Location-Based Transaction Authentication Using Geo-Fencing Signals
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Solution Overview
Problem
Existing systems fail to effectively authenticate transactions on digital cards based on location and time, leading to fraudulent activities and unauthorized access, which negatively impact customer experience and result in overdrafts.
Innovation Solution
A computer-based system that utilizes geo-fencing sensors to triangulate the location of a computing device, employs a machine learning algorithm to authenticate actions based on data beacons generating a primed signal, and dynamically updates a storage module with metadata based on transaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used for digital card transactions, then transaction speed is maintained, but security against fraudulent activities deteriorates
Solution Approach 1:
The system performs preliminary actions by establishing geo-fences and pre-authenticating transactions based on location data before the actual transaction occurs. The machine learning model is trained in advance with historical transaction data to recognize fraudulent patterns, enabling proactive security measures rather than reactive responses.
Solution Approach 2:
The patent introduces location data and machine learning analysis as intermediary elements between the user and the transaction processing system. These intermediaries provide an additional layer of verification that enhances security without requiring direct user intervention, effectively mediating between security requirements and transaction flow.
2Reliability
If location monitoring is implemented for all users, then fraudulent transactions are reduced, but user privacy concerns increase
Solution Approach 1:
The system applies location monitoring selectively rather than universally. Geo-fences are created only for specific high-risk locations or merchants, and location tracking is activated only when relevant to transaction authentication. This localized approach minimizes privacy intrusion while maintaining security effectiveness where needed.
Solution Approach 2:
The patent implements partial monitoring by focusing only on critical transaction points and using machine learning to determine when location verification is necessary. Not all transactions require location authentication - the system applies monitoring partially based on risk assessment, reducing overall privacy impact while maintaining security where threats are detected.
Data Source
AI summary
In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a permission from at least one user of a plurality of users to monitor at least one location associated with a device, utilizing a machine learning algorithm to automatically authenticate at least one action associated with the device based on at least one data beacon generating a primed signal; transmitting a plurality of data from the device to an external data source associated with the at least one action within a predetermined period of time based on the location of the device and the primed signal; and dynamically updating a storage module associated with the device with metadata based on a transmission of the plurality of data associated with the at least one action.