Dynamic Identity Proofing With Risk-Based Checkout Prompts
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Solution Overview
Problem
Conventional systems fail to balance data security and user experience in identity proofing, often requiring manual input or exposing sensitive data, and lack real-time processing of user interaction data for enhanced security and efficiency.
Innovation Solution
A dynamic identity proofing system that uses browser extensions and machine learning models to analyze user interaction data, determining risk scores and prompting for confirmative information to reduce fraud risk, automatically populating secure information on checkout pages when risk is mitigated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual information entry is required for identity proofing, then data security is improved, but user experience deteriorates due to tedious tasks
Solution Approach 1:
The system automatically collects user information through browser extensions and devices without requiring manual entry. The identity proofing process serves itself by utilizing existing browser data, user agent strings, and device characteristics to automatically verify identity while maintaining security.
Solution Approach 2:
The patent replaces the mechanical process of manual information entry with automated electronic data collection and analysis. Machine learning models analyze browser information, device characteristics, and transaction patterns to verify identity without requiring users to manually input data.
2Ease of operation
If automatic population of user information is implemented, then user experience is improved, but data security deteriorates due to exposure to imposters
Solution Approach 1:
The system continuously monitors transaction risk scores and adjusts the level of verification required. Browser extensions provide real-time feedback about user identity characteristics, and the system dynamically determines whether automatic population is safe based on risk assessment from multiple data sources including device fingerprinting and behavior analysis.
Solution Approach 2:
The patent introduces browser extensions and intermediary devices that act as trusted mediators between users and the system. These intermediaries verify user identity through device characteristics and browser information before allowing automatic population, preventing imposters from exploiting the automated system.
3Device complexity
If conventional identity proofing systems are used, then implementation simplicity is maintained, but real-time processing capability deteriorates
Solution Approach 1:
The system performs preliminary data collection and analysis by installing browser extensions that continuously gather user characteristics, device fingerprints, and behavior patterns before transactions occur. This preliminary action enables real-time risk assessment during transactions without adding processing delays.
Solution Approach 2:
The patent implements dynamic identity proofing where the verification process adapts in real-time based on risk scores and transaction context. The system dynamically adjusts what information is collected and how verification is performed, enabling efficient real-time processing while maintaining security through adaptive rather than static procedures.
Data Source
AI summary
Systems as described herein may implement a mechanism for dynamic identity proofing. A dynamic identity proofing system may receive first user input information and browser information from a user device conducting a transaction with an interaction entity. The system may determine a risk score indicating a likelihood the transaction is fraudulent. Based on the risk score exceeding a threshold value, the system may use a machine learning model to determine confirmative information to lower the risk score. The system may cause display of one or more web elements on the checkout page prompting for the confirmative information. The system may determine an updated risk score based on second user input information responsive to the confirmative information. Accordingly, if the updated risk score does not exceed the threshold value, the system may cause the checkout page to be automatically populated with additional secure information.