Mobile App Verification Enrollment With Adaptive Device Stability
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Solution Overview
Problem
The existing mobile application verification (MAV) process for high-risk transactions involves a mandatory waiting period to prevent fraudulent device enrollment, which impedes accessibility and security, creating vulnerabilities for fraudulent access.
Innovation Solution
Implement a device trust computational model that minimizes the enrollment waiting period by using machine learning to evaluate initial authentication strength and historical trust factors, supplemented with contactless OTP card authentication and historical transaction data analysis to enhance fraud detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mandatory waiting period is implemented in the MAV enrollment process, then security against fraudulent device enrollment is improved, but accessibility and user convenience deteriorate
Solution Approach 1:
The patent applies dynamics by making the waiting period adjustable rather than fixed. The system dynamically modifies the waiting period duration based on risk assessment of the enrollment request, allowing shorter periods for low-risk scenarios and longer periods for high-risk scenarios, thus balancing security and accessibility
Solution Approach 2:
The patent changes the parameter of waiting period duration from a static mandatory value to a variable parameter that can be adjusted based on multiple factors including device characteristics, user behavior patterns, and risk indicators, enabling optimized security-acquiescence trade-offs
2Measurement precision
If a long waiting period is imposed, then fraud detection capability is improved, but processing speed and user experience worsen
Solution Approach 1:
The patent performs preliminary risk assessment and fraud detection analysis during the enrollment process itself, gathering device information, user behavior data, and risk indicators upfront. This allows the system to make informed decisions about waiting period duration without requiring unnecessarily long waiting times, thus improving processing speed while maintaining fraud detection capability
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor enrollment requests and adjust waiting period durations based on real-time risk assessments. The feedback loop analyzes device characteristics, user responses, and historical data to optimize the waiting period, improving both fraud detection precision and processing efficiency
Data Source
AI summary
Systems and methods are directed to an improved fraud detection feature in Mobile Application Verification (MAV) device enrollment process. The proposed solution corresponds to a two-prong approach involving a strength factor evaluation of the authentication inputs provided during the initial enrollment process, and a computation of a number of trust parameters values from archived transactional records. A computational model supplemented with a machine learning process is then used to determine a device stability period as a function of the strength factor associated with the user provided authentication data and the computed trust factors.