AML/CFT/CPF endeavors in the crypto space - from blockchain analytics to machine learning - working paper

Aug 4, 2026

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Financial applications of distributed ledger technologies (DLTs) raise regulatory concerns in the crypto sphere. While pseudonymity can protect privacy and support data protection, weak identifiability undermines investigation and enforcement for AML/CFT/CPF objectives. Forensic methods can still trace value transfers across blockchain ecosystems and supply intelligence for regulated entities. The working paper focuses on anomaly detection, the role of machine learning, and how disintermediation reshapes compliance workflows.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Pseudonymity, identifiability, and compliance risks

AML/CFT/CPF overview and regulation context

Pseudonymity and AML/CFT/CPF

Distributed ledgers and blockchain structure

Ledger types and identity management

AML/CFT/CPF endeavors in the crypto space: from blockchain analytics to machine learning ⋆ Nadia Pocher1,2,∗ , Mirko Zichichi2,3 and Stefano Ferretti4,5 1 Institute of Law and Technology, Universitat Autònoma de Barcelona, Bellaterra, Spain 2 Department of Legal Studies, University of Bologna, Bologna, Italy 3 Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain 4 Dipartimento di Scienze Pure e Applicate, University of Urbino Carlo Bo, Urbino, Italy 5 Department of Computer Science and Engineering, University of Bologna, Bologna, Italy Abstract Financial applications of distributed ledger technologies (DLTs) generate regulatory concerns. In the crypto sphere, pseudonymity may safeguard privacy and data protection, but lack of identifiability cripples investigation and enforcement. This challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT/CPF) . Nonetheless, forensic techniques trace transfers across blockchain ecosystems and provide intelligence to regulated entities. This working paper addresses anomaly detection in the crypto space, the role of machine learning, and the impact of disintermediation. Keywords AML/CFT/CPF, blockchain, cryptocurrency, machine learning, forensics 1. Introduction Ever since the launch of Bitcoin [1], the opportunities offered by distributed ledger technologies (DLTs) have driven a fierce excitement for technology [2] . Leveraging distributed systems and cryptography, Nakamoto’s work opened the way to recording and managing information trustworthily without intermediaries. Although the ‘blockchain hype’ goes beyond the financial domain, its first large-scale implementation and leading regulatory debates are financial in nature. The perception of this space as inherently anonymous triggers substantial concerns, and some of its fundamentals clash with accountability. From the early 2010s to the present day, scandals and scams ignite fears of illicit exploitation (e.g., Silk Road, Tornado Cash [3, 4]) . A prime example concerns the fight against money laundering and the financing of terrorism and proliferation (AML/CFT/CPF) . The field is overseen by the Financial Action Task Force (FATF), whose risk indicators guide the understanding of crypto risks [5, 6] . The EU has harmonised its rules since 1991, and a major reform is about to establish a regulation-based single rulebook. Currently, the consolidated version of the AML Directive (AMLD) is Directive Artificial Intelligence Governance Ethics and Law (AIGEL), November 02 - December 19, 2022, Barcelona, Spain ⋆ The contribution of Nadia Pocher and Mirko Zichichi received funding from the EU H2020 R&D programme under the MSCA ITN EJD grant agreement No 814177 Law Science and Technology Joint Doctorate-RIoE. ∗Corresponding author. [Envelope-Open](Envelope-Open nadia.pocher@uab.cat)[ nadia.pocher@uab.cat](Envelope-Open nadia.pocher@uab.cat) (N. Pocher); [mirko.zichichi@upm.es](mirko.zichichi@upm.es) (M. Zichichi); [stefano.ferretti@uniurb.it](stefano.ferretti@uniurb.it) (S. Ferretti) Orcid 0000-0003-1472-2963 (N. Pocher); 0000-0002-4159-4269 (M. Zichichi); 0000-0002-1911-4708 (S. Ferretti) © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) . CWPEURorkroceshopedings http://ceurISSN 1613-ws-0073.org CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org)) (EU) 2015/849 as amended by Directive (EU) 2018/843 . The regime relies on‘regulated entities’, on which duties are imposed to prevent misuses ofthe financial systems and draw the attention of authorities when suspicions arise. They comprise financial entities – e.g., banks, but also cryptoasset service providers (CASPs)– but also non financial businesses and professions.1 In AML/CFT/CPF compliance, operations are typically screened in a partially automated way by software solutions that for crypto transactions are

FAQs

Why does pseudonymity in crypto hinder AML/CFT/CPF enforcement?

Pseudonymity can safeguard privacy, but limited identifiability reduces the effectiveness of investigations and enforcement actions.

How do blockchain forensics support AML/CFT/CPF compliance?

Forensic techniques trace transfers across blockchain ecosystems and provide intelligence to regulated entities.

What is the paper’s focus regarding anomaly detection?

It examines anomaly detection in the crypto space, emphasizing the role of machine learning and the impact of disintermediation.

Aug 4, 2026

Upload by Patrick