Fraud Detection Computing Device for Merchant Resale Transaction Risk Assessment
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Solution Overview
Problem
Current systems fail to effectively identify user computing devices involved in fraudulent or unauthorized payment transactions, particularly in card-not-present transactions, and lack the ability to alert merchants about potentially fraudulent devices reselling stolen goods.
Innovation Solution
A fraud detection computing device that receives and stores device data from user computing devices during transactions, assigns a risk factor to devices identified as fraudulent, and transmits this information to merchants to determine whether to process subsequent resale transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If device data is collected and stored during payment transactions to identify fraudulent devices, then fraud detection capability is improved, but system complexity and data storage requirements increase
Solution Approach 1:
The patent introduces a payment network as an intermediary between merchants and users. The payment network collects device data during transactions and maintains a centralized device information database, freeing individual merchants from implementing complex fraud detection systems while improving overall fraud detection capability through network-wide data aggregation and analysis
Solution Approach 2:
The payment network performs multiple functions: it processes payments, collects device data, stores device information in a centralized database, and provides fraud detection services to multiple merchants simultaneously. This multi-functionality reduces the need for each merchant to independently implement complex fraud detection infrastructure
2Reliability
If device data is collected during transactions, then the ability to identify fraudulent devices is improved, but information security and privacy concerns increase
Solution Approach 1:
The patent extracts only essential device identification data (device identifiers, hardware information) from the transaction process and stores it in a centralized payment network database. This extraction approach collects sufficient information for fraud detection while minimizing the collection of sensitive personal information, thereby reducing information security and privacy risks
Solution Approach 2:
The payment network acts as a trusted intermediary that securely collects, stores, and manages device data. Instead of merchants directly handling sensitive user device information, the payment network intermediates the data collection and provides anonymized device identifiers to merchants for fraud checking, reducing information security risks
3Reliability
If risk factors are assigned to user devices based on transaction analysis, then fraud prevention effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by collecting device data during the initial payment transaction and storing it in the payment network's device information database before resale transactions occur. Device risk profiles are established in advance, enabling rapid fraud assessment during subsequent resale transactions without requiring extensive real-time analysis
Solution Approach 2:
The patent implements differentiated fraud detection approaches: for initial payment transactions, comprehensive device data collection and analysis is performed; for subsequent resale transactions, the system uses pre-established device risk profiles and simpler verification processes. This local quality approach optimizes processing time by applying appropriate levels of scrutiny based on transaction context
Data Source
AI summary
A computer-based method for identifying a user computing device used in a fraudulent transaction is provided. The method includes receiving device data associated with the user computing device during a first payment transaction initiated from the user computing device, wherein the device data is capable of uniquely identifying the user computing device. The method further includes storing the device data within the at least one memory device, and receiving an indication that the first payment transaction initiated from the user computing device was fraudulent. The method further includes assigning a risk factor to the user computing device, and transmitting the risk factor to a merchant, wherein the merchant uses the risk factor to determine whether to process a resale transaction initiated from the user computing device wherein the resale transaction is associated with a resale of goods acquired in association with the first payment transaction.