Professional money laundering is not a fringe problem in the Western Balkans. It is a structural “feature” of how illicit money enters, moves through, and is embedded in the formal economy. A new report by the Global Initiative Against Transnational Organized Crime (GI-TOC) shows that notaries, lawyers, accountants, auditors, real estate brokers, and other professional intermediaries can be drawn into laundering schemes not simply through ignorance, but also through weak oversight, fragmented regulation, and selective use of professional secrecy.

Beneficial ownership transparency has long been treated as a core anti-money laundering tool, but the latest research synthesis from Open Ownership shows that its impact is broader than that. The report, based on a February 2026 research symposium, makes a clear point: if policymakers want effective reforms, they need to measure what beneficial ownership data is actually used for, what changes it creates, and where it falls short.

Modern anti-money laundering (AML) practice evaluates transactions in a behavioral context – not as isolated events but as parts of a customer’s transaction history. Transaction monitoring systems aggregate activity, update risk classifications, trigger enhanced due diligence (EDD), and escalate scrutiny when concerns persist. That institutional design makes recurrence itself costly: repeated interaction with monitored institutions increases review burdens, the likelihood of deeper investigation, and the risk of relationship termination. When monitoring intensity mechanically rises with accumulated activity, frequency of interaction with financial institutions becomes a strategic margin that illicit actors can manage alongside transaction size.

Concealing who actually owns or controls assets is a core money laundering technique. Shell companies, trusts, nominee arrangements, bearer instruments and other opaque structures let criminals hide the people behind transactions and blend illicit proceeds into legitimate economic activity. Policymakers and international bodies have emphasized beneficial ownership transparency as essential to fighting tax evasion, corruption, sanctions circumvention and organized crime. Yet questions remain about whether the people charged with filing suspicious activity reports (SARs) treat non‑transparent ownership as a top priority when they decide which transactions to report.

Money laundering is more than a criminal offence; it is a structural threat to market integrity, competition and consumer welfare across the European Union. The essence of the problem is that many actors – banks, payment firms, crypto‑asset service providers (CASPs), auditors, supervisors and FIUs – must incur concentrated costs to produce benefits that are diffuse and often cross‑border. From an institutional point of view this is the textbook public‑good dilemma: those who would prefer strong AML controls can be disadvantaged if others free‑ride, and individual firms or national supervisors can rationally under‑invest in prevention and follow‑through because the benefits of their effort are shared widely.

This study provides the first systematic analysis of transnational money laundering flows to and from Germany using suspicious transaction reports the German Financial Intelligence Unit (FIU) forwarded to law enforcement between 2020 and 2024. Only reports judged sufficiently substantiated to warrant referral were included. The dataset covers roughly 2.29 million suspicious transactions with an approximated conservative aggregate value of about €2.55 billion (median-based approximation). That median approach intentionally understates extreme values and therefore gives a conservative lower‑bound estimate for economic impact. The analysis examines both counts and volumes of transactions, their geographic distribution, and statistical relationships between flows and country characteristics – the so‑called push and pull factors that make certain origins and destinations more prominent in illicit financial flows.

Blockchain’s transparency and decentralization enable powerful new financial services, but those same properties create opportunities for arbitrage attacks, liquidity exploits, and complex money‑laundering schemes. A recent peer‑reviewed framework published in the International Journal of Intelligent Systems (2026) proposes a layered architecture that combines dynamic pricing, AI‑driven anomaly detection, and compliance checks to reduce those risks while preserving efficiency. This article summarizes the framework, explains why it matters for financial crime prevention, and highlights practical takeaways for compliance teams, DeFi operators, and investigators.

Trade‑based money laundering (TBML) exploits the complexity of cross‑border commerce to conceal and move illicit value. As trade liberalization, digital trade documentation and global supply chains expand, so do opportunities for criminal networks to exploit mismatches in invoices, shipments and trade finance. Recent bibliometric analysis of the TBML literature (2010–November 2025) reveals that TBML has shifted from an obscure technical concern into an increasingly visible research and policy problem. That trend matters for compliance officers, regulators, investigators and financial crime strategists because TBML bypasses many traditional AML controls and thrives where trade documentation, freight handling and customs oversight are weakest.

