Payment Network Geofencing for Multi-Card Fraud Blocking
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Solution Overview
Problem
Existing methods fail to prevent fraudulent transactions on credit cards beyond the blocked card, putting other cards at risk due to unauthorized use.
Innovation Solution
Implementing geofencing and multi-factor authentication to detect and prevent fraudulent transactions by comparing transaction data within a predetermined time frame and proximity, using a payment network's fraud detection engine to restrict transactions and require authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single credit card is blocked after fraudulent activity detection, then the affected card is protected, but other cards remain vulnerable to fraud
Solution Approach 1:
The fraud detection system is extended to provide multi-card protection by analyzing transaction patterns across multiple cards and implementing geo-fencing that restricts transactions from multiple cards within the same geographic location and time window, transforming a single-card blocking mechanism into a multi-card protective system
2Reliability
If geofencing is implemented to restrict transactions within a geographic area, then fraudulent transactions are blocked, but legitimate transactions from the same area may be incorrectly denied
Solution Approach 1:
The geo-fencing mechanism applies different rules to different cards based on their individual risk profiles and transaction histories. First cards (flagged as potentially compromised) trigger geo-fencing that restricts subsequent cards, while legitimate cards with normal transaction patterns are allowed to proceed without restriction, creating localized security measures tailored to specific cards rather than blanket restrictions
3Reliability
If multiple cards are monitored and restricted based on transaction patterns, then fraud spread is prevented, but system complexity increases
Solution Approach 1:
The fraud detection system segments cards into different risk categories (first cards with suspected fraud, second cards potentially at risk) and applies different monitoring and restriction rules to each segment. This segmentation allows the system to manage complexity by handling cards differently based on their risk level rather than applying uniform complex rules to all cards
Data Source
AI summary
A method for monitoring transactions for identifying fraudulent activity is provided. The method may include receiving a transaction request at a payment switch interface from a payment network. The method may include comparing the transaction request to all transactions received that are within a predetermined time immediately preceding a timestamp of the time of execution of the transaction request and that include a geolocation that is within a predetermined proximity to the geolocation of the transaction request. In response to the comparing, the method may include identifying a number of transactions greater than a first threshold that are received within the predetermined time and the geolocation is within the predetermined proximity. Following the identifying, the method may include executing a geofencing by restricting transactions from being executed via the smart card and a plurality of smart cards associated with the payment network that are being executed from within the geolocation.