When Rules Talk to Code: Why Financial Regulation Should Speak to Machines, and Humans

Modern financial regulation confronts a fundamental paradox: while financial markets operate through algorithmic systems executing millions of transactions per second, the rules governing these markets remain written primarily in natural language for human readers. This article argues that regulators should adopt machine-readable and machine-executable regulations as the primary mode of rule promulgation. Drawing on examples from algorithmic trading, anti-money laundering monitoring, and automated regulatory reporting, I demonstrate how executable regulation can reduce interpretive ambiguity, enable real-time compliance, lower implementation costs, and enhance supervisory capacity. The paper proposes a layered regulatory architecture combining canonical legal text with formal computable representations and standardized validation schemas. While acknowledging risks of over-formalization, embedded bias, and reduced flexibility, the paper argues that continuing to write exclusively for human readers effectively delegates lawmaking authority to private coders within regulated institutions. Reclaiming public control over how rules are operationalized requires regulators to publish their own authoritative machine-executable implementations. The process will ensure smooth percolation of regulatory guidance into financial system compliance.