Network Membership Verification for Fraud Detection
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Solution Overview
Problem
Existing payment devices face challenges in detecting fraudulent messages due to the lack of effective systems for determining network membership, which is crucial for ensuring the safety and security of transactions.
Innovation Solution
A computer-implemented method that determines network membership by analyzing interactive messages with personally identifiable information, creating records of user interactions at different locations, and confirming network membership through requests and replies, with a weighing system to prioritize certain locations based on the frequency and consistency of interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If payment devices use traditional transaction processing without network membership verification, then transaction speed is maintained, but fraud detection capability deteriorates
Solution Approach 1:
The system performs preliminary network membership verification before processing transactions. By determining whether users are members of the same network in advance and storing this information, the system can quickly detect fraudulent transactions without adding complex real-time verification steps during the actual transaction processing.
Solution Approach 2:
The patent introduces a network membership determination system as an intermediary layer between the transaction processing system and the payment device. This intermediary verifies network membership status and provides the results to the payment device, allowing fraud detection without complicating the core transaction processing functionality.
2Reliability
If the system verifies network membership for all transactions, then fraud detection accuracy is improved, but transaction processing time increases
Solution Approach 1:
Network membership verification is performed as a preliminary action before transactions occur. The system determines network membership status in advance and stores the results, allowing rapid transaction processing without repeated verification steps. This pre-computation approach ensures high fraud detection accuracy while minimizing time loss during actual transactions.
Solution Approach 2:
The system uses the payment device's existing resources and data to determine network membership. By leveraging information already available at the payment device and using automated verification processes, the system achieves accurate fraud detection without requiring additional time-consuming external verification systems.
3Measurement precision
If the system analyzes multiple interactive messages to determine network membership, then membership determination accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system segments the analysis of interactive messages by organizing them into distinct categories such as location-based messages, temporal proximity messages, and network-based messages. This segmentation allows the system to process and analyze different types of messages separately, improving accuracy while managing complexity through structured data organization.
Solution Approach 2:
The system changes the parameters used for analyzing interactive messages, focusing on key factors such as location, time proximity, and message frequency rather than processing all message details equally. By weighting and prioritizing specific parameters, the system achieves high membership determination accuracy without proportionally increasing data processing complexity.
Data Source
AI summary
Aspects described herein may allow for determining network membership to facilitate detecting fraudulent messages. A computing device may receive, from one or more terminals at a first location, a plurality of interactive messages during a pre-determined time period. Each interactive message may comprise personally identifiable information of a user. The computing device may store a first record and a second record that indicate interactive messages were received from a group of users in temporal proximity to each other at the first location and the second location respectively. The computing device may send a request to confirm users in the subset are members of a network and update a membership list based on a reply received from the user. If further messages are received from devices outside the membership list, an alert may be sent to alert the possibility of a fraudulent message.