A system for preventing deceptive manipulation of participant acquisition programs and method thereof

Abstract

A system 102 for preventing deceptive manipulation of a participant acquisition program includes a storage unit 206 and a computational unit 202. The computational unit 202 executes coded directives for receiving referral data from an initiating user, validating the referral data based on a set of criteria associated with a referred user, calculating a risk rating, ascertaining authenticity of the referral data by contrasting the risk rating with a benchmark risk rating, and managing the referral data. The referral data is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating and denied for financial incentive when the risk rating is more than the benchmark risk rating.

Description

[0001] TITLE OF INVENTION

[0002] A SYSTEM FOR PREVENTING DECEPTIVE MANIPULATION OF PARTICIPANT ACQUISITION PROGRAMS AND METHOD THEREOF

[0003] CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

[0004] The present application claims priority from the Indian patent application having application number 202411031723 filed on 22 April 2024, incorporate herein by a reference.

[0005] FIELD OF INVENTION

[0006] The present invention, in general, relates to the field of digital platforms, and more particularly, relates to a system for preventing deceptive manipulation of participant acquisition programs and method thereof.

[0007] BACKGROUND

[0008] In the digital age, referral programs have become a popular strategy for businesses to attract new users to their platforms. These programs typically involve existing users recommending the platform to potential new users, often in exchange for some form of reward. However, this approach is not without its challenges. One of the main issues is the risk of fraudulent activity, where users attempt to exploit the program for their own gain. This can involve creating fake accounts to claim rewards, rather than genuinely referring new users. This issue is particularly problematic when the rewards offered are in the form of real cash, as opposed to in-app currency or bonuses.

[0009] Existing solutions to this problem have their limitations. Some platforms offer rewards in the form of in-app currency, which cannot be directly withdrawn to a bank account. This can deter fraudulent activity, but it may also make the program less attractive to genuine users. Other platforms that offer real cash rewards do so in a way that makes it difficult for users to exploit the system. However, these measures can also be complex and difficult to manage.

[0010] Therefore, there is a need for a more effective solution to manage referral programs, particularly those that offer real cash rewards. Such a solution should be able to accurately identify and prevent fraudulent activity, while still making the program attractive to genuine users. It should also be flexible and adaptable, allowing for changes to be made as needed without requiring a complete overhaul of the system.

[0011] SUMMARY

[0012] This summary may be provided to introduce concepts related to a system for preventing deceptive manipulation of participant acquisition programs and method thereof, the concepts are further described below in the detailed description. This summary may be not intended to identify essential features of the claimed subject matter nor maybe it intended for use in determining or limiting the scope of the claimed subject matter.

[0013] In accordance with embodiments, a system and method are provided for preventing deceptive manipulation of a participant acquisition program. The system receives referral data from an initiating user recommending a digital platform for a referred user. The referral data is validated based on a set of criteria associated with the referred user. A risk rating is calculated based on the validation of the referral data. The authenticity of the referral data is ascertained by contrasting the risk rating with a benchmark risk rating for a defined duration. The referral data is managed such that it is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating during the defined duration, and denied for financial incentive when the risk rating is more than the benchmark risk rating during the defined duration.

[0014] BRIEF DESCRIPTION OF DRAWINGS

[0015] The detailed description may be described with reference to the accompanying Figures. The same numbers are used throughout the drawings to refer like features and components.

[0016] Figure 1 illustrates a network implementation of a system, in accordance with an embodiment of the present disclosure;

[0017] Figure 2 illustrates a block diagram of the system, in accordance with an embodiment of the present disclosure;

[0018] Figure 3 illustrates a flowchart for preventing deceptive manipulation of participant acquisition programs, in accordance with an embodiment of the present disclosure; and Figure 4 illustrates a flowchart depicting a method for preventing deceptive manipulation of participant acquisition programs, in accordance with an embodiment of the present disclosure.

[0019] DETAILED DESCRIPTION

[0020] Reference throughout the specification to “various embodiments,” “some embodiments,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment. Thus, appearances of the phrases “in various embodiments,” “in some embodiments,” “in one embodiment,” or “in an embodiment” in places throughout the 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.

