Identity Risk System Using Anonymous Profiles for Fraud Detection
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Solution Overview
Problem
Current online security systems are inadequate in identifying and preventing fraudulent activities, particularly those involving compromised user identities, as they focus on behavioral anomalies rather than verifying user identities, leading to vulnerabilities in credential stuffing, account takeover, and other malicious activities.
Innovation Solution
An identity risk determination system that utilizes a data coalition, anonymous interaction profiles, a computational engine, and notification systems to provide a high-fidelity risk score through machine learning algorithms, correlating user behavior across multiple platforms to differentiate between legitimate and illegitimate interactions, thereby enhancing fraud detection and user authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If typical fraud tools focus on identifying risk using techniques to detect anomalies across user network, device, or behavioral interactions, then automated traffic and bots can be identified, but the system knows little about the actual identity of users and cannot effectively prevent credential stuffing and account takeover attacks
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user interaction data across multiple platforms before fraud occurs. It builds anonymous interaction profiles that capture behavioral patterns, device characteristics, and network information in advance, enabling proactive fraud prevention rather than reactive detection
Solution Approach 2:
The patent introduces an intermediary layer of anonymous interaction profiles that mediate between user activity and fraud detection. These profiles serve as a bridge that provides fraud detection capabilities without requiring direct access to sensitive user identity information, thus maintaining privacy while improving security
2Measurement precision
If the system collects and analyzes extensive user interaction data across multiple platforms to build anonymous interaction profiles, then fraud detection accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex fraud detection task into multiple independent components: data collection modules for different platforms, anonymous profile generation, behavioral analysis engines, and risk scoring systems. Each component operates independently and can be developed, maintained, and scaled separately, reducing overall system complexity
Solution Approach 2:
The anonymous interaction profiles serve multiple functions simultaneously: they store user behavioral patterns for fraud detection, provide baseline data for anomaly detection, enable cross-platform user identification without revealing identity, and support both preventive and investigative fraud analysis
3Speed
If real-time fraud analysis is performed using machine learning algorithms on user interaction data, then fraudulent activities can be identified immediately, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary computation by pre-processing user interaction data and generating anonymous interaction profiles during off-peak periods. Machine learning models are trained in advance on historical data, and baseline behavioral patterns are established beforehand, enabling faster real-time inference with reduced computational overhead
Solution Approach 2:
The system dynamically adjusts analysis parameters based on risk levels and traffic patterns. For low-risk interactions, minimal analysis is performed with low computational overhead. For high-risk or anomalous activities, the system intensifies analysis depth and computational resources, optimizing the balance between detection speed and resource consumption
Data Source
AI summary
A method for detecting fraudulent activity on an online system might comprise acquiring data from online service providers about a plurality of interactions of users with the plurality of online service providers, building an identity profile of the user based on the data, receiving, from a client system, an API request, wherein the API request includes information about the user and a new user interaction apparently attempted by the user, comparing the information against the identity profile, and based on the comparing, generating an identity score for the new user interaction. If the identity score exceeds a threshold value, the method can comprise generating an alert, sending the alert to the user, receiving from the user an indication of whether the new user interaction was attempted by the user, and if the new user interaction was not attempted by the user, sending a report to the client system.