Payments vary not only in speed, cost and convenience but in a structural feature that matters enormously for anti-money laundering and countering the financing of terrorism (AML/CFT): whether transactions are validated and recorded through an intermediary that can act as a gatekeeper. Intermediaries such as banks, e-money issuers, payment service providers and hosted wallet custodians can perform customer due diligence (CDD), monitor flows and file suspicious transaction reports. Instruments that do not require such intermediaries – physical cash, self‑hosted crypto wallets and offline variants of retail central bank digital currency (CBDC) – inherently limit those standard points of detection. That simple design difference shapes the incentives of both malicious actors and legitimate users, and it drives regulatory choices and unintended market responses.

Fraud is not just a matter of a lone criminal sending out a few emails or making a few phone calls. The global scamming ecosystem has grown into a highly adaptive, multi-billion-dollar industry that now resembles legitimate business in its structure, specialization, and reach. A recent study published in the International Journal of Law, Crime and Justice describes this development as “evolutionary fraud” – a system in which scams and scam operations continually adapt under pressure from law enforcement, technology, competition, and the search for profit.

Money laundering remains a pervasive threat to financial integrity, enabling crime, corruption, and systemic corruption of public institutions. Recent cross-country research covering 158 countries from 2012 to 2023 shows political stability is consistently associated with lower structural vulnerability to money laundering. Using the Basel AML Index as the outcome measure and the World Bank’s Political Stability and Absence of Violence/Terrorism indicator as the key explanatory variable, the study finds that political stability reduces AML risk even after controlling for democratic quality, legal origin, and macroeconomic factors.

Large language models (LLMs) do not themselves commit violence, but they change the economics and mechanics of persuasion that underpin many fundraising and fraud schemes. By producing high‑volume, culturally tailored narratives, synthetic testimonials and polished outreach materials at low cost, LLMs can reduce barriers that once limited the scale and professionalism of illicit fundraising. The risk is not only that violent or criminal groups will ask an LLM explicitly to write a donation appeal, but that they will hide behind layers of plausible legitimacy – charities, cultural projects, reconstruction campaigns – and use AI to industrialise narratives that convert sympathy into funds. This article summarises recent research and policy findings, compares major provider approaches, reports basic prompt testing results, and offers practical, risk‑based recommendations for industry, financial institutions, regulators and civil society.

Accountants are frequently discussed as procedural technicians – record‑keepers and number crunchers – but the Cullen Commission proceedings and follow‑up interviews show they are also cognitive and institutional actors whose professional scripts materially shape AML outcomes. Analysis of sworn accountant testimony at the Cullen Commission, combined with interviews of accountants, notaries, regulators and legal actors and machine/deep learning sentiment models, reveals that accountants enact distinct evaluative scripts – rooted in training, ethics codes and organizational practice – that both enable and constrain AML performance across real estate, luxury vehicle sales and gaming.

Traditional rule-based anti-money laundering (AML) systems remain the default in many institutions, but they generate overwhelming volumes of false-positive alerts and struggle with complex, multi-step laundering schemes. A recent study on 54,258 SWIFT cross-border transactions from an East African commercial bank presents a practical, high-performing hybrid solution that fuses rule-based heuristics with deep learning embeddings – variational autoencoders (VAEs) for behavioural signals and graph neural networks (GNNs) for relational network structure – and uses an optimised One-Class Support Vector Machine (OCSVM) for semi-supervised anomaly detection. The result is a system that reduces compliance noise, surfaces novel suspicious behaviour, and remains explainable for audit and regulatory review.

Pooled law firm client accounts are central to everyday legal practice: they keep client funds separate from firm funds, enable transactions in conveyancing and commercial work, and protect clients if a firm becomes insolvent. At the same time, the co‑mingling and intermediary role of these accounts create opportunities for misuse. The UK has responded with a complex, multi‑layered regulatory mix that combines professional accounts rules, anti‑money laundering (AML) regulations, statutory criminal offences and sectoral guidance, while both law firms and banks exercise practical oversight. Understanding how these layers interact is essential for assessing effectiveness, managing regulatory burdens, and ensuring proportionality.