[0021] Referring to figure 1, network implementation 100 of a system 102 for a system for preventing deceptive manipulation of participant acquisition programs may be illustrated, in accordance with an embodiment of the present subject matter. The online platform may be a gaming platform, a social media platform, and the like. In one embodiment, the system 102 may comprise a computational unit and a storage unit. Further, the system 102 may be connected to user devices 104 or applications residing over the user devices 104 through a network 106. It may be understood that the system 102 may be communicatively coupled with the user through one or more user devices / applications 104-1, 104-2, . . ., 104-n collectively referred to as a user device 104.

[0022] In one embodiment, the network 106 may be a cellular communication network used by user devices 104 such as mobile phones, tablets, or a virtual device. In one embodiment, the cellular communication network may be the Internet. The user device 104 may be any electronic device, communication device, image capturing device, machine, software, automated computer program, a robot or a combination thereof. The system 102 may be configured to register users over the system 102.

[0023] In one embodiment, the user devices 104 may support communication over one or more types of networks in accordance with the described embodiments. For example, some user devices and networks may support communications over a Wide Area Network (WAN), the Internet, a telephone network (e.g., analog, digital, POTS, PSTN, ISDN, xDSL), a mobile telephone network (e.g., CDMA, GSM, NDAC, TDMA, E-TDMA, NAMPS, WCDMA, CDMA-2000, UMTS, 3G, 4G), a radio network, a television network, a cable network, an optical network (e.g., PON), a satellite network (e.g., VSAT), a packet-switched network, a circuit- switched network, a public network, a private network, and / or other wired or wireless communications network configured to carry data. The aforementioned user devices 104 and network 106 may support wireless local area network (WLAN) and / or wireless metropolitan area network (WMAN) data communications functionality in accordance with Institute of Electrical and Electronics Engineers (IEEE) standards, protocols, and variants such as IEEE 802.11 (“WiFi”), IEEE 802.16 (“WiMAX”), IEEE 802.20x (“Mobile-Fi”), and others. The block diagram of the of the system 102 may be further illustrated in figure 2.

[0024] Referring now to figure 2, various components of the system 102 are illustrated, in accordance with an embodiment of the present subject matter. As shown, the system 102 may include at least one computational unit 202, an I / O interface 204 and a storage unit 206. The storage unit 206 consists of programmed instructions corresponding to a set of modules 208 and data 210. The set of modules 208 may include an intake module 212, a risk identification module 214, a risk evaluation module 216, a judgement module 218, and an incentive control module 220. In one embodiment, the at least one computational unit 202 may be configured to fetch and execute computer-readable instructions, stored in the storage unit 206, corresponding to each module 208. It must be noted that though the invention may be explained considering that the system 102 may be deployed over a remote server and the user device 104 may be communicatively coupled with the system 102 through the network 106. However, it must be noted that the system 102 or modules 208 of the system 102 may also be deployed on the user device 104 itself and the modules 208 perform the same functions as that on the server using the local hardware of the user device 104. By implementing the system 102 on the user device 104, the data privacy of user’s personal data may be maintained as the data never leaves the user device 104.

[0025] In one embodiment, the storage unit 206 may include any computer-readable medium known in the art including, for example, volatile storage unit, such as static random-access storage unit (SRAM) and dynamic random-access storage unit (DRAM), and / or nonvolatile storage unit, such as read-only storage unit (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and storage unit cards. In one embodiment, the programmed instructions may include routines, programs, objects, components, data structures, etc., which perform particular tasks, functions, or implement particular abstract data types. The data 210 may comprise a data repository 222, and other data 224. The other data 224 amongst other things, serves as a repository for storing data processed, received, and generated by one or more components and programmed instructions. The working of the system 102 will now be described in detail referring to figure 2 and figure 3.

[0026] Now referring to figure 2 and 3, figure 2 illustrates a block diagram of the system, in accordance with an embodiment of the present disclosure and figure 3 illustrates a flowchart for preventing deceptive manipulation of participant acquisition programs, in accordance with an embodiment of the present disclosure.

