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Algorithmic Compliance and Regulatory Loss in Digital Assets

Khem Raj Bhatt, Krishna Sharma

TL;DR

It is shown that strong static classification metrics substantially overstate real world regulatory effectiveness, highlighting the fragility of fixed AML enforcement policies in evolving digital asset markets and motivating loss-based evaluation frameworks for regulatory oversight.

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.

Algorithmic Compliance and Regulatory Loss in Digital Assets

TL;DR

It is shown that strong static classification metrics substantially overstate real world regulatory effectiveness, highlighting the fragility of fixed AML enforcement policies in evolving digital asset markets and motivating loss-based evaluation frameworks for regulatory oversight.

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.
Paper Structure (25 sections, 2 equations, 15 figures, 12 tables)

This paper contains 25 sections, 2 equations, 15 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Related Literature
  3. Institutional Setting and Data
  4. Institutional Context: Automated AML in Crypto Markets
  5. Data Source and Sample Construction
  6. Features and Information Structure
  7. Empirical Implementation
  8. Empirical Framework: Regulatory Loss under Deployment
  9. Automated AML as a Regulatory Decision Problem
  10. Regulatory Objective
  11. Enforcement Thresholds, Deployment Design, and Evaluation
  12. Results
  13. Discussion
  14. Conclusion
  15. Supplementary Descriptive Evidence
  16. ...and 10 more sections

Figures (15)

  • Figure 1: Regulatory loss over time ($C_{FN}/C_{FP}=10$).
  • Figure 2: Regulatory loss over time ($C_{FN}/C_{FP}=25$).
  • Figure 3: Optimal threshold $\tau^*$ over time ($C_{FN}/C_{FP}=10$).
  • Figure A1: Illicit prevalence over time in rolling test windows.
  • Figure A2: Distribution of deployed-to-oracle regulatory loss ratios across rolling windows.
  • ...and 10 more figures