Segmented Identity Fraud Risk Scoring System
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Solution Overview
Problem
Credit card lenders face challenges in detecting and preventing identity fraud in credit applications, as sophisticated fraud perpetrators use synthetic, stolen, or manipulated identity information, leading to costly fraudulent transactions and a need for balancing fraud prevention with efficient service for legitimate clients.
Innovation Solution
A method and system that analyze applicant information by searching consumer identity repositories, generating identity characteristics, assigning applications to risk segments, and using predictive scoring models to determine risk scores and types of identity fraud, thereby identifying fraudulent applications and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection methods are used, then fraud prevention is provided, but false positives increase and operational efficiency decreases
Solution Approach 1:
The patent segments the fraud detection process into multiple distinct components: identity characteristics generation, segment assignment based on search results, and predictive scoring model application. This segmentation allows each component to be optimized independently, improving overall detection accuracy while maintaining operational efficiency through automated processing at each stage.
Solution Approach 2:
The system performs preliminary actions by generating identity characteristics and assigning segments before applying the predictive scoring model. This preliminary processing organizes and pre-evaluates applicant information, enabling the final scoring stage to operate more efficiently with pre-processed data, thereby reducing false positives without sacrificing detection accuracy.
2Measurement precision
If comprehensive identity verification is performed, then fraud detection accuracy improves, but processing time increases
Solution Approach 1:
The verification process is divided into segments: searching consumer identity repositories, generating identity characteristics, assigning to risk segments, and applying predictive scoring. This segmentation enables parallel processing and optimization of each stage, achieving comprehensive verification without proportionally increasing total processing time.
Solution Approach 2:
The system changes parameters by transforming raw applicant information into multiple identity characteristics and categorizing applications into different risk segments. This parameter transformation enables more precise fraud detection through multi-dimensional analysis while maintaining efficient processing through automated computational methods.
3Reliability
If multiple identity characteristics are generated and analyzed, then fraud detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex analysis into manageable components: searching repositories, generating characteristics, assigning segments, and scoring. This segmentation reduces system complexity by organizing multiple identity characteristics into structured categories that can be processed independently and systematically.
Solution Approach 2:
The patent introduces intermediary elements including identity characteristics as intermediate data structures and risk segments as intermediate categories. These intermediaries simplify the relationship between raw applicant information and final fraud determination, making the overall system more manageable and interpretable despite analyzing multiple characteristics.
Data Source
AI summary
Certain embodiments of the invention may include systems, methods, and apparatus for determining fraud risk associated with a credit application. According to an exemplary embodiment of the invention, a method is provided for receiving applicant information associated with the application; searching one or more consumer identity repositories for prior usage of the applicant information; generating a plurality of identity characteristics corresponding to the prior usage of the applicant information; assigning the application to one of a plurality of segments based at least in part on the searching; scoring the application with a predictive scoring model to determine a risk score based at least in part on the identity characteristics; determining identity fraud risk types associated with the application; and outputting the risk score and one or more indicators of the determined identity fraud risk types.