# Algorithmic Compliance and Regulatory Loss in Digital Assets

arXiv: https://arxiv.org/abs/2603.04328 · HTML: https://mlsanity.com/paper/2603.04328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)
- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Bitcoin Research with a Transaction Graph Dataset](https://mlsanity.com/paper/2411.10325/) (2024)
- [Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networks](https://mlsanity.com/paper/2509.23101/) (2025)
- [Towards a Public Key Management Framework for Virtual Assets and Virtual Asset Service Providers](https://mlsanity.com/paper/1909.08607/) (2019)

## Abstract (arXiv)

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.