Anti‑money laundering for cryptocurrencies remains a pressing problem: blockchains provide transparent ledgers but preserve pseudonymity, enabling laundering techniques that mix rapid bursts of activity with slow, low‑frequency layering. Conventional approaches frequently focus on static topology or simple temporal encodings and therefore miss important multiscale, nonstationary patterns. The paper “Detecting illicit transactions in bitcoin: a wavelet‑temporal graph transformer approach for anti‑money laundering” presents ChronoWave‑GNN, a graph neural architecture designed to jointly model temporal dynamics and frequency characteristics of transaction graphs to improve detection of illicit activity.

Financial institutions and regulators face a growing challenge: increasingly sophisticated money laundering (ML) operations that hide illicit proceeds through multi-stage, multi-identity transaction chains. Traditional rule-based anti-money laundering (AML) systems are brittle, generate high false-positive rates, and require costly manual review. Supervised machine learning helps, but severe class imbalance between illicit and legitimate samples undermines model accuracy.Graph-based community and clique detection methods are promising for revealing coordinated criminal structures, yet classical algorithms face exponential or prohibitive runtime as transaction networks scale. These limitations expose an urgent need for fundamentally new computational approaches that can sift combinatorially large search spaces to reveal dense or pattern-rich regions where fraud concentrates.

Financial institutions face fraud that is more complex, faster and more distributed than ever – from synthetic identity schemes to cross‑border money‑laundering networks. At the same time, regulators insist on strict data minimisation and strong privacy guarantees under frameworks such as GDPR, PSD2 and the EU AI Act. Those two forces push banks and payment providers toward collaboration researchably without sharing raw customer data.Federated learning (FL) is the most practical approach currently available to reconcile these competing demands: institutions jointly improve models while keeping personal transaction records inside their own systems. But the hype around FL risks glossing over important trade‑offs in accuracy, robustness and operational complexity. The study provides timely, empirically supported guidance for fraud teams considering federated deployments.

Institutional fragility and the rise of informal markets in Southeast Europe create fertile ground for financial crime. Recent research covering Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia and Serbia for 2012–2024 uses dynamic panel estimation to map how governance, macroeconomics and anti-money laundering (AML) regimes interact with informal economic activity. The findings point to persistent informality, unexpected effects from AML measures, and clear policy levers in contract enforcement and economic freedom. For practitioners and policymakers focused on financial crime prevention, these results underline where enforcement, inclusion and regulatory design must be better aligned.

Accounting is usually pictured as a neutral technique for recording transactions and reporting performance. In cartel-affected environments in Mexico, however, accounting works less like an impartial ledger and more like tinted glass: it makes certain things legible – revenues, payrolls, property transfers – while obscuring the violence and coercion that sustain those figures. The study, “Accounting for the cartel” (Critical Perspectives on Accounting, 2026), shows that accounting in cartel contexts does far more than enable money laundering. It actively shapes organisational identities, moral horizons, and public narratives by combining framing and routinising into a practice the author calls systematisation.

Albania has taken important legal steps to align its anti-money laundering and countering the financing of terrorism (AML/CTF) framework with European Union requirements. Since the first national AML law in 2000, successive legislative reforms culminated in Law 9917/2008 and multiple amendments that largely mirror EU AML Directives and FATF standards. The statutory architecture now covers money laundering, terrorist financing, beneficial ownership transparency, risk-based customer due diligence, and a Financial Intelligence Unit (FIU). Yet the story of harmonisation is only half the picture: enforcement and practical implementation remain significant weaknesses that blunt legal gains and leave Albania exposed to illicit finance.

