METHOD FOR ASSIGNING A TRADE INSTRUCTION TO A TRADING SYSTEM BELONGING TO A FINANCIAL INSTITUTION

Disclosed is a system for assigning a trade instruction, in a structured data format, to a trading system capable of processing a trade, wherein the trading system belongs to a financial institution. A data receiving module receives trading information comprising one or more trading keywords from at least one data source in at least one unstructured data format. A data processing module processes the trading information to extract the one or more trading keywords and metadata, associated to the one or more trading keywords, in a structured data format. The data processing module analyzes the one or more trading keywords to categorize the trading information into a trading category. A validation module validates the categorization of the trading information upon referring to a trade domain ontology. A trading module assigns the trading information to a trading system for processing the trade in the trading category upon validating the categorization.

CROSS REFERENCE TO RELATED APPLICATIONS

This present application claims benefit from Indian Complete Patent Application No 201711026706 filed on 27 Jul. 2017 the entirety of which is hereby incorporated by reference

TECHNICAL FIELD

The present subject matter described herein, in general, relates to trade processing. More particularly, a system and method for assigning a trade instruction, to a trading system capable of processing a trade, in a structured data format.

BACKGROUND

Trade processing for a financial institution is a time critical and requires a lot of human skills for processing a trade in an accurate and timely manner. Since any trade involves large financial transaction; any human error(s) may lead to huge loss for an investor. Conventionally, the trade processing is taken care by a custodian on behalf of the investor. Generally, the investor trusts the custodian as he/she have skilled resources with domain knowledge to process the trade on behalf of the investor. These resources understand a requirement of the investor and thereby process the trade in an intended trade instrument as prescribed in the requirement.

Since the trade instruction is received, as of now, in various unstructured data formats including but, not limited to, fax, email (with attachments), digitally in the system, hand written instructions, there is an inherent risk of incorrect or delayed trade being processed because of human fatigue or instructions getting missed.

In addition to the above, the requirement is provided to the custodian by using two channels. First channel, where messages get transmitted electronically by using a bank digital system and hence require very little manual intervention. Second channel, where users send the requirement to the custodian in the unstructured data formats. These, trade instruction, are either handwritten or typed or mix of handwritten and typed data in various formats like pdf and images files. While the conventional system for digital interfaces are available, challenges lie where the trade instruction is not provided directly to the bank digital system and users send the data via the second channel.

SUMMARY

Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to systems and methods for assigning a trade instruction, in a structured data format, to a trading system capable of processing a trade and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in limiting the scope of the claimed subject matter.

In one implementation, a trade instruction processing system for assigning a trade instruction, in a structured data format, to a trading system capable of processing a trade is disclosed. It may be understood that the trading system belongs to a financial institution. The trade instruction processing system may comprise a processor and a memory coupled to the processor. The processor may execute a plurality of modules present in the memory. The plurality of modules may comprise a data receiving module, a data processing module, a validation module, and a trading module. The data receiving module may receive trading information from at least one data source in at least one unstructured data format. The trading information may be received in the form of image or facsimile (containing handwritten information, typed information or a combination of both). It may be understood that the trading information comprises one or more trading keywords. In order to extract the one or more keywords, the data processing module processes the trading information by an image processing using a Long Short Term Memory (LSTM) technique and a reinforcement learning technique. The data processing module, upon processing the image file, extracts the one or more trading keywords and metadata, associated to the one or more trading keywords, from the trading information in a structured data format. In one aspect, the trading information is processed by using at least one data processing technique. The data processing module may further analyze the one or more trading keywords to categorize the trading information into a trading category of a plurality of trading categories. The validation module may validate the categorization of the trading information upon referring to a trade domain ontology. In one aspect, the trade domain ontology may comprise a predefined mapping of the one or more trading keywords and at least one trading category. The trading module may assign the trading information to a trading system for processing a trade in the trading category, as categorized, upon validating the categorization.

In another implementation, a method for assigning a trade instruction, in a structured data format, to a trading system capable of processing a trade is disclosed. It may be understood that the trading system belongs to a financial institution. In order to assign the trade instruction, trading information may be received from at least one data source in at least one unstructured data format. In one aspect, the trading information may be received in the form of image or facsimile (containing handwritten information, typed information or a combination of both). It may be understood that the trading information comprises one or more trading keywords. In order to extract the one or more keywords, the trading information may be processed by an image processing using a Long Short Term Memory (LSTM) technique and a reinforcement learning technique. In one aspect, upon image processing, the trading information may be processed to extract the one or more trading keywords and metadata, associated to the one or more trading keywords, may be extracted from the trading information in a structured data format. In one aspect, the trading information may be processed by using at least one data processing technique. Upon extracting the one or more trading keywords, the one or more trading keywords may be analyzed to categorize the trading information into a trading category of a plurality of trading categories. Subsequently, the categorization of the trading information may be validated upon referring to a trade domain ontology. In one aspect, the trade domain ontology may comprise a predefined mapping of the one or more trading keywords and at least one trading category. Post validation, the trading information may be assigned to a trading system for processing the trade in the trading category, as categorized, upon validating the categorization. In one aspect, the aforementioned method for assigning the trade instruction to the trading system may be performed by a processor using programmed instructions stored in a memory.

