Reconstruction Engine for Unified Fraud Detection
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Solution Overview
Problem
The distribution of data across multiple data centers makes it challenging to obtain a complete representation of a user's activity, hindering effective fraud detection and system performance analysis, as existing fraud detection systems struggle to collect data in real time without impacting customer experience.
Innovation Solution
A method and system for reconstructing data packets from multiple data centers into a unified format, using a reconstruction engine to process user requests and sessions, and a fraud detection system that combines data from various centers to analyze user activity and detect fraudulent behavior in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is distributed across multiple data centers to handle increased traffic and improve processing speed, then system productivity and response time are improved, but the ability to obtain a complete representation of user activity deteriorates
Solution Approach 1:
A data reconstruction engine is introduced as an intermediary component that receives distributed data packets from multiple data centers, reconstructs them into complete user activity representations, and makes them available for fraud detection. This mediator bridges the gap between distributed processing and holistic data analysis without requiring changes to the existing data center architecture.
2Measurement precision
If fraud detection systems collect data from all data centers in real time to improve fraud detection capability, then detection precision is improved, but system complexity and impact on customer experience worsen
Solution Approach 1:
The fraud detection function is extracted from the core data processing path and placed in a separate analytical layer. The reconstruction engine extracts complete user activity data from distributed packets, and fraud detection analyzes this reconstructed data independently, allowing real-time fraud detection without adding complexity to the primary data processing flow or impacting customer experience.
3Quantity of substance
If data packets are distributed across multiple data centers to handle increased traffic, then system capacity is improved, but the difficulty of detecting and measuring complete user activity worsens
Solution Approach 1:
The reconstruction engine merges distributed data packets from multiple data centers into complete user activity representations by combining fragmented information. This merging process restores the holistic view of user activity that was fragmented during distribution, enabling effective detection and measurement of complete user behavior patterns across the distributed system.
Data Source
AI summary
Described are computer-based methods and apparatuses, including computer program products, for detecting and interdicting fraudulent activity on a network. An user utilizes a transmitting device to transmit user requests that are split between a plurality of data centers for processing. The user requests are captured at the data centers. The user requests are unified into an user session. The user session can be analyzed for fraud detection, marketing analysis, network intrusion detection, customer service analysis, and/or performance analysis. If fraudulent activity is detected, then the user can be interdicted to prevent further fraudulent activity.