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Algorithmic Compliance and Regulatory Loss in Digital Assets

Khem Raj Bhatt, Krishna Sharma

arXiv 4 Mar 2026 · Machine Learning

arXiv:2603.04328 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

32
references
65
in-text mentions
32
distinct cited
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5,965
main-text words

appendix boundary found by appendix_command · 68% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Elkan, Charles (2001) The foundations of cost-sensitive learning1.00054100%
2Danielsson, Jon and James, Kevin R and Valenzuela, Marcela and Zer,… (2016) Model risk of risk models0.92844100%
3Anagnostopoulos, Ioannis (2018) Fintech and regtech: Impact on regulators and banks0.84333100%
4Baek, Chung and Elbeck, Matt (2015) Bitcoins as an investment or speculative vehicle? A first look0.84333100%
5Baur, Dirk G and Hong, KiHoon and Lee, Adrian D (2018) Bitcoin: Medium of exchange or speculative assets?0.84333100%
6Biais, Bruno and Bisiere, Christophe and Bouvard, Matthieu and Casam… (2023) Equilibrium bitcoin pricing0.84333100%
7Gama, João and Zliobaitė, Indrė and Bifet, Albert and Pechenizkiy, M… (2014) A survey on concept drift adaptation0.84333100%
8Oztas, Berkan and Cetinkaya, Deniz and Adedoyin, Festus and Budka, M… (2024) Transaction monitoring in anti-money laundering: A qualitative analysis and points of view from industry0.84333100%
9Widmer, Gerhard and Kubat, Miroslav (1996) Learning in the presence of concept drift and hidden contexts0.84333100%
10Bailey, David and Borwein, Jonathan and Lopez de Prado, Marcos and Z… (2017) The probability of backtest overfitting0.64422100%

Showing the top 10 of 32 scored citations.