In yet another implementation, non-transitory computer readable medium embodying a program executable in a computing device for assigning a trade instruction, in a structured data format, to a trading system capable of processing a trade, wherein the trading system belongs to a financial institution is disclosed. The program may comprise a program code for receiving trading information from at least one data source in at least one unstructured data format. In one aspect, the trading information may be received in the form of image or facsimile (containing handwritten information, typed information or a combination of both). It may be understood that the trading information comprises one or more trading keywords. The program may further comprise a program code for processing the trading information by an image processing using a Long Short Term Memory (LSTM) technique and a reinforcement learning technique. In one aspect, upon processing the trading information, the one or more trading keywords and metadata, associated to the one or more trading keywords, may be extracted from the trading information in a structured data format, wherein the trading information is processed by using at least one data processing technique. The program may further comprise a program code for analyzing the one or more trading keywords to categorize the trading information into a trading category of a plurality of trading categories. The program may further comprise a program code for validating the categorization of the trading information upon referring to a trade domain ontology (220), wherein the trade domain ontology (220) comprises a predefined mapping of the one or more trading keywords and at least one trading category. The program may further comprise a program code for assigning the trading information to a trading system for processing a trade in the trading category, as categorized, upon validating the categorization.

DETAILED DESCRIPTION

Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words “comprising,” “having,” “containing,” and “including,” and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice, the exemplary, systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.

Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.

The proposed invention facilitates to assign a trade instruction, in a structured data format, to a trading system capable of processing a trade is disclosed. It may be understood that the trading system belongs to a financial institution. It may further be understood that the trade instruction comprises trading information including a certain amount to be invested in a specific trade instrument along with other details. The other details may include, but not limited to, date of investment, country, currency and mode of payment.

The proposed invention automates the process of reading and thereby assigning the trade instruction to the trading system for processing the trade. In one aspect, the trade may be processed upon investing the amount in various types of trade instruments. Examples of the trade instruments may include, but not limited to, derivatives, mutual fund, forex, and equity shares. In order to assign the trade, the trading information is received in an unstructured data format. In one aspect, the trading information may be received in the form of image or facsimile (containing handwritten information, typed information or a combination of both). It may be understood that the trading information comprises one or more trading keywords. In order to extract the one or more keywords, the trading information may be processed by an image processing using a Long Short Term Memory (LSTM) technique and a reinforcement learning technique. Upon image processing, the trading information is processed to extract the one or more trading keywords and metadata, associated to the one or more trading keywords, may be extracted from the trading information in a structured data format.

Subsequently, the one or more trading keywords may be analyzed to categorize the trading information into a trading category of a plurality of trading categories, wherein the plurality of categories are having different aspects of the trade instruction that the financial institution has received. Further, the categorization of the metadata may be validated upon referring to a trade domain ontology and thereby the trading information may be assigned to the trading system for processing the trade. Thus, upon assigning the trade system, the proposed invention facilitates to avoid human errors and save efforts spend on manual activities by respective brokers, agents associated to the financial institution.

While aspects of described system and method for assigning the trade instruction, in the structured data format, to the trading system capable of processing the trade may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.

Referring now toFIG. 1, a network implementation100of a trade instruction processing system102for assigning a trade instruction, in a structured data format, to a trading system101capable of processing a trade. In one aspect, the trading system101belongs to a financial institution is disclosed. In order to assign the trade instruction, initially, the trade instruction processing system102receives trading information comprising one or more trading keywords from at least one data source in at least one unstructured data format. In order to extract the one or more keywords, the trade instruction processing system102processes the trading information by an image processing using a Long Short Term Memory (LSTM) technique and a reinforcement learning technique. In one aspect, the image processing system102processes the trading information to extract the one or more trading keywords and metadata, associated to the one or more trading keywords, in a structured data format. Upon extracting the one or more trading keywords, the trade instruction processing system102analyzes the one or more trading keywords to categorize the trading information into a trading category of a plurality of trading categories. Subsequently, the trade instruction processing system102validates categorization of the trading information upon referring to a trade domain ontology. In one aspect, the trade domain ontology may comprise a predefined mapping of the one or more trading keywords and at least one trading category. Post validation, the trade instruction processing system102assigns the trading information to a trading system101for processing the trade in the trading category, as categorized, upon validating the categorization.

