Real-time Sanctionable Individual Identification via Machine Intelligence
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Solution Overview
Problem
Current systems for identifying sanctionable individuals or entities are slow to update and distribute lists, allowing bad actors to evade detection and resulting in costly penalties for regulated entities.
Innovation Solution
A system utilizing machine intelligence for real-time analysis of online digital content, combined with blockchain-based consensus weighting mechanisms, to identify and block sanctionable individuals or entities by monitoring electronic content sources, calculating confidence scores, and adjusting decision reliability scores for blockchain participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional list update and distribution systems are used, then regulatory compliance is maintained, but the system is slow to identify and block sanctionable individuals, allowing bad actors to evade detection
Solution Approach 1:
The system performs preliminary actions by continuously monitoring electronic content sources and performing electronic textual analysis to identify potential sanctionable individuals before they can conduct unauthorized transactions. The machine intelligence system proactively searches for names and analyzes content in real-time, rather than waiting for periodic list updates. This preliminary identification and blocking mechanism prevents bad actors from exploiting the time delay between list updates.
Solution Approach 2:
The patent replaces the traditional mechanical manual list update and distribution system with an automated machine intelligence system. Instead of manually compiling, updating, and distributing sanction lists, the system uses automated electronic content analysis, natural language processing, and machine learning algorithms to continuously monitor and identify sanctionable individuals in real-time, significantly improving speed while maintaining reliability through automated validation.
2Productivity
If real-time monitoring of electronic content sources is implemented, then identification speed is improved, but system complexity increases
Solution Approach 1:
The machine intelligence system performs self-service by autonomously monitoring electronic content sources, performing electronic textual analysis, identifying names, and determining whether individuals are sanctionable without requiring manual intervention. The system automatically updates blocks with identified individuals and calculates confidence scores, enabling high productivity in transaction monitoring while managing complexity through automation rather than human operators.
Solution Approach 2:
The monitoring system is designed with multi-functionality to handle diverse electronic content sources including social media platforms, news websites, and other digital platforms. The machine intelligence system performs multiple functions: content monitoring, textual analysis, name identification, sanctionability determination, and block updating, all within a single integrated system. This universal approach improves productivity across multiple transaction types while consolidating complexity into one system rather than requiring separate monitoring mechanisms for each source.
3Measurement precision
If machine intelligence analysis is used to identify sanctionable individuals, then identification accuracy is improved, but false positives may increase
Solution Approach 1:
The system implements feedback mechanisms by calculating confidence scores for each identified individual based on the strength of evidence from electronic content analysis. The machine intelligence system continuously refines its identification accuracy by learning from past results and adjusting its analysis parameters. This feedback loop improves measurement precision over time while allowing operators to set confidence thresholds that minimize false positives by only blocking individuals with high-confidence identifications.
Data Source
AI summary
Machine learning based techniques are described for identifying sanctionable persons via monitoring a plurality of electronic content sources. This may allow for more rapid identification of prohibited or restricted transactions. A trained sentiment analysis classifier may classify a particular electronic content item as containing sanctionable conduct. An electronic textual analysis of the electronic content item may be performed to identify one or more individual names within the particular electronic content item. An indication as to whether the one or more individual names have been identified as individuals who may be subject to one or more sanction requirements that prohibit one or more online actions may be electronically stored in a data table. Various operations may be performed to block or otherwise restrict online accounts associated with the individual from performing online activities.