Money laundering is not an optional add‑on for organized criminal enterprises; it is the operational mechanism that converts criminal proceeds into usable capital. Recent research frames laundering choices as strategic decisions by criminal organizations (COs) that balance costs, risks, and scale. Two broad laundering pathways emerge from their model: smurfing – breaking illicit sums into many small cash transactions or purchases of semi‑durable and durable goods – and asset laundering – using the financial system through transaction layering or portfolio diversification. Each channel has different cost/risk trade‑offs, different economic effects, and crucially, different policy implications. Understanding how criminals substitute between these methods is essential to design effective anti‑money laundering (AML) strategies that do more than shift the problem between sectors.

A behaviourist approach reframes customer due diligence by focusing on observable, measurable patterns rather than solely on static identity data. Financial institutions already collect transactional records, device signals and communication logs; combining these with psychological and behavioural insights turns disparate traces into a dynamic profile that helps spot potential money laundering and terrorist financing (ML/TF) risks earlier. Behavioural indicators can reveal stimulus–response chains, reinforced patterns and deviations from a customer’s normal profile that may signal preparatory or active involvement in illicit activity.

Sanctions as a coercive tool have long relied on the U.S. dollar as their most reliable chokepoint. The argument is simple: most international transactions – especially high-value commodity and arms payments – clear in U.S. dollars, which pass through U.S.-regulated settlement and correspondent banking systems and become visible to U.S. enforcement. That visibility enables intervention, asset freezes, and criminal prosecutions against banks and intermediaries that enable sanctioned trade. But recent experience challenges this core assumption. Emerging practices – bilateral currency swaps, lender-controlled commodity revenue accounts, barter and in-kind exchanges, expanding non-dollar development finance, and the growth of cryptocurrencies and opaque crypto off-ramps – have hollowed out the dollar chokehold and created practical routes for embargoed states and proliferators to acquire weapons, dual-use technologies, and revenue despite intense sanctions regimes.

Anti‑money laundering programs face a structural problem: criminals deliberately fragment transactions across banks and product lines so that no single institution sees a suspicious pattern. Traditional responses — centralized data lakes or ad hoc manual sharing — either create unacceptable privacy and security risks or remain ineffective because they cannot see the cross‑institution picture. Federated data governance rethinks that trade‑off. It lets banks collectively improve detection models and surface cross‑institution patterns while keeping underlying customer data local and protected. The result is improved detection power without wholesale exposure of sensitive records.

Introduction: why realistic synthetic AML data matters

Financial crime researchers and practitioners face a persistent obstacle: the scarcity of realistic, shareable transaction datasets. Real bank logs are highly sensitive and often impossible to publish; when available they risk re-identification. That shortage limits reproducible research, fair model comparison and the development of systems that actually work in regulated environments. AMLNet responds to that need by offering a knowledge-driven multi-agent system that both generates large-scale, regulation-aware synthetic transaction streams and runs an ensemble detection pipeline against them. The work aims to strike a pragmatic balance: realistic behavioral and network structure, explicit regulatory alignment, and detection methods that generalize beyond the generator itself.

Introduction

Older adults are increasingly targeted by sophisticated financial fraud schemes such as investment scams, identity theft, and imposter scams. Cognitive decline, social isolation, and trust-based relationships make them particularly vulnerable to exploitation. Despite the severity of these risks, older victims often underreport incidents, leading to significant financial losses. This calls for effective educational strategies that address both the cognitive and emotional needs of older adults to improve their ability to detect and resist fraud.

Introduction

Financial networks constitute complex systems that enable global monetary transactions and investments, including banks, payment processors, credit unions, and cryptocurrency platforms. These entities are vulnerable to multiple forms of fraud such as identity theft, transaction fraud, payment fraud, and securities trading fraud. The rapid digitization and scale of financial ecosystems have increased the urgency for sophisticated fraud detection mechanisms. Artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), and hybrid models, has emerged as a critical technology for addressing this challenge by analyzing vast datasets, uncovering hidden patterns, and adapting dynamically to evolving fraudulent tactics.

