Transaction Authentication via Third-Party Data Comparison
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Solution Overview
Problem
Current systems for authorizing financial transactions are inadequate due to reliance on complex and expensive heuristics, leading to incorrect flags for authorized or unauthorized transactions, asynchronous analysis, and lack of real-time user data, resulting in incomplete fraud detection.
Innovation Solution
A system and method that utilizes third-party data, such as social media data, to authorize financial transactions by comparing transaction data to user data from approved merchants, locations, behaviors, and calendar events, allowing for real-time authentication and reducing reliance on heuristic algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex heuristics and computational power are used to determine if a transaction matches the profile or behavior of the authorized end-user, then fraud detection capability is improved, but computational complexity and cost increase
Solution Approach 1:
The patent introduces third-party data sources (social media networks, location services, calendar applications) as intermediaries to provide contextual information about the end-user. These intermediaries supply data such as user location, approved merchants, calendar events, and behavior patterns that serve as mediators between the transaction data and the authorization decision, reducing the need for complex computational analysis of transaction patterns alone
Solution Approach 2:
The patent replaces the mechanical system of complex heuristic algorithms with an information-based system that queries external data sources. Instead of computationally intensive pattern matching and behavioral analysis, the system substitutes direct queries to third-party services that return pre-processed contextual information, significantly reducing computational complexity while maintaining or improving fraud detection accuracy
2Reliability
If heuristics are used for fraud detection, then some fraud detection is achieved, but false positives and false negatives occur
Solution Approach 1:
The patent implements feedback mechanisms where third-party data sources continuously provide updated information about the end-user's location, calendar events, and behavior patterns. This real-time feedback allows the authorization system to dynamically adjust its decisions based on current contextual information, reducing false positives by confirming legitimate transactions and false negatives by detecting anomalies that deviate from the user's established patterns
Solution Approach 2:
The system performs preliminary actions by pre-obtaining and storing contextual information from third-party sources before transaction authorization is needed. User profiles, approved merchant lists, calendar events, and location data are retrieved in advance and cached, enabling rapid comparison with transaction data without requiring complex real-time analysis, thereby improving both accuracy and speed
3Productivity
If asynchronous analysis is used to detect fraud, then computational load is reduced, but transactions are allowed to complete before fraud is detected
Solution Approach 1:
The patent applies preliminary action by pre-querying and caching contextual data from third-party sources before the authorization decision is needed. User profiles, location data, calendar events, and approved merchant lists are retrieved in advance and stored locally, enabling synchronous comparison with incoming transaction data without delaying the authorization process, thus achieving both speed and timeliness
Data Source
AI summary
The present invention relates to system and method for authorizing a financial transaction using user-data collected from third party websites, such as Social Media Networks. In operation, collected user-data may be compared to a financial-data collected during a financial transaction to identify potential fraud and/or other discrepancies, confirming the identity of the user with a greater degree of accuracy.