Distributed User Model Adaptation for Real-Time Fraud Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack effective methods for monitoring user authenticity during user activities on application servers in distributed computer systems, failing to adequately detect and respond to fraudulent behavior in real-time.
Innovation Solution
A distributed server system comprising an application server and a user-model server that adapts an existing user model session-by-session by extracting features from user activities, determining a total risk-score value, and triggering corrective actions such as signing out, two-factor authentication, locking, or alert functions when the risk-score exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user behavior patterns are compared with predefined rules to identify fraudulent activity, then user authenticity monitoring is performed, but the system cannot adapt to evolving fraudulent behaviors in real-time
Solution Approach 1:
The patent implements dynamic user models that are continuously adapted session-by-session based on observed user activities. Instead of using static predefined rules, the system dynamically updates user behavior profiles to reflect current patterns, enabling real-time detection of fraudulent activities while adapting to evolving threats.
Solution Approach 2:
The system incorporates feedback mechanisms where user activity data is continuously collected, analyzed, and used to update user models. The risk score calculations provide feedback on user authenticity, and this information feeds back into refining the user models, creating a closed-loop system that improves detection accuracy over time.
2Ease of manufacture
If static predefined rules are used for fraud detection, then implementation is simple, but the system generates false positives and cannot detect novel fraudulent patterns
Solution Approach 1:
The patent changes the fundamental parameter from static rules to dynamic risk scores that are calculated based on multiple user activity parameters. The system evaluates various parameters such as login patterns, device information, and behavior sequences, combining them into a comprehensive risk score that adapts to each user's unique profile.
Solution Approach 2:
The system creates a composite user model that integrates multiple types of user activity data and behavior patterns. By combining various indicators and features into a unified risk assessment framework, the system achieves more accurate fraud detection while maintaining implementation feasibility through modular architecture.
3Measurement precision
If comprehensive user activity analysis is performed session-by-session, then detection precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the user activity analysis into distinct components and phases. User activities are divided into discrete events that are processed individually, and the risk assessment is broken down into multiple evaluation stages. This segmentation allows for efficient processing while maintaining comprehensive analysis.
Solution Approach 2:
The system implements risk score calculations that focus on the most relevant user activity parameters for each specific context. Rather than analyzing every possible parameter uniformly, the system applies partial analysis concentrated on high-risk indicators, achieving effective fraud detection with reduced computational overhead.
Data Source
AI summary
Systems and methods for monitoring user authenticity during user activities in a user session on an application server is provided. The method being carried out in a distributed manner by a distributed server system. The method comprises a user modeling-process and a user-verification process. The user-modeling process is performed on a user-model server in which a user model is adapted session-by-session to user activity data received from the application server. The user-verification process is performed on the application server on the basis of the user model adapted on the user-model server. The user-verification process comprises comparing the user model with features extracted from user activity in the user session on the application server and determining a total risk-score value based on the comparison. If the total risk-score value is greater than a given threshold, a corrective action is performed.