Trust Messaging Layer for Double-Blind Fraud Alert Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data sharing consortiums face challenges in maintaining privacy and confidentiality of sensitive client datasets, leading to privacy risks, compliance issues, and operational inefficiencies, which deter institutions from participating fully and delay the dissemination of critical risk information.
Innovation Solution
A system that enables double-blind risk alerts using pseudonymous identifiers and a confidential matching engine for privacy-preserving record linkage, allowing entities to share and receive alerts without revealing personally identifiable information or proprietary details, leveraging secure communication protocols and cryptographic standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entities contribute personally identifiable information to a centralized repository for risk detection, then collective risk detection capability is improved, but privacy and security of client data deteriorates
Solution Approach 1:
The patent extracts only the essential identifying characteristics needed for risk detection (name, date of birth, last four digits of ID) while leaving out sensitive PII. This allows entities to participate in collective risk detection without exposing full personally identifiable information to the centralized repository.
Solution Approach 2:
The patent introduces a trusted intermediary platform that mediates between entities and the centralized repository. This intermediary verifies and standardizes the limited identifying characteristics before they enter the repository, ensuring privacy protection while enabling effective risk detection through standardized data formats.
2Reliability
If entities share complete client data in consortiums, then effectiveness of risk detection is improved, but compliance with data protection regulations deteriorates
Solution Approach 1:
The patent applies different data sharing levels to different entities based on their needs and risk profiles. Each entity contributes only the specific identifying characteristics relevant to their risk detection requirements, rather than sharing all client data uniformly. This localized approach maintains detection effectiveness while reducing regulatory compliance risks.
3Quantity of substance
If smaller institutions participate in data consortiums, then completeness of datasets is improved, but data quality and reporting standards deteriorate
Solution Approach 1:
The patent implements universal data standards that enable entities of all sizes to contribute data in a standardized format. The standardized identifying characteristics (name, date of birth, last four digits of ID) can be collected and processed by any entity regardless of size, allowing smaller institutions to participate meaningfully while maintaining consistent data quality across the entire consortium.
4Adaptability or versatility
If institutions participate in centralized data pooling, then scope of risk detection is improved, but operational overhead and complexity deteriorate
Solution Approach 1:
The patent segments the data sharing process into distinct components: entities collect and standardize limited identifying characteristics locally, the centralized repository stores only these standardized segments, and matching algorithms compare only these segmented data points. This segmentation reduces operational complexity while maintaining broad risk detection scope across multiple entities.
Data Source
AI summary
Examples provide a process including receiving, from a first subscriber, a payload comprising an alert indicative of fraudulent activity associated with a user. The process further includes identifying, based in part on first data associated with the user and included in the payload, a second subscriber associated with the user, transmitting a request the second subscriber to provide second data associated with the user, and receiving, from the second subscriber, the second data associated with the user. The process further includes generating a comparison between one or more first fields included in the first data and one or more second fields included in the second data, and outputting results of the comparison to the second subscriber. The results are output to the second subscriber within a trust service messaging layer of an alerting network.