Targeted Fraud Alerts Using Message Clustering and User Profiles
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Solution Overview
Problem
The increasing threat of online fraud, particularly phishing and identity theft, is exacerbated by the dynamic nature of fraudulent messages and the limitations of current prevention methods, which often disrupt user experience with intrusive warnings.
Innovation Solution
A computer system employs a hardware processor to analyze message similarity and user behavior patterns to identify potential targets for targeted security notifications, using a generative language model to assess message content and user activity, and transmit personalized alerts when certain conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If warning messages are flooded to users about fraudulent messages, then security awareness is improved, but user experience deteriorates and notification effectiveness decreases
Solution Approach 1:
The patent applies local quality by sending notifications selectively to specific user groups rather than universally. The system identifies recipients with similar device usage patterns to the fraud victim and sends targeted notifications only to them, making the security warning both relevant and non-intrusive for the right audience while maintaining user experience for others.
Solution Approach 2:
The system applies partial action by sending notifications to only a subset of users who match the fraud victim's profile characteristics, rather than notifying all users. This partial targeting ensures security awareness is raised where most needed without overwhelming the broader user base with unnecessary alerts.
2Reliability
If generic security warnings are sent to all users, then security coverage is improved, but notification relevance and effectiveness deteriorate
Solution Approach 1:
The patent implements local quality by customizing notifications for specific user segments based on their device usage patterns. Rather than generic warnings to all users, the system identifies and notifies only those whose device characteristics match the fraud victim's profile, ensuring high relevance and precision for the targeted audience.
Solution Approach 2:
The system performs preliminary action by pre-identifying and profiling fraud victims based on their device usage patterns before sending notifications. This advance profiling enables the system to later target similar users with relevant notifications, improving both coverage and relevance through preparatory analysis.
3Measurement precision
If continuous monitoring of all messages is performed, then fraud detection accuracy is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent applies the extraction principle by isolating and analyzing only the critical characteristics of fraudulent messages and victim profiles, rather than monitoring and processing all message content in full detail. The system extracts key patterns (such as message sender, timing, and basic content features) to create fraud indicators, reducing complexity while maintaining detection accuracy.
Solution Approach 2:
The system applies partial action by focusing monitoring resources on specific high-risk message patterns and user profiles identified through initial analysis. Rather than continuously analyzing all messages from all users with equal depth, the system applies enhanced monitoring selectively to messages matching fraud patterns, reducing overall system complexity while maintaining high detection accuracy for relevant cases.
Data Source
AI summary
Some embodiments implement a targeted notification of potential victims of online fraud, such as fishing, among others. A security server protects a plurality of client devices by using telemetry data harvested from each device to construct a device-specific client profile. The client profile indicates a typical manner in which the respective device is used (e.g., which applications are installed and how much each installed application is being used). Features extracted from incoming messages are used to identify fraudulent messages and divide an incoming message stream into clusters. In response to identifying a cluster including fraudulent messages, some embodiments identify the recipients of cluster messages, and select a notification target from among the protected client devices according to a similarity between a client profile of the notification target and client profiles of the message recipients. A security notification is sent to the notification target.