Identity Fraud Detection via Disambiguated Entity Records
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Solution Overview
Problem
Current systems face challenges in efficiently detecting identity-based fraud, particularly in verifying the legitimacy of requests for credit, payments, or benefits, as sophisticated fraud perpetrators can misuse identity information, leading to significant revenue loss and costly delays.
Innovation Solution
A system and method that receive entity-supplied information, such as names and social security numbers, query public or private databases, and use computer processors to determine validity by comparing data, creating disambiguated records, scoring parameters, and identifying fraud indicators, thereby enhancing fraud detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection systems verify identity information through multiple databases, then fraud detection capability is improved, but processing time and system complexity increase
Solution Approach 1:
The patent segments the fraud detection process into distinct modules: entity-supplied information collection, independent information querying from multiple databases, validity determination through comparison, and fraud indicator identification. This modular segmentation allows parallel processing of different information sources while maintaining comprehensive verification, thereby reducing overall processing time without compromising detection capability.
Solution Approach 2:
The system performs preliminary actions by pre-querying databases for independent information about the entity before the actual fraud detection decision is made. Entity records are created and validated in advance, with validity indications determined through preliminary comparison with database information. This preliminary validation accelerates the final fraud detection process by having verification data ready beforehand.
2Measurement precision
If comprehensive data comparison is performed to determine validity, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific critical comparison points rather than uniformly processing all data. The system determines validity indications by comparing entity-supplied information with independent information at key identification points (name, address, SSN), and only performs detailed analysis on records that show local mismatches or anomalies. This selective approach maintains high detection accuracy while reducing overall computational complexity.
Solution Approach 2:
The system changes parameters by transforming raw data into standardized formats and using weighted scoring mechanisms. Entity records are assigned validity indications based on parameter comparisons, and fraud indicators are identified by analyzing deviations in specific parameters such as address format, name variations, and SSN validation. This parameter-based approach simplifies complex comparisons by converting them into standardized evaluation metrics.
3Reliability
If multiple independent information sources are queried, then fraud indicator identification is improved, but data processing load increases
Solution Approach 1:
The patent extracts only the essential independent information needed for fraud detection from multiple databases, rather than processing complete data sets. The system queries databases for specific entity records containing name, address, and SSN information, and extracts only the relevant fields needed for comparison with entity-supplied information. This extraction approach maintains comprehensive fraud indicator identification while significantly reducing data processing load by eliminating unnecessary data.
4Measurement precision
If entity records are created and validated through database comparison, then identity verification accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent implements continuity of useful action by maintaining entity records in a validated state once created, rather than re-verifying the same information repeatedly. The system creates entity records with validity indications determined through database comparison, and these validated records are reused for subsequent fraud detection queries involving the same entity. This continuous validation approach maintains high identity verification accuracy while improving processing speed by eliminating redundant verification steps.
Data Source
AI summary
Certain embodiments of the disclosed technology include systems and methods for increasing efficiency in the detection of identity-based fraud indicators. A method is provided that includes: receiving entity-supplied information comprising at least a name, a social security number (SSN), and a street address associated with a request for a payment or a benefit; querying one or more databases with the entity-supplied information; receiving a plurality of information in response to the querying; determining a validity indication of the entity supplied information; creating disambiguated entity records; determining relationships among the disambiguated records; scoring, based at least in part on determining the relationships among the disambiguated entity records, at least one parameter of the entity-supplied information; determining one or more indicators of fraud based on the scoring; and outputting, for display, one or more indicators of fraud.