Automated Scam Detection System Using Deterministic and Probabilistic Analysis
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Solution Overview
Problem
Conventional anti-fraud tools are inadequate in detecting sophisticated scams where legitimate customers use correct credentials and devices, making it difficult to distinguish between legitimate and fraudulent communications, especially with the rise of AI and deepfake technology.
Innovation Solution
An automated system using deterministic and probabilistic analyses, including machine learning models and Natural Language Processing, to evaluate communication attributes, generate a scam risk score, and provide recommendations based on the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-fraud tools are used to detect scams, then they can identify obvious fraudulent activities with mismatched credentials, but they fail to detect sophisticated scams where legitimate customers use correct credentials and devices
Solution Approach 1:
The detection system is divided into multiple independent analysis modules: deterministic check module, probabilistic analysis module, machine learning model module, and attachment content analysis module. Each module handles specific aspects of scam detection, allowing the system to process complex scam patterns through specialized segments rather than a single monolithic system.
Solution Approach 2:
The system combines multiple detection approaches (deterministic rules, probabilistic models, machine learning, and attachment content analysis) into a composite detection framework. This composite approach integrates different methodologies to achieve higher reliability in detecting sophisticated scams while managing complexity through structured integration.
2Reliability
If consumers rely on training and intuition to recognize scams, then they may identify some fraudulent patterns, but rapidly advancing scam technology including AI and deepfake tools outpace consumer ability to discern legitimate from fraudulent communications
Solution Approach 1:
The system performs preliminary automated analysis of communications before consumers need to manually evaluate them. The deterministic checks, probabilistic analyses, and machine learning models pre-assess communications for scam indicators, providing consumers with pre-processed risk assessments that save time and improve accuracy.
Solution Approach 2:
The automated detection system acts as an intermediary between the complex world of sophisticated scams and the consumer. It translates complex scam patterns into simple risk scores and recommendations, mediating the information gap between advanced scam techniques and consumer understanding.
3Reliability
If conventional scam detection systems focus on analyzing attachments for malware signatures and entropy, then they can detect obvious malicious files, but they fail to examine actual attachment content for concealed scam messages
Solution Approach 1:
The system extracts and separately analyzes the actual content of attachments (text, images, documents) from the file structure. By taking out the content for separate examination, the system can detect scam messages concealed within attachments without being limited by traditional malware signature approaches.
Solution Approach 2:
The system adds a new dimension of analysis by examining attachment content semantically rather than just structurally. Instead of only analyzing file properties and signatures, it introduces content-based analysis in a different dimensional space, detecting scams based on what the attachment says rather than just how it's structured.
4Measurement precision
If a comprehensive analysis system examining all communication attributes is implemented, then scam detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system dynamically adjusts its analysis depth and methodology based on risk indicators. Deterministic checks provide quick initial assessment, and only communications that trigger certain conditions proceed to more time-consuming probabilistic and machine learning analyses, creating a dynamic processing workflow that optimizes time versus precision.
Solution Approach 2:
The system applies partial analysis to all communications (deterministic checks) and excessive/detailed analysis only where needed (probabilistic and ML models). This partial/excessive approach ensures all communications receive baseline scrutiny while concentrating computational resources on suspicious cases.
Data Source
AI summary
A system for scam detection and prevention, the system including one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations including: receiving a communication comprising communication information; parsing the received communication information to extract attributes of the communication information; performing a series of deterministic checks on the attributes; performing a series of probabilistic analyses on the attributes, wherein the probabilistic analyses comprise using machine learning models trained on known legitimate communications and known fraudulent communications; aggregating the results of the deterministic checks and probabilistic analyses to generate a scam risk score; generating recommendations specific based on the generated scam risk score, deterministic checks, and probabilistic analyses; and presenting the scam risk score and the recommendations to a user.