Location Comparison Engine for Wireless Fraud Prevention
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Solution Overview
Problem
Current systems for fraud detection and user authentication in wireless transactions often result in false positives and fail to prevent fraudulent activities effectively, as they primarily rely on detection methods rather than prevention, and lack precise location-based verification.
Innovation Solution
A Location Comparison Engine that compares the location of a wireless device from a wireless network with the location of the user from another data network to determine proximity, thereby validating user identity and reducing fraudulent activities by assigning algorithmic values to the comparison results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detection methods are used for fraud detection and user authentication, then fraudulent activities can be identified, but false positives occur and prevention capability is insufficient
Solution Approach 1:
The system performs preliminary location verification by comparing the wireless device's network-derived location with the user's actual location before allowing transactions to proceed. This preventive approach validates user identity upfront rather than merely detecting fraud after it occurs, thereby reducing false positives while maintaining reliability.
2Reliability
If location-based verification is implemented, then fraudulent transactions are reduced, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a location comparison engine that receives location data from multiple sources, a validation module that compares these locations, and a results processing system that acts on the validation outcome. This segmentation allows each module to perform a specific function independently, managing complexity while maintaining high transaction security through multi-source location verification.
3Measurement precision
If multiple data networks are integrated for location comparison, then validation accuracy improves, but information integration difficulty increases
Solution Approach 1:
The location comparison engine serves as an intermediary that receives location information from multiple independent data networks (wireless network and other data networks), standardizes this information, and performs comparison without losing critical location data. This intermediary structure enables accurate proximity validation while managing the integration complexity of multiple sources.
Data Source
AI summary
A system and method for automatically comparing obtained wireless device location information from a wireless network and comparing that location with another independent source geographic location is provided. Location information is derived from two or more sources in a multiplicity of ways and a comparison is made within a Location Comparison Engine. The Location Comparison Engine makes use of databases that assist in resolving obtained raw positioning information and converting that positioning information into one or more formats for adequate location comparison. Results of the location comparison are deduced to determine if the wireless device is in some proximity to some other activity source location. Other location information used for comparison may be obtained from a multiplicity of sources, such as another network based on some activity of the wireless device user, another wireless device via a wireless network, or any system capable of providing location information to the Location Comparison Engine.