[2603.04328] Algorithmic Compliance and Regulatory Loss in Digital Assets

Computer Science > Machine Learning arXiv:2603.04328 (cs) [Submitted on 4 Mar 2026] Title:Algorithmic Compliance and Regulatory Loss in Digital Assets Authors:Khem Raj Bhatt, Krishna Sharma View a PDF of the paper titled Algorithmic Compliance and Regulatory Loss in Digital Assets, by Khem Raj Bhatt and Krishna Sharma View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG); Econometrics (econ.EM) Cite as: arXiv:2603.04328 [cs.LG]   (or arXiv:2603.04328v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.04328 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Krishna Sharma Dr. [view email] [v1] ...