8 articles tagged with #regulatory-tech. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce DeXposure-Claw, an AI-powered supervision system designed to monitor DeFi credit risks by combining graph time-series forecasting with structured evidence gates to reduce false alarms in regulatory decision-making. The system includes a new evaluation benchmark aligned with regulatory standards, validated on five years of real blockchain data.
ampersend and TRM Labs have partnered to integrate real-time compliance screening into AI agent transactions, enabling autonomous systems to conduct compliant commerce at scale. This collaboration addresses a critical gap in enterprise adoption of AI-driven trading and commerce by combining ampersend's transaction infrastructure with TRM Labs' compliance expertise.
Researchers have developed AWASH, a multimodal AI detection framework that identifies corporate AI-washing—exaggerated or fabricated claims about AI capabilities across corporate disclosures. The system analyzes text, images, and video from financial reports and earnings calls, achieving 88.2% accuracy and reducing regulatory review time by 43% in user testing with compliance analysts.
Researchers have developed an open-source benchmark dataset to evaluate AI systems' compliance with the EU AI Act, specifically focusing on NLP and RAG systems. The dataset enables automated assessment of risk classification, article retrieval, and question-answering tasks, achieving 0.87 and 0.85 F1-scores for prohibited and high-risk scenarios.
US Customs and Border Protection has partnered with Tru Identity to implement AI-driven cargo screening technology aimed at enhancing trade compliance and reducing illegal imports. The initiative leverages artificial intelligence to strengthen supply chain security and protect government revenue through more sophisticated border enforcement mechanisms.
Researchers propose a multi-LLM system with hybrid retrieval-augmented generation to automate German IT-Grundschutz security certifications, addressing NIS2 compliance demands and specialist shortages. The architecture combines large language models with knowledge graphs to streamline certification phases while maintaining security quality standards.
Researchers propose OFMU, a bi-level optimization framework designed to enable large language models to selectively unlearn specific data without full retraining, addressing privacy and regulatory compliance needs. The method balances forgetting targeted information while maintaining model performance through hierarchical optimization with theoretical convergence guarantees.
Researchers have developed PASTA, a scalable AI compliance evaluation framework that can assess multiple policies simultaneously using LLM-powered analysis. The system evaluates five major AI policies in under two minutes for approximately $3, with expert validation showing strong alignment with human judgment.
