Dynamic Fraud Blacklist for Near Real-Time Detection
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Solution Overview
Problem
Existing fraud detection techniques often rely on a 'one size fits all' approach, leading to inaccurate results such as false positives and difficulty in identifying and blocking fraudulent activity in real-time across multiple user accounts.
Innovation Solution
A dynamic fraudulent user blacklist is implemented, which identifies and updates patterns of fraudulent activity across multiple accounts in real-time, allowing for near real-time detection and prevention of similar attacks by recognizing patterns associated with the same fraudster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generalized fraudulent user patterns are used with a one size fits all approach, then fraud detection can be implemented across multiple accounts, but the accuracy of detection deteriorates leading to false positives
Solution Approach 1:
The patent segments the fraudulent user pattern detection by creating individual forensic profiles for each user account that are then compared against a dynamic blacklist. This allows the system to apply fraud detection specifically tailored to each account's unique patterns rather than using a single generalized approach, thereby maintaining high detection accuracy across multiple accounts without producing false positives.
2Productivity
If traditional fraud detection techniques are used, then fraud analysis can be performed, but the ability to identify and block fraudulent activity in real-time deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously building and updating forensic profiles for each user account and maintaining a dynamic blacklist of fraudulent patterns in advance. When a new transaction or activity occurs, the system can immediately compare it against the pre-established profiles and blacklist, enabling real-time detection and blocking of fraudulent activity without requiring complex analysis at the moment of detection.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors user activities, updates forensic profiles based on new information, and dynamically adjusts the blacklist. This feedback loop allows the system to learn from new fraudulent patterns and improve its real-time detection capability over time, maintaining both high productivity and speed in fraud analysis.
3Ease of manufacture
If static blacklist approaches are used, then implementation is simple, but the system cannot adapt to new fraudulent patterns across multiple accounts
Solution Approach 1:
The patent transforms the static blacklist into a dynamic system that automatically updates based on new fraudulent patterns detected across user accounts. The forensic profiles and blacklist are continuously refined as the system analyzes new data, allowing the system to adapt to emerging fraud techniques while maintaining the simplicity of the underlying implementation architecture. The dynamic nature is achieved through automated profile comparison and pattern recognition rather than complex manual updates.
Data Source
AI summary
Various embodiments are generally directed to detecting fraudulent activity on a user account based at least in part on a dynamic fraudulent user blacklist. The fraudulent activity may be identified based on a similarity of forensic profiling across multiple user accounts, for example, fraudulent activity occurring by the same fraudster or perpetrator may have a similar or identical fraudulent pattern across the multiple user accounts. By identifying the fraudulent user patterns associated the same fraudster and dynamically updating a blacklist to include these fraudulent user patterns, the same types of attacks may be prevented on the other existing user accounts.