Media Feed Analysis Using Entity-Risk Filtering for Compliance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Financial institutions face challenges in complying with stringent government regulations due to data overload, high false positive rates, and inefficiencies in manual and automated compliance review systems, leading to increased costs and risks of non-compliance.
Innovation Solution
A system utilizing custom-built taxonomies, natural language processing (NLP), and machine learning (ML) to extract and filter highly relevant documents, reducing false positives by identifying and highlighting entities and risks relevant to a particular enterprise, with optional expert review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual compliance review methods are used, then analyst bias and inconsistencies are reduced through human judgment, but productivity is low and time consumption is high due to the increasing volume of news articles
Solution Approach 1:
The patent introduces an automated alerting system as an intermediary between the large volume of news articles and human compliance analysts. The system processes articles through NLP, entity recognition, and risk classification to generate structured alerts, thereby filtering and prioritizing content before human review. This intermediary layer increases productivity by handling initial screening while maintaining reliability through careful alert design that preserves contextual nuance for analyst judgment.
2Productivity
If automated alerting systems are deployed, then productivity increases through high-volume processing, but measurement precision deteriorates due to high false positive rates and generalized risk detection
Solution Approach 1:
The patent applies local quality by transitioning from generalized risk detection to entity-specific and client-specific risk detection. The system uses entity recognition to identify specific organizations, individuals, and entities mentioned in articles, then applies client-specific risk taxonomies to evaluate only relevant risks. This localized approach to risk assessment improves measurement precision by reducing false positives while maintaining high productivity through automated processing.
Solution Approach 2:
The patent changes the parameters of risk detection from generic keywords to structured entity-risk relationships with confidence scores. The system transforms unstructured article text into structured feature vectors containing entity identifiers, risk types, and confidence levels. This parameter transformation enables more precise measurement of actual compliance risk while maintaining high-volume automated processing capability.
3Reliability
If compliance departments are expanded with more workers, then reliability improves through increased review capacity, but device complexity increases due to coordination and management overhead
Solution Approach 1:
The patent enables the compliance monitoring system to serve itself by implementing automated article processing, alert generation, and priority ranking without requiring proportional increases in human staff. The system autonomously processes incoming articles, applies risk classification, and presents prioritized alerts to analysts, thereby maintaining or improving reliability through consistent automated processing while avoiding the organizational complexity of expanding compliance departments.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support relevance-based analysis and filtering of documents and media for one or more enterprises. Aspects disclosed herein leverage custom-built taxonomies, natural language processing (NLP), and machine learning (ML) for identifying and extracting features from highly-relevant documents. The extracted features are vectorized and then filtered based on entities (e.g., enterprises, organizations, individuals, etc.) and compliance-based risks (e.g., illegal or non-compliant activities) that are highly relevant to a particular client. The filtered feature vectors are used to identify and highlight relevant information in the corresponding documents, enabling decision making to resolve compliance-related risks. The aspects described herein generate fewer false positive or otherwise less relevant results than conventional document screening applications or manual techniques.