固定AML规则在加密市场中易失效，动态调整才能降低监管损失。
Algorithmic Compliance and Regulatory Loss in Digital Assets
- 用滚动评估发现静态分类指标夸大实际监管效果
- 时间非平稳性导致监管阈值不稳定，监管损失持续偏高
- 核心问题是决策规则校准失准，适合监管科技研究者参考
我们研究了基于机器学习的加密货币反洗钱（AML）执法系统在实际部署中的表现。通过对比特币交易数据进行前瞻性与滚动评估，发现强静态分类指标严重夸大了真实世界的监管有效性。时间非平稳性导致成本敏感的执法阈值显著不稳，相对于动态最优基准，产生大量且持续的超额监管损失。根本原因在于决策规则的校准偏差，而非预测准确率本身下降。这些发现凸显了固定式AML政策在不断演化的数字资产市场中的脆弱性，并推动以损失为导向的监管评估框架。
原文摘要 · Abstract (English)
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.
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