Description:
FIELD OF INVENTION
[0001] The embodiments of the present disclosure generally relate to a field of remote user identity verification and specifically to a system and a method for validating location information during remote user identity verification using social media location information.

BACKGROUND OF INVENTION
[0002] The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] In prevalent times, financial institutions and service providers are increasingly adopting user identification methods like Video Know Your Customer (vKYC) for remote user identity verification. While standard vKYC processes verify user identity documents and conduct live video interactions, they often lack an additional layer of historical location validation leading to fraudsters exploiting this gap by misrepresenting their actual location. Similarly, existing fraud detection systems focus on transaction and behavioral analysis of the user but do not incorporate historical location data from associated social media to cross-check real-time location claims of the user.
[0004] There is therefore a need to address these challenges by cross-referencing the user’s current physical location with historically tagged location data from their social media posts so as to detect anomalies that indicate fraudulent activity leading to strengthening of security and reliability of the remote user identity verification process.

OBJECTS OF THE INVENTION
[0005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.
[0006] It is an object of the present disclosure to provide a system and a method to validate location information during remote user identity verification using social media location information.
[0007] It is an object of the present disclosure to provide the system and the method that enhances location authentication by combining real-time reverse geocoding with historical social media location information leading to strengthened identity verification by cross-referencing a user’s current location with their past tagged locations.
[0008] It is an object of the present disclosure to provide the system and the method to improve fraud detection by calculating an average distance between historical user locations and current user location or by comparing a dominant historical country information with current country information resulting in identifying inconsistencies that indicate potential fraudulent activity.
[0009] It is an object of the present disclosure to provide the system and the method to allow financial institutions to set custom thresholds for acceptable location deviations or country mismatches, making it adaptable to varying risk profiles and regulatory requirements.
[0010] Another object of the present disclosure is to prioritize user-centric privacy by operating with explicit user consent, ensuring compliance with data protection regulations while still enabling effective identity validation.
[0011] Yet another object of the present disclosure is to provide the system and the method to use a distance matrix Application Programming Interface (API) to calculate distance between the user’s current and past locations and then computing an average distance for location validation.
[0012] Yet another object of the present disclosure is to determine a dominant country from social media location tags against the current location’s country.
[0013] Another object of the present disclosure is to provide the system and the method that allows configuration of acceptable distance thresholds and matching criteria which is tailored to different risk levels.
[0014] Another object of the present disclosure is to provide the system and the method for enabling seamless Video Know Your Customer (vKYC) integration by automatically integrating vKYC validation results into a decision making process.

SUMMARY OF THE INVENTION
[0015] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0016] In an aspect, the present disclosure relates to a method for validating location information during a remote user identity verification. The method uses an identity verification server communicatively coupled to an agent portal to receive an affirmative consent from a user to fetch a current location information of the user, and location coordinates from an associated social media platform, upon initiation of a remote user identity verification session. The method enables to fetch the current location information of the user via a reverse geocoding mechanism, and the location coordinates from one or more historical tagged locations present on the associated social media platform. The method facilitates to calculate a distance information between an average historical tagged location distance and the fetched current location information of the user, wherein the average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations. Further, the method includes extracting a country name having a maximum count of occurrences with respect to the one or more historical tagged locations. In addition, the method includes determining whether at least the calculated average distance is less than a pre-configured distance threshold, and the extracted country name having the maximum count of occurrences match the current location information of the user, wherein in response to a negative determination, a flag is issued with respect to the remote user identity verification session.
[0017] In an embodiment, the distance information between the average historical tagged location distance and the fetched current location information of the user is determined using a distance matrix application programming interface (API).
[0018] In an embodiment, the issued flag corresponds to one or more varying risk levels associated with the remote user identity verification.
[0019] In an embodiment, the remote user identity verification session is halted upon issuance of the flag.
[0020] In an embodiment, the location coordinates are fetched from the one or more historical tagged locations using a geocoding API.
[0021] In an embodiment, the current location information of the user is captured via the reverse geocoding mechanism of geospatial data captured during the remote user identity verification session.