Although the present disclosure is explained considering that the trade instruction processing system102is implemented on a server, it may be understood that the trade instruction processing system102may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment. It will be understood that the trade instruction processing system102may be accessed by multiple users through one or more user devices104-1,104-2. . .104-N, collectively referred to as user104or stakeholders, hereinafter, or applications residing on the user devices104. In one implementation, the trade instruction processing system102may comprise the cloud-based computing environment in which a user may operate individual computing systems configured to execute remotely located applications. Examples of the user devices104may include, but are not limited to, a IoT device, IoT gateway, portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices104are communicatively coupled to the trade instruction processing system102through a network106.

Referring now toFIG. 2, the trade instruction processing system102is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the trade instruction processing system102may include at least one processor202, an input/output (I/O) interface204, and a memory206. The at least one processor202may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor202is configured to fetch and execute computer-readable instructions stored in the memory206.

The memory206may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory206may include modules208and data210.

The modules208include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules208may include a data receiving module212, a data processing module214, a validation module216, a trading module218, and other modules220. The other modules220may include programs or coded instructions that supplement applications and functions of the trade instruction processing system102. The modules208described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the trade instruction processing system102.

The data210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules208. The data210may also include a trade domain ontology220and other data222. The other data222may include data generated as a result of the execution of one or more modules in the other modules218.

As there are various challenges observed in the existing art, the challenges necessitate the need to build the trade instruction processing system102for assigning a trade instruction, in a structured data format, to a trading system101capable of processing a trade. In order to facilitate the communication, at first, a user may use the user device104to access the trade instruction processing system102via the I/O interface204. The user may register them using the I/O interface204to use the trade instruction processing system102. In one aspect, the user may access the I/O interface204of the trade instruction processing system102. In order to assigning the trade instruction to the trading system101, the trade instruction processing system102may employ the data receiving module212, the data processing module214, the validation module216, and the trading module218. The detail functioning of the modules is described below with the help of figures.

The data receiving module212receives trading information comprising one or more trading keywords from at least one data source in at least one unstructured data format. In one aspect, the trading information may be received in the form of image or facsimile (containing handwritten information, typed information or a combination of both). It may be understood that the trading information comprises one or more trading keywords. Examples of the unstructured data format may include, but not limited to, PDF, JPEG, JPG, email, and facsimile. Upon receipt of the trading information, the data processing module214processes the trading information. The trading information may be processed by using at least one data processing technique. Examples of the at least one data processing technique may include, but not limited to, an image processing technique, a machine learning technique, a reinforcement learning technique, and a Natural Language Processing (NLP) technique. In one aspect, the machine learning technique may use a Long Short Term Memory (LSTM) technique. In order to extract the one or more keywords, the data processing module214processes the trading information by an image processing using a Long Short Term Memory (LSTM) technique and a reinforcement learning technique. In one embodiment, the data processing module214upon processing the image, extracts the one or more trading keywords and metadata in a structured data format.

Upon processing the trading information, the data processing module214analyzes the one or more trading keywords along with machine learning techniques to categorize the trading information into a trading category of a plurality of trading categories. In one embodiment, the data processing module214categorizes the trading information into the trading category upon implementing the machine learning algorithm on based on the one or more trading keywords as identified from the trading information. Examples of the plurality of trading categories may comprise ‘derivatives’, ‘forex’, ‘mutual fund’, and ‘equity share’ etc. In one embodiment, the trading information may be categorized based on mapping of the one or more trading keywords and one or more parameters associated to the trading category. The trading keywords may comprise at least one of trade type, trade date, account number, broker details, and currency information.

In order to elucidate the functioning of the data receiving module212and the data processing module214, consider an example where the trading information is received from a person ‘X’ via an email, email with attachment or scanned document or any other business application containing these scanned document. It is to be noted that email or the document, comprising the trading information, indicates that an amount of $15000 is to be invested on date Jul. 1, 2017 in mutual funds offered by ‘ABC’ company. From the above email, it has been further noted that the email comprising trading keywords as ‘amount’, ‘date’, ‘mutual funds’ and the metadata as ‘$15000’, ‘July 1st, 2017’, ‘ABC’ company’ for the trading keywords respectively. Since the trading information, as aforementioned, is in unstructured data format, can be handwritten text, typed text or mix of handwritten and typed text the data processing module214processes the trading information to extract the trading keywords and the metadata associated to the trading keywords from the source. It may be noted that the trading keywords and the metadata may be extracted by using the Natural Language Processing (NLP) technique. Here the metadata indicates the values pertaining to the trading keywords.

Since the trading information indicates that the trade is to be processed in ‘mutual funds’ category, the data processing module214analyzes the trading keywords (i.e. ‘amount’, ‘date’, and ‘mutual funds’) and categorizes the trading information into the ‘mutual fund’ category. Thus, in this manner, the trading information is categorized into at least one trading category.

