Identity Confidence Scoring System for Fraud Detection
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Solution Overview
Problem
Current methods fail to effectively verify the identity of individuals, particularly in financial transactions, as fraudsters can provide false or manipulated identities that evade detection by matching with legitimate identities without reported fraudulent activity.
Innovation Solution
A method and system for evaluating identity information by developing a confidence score based on header data from multiple data sources, using a scoring system to assess the likelihood that the provided identity matches the actual identity, and providing a score to determine authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional identity verification methods are used to check identity information against fraud databases, then legitimate identities can be verified, but fraudsters providing synthetic or manipulated identities can evade detection
Solution Approach 1:
The patent segments identity verification into multiple independent confidence scores: header data confidence score (evaluating identity information quality), body data confidence score (evaluating supporting documentation), and cross-data source confidence scores. Each segment evaluates different aspects of identity authenticity, allowing the system to detect fraudsters by analyzing discrepancies across segments rather than relying on a single verification method.
Solution Approach 2:
The patent creates a universal confidence scoring system that can evaluate multiple types of identity information (header data, body data, supporting documents) from diverse data sources simultaneously. The scoring framework is multi-functional, handling synthetic identities, manipulated identities, and legitimate identities through the same unified evaluation process, making it adaptable to various fraud techniques while maintaining consistent verification standards.
2Reliability
If comprehensive data from multiple sources is collected to improve identity verification, then fraud detection capability increases, but system complexity and data processing requirements increase
Solution Approach 1:
The patent divides the complex verification system into modular segments: header data evaluation module, body data evaluation module, confidence score calculation module, and threshold comparison module. Each module handles a specific aspect of verification, making the overall complex system manageable through functional segmentation. This modular approach allows independent optimization and maintenance of each component while achieving comprehensive fraud detection.
Solution Approach 2:
The patent transforms complex multi-source data into simplified confidence score parameters through standardized evaluation functions. By converting diverse data types (names, addresses, social security numbers, supporting documents) into unified confidence scores with defined thresholds, the system reduces parameter complexity while preserving verification accuracy. The parameter transformation enables straightforward comparison and decision-making despite the underlying data complexity.
Data Source
AI summary
Identity data for an applicant opening a bank account is provided to an ID confidence scoring system. The scoring system accesses a multi-source data management system, using queries that include a base component, a link component and a function component. Data records maintained by the multi-source data management system include header data having identity data elements, with the header data analyzed pursuant to the queries. Queries may also be provided to an entity resolution system having data records organized in data networks, each data network corresponding to a single entity. Query results are used to develop an ID confidence score for applicant identity data.