Scam Detection via Attachment Content and Identity Correlation
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Solution Overview
Problem
Conventional anti-fraud tools are inadequate for detecting sophisticated scams where legitimate customers initiate transactions, as they rely on outdated indicators and fail to analyze the content of communications like PDFs and images, leaving a significant gap in scam detection.
Innovation Solution
An automated system using deterministic and probabilistic analyses, including machine learning models and Natural Language Processing, to assess communication attributes, generate a scam risk score, and provide recommendations, while also analyzing attachments for embedded scams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-fraud tools are used to detect scams, then they can identify impersonation attempts with mismatched credentials, but they fail to detect sophisticated scams where legitimate customers use correct credentials and devices
Solution Approach 1:
The system dynamically adapts its detection approach by switching between traditional credential verification and advanced attachment content analysis based on the communication context. The multi-modal analysis framework allows the system to evolve its detection capabilities alongside changing scam tactics, maintaining reliability while improving adaptability.
Solution Approach 2:
The patent introduces a new dimension of analysis by examining the actual content within attachments (images, text, audio) rather than treating them as opaque files. This dimensional shift from metadata-only analysis to content-aware analysis enables detection of scams concealed within attachment contents, resolving the contradiction between detecting traditional and sophisticated scams.
2Measurement precision
If consumers rely on training and intuition to recognize scams, then they may identify obvious fraudulent patterns, but they cannot distinguish between legitimate communications and sophisticated AI-generated scams
Solution Approach 1:
The system acts as an intermediary between the consumer and the communication, performing automated attachment content analysis and providing scam risk assessments. This intermediary function relieves consumers of the burden of detecting sophisticated scams themselves, bridging the gap between consumer capability and scam complexity through automated intelligent analysis.
3Productivity
If conventional systems analyze attachments by assessing potential maliciousness without reading content, then they can quickly identify obvious malware, but they miss scams concealed in the text or images of documents
Solution Approach 1:
The system performs preliminary quick assessment of attachments using traditional methods (entropy measurement, malware signature detection) to rapidly identify obviously malicious files. For attachments that pass or fail this preliminary check, the system then applies content reading analysis to detect concealed scams, creating a two-stage process that maintains productivity while improving detection completeness.
Solution Approach 2:
The attachment analysis process is segmented into multiple stages: initial rapid assessment (entropy, signatures), followed by conditional content reading (text extraction from images, PDF content analysis, audio transcription). This segmentation allows the system to maintain high productivity for obvious cases while applying thorough content analysis only when needed, resolving the contradiction between speed and completeness.
Data Source
AI summary
A system for verifying an identity of a counterparty includes one or more processors and a memory storing instructions that, when executed, cause the system to receive identifying information from a user, transmit a consent prompt to the counterparty via an associated communication channel, receive consent and additional data including a full name and approximate location, determine whether the name is historically associated with the identifying information, analyze whether the identifying information corresponds to a temporary account based on service provider characteristics, correlate a device fingerprint of the counterparty's device with historical records linked to the identifying information, generate a scam risk score based on results of the determining and analyzing operations, and present the scam risk score and one or more risk-based recommendations to the user through a privacy-preserving interface.