[0022] In an aspect, the present disclosure relates to a system for validating location information during a remote user identity verification. The system may include one or more processors operatively configured to an identity verification server communicatively coupled to an agent portal, a memory operatively coupled to the one or more processors, wherein the memory comprises processor-executable instructions, which on execution, cause the one or more processors to receive an affirmative consent from a user to fetch a current location information of the user, and location coordinates from an associated social media platform, upon initiation of a remote user identity verification session. Next, the current location information of the user is fetched via a reverse geocoding mechanism, and the location coordinates from one or more historical tagged locations present on the associated social media platform. The processor then calculates a distance information between an average historical tagged location distance and the fetched current location information of the user, wherein the average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations. Further, the processor extracts a country name having a maximum count of occurrences with respect to the one or more historical tagged locations. Furthermore, the processor determines whether at least the calculated average distance is less than a pre-configured distance threshold, and the extracted country name having the maximum count of occurrences match the current location information of the user, wherein in response to a negative determination, a flag is issued with respect to the remote user identity verification.
[0023] In an aspect, the present disclosure relates to non-transitory computer-readable medium comprising processor-executable instructions that may cause a processor to use an identity verification server communicatively coupled to an agent portal to validate location information during a remote user identity verification. The processor may receive an affirmative consent from a user to fetch a current location information of the user, and location coordinates from an associated social media platform, upon initiation of a remote user identity verification session. Further, the processor may fetch the current location information of the user via a reverse geocoding mechanism, and the location coordinates from one or more historical tagged locations present on the associated social media platform. Furthermore, the processor may calculate a distance information between an average historical tagged location distance and the fetched current location information of the user, wherein the average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations. In addition, the processor may extract a country name having a maximum count of occurrences with respect to the one or more historical tagged locations. The processor may then determine whether at least the calculated average distance is less than a pre-configured distance threshold, and the extracted country name having the maximum count of occurrences match the current location information of the user, wherein in response to a negative determination, a flag is issued with respect to the remote user identity verification.

BRIEF DESCRIPTION OF DRAWINGS
[0024] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0025] FIG. 1 illustrates an exemplary block diagram representation of a network architecture implementing a proposed system for validating location information during a remote user identity verification, in accordance with an embodiment of the present disclosure.
[0026] FIG. 2 illustrates exemplary functional units of the proposed system, in accordance with an embodiment of the present disclosure.
[0027] FIG. 3A illustrates a workflow that shows a series of steps for validation of the location information during the remote user identity verification, in accordance with an embodiment of the disclosure.
[0028] FIG. 3B illustrates a workflow that shows a series of steps for calculating an average of multiple tagged locations, in accordance with an embodiment of the disclosure.
[0029] FIG. 4 is a flow diagram depicting a proposed method for validating the location information during the remote user identity verification, in accordance with an embodiment of the present disclosure.
[0030] FIG. 5 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be utilized in accordance with embodiments of the present disclosure.
[0031] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION
[0032] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0033] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details.
[0034] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently.
[0035] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0036] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0037] In prevalent times, financial institutions and service providers are increasingly utilizing Video Know Your Customer (vKYC) process as a mechanism for remote user identity verification. While traditional vKYC processes authenticate user identity documents and facilitate live video interactions, they often overlook historical location validation, creating an opportunity for fraudsters to falsify their actual whereabouts. Likewise, current fraud detection systems primarily analyze transactions and user behavior but fail to integrate historical location data from linked social media accounts to verify real-time location claims. The present disclosure provides a system and a method for validating location information during the remote user identity verification. During a vKYC session, user consent is obtained for accessing social media location data along with current location coordinates. Further, current user location is determined via reverse geocoding and historical tagged locations are extracted from the social media. Further, a distance matrix API is used to compute distances between past tagged locations and the current user location while identifying most frequently occurring country. Thereafter, the system checks if an average distance exceeds a set threshold or if a dominant historical country differs from a current one. If discrepancies are found, the system flags the user for further review, and outcome is integrated into the vKYC process for final verification by an agent. Various embodiments of the present disclosure will be explained in detail with reference to FIGs. 1-4.
[0038] FIG. 1 illustrates an exemplary block diagram representation of a network architecture 100 implementing a proposed system 114 for remote user identity verification, according to embodiments of the present disclosure. The system 114 includes an identity verification server 102 that is communicatively coupled to an agent portal 104. The identity verification server 102 may be connected to one or more computing devices via a communication network 106. The identity verification server 102 may be connected to a database 120.
