Computational Models for Blockchain Regulation and Compliance

Abstract

The regulatory landscape for blockchain and digital assets has shifted from benign neglect to aggressive enforcement. The European Union's Markets in Crypto-Assets Regulation (MiCA), effective June 2024, imposes licensing, capital, and disclosure requirements on crypto-asset service providers. The United States applies a patchwork of securities law (SEC), commodities law (CFTC), banking regulation (OCC), and anti-money laundering rules (FinCEN) to blockchain activities. Switzerland's DLT Act, Singapore's Payment Services Act, and the FATF Travel Rule add further jurisdictional complexity. For blockchain-based organisations operating across borders, compliance is a computational problem: transactions must be classified, screened, reported, and audited in real time against regulatory rules that vary by jurisdiction, asset type, counterparty status, and transaction size. We present the Regulatory Compliance Computation Framework (RCCF), evaluating five computational compliance models -- rule-based transaction screening, machine learning-driven risk scoring, on-chain compliance smart contracts, zero-knowledge regulatory proofs, and automated regulatory reporting pipelines -- across four regulatory regimes (MiCA, US multi-agency, Swiss DLT Act, FATF Travel Rule). Our Compliance Model Effectiveness Score (CMES) measures regulatory coverage, classification accuracy, processing throughput, privacy preservation, and cross-jurisdictional portability. Zero-knowledge regulatory proofs achieve the highest CMES (0.912) by enabling verifiable compliance attestation without exposing underlying transaction data, while rule-based screening achieves the highest throughput (14,200 transactions per second) for straightforward Travel Rule compliance.