Algorithmic Compliance and Regulatory Loss in Digital Assets
This paper highlights the fragility of fixed AML enforcement policies in digital asset markets, posing a problem for regulators and financial institutions relying on such systems.
This paper investigates machine learning-based anti-money laundering (AML) enforcement systems in cryptocurrency, revealing that strong static classification metrics significantly overestimate real-world regulatory effectiveness. Temporal nonstationarity leads to unstable enforcement thresholds, causing large and persistent excess regulatory losses compared to dynamic benchmarks.
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.