Trust Designation Platform for Anonymous Merchant Applications
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Solution Overview
Problem
Merchant applications face challenges in verifying the credibility of users, leading to fraudulent transactions due to anonymity, which wastes resources and affects customer service, as they cannot confirm if the user accessing the account is the actual credible user.
Innovation Solution
A trust designation platform using machine learning to determine a trust designation for users based on transaction data, account data, location data, browser fingerprint data, and credit data, providing a trust token that designates users as trusted or untrusted, allowing the user to remain anonymous while their trustworthiness is displayed to others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users remain anonymous in merchant applications, then user privacy is protected, but fraudulent transactions increase due to inability to verify user credibility
Solution Approach 1:
The system segments user information into two distinct parts: identity data (kept private for anonymity) and trustworthiness data (shared for verification). The machine learning model processes multiple data dimensions (transaction history, account information, location data, browser fingerprints, credit data) separately and aggregates them into a trust score, allowing verification without exposing personal identity.
Solution Approach 2:
The patent introduces a trust score as an intermediary element between user identity and transaction verification. Instead of directly exposing user identity information, the system uses the machine learning-generated trust score as a mediator that represents user credibility without revealing personal details. This intermediary enables verification while preserving anonymity.
2Reliability
If traditional verification methods are used to confirm user identity, then fraudulent transactions are reduced, but user privacy and anonymity are compromised
Solution Approach 1:
The system extracts the essential verification attribute (trustworthiness) from the complete user identity package. By taking out only the necessary credibility indicators and processing them through machine learning, the system achieves verification without extracting or exposing sensitive personal information such as names, addresses, or other identifying details.
Solution Approach 2:
The trust score functions as a disposable verification token that can be generated, used, and discarded without exposing permanent personal information. Each trust score is context-specific and can be regenerated as needed, providing verification capability without creating lasting exposure of user privacy.
3Measurement precision
If machine learning models process multiple user data dimensions, then trust assessment accuracy improves, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it processes diverse data types (transaction data, account data, location data, browser fingerprints, credit data), performs pattern recognition across different user behaviors, generates trust scores, and can be applied across various merchant applications. This multi-functionality consolidates what would otherwise require separate systems for each verification task.
Solution Approach 2:
The system transforms multiple complex data parameters into a single simplified output parameter (trust score). By changing the parameter representation from multiple raw data dimensions to one aggregated trust metric, the system maintains high assessment accuracy while simplifying the interface and reducing the apparent complexity for end users and merchants.
Data Source
AI summary
A device receives a request for a trust designation for a user that is to utilize a merchant application to interact with one or more other users, wherein the merchant application includes one or more interfaces that allow the user to interact with the one or more other users while remaining anonymous or partially anonymous. The device obtains user data for the user based on information included in the request. The device determines the trust designation for the user by using a data model that has been trained using machine learning to process the user data. The device permits at least one of the one or more interfaces of the merchant application to display the trust designation, wherein the user remains anonymous or partially anonymous while the trust designation is displayed.