Understanding Money Laundering Through Linguistic Analysis

Money laundering is a critical issue that facilitates serious crimes, enables criminal enterprises to expand, and destabilizes financial systems and economies worldwide. Despite extensive research into anti-money laundering (AML) strategies and legislation, there remains limited focus on the language and behaviors of individuals directly involved in money laundering activities. Dark-web forums provide a unique environment where illicit knowledge exchange occurs anonymously, serving as hotspots for criminal learning and collaboration. This article explores the linguistic mechanisms used in one such dark-web forum, known as Laundromat, dedicated to discussions on money laundering.

Introduction

Money laundering has become a critical concern at both national and international levels, threatening financial institutions and economies worldwide. It involves disguising the origins of illegally obtained funds, typically through methods such as placement, layering, and integration. With the rise of digital banking, cryptocurrencies, and complex cross-border transactions, the detection and prevention of money laundering have become increasingly challenging. This study provides a detailed bibliometric analysis of global research trends on money laundering and associated scams, drawing on 262 publications up to early 2025 indexed in the Scopus database.

Banks in developed countries face a significant challenge today: they must simultaneously prevent financial crimes such as money laundering (ML) and terrorist financing (TF) while ensuring broad financial inclusion (FI). These goals often conflict, as stringent anti-money laundering and countering the financing of terrorism (AML/CFT) regulations can lead to the exclusion of certain customers from the formal financial system. This article explores how banks can manage this conflict effectively, based on insights from a recent qualitative study conducted in Finland, a highly developed country with robust AML/CFT frameworks.

Introduction: The Need for Uniform Beneficial Ownership Registers

The fight against money laundering and terrorist financing within the European Union has long been hampered by inconsistencies in how Member States define and maintain Beneficial Ownership Registers (BORs). Initiated under Directive (EU) 2015/849 (the Fourth Anti-Money Laundering Directive, 4AMLD), the establishment of National Central Registers (NCRs) aimed to increase transparency in business transactions and prevent the concealment of illicit financial flows. However, the uneven interpretation and application of these rules across Member States have weakened their effectiveness, calling for urgent reforms to harmonise the regulatory framework.

Terrorist groups have not abandoned traditional finance, but they are testing how virtual assets can be integrated into existing funding operations. This integration is not uniform or widespread, yet it raises new investigative challenges because intermediaries that convert virtual assets to fiat – or that provide hybrid services – can simplify transfers and in some cases obfuscate the trail. This article summarises recent findings on intermediary use in terrorism financing, illustrates key operational patterns from documented cases, and highlights the data and investigative angles authorities should prioritise.

The European Commission’s 2021 AML/CFT package aims to rewrite how the EU prevents money laundering and terrorist financing. Central to that package is the proposed Anti‑Money Laundering Authority (AMLA), a Union body charged with strengthening supervision, improving consistency across member states, and supporting cross‑border operational cooperation. For financial crime practitioners, compliance teams and investigators the two most consequential shifts are the greater ambition for joint FIU activity across borders and the decision to assign long‑term hosting, management and development of FIU.net to a Union body. Both moves promise better cross‑border intelligence flows – but they also push data protection to the center of the debate.

The word “terrorism” is everywhere in the news today, but have you ever stopped to think about where this term comes from and what it really means?

Introduction

Organized crime, particularly Mafia infiltration, has long distorted economic and financial systems in various regions, especially in Italy. The presence of Mafia-connected firms disrupts fair competition, impairs innovation, and creates an uneven market landscape. This article explores recent research that examines how targeted anti-Mafia police operations influence banks’ lending behaviors and the broader economic environment.

The rapid rise of artificial intelligence (AI) is reshaping many areas of our lives, not least the complex and critical field of financial security. A recent study explores how AI is changing the fight against money laundering across Europe. The research provides compelling evidence on whether AI primarily supports anti-money laundering (AML) efforts or if it inadvertently aids organised criminals by empowering their schemes.

The global video game industry reached $187.7 billion in revenue in 2024 and draws roughly 3.4 billion players worldwide. That scale and diversity create a complex environment where illicit actors can hide within legitimate activity. While regulators and researchers have devoted considerable attention to content moderation and countering recruitment and propaganda in games, the financial crime risks in this ecosystem remain comparatively overlooked. This article synthesises recent research and operational findings to explain how online gaming and gaming-adjacent platforms can be misused for money laundering and fundraising by terrorist and extremist actors, where current regulatory gaps lie, and what practical first steps policymakers and practitioners should take.

