Contextual Web-Page Fraud Scoring via UI Element Segmentation
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Solution Overview
Problem
Current security measures for electronic devices and systems are inadequate in distinguishing between legitimate and fraudulent user interactions, particularly in contexts where monetary exposure is involved, as they fail to effectively assess the risk associated with specific User Interface elements and user behavior.
Innovation Solution
The implementation of a system that performs contextual mapping and analysis of web-page elements to assign fraud-relatedness score-values, combining these with user-specific behavioral characteristics to differentiate between fraudulent and legitimate transactions, and generate possible-fraud notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used to verify user identity, then authentication is achieved, but the system cannot distinguish between legitimate and fraudulent transactions
Solution Approach 1:
The patent segments the web page into multiple UI elements and assigns different fraud risk levels to each element. This segmentation allows the system to focus security analysis on specific high-risk elements rather than treating the entire page uniformly, thereby improving fraud detection accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The patent applies local quality by implementing context-dependent security measures tailored to each UI element's fraud risk profile. High-risk elements receive enhanced security scrutiny while low-risk elements maintain normal processing, optimizing the balance between detection accuracy and system complexity through localized security enhancement.
2Reliability
If comprehensive user behavior analysis is implemented to detect fraud, then fraud detection capability improves, but processing time and system resource consumption increase
Solution Approach 1:
The patent implements partial action by applying full behavioral analysis only to high-risk UI elements while using simplified or no analysis for low-risk elements. This selective approach maintains strong fraud detection capability for critical areas while minimizing unnecessary processing time for routine interactions, effectively balancing detection capability with processing efficiency.
Solution Approach 2:
The system uses pre-collected user behavioral characteristics and profiles to automatically assess fraud risk without requiring real-time comprehensive analysis for every interaction. This self-service approach leverages existing data to quickly evaluate transactions, reducing processing time while maintaining detection capability through intelligent use of historical information.
3Reliability
If fraud risk assessment is applied to all UI elements uniformly, then security coverage is maximized, but false positives increase
Solution Approach 1:
The patent assigns different fraud risk levels to different UI elements based on their specific characteristics and potential for fraudulent use. This local quality approach ensures that security assessment precision is optimized for each element type, reducing false positives by applying appropriate scrutiny levels rather than uniform assessment across all elements.
Solution Approach 2:
The system dynamically adjusts fraud risk parameters and thresholds based on the specific UI element being interacted with, user behavior patterns, and contextual factors. This parameter adaptation allows the security coverage to remain comprehensive while improving assessment accuracy by tailoring detection sensitivity to each situation, thereby reducing false positives.
Data Source
AI summary
Devices, systems, and methods of contextual mapping of web-page elements and other User Interface elements, for the purpose of differentiating between fraudulent transactions and legitimate transactions, or for the purpose of distinguishing between a fraudulent user and a legitimate user. User Interface elements of a website or webpage or application or other computerized service, are contextually analyzed. A first User Interface element is assigned a low fraud-relatedness score-value, since user engagement with the first User Interface element does not create a security risk or a monetary exposure. A second, different, User Interface element is assigned a high fraud-relatedness score-value, since user engagement with the second User Interface element creates a security risk or a monetary exposure. The fraud-relatedness score-values are taken into account, together with user-specific behavioral characteristics, in order to determine whether to generate a possible-fraud notification, or as part of generating a possible-fraud score for a particular set-of-operations.