[0039] The identity verification server 102 may include, but is not limited to, a stand-alone server, a remote server, a cloud computing server, a dedicated server, a web server, an application server, a video conferencing server, a database server, an Artificial Intelligence/machine learning server, a security server, hardware supporting a part of a cloud service or system, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof, and the like.
[0040] The remote user identity verification is performed by a network of interconnected servers, where the web servers may host an online platform for user registration, document uploads, and video call initiation, while also providing an interface for both users and agents. The application server may manage core KYC logic, orchestrating workflows, processing verification requests, and integrating with databases and third-party services. The video conferencing servers may facilitate live video interactions, ensuring real-time audio and video streaming. The database servers may securely store sensitive customer data, including documents and recordings along with audit trails. The Artificial Intelligence/machine learning servers may power crucial features like facial recognition, liveness detection, Optical Character Recognition (OCR), and fraud detection, requiring significant processing power. Finally, the security servers may enforce robust security measures, including encryption and access control, to protect sensitive data and ensure compliance with regulatory requirements.
[0041] The communication network 106 may be a wired communication network or a wireless communication network. The wireless communication network may be any wireless communication network capable of transferring data between entities of that network such as, but is not limited to, a carrier network including a circuit-switched network, a public switched network, a Content Delivery Network (CDN) network, a Long-Term Evolution (LTE) network, a New Radio (NR), a Global System for Mobile Communications (GSM) network and a Universal Mobile Telecommunications System (UMTS) network, an Internet, intranets, Local Area Networks (LANs), Wide Area Networks (WANs), mobile communication networks, combinations thereof, and the like.
[0042] The system 114 may be implemented by way of a single device or a combination of multiple devices that may be operatively connected or networked together. For example, the system 114 may be implemented by way of a standalone device such as the centralized server (and/or a decentralized server or node(s)), and the like, and may be communicatively coupled to a computing device 110. In another example, the system 114 may be implemented in/ associated with the computing device 110. In yet another example, the system 114 may be implemented in/associated with respective computing devices 110-1, 110-2, …..., 110-N (individually referred to as computing device 110, and collectively referred to as computing devices 110), associated with one or more user 112-1, 112-2, …..., 112-N (individually referred to as the user 112, and collectively referred to as the users 112). In such a scenario, the system 114 may be replicated in each of the computing devices 110. The users 112 may be a user, but are not limited to, new customers opening financial accounts, individuals accessing certain online services, individuals involved in high-value transactions, user obtaining certain government services, customers purchasing SIM cards, anyone needing remote identity verification, and the like. In some instances, the user 112 may include an entity or an administrator, who is in conversation with the computing device 110 via the agent portal 104. The agent portal 104 may be, but not limited to, a financial institution, a bank, a fintech company, cryptocurrency exchanges, telecommunications companies and the like.
[0043] The computing device 110 may be at least one of, an electrical, an electronic, and an electromechanical device. The computing device 110 may include, but is not limited to, a mobile device, a smart- phone, a Personal Digital Assistant (PDA), a tablet computer, a phablet computer, a wearable device, a Virtual Reality/Augmented Reality (VR/AR) device, a laptop, a desktop, a server, and the like. The system 114 may be implemented in hardware or a suitable combination of hardware and software. The database 108 may be, but not limited to a user profile database, a document database, a video recording database, a facial recognition database, a transaction and activity logs database, and the like.
[0044] In an embodiment, upon initiation of a remote user identity verification session, the identity verification server 102 that is communicatively coupled to the agent portal 104 may receive an affirmative consent from the user 112 to fetch a current location information of the user 112 along with location coordinates from an associated social media platform 116. The social media platform 116 may be, for example, a general social networking platform, a professional networking platform, a visual and media sharing platform, a messaging and communication platform, a location based social media platform, and the like. In an embodiment, the current location information of the user is captured via a reverse geocoding mechanism of geospatial data captured during the remote user identity verification session. The reverse geocoding mechanism bridges a gap between machine readable coordinates and human understandable addresses, making location data more accessible and useful. The reverse geocoding mechanism converts latitude and longitude coordinates of the location coordinates into a human readable address by taking the location coordinates as input, searching a geospatial database for matching addresses, and then outputting address details.
[0045] The identity verification server 102 may then fetch the current location information of the user 112 via the reverse geocoding mechanism along with the location coordinates from one or more historical tagged locations present on the associated social media platform 116. In an embodiment, the location coordinates are fetched from the one or more historical tagged locations using a geocoding API.
