Compromised PII Modeling for Identity Theft Risk Scores
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Solution Overview
Problem
The Dark Web serves as a rampant source of illegal activities, with compromised Personally Identifiable Information (PII) being easily accessible and difficult to monitor, leading to a high incidence of identity theft and fraud, particularly affecting individuals aged 20-49, who account for a majority of reported fraud cases.
Innovation Solution
A system is developed to generate a Dark Web Risk Score (DWRS) using predictive models trained with machine learning algorithms, analyzing PII from various web sources, including the Dark Web, to assess the likelihood of future fraudulent activities and provide personalized risk reports and recommendations to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PII is monitored on the Dark Web, then fraud detection capability is improved, but monitoring difficulty increases due to anonymous and unmonitored nature of the Dark Web
Solution Approach 1:
The patent uses machine learning models as intermediaries to detect compromised PII on the Dark Web. These models analyze patterns and anomalies in dark web data to identify fraud risks without requiring direct human monitoring of the anonymous dark web environment, thus improving detection capability while managing monitoring difficulty
Solution Approach 2:
The patent replaces manual monitoring mechanisms with automated machine learning algorithms. The system automatically scans, analyzes, and detects compromised PII on the Dark Web using AI-driven tools, eliminating the need for human operators to directly monitor the difficult-to-access dark web environment
2Measurement precision
If machine learning algorithms are used to generate risk scores, then predictive accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex predictive modeling task into multiple specialized machine learning models, each trained for specific fraud detection purposes. This segmentation allows the system to achieve high predictive accuracy through specialized algorithms while managing overall system complexity through modular architecture
Solution Approach 2:
The patent develops multi-functional machine learning models that can handle various types of PII data and fraud scenarios simultaneously. These universal models reduce system complexity by consolidating multiple detection functions into single versatile algorithms rather than requiring separate specialized models for each fraud type
3Measurement precision
If comprehensive PII data is collected from multiple web sources, then risk assessment accuracy is improved, but data management complexity increases
Solution Approach 1:
The patent merges data collection and management operations into a unified system that simultaneously gathers PII information from multiple web sources including the Dark Web, Surface Web, and Deep Web. This consolidation improves risk assessment accuracy through comprehensive data while managing complexity through integrated data pipelines and centralized processing
Data Source
AI summary
One or more implementations include methods, systems, and/or devices to help protect consumers from fraudulent activity using compromised PII. For example, systems and methods can be implemented that enable the detection and prevention of consumer-focused identity theft, generates a risk score and is powered by a predictive model using machine learning techniques and tools and presents information and recommended action a user can take in reports.