After categorizing the trading information into the at least one trading category, the validation module216validates the categorization of the trading information. The categorization of the trading information is validated to ensure, before investment, that the trading information is categorized in the trading category as intended by the investor. In one aspect, the trading information may be validated upon referring to a trade domain ontology222. In one embodiment, the trade domain ontology222may comprise a predefined mapping of the one or more trading keywords and at least one trading category.

For example, the trade domain ontology database222comprises the mapping of the trading keywords (such as ‘mutual fund’, ‘NAV’, “Net Asset Value”, ‘Unit’) with ‘mutual fund’. Similarly, the trade domain ontology222comprises the mapping of the trading keywords (such as ‘equity share’ and ‘Sensex’) with ‘equity share’. Post validation, the trading module218assigns the trading information, indicating the trade instruction, to a trading system101for processing the trade, as categorized, upon validating the categorization.

To elucidate the functioning of the validation module216and the trading module218, consider the example same as aforementioned. Since the trading information, as in the above email, is categorized in the ‘mutual fund’ category, the validation module216validates the categorization of the trading information. To validate the categorization, the validation module216refers the trade domain ontology222to ensure that the trading information is categorized correctly. Upon referring to the trade domain ontology222, it must be understood that the trading keywords (amount’, ‘date’, and ‘mutual funds’), as identified, are mapped with the ‘mutual fund’ category. This validates that the categorization of the trading information is being done correctly.

Upon validation, the trading module218assigns the trading information, including the metadata pertaining to the trading keywords, to a trading system101for processing the trade. In other words, the trading module218assigns ‘$15000’, ‘July 1st, 2017’, ‘ABC’ company’ (pertaining to the ‘amount’ to be invested, ‘date’ on which the amount to be invested, and ‘mutual fund’ in which the amount to be invested) to the trading system101for processing the trade. Thus, in this manner, the trade instruction processing system102automates the trading process with robust capabilities, avoid human errors and save efforts spend on manual activities by respective operators.

Referring now toFIG. 3, a data flow within the trade instruction processing system102for assigning a trade instruction, in a structured data format, to a trading system101capable of processing a trade is shown, in accordance with an embodiment of the present subject matter. As shown in theFIG. 3, various input systems, hereinafter referred to as input system302, are coupled with the trade instruction processing system102. The data receiving module212receives trading information in the form of image or facsimile (containing handwritten information, typed information or a combination of both). In one embodiment, the trading information comprises one or more trading keywords from the input system302in at least one unstructured data format. Since the trading information is in the image and unstructured data form, the data processing module214processes the trading information by an image processing using a Long Short Term Memory (LSTM) technique and a reinforcement learning technique, as shown in block302. Subsequently, the data processing module214extracts the one or more trading keywords and metadata, associated to the one or more trading keywords, by implementing Natural Language Processing (NLP) technique, as shown in block304. Post extraction of the one or more trading keywords and the metadata, the data processing module214analyzes the one or more trading keywords to categorize the trading information into a trading category. Thereafter, the validation module216validates the categorization of the trading information upon referring to a trade domain ontology220, as shown in block306. Once the categorization of the trading information is validated, the trading module218assigns the trading information, to a trading system101, via an interface, for processing the trade in the trading category.

Referring now toFIG. 4, a method400for assigning a trade instruction, in a structured data format, to a trading system101capable of processing a trade is shown, in accordance with an embodiment of the present subject matter. The method400may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method400may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.

At block402, trading information comprising one or more trading keywords may be received from at least one data source in at least one unstructured data format. In one implementation, the trading information may be received by the data receiving module212.

At block404, the trading information may be processed to extract the one or more trading keywords and metadata, associated to the one or more trading keywords, in a structured data format. In one aspect, the trading information may be processed by using at least one data processing technique. In one implementation, the trading information may be processed by the data processing module214.

At block406, the one or more trading keywords may be analyzed to categorize the trading information into a trading category of a plurality of trading categories. In one implementation, the one or more trading keywords may be analyzed by the data processing module214.

At block408, the categorization of the trading information may be validated upon referring to a trade domain ontology220. In one aspect, the trade domain ontology220may comprise a predefined mapping of the one or more trading keywords and at least one trading category. In one implementation, the categorization of the trading information may be validated by the validation module216.

At block410, the trading information may be assigned to a trading system101for processing a trade in the trading category, as categorized, upon validating the categorization. In one implementation, the trading information may be assigned by the trading module218.

Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.

Some embodiments enable a system and a method to automate manual trade processing and analysis.

Some embodiments enable a system and a method to facilitate users with faster services with high accuracy rates.

Some embodiments enable a system and a method to avoid manual efforts on the entire process.

Some embodiments enable a system and a method to avoid human error causing financial impact.

Although implementations for methods and systems for assigning a trade instruction, in a structured data format, to a trading system101capable of processing a trade have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for assigning the trade instruction, in the structured data format, to the trading system101.