[0046] The identity verification server 102 may calculate a distance information between an average historical tagged location distance and the fetched current location information of the user 112. The average historical tagged location distance may correspond to an average of distance information of the fetched one or more historical tagged locations. In an embodiment, the distance information between the average historical tagged location distance and the fetched current location information of the user 112 may be determined using a distance matrix application programming interface (API).
[0047] The identity verification server 102 may extract a country name having a maximum count of occurrences with respect to the one or more historical tagged locations.
[0048] The identity verification server 102 may initially determine whether the calculated average distance is less than a pre-configured distance threshold. Then the identity verification server 102 may determine if the extracted country name having the maximum count of occurrences match the current location information of the user 112. In cases, when a response to both the determinations is negative, a flag is issued with respect to the remote user identity verification, else when the response is positive, the identification process for the user 112 is proceeded further. In both the cases, the agent portal 104 is informed on how to proceed further with the user 112. In an embodiment, the issued flag corresponds to one or more varying risk levels associated with the remote user identity verification. Upon issuance of the flag, the remote user identity verification session is halted.
[0049] The positive response indicates a correct identity match of the user 112 which confirms successful verification of user authenticity, correct user identity and documents validation. Implications of the positive response leads to granting an access to a user account, increased trust and security with respect to the user identity, streamlined onboarding of the user 112 with reduced risks.
[0050] In some implementations, the database 108 may include data and modules. As an example, the data may be stored in the database 108. In an embodiment, the data may be stored in a memory in the form of various data structures. Additionally, the data may be organized using data models, such as relational or hierarchical data models.
[0051] In an embodiment, the data stored in the database 108 may be processed by the modules of the system 114. The modules may be stored within the memory. In an example, the modules communicatively coupled to the processor configured in the system, may also be present outside the memory, and implemented as hardware. As used herein, the term modules refer to an Application-Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and the memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
[0052] Further, the system 114 may also include other units such as a display unit, an input unit, an output unit, and the like, however the same are not shown in FIG. 1, for the purpose of clarity. Also, in FIG. 1 only a few units are shown, however, the system 113 or the network architecture 100 may include multiple such units or the system 114/network architecture 100 may include any such numbers of the units, obvious to a person skilled in the art or as required to implement the features of the present disclosure. The system 114 may be a hardware device including a processor (not shown) executing machine-readable program instructions to extract data from the financial instrument.
[0053] Execution of the machine-readable program instructions by the processor may enable the system 114 to execute remote user identity verification. The “hardware” may comprise a combination of discrete components, an integrated circuit, an application-specific integrated circuit, a field-programmable gate array, a digital signal processor, or other suitable hardware. The “software” may comprise one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code, or other suitable software structures operating in one or more software applications or on one or more processors. The processor may include, for example, but are not limited to, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and any devices that manipulate data or signals based on operational instructions, and the like. Among other capabilities, the processor may fetch and execute computer-readable instructions in the memory operationally coupled with the system 114 for performing tasks such as data processing, input/ output processing, and/or any other functions. Any reference to a task in the present disclosure may refer to an operation being or that may be performed on data.
[0054] FIG. 2 illustrates, at 200, exemplary functional units of the system 114, in accordance with an exemplary embodiment of the present disclosure. The system 114 may include the one or more processor(s) 202. The one or more processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in memory 204. The memory 204 may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0055] In an embodiment, the system 114 may also include an interface(s) 206. The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication with various other devices coupled to the one or more processor(s) 202. The interface(s) 206 may also provide a communication pathway for one or more components of the one or more processor(s) 202. Examples of such components include, but are not limited to, processing engine(s) 208 and database 222. The database 222 corresponds to the database 108 referred to in FIG. 1.
[0056] In an embodiment, the processing engine(s) 208 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may include a processing resource (for example, one or more processors), to execute such instructions.
[0057] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 208. In such examples, the processor(s) 202 may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system 114 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry. The database 222 may include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208. In an embodiment, the processing engine(s) 208 may include a receiving unit 210, a fetching unit 212, a calculating unit 214, an extracting unit 216, a determination unit 218, and other units(s) 220.