Introduction

Financial crime, especially fraud and money laundering, has become increasingly complex with the rise of digital transactions and international financial networks. Traditional investigative methods struggle to keep up with the volume and sophistication of illicit activities. The WeirdFlows pipeline, introduced by researchers from Italian universities and the Anti Financial Crime Digital Hub, presents a novel method that leverages network analysis to detect suspicious transaction flows without relying on predefined patterns or labeled training data. This approach focuses on interpretability and scalability, essential for aiding Anti-Financial Crime (AFC) analysts in formal investigations.

Universities have become prime targets for foreign intelligence and state-directed technology acquisition. Their openness, interdisciplinary collaborations, and global student and staff mobility make them fertile ground for actors seeking novel technologies, dual‑use capabilities, or intellectual property. Cases of alleged theft, recruitment of insiders, and covert influence show the scale of the threat: compromised projects can accelerate foreign military or commercial capability, erode public trust, and put researchers at legal and personal risk. Governments have begun to respond with export controls, foreign activity reporting, and tailored research‑security policies, yet implementation across higher education remains inconsistent. Many institutions are under‑resourced and worry that heavy‑handed controls will damage academic freedom, collaboration, and funding flows. The central policy challenge is therefore to reduce the security risk without suffocating the knowledge ecosystem that universities exist to sustain.

Understanding the Challenge of Cybercrime and Money Mules

Cybercrime, particularly financial-economic cybercrime, is a rapidly growing threat globally, with millions of victims suffering significant financial losses. In the Netherlands alone, over two million people fell victim to cybercrime in recent years, with damages running into hundreds of millions of euros. Among these crimes, online fraud dominates, and a key component enabling these offenses is the use of money mules — individuals who allow their bank accounts to be used to launder stolen funds.

Generative AI and large language models are changing how information is created, consumed, and acted upon. For financial crime professionals — compliance officers, fraud investigators, AML analysts, bank risk managers, and policy - makers—this change is not abstract. It affects how illicit finance is detected, how terrorist networks raise and move funds, and how regulators and firms balance privacy, accuracy, and operational speed. Drawing on Christopher Wall’s analysis of AI’s role in counterterrorism, this article reframes those insights specifically for the financial crime community and highlights strategic choices that will determine whether AI reduces harm or amplifies risk.

Understanding the Challenge of Illicit Financial Flows

Illicit financial flows (IFFs) pose a significant threat to economies worldwide, undermining governance, distorting markets, and draining resources from development. Despite decades of global efforts against money laundering, including the establishment of the Financial Action Task Force (FATF) in 1989, money laundering continues unabated. One major challenge is the lack of reliable data and effective methodologies to measure and assess these risks accurately. Many National Risk Assessments (NRAs) rely heavily on expert opinions, which can be subjective and outdated, limiting their usefulness in addressing the dynamic nature of illicit flows.

Introduction: The Double-Edged Sword of Cryptocurrencies

Cryptocurrencies continue to attract millions due to their accessibility, decentralization, and investment opportunities. However, these same characteristics have made them a prime target for financial criminals. In 2024, cryptocurrency platform hacks resulted in stolen funds amounting to $2.2 billion, a 21% increase from the previous year. Fraud, scams, organized crime, and money laundering are increasingly connected to Crypto Asset Service Providers (CASPs), who operate exchanges, wallets, and other services. This growing association has drawn intense regulatory scrutiny globally. CASPs face significant hurdles as they try to keep pace with fast-evolving regulations while adopting necessary technological and operational changes.

The Impact of Digitalization on Financial Crime

Digital transformation has reshaped the European financial industry, bringing both significant benefits and new risks. he convenience of online banking, mobile payment services, and digital financial products has enhanced customer experience and operational efficiency for banks. However, these advancements have also opened new avenues for criminals to launder money digitally, posing a serious threat to society, democracy, and the rule of law.

