Electronic Profile Security with Application-State Fraud Checks
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Solution Overview
Problem
Existing systems lack user participation in security and fraud check processes for user electronic profile activities, relying solely on inference without user feedback.
Innovation Solution
A computer-based method and system that utilizes application analytics data to determine a reduced set of fraud checks based on user interaction with a software application, generating fraud determinations and authorization notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full set of fraud checks is performed for all profile activity authorization requests, then security and fraud detection accuracy is improved, but computational intensity and processing time increase
Solution Approach 1:
The system dynamically adjusts the set of fraud checks based on real-time application analytics data. When the application is actively open and running, a reduced set of fraud checks is performed. When the application is closed or not responding, a full set of fraud checks is executed. This dynamic adaptation resolves the contradiction by making the security process flexible rather than static.
Solution Approach 2:
The system changes the parameter of fraud check intensity based on application state. By monitoring whether the application is open or closed, the system adjusts the scope and depth of fraud checks performed. This parameter change allows the system to maintain high security when needed while improving processing speed during normal operation.
2Ease of operation
If application analytics data is accessed and processed to determine fraud check scope, then user participation in security is improved, but system complexity increases
Solution Approach 1:
The system automatically monitors application state and determines the appropriate fraud check scope without requiring explicit user input. The application analytics data self-reporting mechanism allows the system to infer user intent and adjust security measures autonomously, simplifying the user experience while maintaining sophisticated security.
Solution Approach 2:
The system uses feedback from application analytics data to continuously adjust fraud check behavior. By monitoring application open/close events and responsiveness, the system receives real-time feedback about user engagement and adapts its security process accordingly, creating a closed-loop system that balances security and usability.
3Productivity
If reduced fraud checks are performed when application is open, then processing efficiency is improved, but security coverage may be reduced
Solution Approach 1:
The system dynamically switches between reduced and full fraud check modes based on real-time application state. When the application is open and responsive, reduced checks provide efficient processing. When the application closes or becomes unresponsive, the system automatically transitions to full fraud checks, ensuring comprehensive security coverage when user verification is most needed.
Solution Approach 2:
The system performs fraud checks with varying intensity based on periodic monitoring of application state. Rather than using a fixed check regime, the system periodically reassesses application responsiveness and adjusts fraud check scope accordingly, creating a rhythm of intensive and light security checks that balances efficiency and coverage.
Data Source
AI summary
Systems and methods of the present disclosure include computer systems for improving data security. To do so, an authorization request associated with a user profile is received, including a time-stamp for a profile activity, a profile identifier, and a value. Application analytics data for a software application on a user device associated with the user profile is accessed, including an open event indicator indicating a time of a loading, and a close event indicator indicating a time of a termination. An open period of the software application at the time-stamp is determined based on the application open event indicator and the application close event indicator. A reduced set of fraud checks is selected and executed when the time-stamp associated with the profile activity is within the open period of the software application. A fraud determination is generated, and an authorization notification is generated based on the fraud determination.