Network Address Location Clustering for Fraud Risk Reduction
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Solution Overview
Problem
Existing databases that map network addresses to locations often become outdated due to frequent changes in network addresses and location information, especially for mobile devices, leading to inaccurate location data for transaction processing.
Innovation Solution
A service provider server maintains a database that filters out cellular data connections, applies clustering criteria to determine representative location information for network addresses, and uses a third-party database for validation, ensuring accurate and reliable location mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If databases map network addresses to location information, then location data is available for transaction processing, but the data becomes outdated due to frequent changes in network addresses and location information
Solution Approach 1:
The system performs preliminary actions by pre-establishing clustering criteria and pre-processing location data before transactions occur. The server proactively groups location information into clusters and determines representative locations in advance, so when a transaction occurs, the location data is already organized and ready, eliminating time lag issues.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring location information changes and updating the database accordingly. When location information changes, the server detects this change, updates the clustering, and recalculates representative locations, ensuring the database always reflects current location data while maintaining accuracy.
2Quantity of substance
If the system processes all network addresses and location information, then comprehensive data is maintained, but processing complexity and computational resources increase
Solution Approach 1:
The system extracts and separates essential information from the broader dataset. By filtering out redundant location data and retaining only the necessary information for clustering, the system reduces processing complexity while maintaining comprehensive coverage of important location patterns.
Solution Approach 2:
The system merges similar location information into unified clusters. Instead of processing each individual location entry separately, the server groups locations that are spatially close or pattern-wise similar into single cluster representations, significantly reducing the quantity of data that needs processing while preserving the essence of location information.
3Measurement precision
If the system uses multiple databases for validation and representation, then location accuracy is improved, but system complexity and data management difficulty increase
Solution Approach 1:
The system designs the database structure to serve multiple functions simultaneously. The same database table stores both raw location information and processed cluster data, and the server performs multiple operations (filtering, clustering, validation) using the same data structure, eliminating the need for separate specialized databases and reducing overall system complexity.
Data Source
AI summary
A method is provided that includes accessing, by a server provider server of a service provider, a database storing associations between network addresses and locations. Additionally, the method includes determining a subset of the database corresponding to a first network address, each association included in the subset corresponding to an association between the first network address and a respective location. The method also includes in response to determining that the subset of the database satisfies one or more clustering criteria, calculating a representative location corresponding to the first network address, and storing an association between the first network address and the representative location in a second database.