[0027] Step 302 involves the intake module 212 receiving referral data from an initiating user. In one embodiment, the computational unit 202 may be configured to execute programmed instructions corresponding to the intake module 212 for receiving a referral data from at least one initiating user corresponding to a recommended account of at least one referred user for recommending a digital platform for the at least one referred user by the at least one initiating user. This data pertains to a recommended account of a referred user, who has been recommended to use a digital platform by the initiating user. The action of receiving referral data initiates the verification process. The referral data includes the identification of the referred user's account and its association with the initiating user's account within the system. The purpose of this action is to establish a link between the new user (referred) and the existing user (initiating) for the digital platform's referral program. The system recognizes the tangible rewards, which include real cash benefits that the initiating user may receive if the referral is validated as genuine. This step establishes the expectation for the initiating user and the monetary incentive at stake. The intake module 212 captures and records the referral information provided by the initiating user, including the identification of the referred user's account and the association of this account with the initiating user's account. The storage unit 206 holds the referral data for subsequent processing.

[0028] The goal of step 302 is to begin the referral verification process, designed to ensure that only validated referrals result in the disbursement of financial incentives. This is achieved by collecting detailed referral data that will later be validated against a set of criteria to determine the authenticity of the referral and the eligibility for the reward.

[0029] Step 304 involves the process of validating referral data that is received from users who initiate the referral process. This step is conducted by a risk identification module 214, which is a component of the computational unit 202 configured to execute directives stored within a storage unit 206. The risk identification module 214 performs an automated check of the referral data against a predefined set of criteria related to the referred user. In one embodiment, the computational unit 202 may be configured to execute programmed instructions corresponding to the risk identification module 214 for validating the referral data of the at least one initiating user. The automated validation of the referral data of the at least one initiating user is performed based on a set of criteria associated with the at least one referred user. Within step 304, several sub-steps are carried out: a. The criteria for validation include a range of factors such as the activities performed by the referred user on the digital platform, how long the referred user remains active on the platform, any monetary transactions performed by the referred user, the validation of the referred user's device, and the geographical location of the device. These factors are assessed to determine the legitimacy of the referral. b. Part of the validation process includes examining the devices used by the referred users to identify any signs that suggest the creation of multiple accounts from the same device, which is a common method used to exploit referral programs. c. The risk identification module 214 may incorporate a machine learning model that is trained to detect patterns that are indicative of fraudulent activities. This model uses historical data to improve its ability to identify potential fraud. d. Additionally, the module may include a database that stores historical referral data along with associated risk ratings. This database is used to compare new referral data against past data to spot any irregularities or patterns that resemble known fraudulent activities.

[0030] The actions performed in step 304 and its sub-steps are executed through software components interacting with the data. The risk identification module 214 analyses the incoming referral data against the established criteria to identify potential risks. The machine learning model within the module enhances the detection capabilities, and the historical data serves as a benchmark for making informed decisions. The objective of these actions is to ensure that the referral program rewards are given for legitimate referrals, thereby safeguarding the digital platform from potential exploitation and preserving the integrity of the referral program.

[0031] Step 306 involves the calculation of a risk rating following the automated validation of referral data provided by an initiating user. This step is conducted by a risk evaluation module 216 within a system. In one embodiment, the computational unit 202 may be configured to execute programmed instructions corresponding to the risk evaluation module 216 for calculating a risk rating based on the automated validation of the referral data of the initiating user. The module operates based on coded directives that analyze the referral data, which includes activities performed by the referred user on a digital platform, user retention, monetary transactions, device validation, and spatial location. Sub-step 306 specifies that the risk rating is assigned by a risk identification module 214, which prioritizes various criteria to determine the risk rating. The prioritization is based on the significance of each factor in indicating potential fraudulent behaviour, as established through historical data analysis and pattern recognition.

[0032] The risk identification module 214 may utilize machine learning techniques to identify patterns that suggest fraudulent activity and compare current referral data against a database of historical referral data to refine the accuracy of the risk rating. The outcome of Step 306, the numerical risk rating, is used in subsequent steps to assess the authenticity of the referral data and to guide the management of financial incentives for the initiating user. The process ensures that the referral program rewards genuine referrals while mitigating the risk of fraudulent exploitation.