Understanding Fear of Crime Beyond Crime Rates

Fear of crime has been a topic of research since the 1960s, recognized as a complex social phenomenon that affects both individual well-being and societal cohesion. Traditionally, fear of crime was linked directly to crime rates or personal victimization experiences. However, recent studies suggest that fear of crime often expresses broader social insecurities rather than reflecting actual crime risks alone. These generalized insecurities include anxieties about economic stability, social trust, and welfare state effectiveness.

Since the Paris and Brussels attacks (2015–2016) and a string of high‑profile financial scandals, EU institutions concluded that the patchwork of national rules, uneven supervision, and overloaded financial intelligence units (FIUs) were leaving systemic gaps exploitable by criminals and terrorist networks. The European Commission’s assessments and impact analyses framed the problem bluntly: legal divergence, weak oversight of obliged entities (e.g., banks and an expanding list of non‑financial gatekeepers), inconsistent FIU cooperation, and an explosion of low‑value but high‑volume suspicious transaction reports (STRs) had turned prevention into a low‑value, high‑cost exercise.

Introduction to AMLA and the EU’s AML/CFT Framework

The European Union (EU) has taken a significant step forward in fighting financial crime with the establishment of the Anti-Money Laundering Authority (AMLA). This new entity represents a major milestone in the institutional framework aimed at combating money laundering and terrorist financing (AML/CFT). The creation of AMLA addresses longstanding challenges in the fragmented AML supervisory landscape within the EU, particularly those exposed by high-profile cross-border money laundering scandals involving EU banks.

Money laundering remains one of the most significant financial crimes worldwide. Criminals generate illicit proceeds that must be concealed and integrated into the legal economy to maintain their value and usability. According to the United Nations Office on Drugs and Crime, money laundering accounts for approximately 2 to 5 percent of global GDP — equating to $800 billion to $2 trillion annually. Combating this crime requires not only depriving criminals of their financial gains but also preventing the concealment and reintegration of illicit funds into legitimate financial systems. This is the core purpose of anti-money laundering (AML) frameworks, which have evolved over decades into complex regimes combining repression and prevention.

Understanding the Profiles and Tactics of Online Romance Fraudsters

Online romance fraud continues to be a significant form of cybercrime, exploiting emotional vulnerabilities to commit financial crimes. A detailed analysis of fifty convicted Nigerian online romance scammers by the Economic and Financial Crimes Commission (EFCC) reveals crucial insights into the offenders’ profiles, operational methods, and the demographics of their victims.

Introduction to the EPPO and Its Current Role

The establishment of the European Public Prosecutor’s Office (EPPO) by Council Regulation (EU) 2017/1939 marked a significant milestone in European criminal justice. This supranational body introduced a vertical cooperation model, diverging from prior horizontal collaborations between Member States through agencies like Eurojust. The EPPO’s primary mandate is to investigate, prosecute, and bring offenders to justice for crimes affecting the financial interests of the European Union (EU), operating in coordination with national courts.

The Growing Importance of Combating Money Laundering in Bulgaria

Money laundering remains a significant threat to financial stability and legality worldwide, and Bulgaria is no exception. Due to its strategic position as an EU member and a regional player, Bulgaria faces unique challenges from cross-border crimes involving illicit financial flows. The recent study by Kostova and Zhelev (2024) highlights how the integration of modern technologies such as artificial intelligence (AI) and machine learning (ML) can dramatically improve the detection and prevention of money laundering in Bulgaria. These innovations allow supervisory authorities to analyze vast amounts of transaction data quickly and accurately, thus enhancing the effectiveness of audits and tax inspections.

The creation of the Authority for Anti-Money Laundering and Countering the Financing of Terrorism (AMLA) is the most consequential institutional change in the EU’s AML/CFT architecture in decades. The reform package adopted in 2024 replaces the fragmented patchwork of minimum harmonisation directives with a directly applicable AML Regulation, clarifies supervisory roles, and places an EU-level enforcement actor at the core of cross-border AML oversight. For practitioners, compliance officers, and enforcement professionals, understanding AMLA’s remit, tools and limits is essential: AMLA introduces direct decision‑making and sanctioning powers over a defined universe of high‑risk, cross‑border financial institutions, while leaving most day‑to‑day supervision with national authorities under AMLA’s oversight.

