ARCCS: An Automated Regulatory Compliance Checking System
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Abstract (arXiv)
Regulatory compliance checking - deciding whether a target document satisfies the obligations of a regulation - requires interpreting dense legal text, identifying which provisions apply, and grounding each decision in explicit evidence. We present ARCCS, an end-to-end, automated, agentic, and regulation-agnostic Legal NLP system for compliance checking. ARCCS decomposes raw regulatory text into atomic, traceable requirements and evaluates a target document against them using retrieved evidence, confidence scores, and human-interpretable justifications. This design decouples compliance assessment from any fixed regulatory template or predefined rule set, enabling the pipeline to operate over regulations of varying size and structure. We evaluate ARCCS in two complementary settings. First, in a GDPR policy-document evaluation, LLM-based judges find its decisions and justifications legally and evidentially consistent in up to 96.67% of the assessed cases. Second, on an EU public-procurement benchmark comprising more than 1,200 individual rule checks, the system attains 98.8% accuracy in violation detection. ARCCS is, to our knowledge, the first fully open-source system for end-to-end regulatory compliance checking and auditable report generation.
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References · 11
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- 2024Rethinking Legal Compliance Automation: Opportunities with Large Language Models
- 2026CTRAG: An In-Context Retrieval-based Framework for Automated Compliance Checking using LLMs
- 2026Code-as-Auditor: Executable Compliance Reasoning via Regulation-to-Code
- 2025Judging by the Rules: Compliance-Aligned Framework for Modern Slavery Statement Monitoring
- 2025Explainable Compliance Detection with Multi-Hop Natural Language Inference on Assurance Case Structure
- 2026ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection
- 2026REGREACT: Self-Correcting Multi-Agent Pipelines for Structured Regulatory Information Extraction
- 2025GraphCompliance: Aligning Policy and Context Graphs for LLM-Based Regulatory Compliance