[0033] Step 308 involves the process of determining the authenticity of referral data by comparing a calculated risk rating with a benchmark risk rating over a specified duration. This step is pivotal in deciding whether the referral data is genuine and if the initiating user qualifies for a financial incentive. The actions within this step 308 include: a. Comparing the risk rating with a benchmark risk rating: This action involves the analysis of the risk rating against a pre-set benchmark risk rating. The benchmark risk rating acts as a standard to evaluate the referral data. It is adjusted based on the initiating user's history with the referral program. A history of effective referrals may lead to a decrease in the benchmark risk rating, while a history of ineffective referrals may result in an increase. b. Specified duration: The assessment of the referral data is conducted over a specified duration. This duration can vary based on the initiating user's history with the referral program. Users with a history of effective referrals may be subject to a shorter duration, while those with a history of ineffective referrals may undergo a longer evaluation period. c. Judgement module 218: In one embodiment, the computational unit 202 may be configured to execute programmed instructions corresponding to the judgement module 218 for ascertaining authenticity of the referral data of the at least one initiating user by contrasting the risk rating with a benchmark risk rating for a defined duration. This module is tasked with executing the comparison and making the determination of authenticity. It utilizes the risk rating and the benchmark risk rating to assess whether the referral data is genuine. If the risk rating is below the benchmark risk rating, the referral data is considered authentic. If the risk rating exceeds the benchmark risk rating, the referral data is considered inauthentic. If the risk rating equals the benchmark risk rating, the referral data may be subject to further validation. d. Feedback mechanism: The module responsible for risk identification updates the criteria for assessment based on feedback regarding the accuracy of the risk rating. This mechanism ensures the continuous refinement of the assessment process, enhancing its effectiveness over time.

[0034] In essence, Step 308 and its associated sub-steps involve a methodical approach to evaluating the legitimacy of referral data by comparing the risk rating against a benchmark risk rating within a specified duration. The process adapts to the user's track record with the referral program and incorporates feedback to improve the assessment process.

[0035] Step 310 involves the management of referral data by an incentive control module 220 within a system designed to prevent deceptive manipulation of a participant acquisition program. This step determines the outcome of the referral process in terms of financial rewards for the initiating user. The actions within Step 310 include the sanctioning or denial of financial rewards based on the risk rating in comparison to a benchmark risk rating during a defined duration. If the risk rating is less than the benchmark risk rating, the referral data of the initiating user is approved for financial reward. If the risk rating exceeds the benchmark risk rating, the referral data is rejected for financial reward, indicating a high risk of fraud. This mechanism ensures that only genuine referrals lead to financial rewards, thereby protecting the integrity of the referral program. Sub-step of step 310 introduces a variable element to the defined duration for investigation of referrals. This duration is adjusted based on the initiating user's history of referrals. A history of effective referrals leads to a decreased duration, allowing for quicker reward processing. A history of ineffective referrals leads to an increased duration, providing additional time for thorough investigation.

[0036] Sub-step of 310 addresses scenarios where the risk rating is equal to the benchmark risk rating. In such cases, the referral data is placed into a pending state for additional validation. This implies that further checks or time may be required to conclusively determine the authenticity of the referral data before any financial reward is granted or denied.

[0037] In one embodiment, the computational unit 202 may be configured to execute programmed instructions corresponding to the incentive control module 220 for managing the referral data of the at least one initiating user, wherein the referral data of the at least one initiating user is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating during the defined duration, wherein the referral data of the at least one initiating user is denied for financial incentive when the risk rating is more than the benchmark risk rating during the defined duration. The incentive control module 220 operates based on the risk rating calculated by the risk evaluation module 216 and the benchmark risk rating, which may be dynamically adjusted based on the initiating user's history of referrals. The module may utilize algorithms, databases, and models trained to assess risk and manage rewards accurately. The goal is to reward genuine referrals while preventing fraud, thereby maintaining the scalability and attractiveness of the referral program without compromising on security.

[0038] Now referring to figure 4 illustrates a flowchart depicting a method for preventing deceptive manipulation of participant acquisition programs, in accordance with an embodiment of the present disclosure. The method 400 comprises various steps that are described in paragraphs below.