[0058] The other unit(s) 220 may implement functionalities that supplement applications/ functions performed by the system 114. In another embodiment, the system 114, through the other unit(s) 220, may validate location information during a remote user identity verification.
[0059] The identity verification server that is communicatively coupled to the agent portal may receive using the receiving unit 210, an affirmative consent from the user 112 to fetch the current location information of the user 112, and location coordinates from an associated social media platform, upon initiation of a remote user identity verification session.
[0060] The fetching unit 212 may fetch the current location information of the user 112 via a reverse geocoding mechanism, and the location coordinates from one or more historical tagged locations present on the associated social media platform.
[0061] The calculating unit 214 may calculate a distance information between an average historical tagged location distance and the fetched current location information of the user. The average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations. The distance information between the average historical tagged location distance and the fetched current location information of the user may be determined using the distance matrix API.
[0062] The extracting unit 216 may extract a country name having a maximum count of occurrences with respect to the one or more historical tagged locations.
[0063] The determination unit 218 may determine whether at least the calculated average distance is less than a pre-configured distance threshold and the extracted country name having the maximum count of occurrences match the current location information of the user.
[0064] When the determination is negative, a flag is produced that represents the risk levels associated with the remote user identity verification session. In response to the negative determination, a flag is issued with respect to the remote user identity verification session. The issued flag corresponds to one or more varying risk levels that are associated with the remote user identity verification. Further, the remote user identity verification session is halted upon issuance of the flag. During the vKYC process, the risk levels may be assessed to determine a likelihood of fraud or other malicious activity. These levels may help organizations to decide whether to approve or reject the user’s identity, or to require additional verification steps. As may be appreciated, using and maintaining dynamic risk assessment models during remote user identity verification proves highly advantageous, as it allows the organizations to adapt swiftly to evolving fraud patterns, thereby enhancing security and minimizing potential losses. Regularly updating these models ensures that they remain effective in identifying and mitigating emerging threats, providing a proactive defense against sophisticated fraudulent activities. Furthermore, adherence to relevant regulations, such as vKYC, is crucial for legal compliance and fosters trust with the users, further solidifying benefits of a robust and adaptable risk assessment framework.
[0065] FIG. 3A illustrates a workflow 300 showing a series of steps for validation of the location information during the remote user identity verification, in accordance with an embodiment of the disclosure.
[0066] With respect to FIG. 3A, a data extraction module may initiate a first query with respect to obtaining location information permission (at step 302) from a customer portal that is being executed on the computing device 110 of the user 112. At step 304, the user’s current location coordinates are determined. The current location coordinates are then sent to a reverse geocoding API, at step 306. The reverse geocoding API then returns the user’s current address as output, at step 308. Further, the data extraction module initiates a query with respect to obtaining the user’s consent and authorization to access social media information, at step 310. For this, an API request is sent to the social media, at step 312. The API fetches the user account’s tagged location data, at step 314. In addition, at step 316, a random tagged location is fetched month-wise and is sent to a geocoding API which converts it to coordinates, at step 318.
[0067] A distance calculation module uses distance matrix APIs to determine distances between historical and current locations. For this, the distance calculation module obtains data from the data extraction module. At step 320, all the tagged locations are fetched from the tagged location data, at step 314. At step 322, all the fetched tagged locations are used to fetch a particular country which has a maximum number of occurrences. In addition, a data analysis module computes an average of distances that are present between historical and current locations, or alternatively, identifies the most frequently visited country from the historical location data and compares with the country of the current location.
[0068] Thereafter, a validation module assesses, at step 328, whether the computed average distance exceeds a pre-configured threshold or, at step 330, if the maximum occuring country location differs from the current country’s location. In such cases, the remote user identification session is flagged, at steps 332 and 334, for further review.
[0069] A set threshold is fetched, at step 340, and is used to determine if the average distance is greater than the threshold distance, at step 328. The set threshold, at step 340, allows configuration of acceptable distance thresholds and matching criteria that may be tailored to different risk levels.
[0070] Otherwise, the remote user identification session is allowed to proceed further, at step 336 and step 338. Subsequently, a validation outcome, which includes data that is used to raise a flag or verify the user, is integrated into the vKYC decision process present at the agent portal 104 as a final result, at step 342. This enables a verification agent using the agent portal 104 to make a final decision regarding the user’s identity verification.
