, the disclosure is considered to include any computer-readable medium or other equivalents and successor media, in which data or instructions may be stored.
Although the present application describes specific embodiments which may be implemented as computer programs or code segments in computer-readable media, it is to be understood that dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays and other hardware devices, can be constructed to implement one or more of the embodiments described herein. Applications that may include the various embodiments set forth herein may broadly include a variety of electronic and computer systems. Accordingly, the present application may encompass software, firmware, and hardware implementations, or combinations thereof. Nothing in the present application should be interpreted as being implemented or implementable solely with software and not hardware.
Although the present specification describes components and functions that may be implemented in particular embodiments with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same or similar functions are considered equivalents thereof.
The illustrations of the embodiments described herein are intended to provide a general understanding of the various embodiments. The illustrations are not intended to serve as a complete description of all the elements and features of apparatus and systems that utilize the structures or methods described herein. Many other embodiments may be apparent to those of skill in the art upon reviewing the disclosure. Other embodiments may be utilized and derived from the disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the disclosure. Additionally, the illustrations are merely representational and may not be drawn to scale. Certain proportions within the illustrations may be exaggerated, while other proportions may be minimized. Accordingly, the disclosure and the figures are to be regarded as illustrative rather than restrictive.
One or more embodiments of the disclosure may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any particular invention or inventive concept. Moreover, although specific embodiments have been illustrated and described herein, it should be appreciated that any subsequent arrangement designed to achieve the same or similar purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all subsequent adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the description.
The Abstract of the Disclosure is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, various features may be grouped together or described in a single embodiment for the purpose of streamlining the disclosure. This disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may be directed to less than all of the features of any of the disclosed embodiments. Thus, the following claims are incorporated into the Detailed Description, with each claim standing on its own as defining separately claimed subject matter.
The above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims, and their equivalents, and shall not be restricted or limited by the foregoing detailed description.