[0039] At step 402, the system 102 may be configured for receiving, by an intake module 212, a referral data from at least one initiating user corresponding to a recommended account of at least one referred user for recommending a digital platform for the at least one referred user by the at least one initiating user.

[0040] At step 404, the system 102 may be configured for validating, by a risk identification module 214, the referral data of the at least one initiating user, wherein the automated validation of the referral data of the at least one initiating user is performed based on a set of criteria associated with the at least one referred user.

[0041] At step 406, the system 102 may be configured for calculating, by a risk evaluation module 216, a risk rating based on the automated validation of the referral data of the initiating user.

[0042] At step 408, the system 102 may be configured for ascertaining, by a judgement module 218, authenticity of the referral data of the at least one initiating user by contrasting the risk rating with a benchmark risk rating for a defined duration.

[0043] At step 410, the system 102 may be configured for managing, by an incentive control module 220, the referral data of the at least one initiating user, wherein the referral data of the at least one initiating user is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating during the defined duration, wherein the referral data of the at least one initiating user is denied for financial incentive when the risk rating is more than the benchmark risk rating during the defined duration.

[0044] In one embodiment, the referral data includes tangible rewards corresponding to the initiating user’s account. The set of criteria associated with the at least one referred user comprises at least one activity performed by the referred user on the digital platform, a measure of the referred user's retention on the digital platform, one or more monetary transactions performed by the referred user on the digital platform, validation of the referred user's device, a spatial location of the referred user's device and like. The at least one referred user comprises a plurality of referred users, wherein the set of criteria associated with each of the plurality of referred users includes the spatial location of each referred user's device, wherein if the spatial location of all referred users' devices is the same, the risk rating is increased. The benchmark risk rating is dynamically adjusted based on at least one initiating user's previous referral track record, Wherein the previous referral track record includes information about the outcomes of previous referrals made by the initiating user, wherein if the at least one initiating user has a history of effective referrals, the benchmark risk rating is decreased, and if the initiating user has a history of ineffective referrals, the benchmark risk rating is increased. The device validation includes devicebased checks which ascertain if the phones of the referrals can be deemed as high risk for replicated accounts. The defined duration is dynamic for investigation of referrals based on the at least one initiating user's previous referral track record, Wherein the previous referral track record includes information about the outcomes of previous referrals made by the initiating user, wherein if the at least one initiating user has a history of effective referrals, the defined duration is decreased, and if the initiating user has a history of ineffective referrals, the defined duration is increased. The risk rating is determined by a risk identification module 214 which assigns the risk rating to the referral based on various prioritized criteria. The referral data of the at least one initiating user is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating during the defined duration, wherein the referral data of the at least one initiating user is denied for financial incentive when the risk rating is more than the benchmark risk rating during the defined duration, and wherein the referral data of the at least one initiating user is placed into a pending state for further validation when the risk rating is equal to the benchmark risk rating during the defined duration. The risk identification module 214 further comprises a machine learning model trained to identify patterns indicative of fraudulent activity in the referral data. The risk identification module 214 further comprises a database storing historical referral data and associated risk ratings, and wherein the risk identification module 214 is configured to compare the referral data with the historical referral data to determine the risk rating. The risk identification module 214 is further configured to update the set of criteria based on feedback received from the judgement module 218 regarding the accuracy of the risk rating.

[0045] Although implementations for the system 102 and the method 400 for preventing deceptive manipulation of a participant acquisition program, have been described in language specific to structural features and methods, it must be understood that the claims are not limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for the system 102 and the method 400 for preventing deceptive manipulation of a participant acquisition program.