[0071] FIG. 3B illustrates a workflow 350 showing a series of steps for calculating an average of the multiple tagged locations, in accordance with an embodiment of the disclosure.
[0072] FIG. 4 illustrates a sequence flow 400 for validating the location information during the remote user identity verification, in accordance with an embodiment of the present disclosure. With respect to FIG. 4, upon initiation of a remote user identity verification session, at step 402, the identity verification server communicatively coupled to an agent portal may receive an affirmative consent from a user to fetch a current location information of the user and location coordinates from an associated social media platform. The identity verification server may then, at step 404, fetch the current location information of the user via a reverse geocoding mechanism and the location coordinates from one or more historical tagged locations present on the associated social media platform. Further, the identity verification server may, at step 406, calculate a distance information between an average historical tagged location distance and the fetched current location information of the user. The average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations. The identity verification server may then, at step 408, extract a country name having a maximum count of occurrences with respect to the one or more historical tagged locations. At step 410, the identity verification server may determine whether at least the calculated average distance is less than a pre-configured distance threshold and the extracted country name having the maximum count of occurrences match the current location information of the user. In response to a negative determination, a flag is issued with respect to the remote user identity verification session.
[0073] Unlike existing solutions, the present disclosure provides a system and a method for validating the location information during the remote user identity verification using the social media location information. The disclosed system and a method facilitates to strengthen location based user authentication via real-time and historical data cross referencing leading to improved fraud detection by identifying location inconsistencies, customizable risk thresholds for diverse regulatory needs, user-centric privacy with explicit consent, efficient distance calculation using APIs, accurate country matching, configurable validation criteria, and seamless vKYC integration for streamlined decision making.
[0074] FIG. 5 illustrates an exemplary computer system 500 in which or with which embodiments of the present disclosure may be implemented. As shown in FIG. 5, the computer system 500 may include an external storage device 510, a bus 520, a main memory 530, a read-only memory 540, a mass storage device 550, communication port(s) 560, and a processor 570. A person skilled in the art will appreciate that the computer system 500 may include more than one processor and communication ports. The processor 570 may include various modules associated with embodiments of the present disclosure. The communication port(s) 560 may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) 560 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 500 connects. The main memory 530 may be random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory 540 may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for the processor 570. The mass storage device 550 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage device 550 includes, but is not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks.
[0075] The bus 520 communicatively couples the processor 570 with the other memory, storage, and communication blocks. The bus 520 may be, e.g. a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB, or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 570 to the computer system 500.
[0076] Optionally, operator and administrative interfaces, e.g. a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus 520 to support direct operator interaction with the computer system 500. Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) 560. Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system 500 limit the scope of the present disclosure.
[0077] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
[0078] Since many modifications, variations, and changes in detail can be made to the described preferred embodiments of the invention, it is intended that all matters in the foregoing description and shown in the accompanying drawings be interpreted as illustrative and not in a limiting sense. Thus, the scope of the invention should be determined by the appended claims and their legal equivalents.
ADVANTAGES OF THE PRESENT DISCLOSURE
[0079] The present disclosure provides a system and a method to validate location information during remote user identity verification using social media location information.
[0080] The present disclosure enhances location authentication by combining real-time reverse geocoding with historical social media location information leading to strengthened identity verification by cross-referencing a user’s current location with their past tagged locations.
[0081] The present disclosure improves fraud detection by calculating an average distance between historical user locations and current user location or by comparing a dominant historical country information with current country information resulting in identifying inconsistencies that indicate potential fraudulent activity.
[0082] The present disclosure allows financial institutions to set custom thresholds for acceptable location deviations or country mismatches, making it adaptable to varying risk profiles and regulatory requirements.
[0083] The present disclosure prioritizes user-centric privacy by operating with explicit user consent, ensuring compliance with data protection regulations while still enabling effective identity validation.
[0084] The present disclosure uses a distance matrix Application Programming Interface (API) to calculate distance between the user’s current and past locations and then computing an average distance for location validation.
[0085] The present disclosure determines a dominant country from social media location tags against the current location’s country.
[0086] The present disclosure allows configuration of acceptable distance thresholds and matching criteria which is tailored to different risk levels.
[0087] The present disclosure enables seamless Video Know Your Customer (vKYC) integration by automatically integrating validation results into the vKYC decision making process.
