Algorithmic Compliance and Regulatory Loss in Digital Assets

TL;DR

This paper examines the limitations of static machine learning enforcement systems in cryptocurrency AML, highlighting their instability and excess regulatory losses due to nonstationarity and miscalibration.

Contribution

It reveals the fragility of fixed AML policies in digital assets and proposes loss-based evaluation frameworks for better regulatory oversight.

Findings

Static metrics overstate real-world effectiveness

Enforcement thresholds are unstable over time

Miscalibration causes regulatory losses

Abstract

We study the deployment performance of machine learning based enforcement systems used in cryptocurrency anti money laundering (AML). Using forward looking and rolling evaluations on Bitcoin transaction data, we show that strong static classification metrics substantially overstate real world regulatory effectiveness. Temporal nonstationarity induces pronounced instability in cost sensitive enforcement thresholds, generating large and persistent excess regulatory losses relative to dynamically optimal benchmarks. The core failure arises from miscalibration of decision rules rather than from declining predictive accuracy per se. These findings underscore the fragility of fixed AML enforcement policies in evolving digital asset markets and motivate loss-based evaluation frameworks for regulatory oversight.