Claims

WE CLAIM:

1. A system (102) for preventing deceptive manipulation of a participant acquisition program, wherein the system (102), comprising: a storage unit (206); and a computational unit (202), wherein the computational unit (202) may be configured to execute coded directives stored in the storage unit for: receiving, by an intake module (212), a referral data from at least one initiating user corresponding to a recommended account of at least one referred user for recommending a digital platform for the at least one referred user by the at least one initiating user; validating, by a risk identification module (214), the referral data of the at least one initiating user, wherein the automated validation of the referral data of the at least one initiating user is performed based on a set of criteria associated with the at least one referred user; calculating, by a risk evaluation module (216), a risk rating based on the automated validation of the referral data of the initiating user; ascertaining, by a judgement module (218), authenticity of the referral data of the at least one initiating user by contrasting the risk rating with a benchmark risk rating for a defined duration; and managing, by an incentive control module (220), the referral data of the at least one initiating user, wherein the referral data of the at least one initiating user is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating during the defined duration, wherein the referral data of the at least one initiating user is denied for financial incentive when the risk rating is more than the benchmark risk rating during the defined duration.

2. The system of claim 1, wherein the referral data includes tangible rewards corresponding to the initiating user’s account.

3. The system of claim 1, wherein the set of criteria associated with the at least one referred user comprises at least one activity performed by the referred user on the digital platform, a measure of the referred user's retention on the digital platform, one or more monetary transactions performed by the referred user on the digital platform, validation of the referred user's device, a spatial location of the referred user's device and like.

4. The system of claim 1, wherein the at least one referred user comprises a plurality of referred users, wherein the set of criteria associated with each of the plurality of referred users includes the spatial location of each referred user's device, wherein if the spatial location of all referred users' devices is the same, the risk rating is increased.

5. The system of claim 1, wherein the benchmark risk rating is dynamically adjusted based on at least one initiating user's previous referral track record, Wherein the previous referral track record includes information about the outcomes of previous referrals made by the initiating user, wherein if the at least one initiating user has a history of effective referrals, the benchmark risk rating is decreased, and if the initiating user has a history of ineffective referrals, the benchmark risk rating is increased.

6. The system of claim 3, wherein the device validation includes device-based checks which ascertain if the phones of the referrals can be deemed as high risk for replicated accounts.

7. The system of claim 1, wherein the defined duration is dynamic for investigation of referrals based on the at least one initiating user's previous referral track record, Wherein the previous referral track record includes information about the outcomes of previous referrals made by the initiating user, wherein if the at least one initiating user has a history of effective referrals, the defined duration is decreased, and if the initiating user has a history of ineffective referrals, the defined duration is increased.

8. The system of claim 1, wherein the risk rating is determined by a risk identification module (214) which assigns the risk rating to the referral based on various prioritized criteria, wherein the risk identification module (214) further comprises a machine learning model trained to identify patterns indicative of fraudulent activity in the referral data, wherein the risk identification module (214) further comprises a database storing historical referral data and associated risk ratings, and wherein the risk identification module (214) is configured to compare the referral data with the historical referral data to determine the risk rating, wherein the risk identification module (214) is further configured to update the set of criteria based on feedback received from the judgement module (218) regarding the accuracy of the risk rating.

9. The system of claim 1, wherein the referral data of the at least one initiating user is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating during the defined duration, wherein the referral data of the at least one initiating user is denied for financial incentive when the risk rating is more than the benchmark risk rating during the defined duration, and wherein the referral data of the at least oneinitiating user is placed into a pending state for further validation when the risk rating is equal to the benchmark risk rating during the defined duration.

10. A method for preventing deceptive manipulation of a participant acquisition program, wherein the method, comprising steps of: receiving, by an intake module (212), a referral data from at least one initiating user corresponding to a recommended account of at least one referred user for recommending a digital platform for the at least one referred user by the at least one initiating user; validating, by a risk identification module (214), the referral data of the at least one initiating user, wherein the automated validation of the referral data of the at least one initiating user is performed based on a set of criteria associated with the at least one referred user; calculating, by a risk evaluation module (216), a risk rating based on the automated validation of the referral data of the initiating user; ascertaining, by a judgement module (218), authenticity of the referral data of the at least one initiating user by contrasting the risk rating with a benchmark risk rating for a defined duration; and managing, by an incentive control module (220), the referral data of the at least one initiating user, wherein the referral data of the at least one initiating user is sanctioned for financial incentive when the risk rating is less than the benchmark risk rating during the defined duration, wherein the referral data of the at least one initiating user is denied for financial incentive when the risk rating is more than the benchmark risk rating during the defined duration.

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