, Claims:
1. A method (400) for validating location information during a remote user identity verification, said method (400) comprising:
receiving (402), by an identity verification server (102) communicatively coupled to an agent portal (104), an affirmative consent from a user (112) to fetch a current location information of the user (112), and location coordinates from an associated social media platform (116), upon initiation of a remote user identity verification session;
fetching (404), by the server (102), the current location information of the user via a reverse geocoding mechanism, and the location coordinates from one or more historical tagged locations present on the associated social media platform (116);
calculating (406), at the server (102), a distance information between an average historical tagged location distance and the fetched current location information of the user, wherein the average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations;
extracting (408), by the server (102), a country name having a maximum count of occurrences with respect to the one or more historical tagged locations; and
determining (410), by the server (102), whether at least the calculated average distance is less than a pre-configured distance threshold, and the extracted country name having the maximum count of occurrences match the current location information of the user, wherein in response to a negative determination, a flag is issued with respect to the remote user identity verification session.
2. The method (400) as claimed in claim 1, wherein the distance information between the average historical tagged location distance and the fetched current location information of the user is determined using a distance matrix application programming interface (API).
3. The method (400) as claimed in claim 1, wherein the issued flag corresponds to one or more varying risk levels associated with the remote user identity verification.
4. The method (400) as claimed in claim 1, wherein the remote user identity verification session is halted upon issuance of the flag.
5. The method (400) as claimed in claim 1, wherein the location coordinates are fetched from the one or more historical tagged locations using a geocoding API.
6. The method (400) as claimed in claim 1, wherein the current location information of the user is captured via the reverse geocoding mechanism of geospatial data captured during the remote user identity verification session.
7. A system (114) for validating location information during a remote user identity verification, the system (114) comprising:
one or more processors (202) operatively configured to an identity verification server (102) communicatively coupled to an agent portal (104); and
a memory (204) operatively coupled to the one or more processors (202), wherein the memory (202) comprises processor-executable instructions, which on execution, causes the one or more processors (202) to:
receive an affirmative consent from a user (112) to fetch a current location information of the user (112), and location coordinates from an associated social media platform (116), upon initiation of a remote user identity verification session;
fetch the current location information of the user (112) via a reverse geocoding mechanism, and the location coordinates from one or more historical tagged locations present on the associated social media platform (116);
calculate a distance information between an average historical tagged location distance and the fetched current location information of the user (112), wherein the average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations;
extract a country name having a maximum count of occurrences with respect to the one or more historical tagged locations; and
determine whether at least the calculated average distance is less than a pre-configured distance threshold, and the extracted country name having the maximum count of occurrences match the current location information of the user (112), wherein in response to a negative determination, a flag is issued with respect to the remote user identity verification.
8. The system (114) as claimed in claim 7, wherein the distance information between the average historical tagged location distance and the fetched current location information of the user is determined using a distance matrix application programming interface (API).
9. The system (114) as claimed in claim 7, wherein the issued flag corresponds to one or more varying risk levels associated with the remote user identity verification.
10. The system (114) as claimed in claim 7, wherein the remote user identity verification session is halted upon issuance of the flag.
11. The system (114) as claimed in claim 7, wherein the location coordinates are fetched from the one or more historical tagged locations using a geocoding API.
12. The system (114) as claimed in claim 7, wherein the current location information of the user (112) is determined via the reverse geocoding mechanism of geospatial data captured during the remote user identity verification session.
13. A non-transitory computer-readable medium comprising processor-executable instructions that cause a processor (202) operatively configured to an identity verification server (102) communicatively coupled to an agent portal (104) to:
receive an affirmative consent from a user (112) to fetch a current location information of the user (112), and location coordinates from an associated social media platform (116), upon initiation of a remote user identity verification session;
fetch the current location information of the user (112) via a reverse geocoding mechanism, and the location coordinates from one or more historical tagged locations present on the associated social media platform (116);
calculate a distance information between an average historical tagged location distance and the fetched current location information of the user (112), wherein the average historical tagged location distance corresponds to an average of distance information of the fetched one or more historical tagged locations;
extract a country name having a maximum count of occurrences with respect to the one or more historical tagged locations; and
determine whether at least the calculated average distance is less than a pre-configured distance threshold, and the extracted country name having the maximum count of occurrences match the current location information of the user (112), wherein in response to a negative determination, a flag is issued with respect to the remote user identity verification.