Introduction

Cyberfraud has become a significant criminal activity worldwide, impacting individuals and economies alike. West African countries, including Côte d’Ivoire, have become notable hotspots for cybercriminal activity, especially scams such as romance fraud and sextortion. Understanding how these cyberfraudsters manage and launder their illicit earnings is crucial for developing effective countermeasures. A recent field study conducted by Swiss and Ivorian research teams sheds light on the strategies Ivorian cybercriminals employ to handle their criminal proceeds and the challenges faced by law enforcement in curbing these activities.

Introduction

Financial sanctions have long been a key tool used by regulatory authorities to combat illegal activities by targeting the economic incentives behind criminal behavior. Traditional sanctions focus on freezing or blocking assets such as bank accounts, real estate, and vessels owned by sanctioned individuals or entities. However, the rise of cryptocurrencies has introduced new challenges to enforcement efforts. The decentralized and often pseudonymous nature of crypto assets provides criminals with novel ways to evade sanctions and continue illicit operations. This article examines the effectiveness of sanctions applied in the crypto ecosystem, particularly focusing on Bitcoin, and evaluates whether these measures successfully deter sanctioned entities from using cryptocurrencies for illegal activities.

Understanding Economic Crime and Its Challenges

Economic crime, which includes illegal financial activities such as money laundering, corruption, tax fraud, and financial abuse, poses a significant threat to modern societies and economies. As economies become more complex and globalized, the scale and sophistication of these crimes have increased drastically. White-collar criminals — often individuals in high social or professional positions — exploit their privileges to carry out illegal acts concealed within seemingly legitimate business operations. These crimes not only undermine legal systems but also erode public trust in institutions and economic stability.

Introduction: Addressing AML Compliance Challenges

Money laundering represents a significant portion of the global economy, estimated at 2-5% of the world’s GDP, and poses a critical risk by funding criminal networks and terrorism. Despite the extensive efforts to curb these illicit activities, only a small fraction of laundered funds are captured, particularly in Europe, where it is estimated that about 1.1% of such funds are detected. The European Union’s Fifth Anti-Money Laundering Directive (5AMLD), enacted in 2018, expands regulatory requirements by broadening transparency measures, including mandates for publicly accessible Ultimate Beneficial Owner (UBO) registries and extending due diligence obligations to various entities, including small businesses.

The October 7, 2023 attacks and the subsequent Iron Swords campaign exposed a worrying dynamic: modern terrorist groups are using online crowdfunding and cryptocurrencies to raise, move and hide funds. These digital venues let organizers solicit global support quickly, often under humanitarian pretexts, and route donations in ways that frustrate traditional financial controls. Far from marginal, documented cases and seizure operations show these channels can yield substantial sums and help sustain operations while reducing vulnerability to sanctions and bank oversight.

Why lawyers matter in money laundering is not just a rhetorical question. They sit at the intersection of legal structures, corporate vehicles, financial flows and reputational defence. Michael Levi’s review of case studies and datasets from the Global North and South shows that lawyers sometimes play decisive roles in the concealment, movement and legitimisation of contested wealth. Yet the picture is far from straightforward: much lawyer involvement is routine, lawful and legitimate; proving criminal intent or culpable knowledge is hard; and regulatory responses are uneven and contested.

Anti‑money laundering (AML) regimes have become a near‑universal feature of modern governance. Worldwide, governments have adopted complex laws based on Financial Action Task Force (FATF) standards and compelled banks, lawyers, accountants and other “gatekeepers” to collect information, monitor flows and report suspicious activity. Yet three decades into this global experiment, the evidence suggests the policy’s core design is delivering negligible impact against the criminal proceeds it set out to disrupt. This article summarizes the problem, quantifies the scale of failure as the evidence allows, explains why the failure persists, and outlines what must change to make AML meaningful rather than mostly performative.