Enforcement Network Research Articles

The role and importance of financial intelligence in identifying and tracing criminal assets

The paper examines the basic principles of improving efficiency and providing law enforcement officers, as well as authorized bodies, in whose management the seized property is transferred, with instructions on the legal grounds, procedural and tactical features of detection, search and seizure of property. It was determined that financial intelligence is a complex system aimed at detecting operations related to the legalization of proceeds obtained through crime. This system can act as a component of the pre-trial investigation in criminal proceedings related to the research. Asset search activities should include elements of economic and monetary intelligence. It has been established that in Ukraine, the State Financial Monitoring Service, subordinate to the Ministry of Finance of Ukraine, is entrusted with the functions of financial intelligence, which implements the state policy in the field of prevention and countermeasures against the legalization (laundering) of proceeds obtained through crime, the financing of terrorism, and the proliferation of weapons of mass destruction. It is argued that financial intelligence is not a function of the National Agency of Ukraine for detection, search and management of assets obtained from corruption and other crimes. In turn, the DSFM collects, processes and analyzes information on financial transactions subject to mandatory financial monitoring and other transactions related to money laundering. It was investigated that the bodies of foreign countries are similar: Financial Crimes Enforcement Network (FinCEN) - the US financial intelligence agency; Financial Transactions and Reports Analysis Center (FINTRAC), Canada's financial intelligence agency; Zentralstelle für Verdachtsanzeigen – German financial intelligence agency; TRACFIN – financial intelligence unit of the French Republic; The Inland Revenue Service NCIS/ECU is the UK's financial intelligence agency. Having analyzed the relevant legislation of ARMA and the State Financial Monitoring Service, we come to the conclusion that the terms "asset discovery" and "asset search" are explicitly defined at the legislative level only for ARMA. The profile legislation of the State Financial Monitoring Service does not reflect or explain the essence of these terms, however, in the course of implementing measures to prevent and counter the legalization (laundering) of proceeds obtained through crime, the State Financial Monitoring Service conducts financial investigations with the aim of finding information about the assets of questionable origin.

Navigating the Complexity of Money Laundering: Anti–money Laundering Advancements with AI/ML Insights

Abstract This study explores the fusion of artificial intelligence (AI) and machine learning (ML) methods within anti–money laundering (AML) frameworks using data from the US Treasury’s Financial Crimes Enforcement Network (FinCEN). ML and deep learning (DL) algorithms—such as random forest classifier, elastic net regressor, least absolute shrinkage and selection operator (LASSO) regression, gradient boosting regressor, linear regression, multilayer perceptron (MLP) classifier, convolutional neural network (CNN), random forest regressor, and K-nearest neighbor (KNN)—were used to forecast variables such as state, year, and transaction types (credit card and debit card). Hyperparameter tuning through grid search and randomized search was used to optimize model performance. The results demonstrated the efficacy of AI/ML algorithms in predicting temporal, spatial, and industry-specific money-laundering patterns. The random forest classifier achieved 99.99% average accuracy in state prediction, while the gradient boosting regressor and random forest classifier excelled in predicting year and state simultaneously, and credit card transactions, respectively. MLP and CNN showed promise in the context of debit card transactions. The gradient boosting regressor performed competitively with low mean squared error (MSE) (2.9) and the highest R-squared (R 2) value of 0.24, showcasing its pattern-capturing proficiency. Logistic regression and random forest classifier performed well in predicting credit card transactions, with area under the receiver operating characteristic curve (ROC_AUC) scores of 0.55 and 0.53, respectively. For debit card prediction, MLP achieved a precision of 0.55 and recall of 0.42, while CNN showed a precision of 0.6 and recall of 0.54, highlighting their effectiveness. The study recommends interpretability, hyperparameter optimization, specialized models, ensemble methods, data augmentation, and real-time monitoring for improved adaptability to evolving financial crime patterns. Future improvements could include exploring the integration of blockchain technology in AML.

Integrating data, knowledge, and expertise for policy advice: understanding the practices of Dutch organized crime control professionals

Abstract Current research on data in policy has primarily focused on street-level bureaucrats, neglecting the changes in the work of policy advisors. This research fills this gap by presenting an explorative theoretical understanding of the integration of data, local knowledge and professional expertise in the work of policy advisors. The theoretical perspective we develop builds upon Vickers’s (1995, The Art of Judgment: A Study of Policy Making, Centenary Edition, SAGE) judgments in policymaking. Empirically, we present a case study of a Dutch law enforcement network for preventing and reducing organized crime. Based on interviews, observations, and documents collected in a 13-month ethnographic fieldwork period, we study how policy advisors within this network make their judgments. In contrast with the idea of data as a rationalizing force, our study reveals that how data sources are selected and analyzed for judgments is very much shaped by the existing local and expert knowledge of policy advisors. The weight given to data is highly situational: we found that policy advisors welcome data in scoping the policy issue, but for judgments more closely connected to actual policy interventions, data are given limited value.

Transnational Affective Circuitry: Public Information Campaigns, Affective Governmentality, and Border Enforcement

Geographers have been central to identifying and exploring the shifting spatialities of border enforcement and how different enforcement strategies alter the geography of state sovereignty. Migration-related public information campaigns (PICs) are one strategy that has received increasing attention from geographers and social scientists more broadly in recent years. Although existing research examines the sites and spaces where PICs are distributed, as well as the affective content of their messaging, little research has examined the development of campaigns and the transnational connections that enable their deployment. This article draws on work in the fields of carceral circuitry and transnational enforcement networks to expand our understanding of affective governmentality as a transnational strategy of border governance. Based on data collected as part of a large-scale comparative study of the use of PICs by the U.S. and Australian governments, we argue that this form of affective governmentality relies on transnational circuits through which people, money, and knowledge move to enable the development and circulation of affective messaging. In doing so, we develop the concept of transnational affective circuitry to refer to the often contingent, temporary relations and connections that enable PICs to operate as a form of transnational affective governmentality aimed at hindering unauthorized migration. Our analysis illustrates the transnational connections that enable increasingly expansive and creative forms of border enforcement to emerge while also expanding the scope of examinations of affective governmentality to attend to the relations that undergird and enable this form of transnational governance.

Does decentralised finance equal deregulated finance?

DeFi is a fast-growing sector of the blockchain ecosystem. In this paper, readers are introduced to key concepts in DeFi, including use cases for the technology, differences between permissionless and permissioned DeFi, and the respective advantages of participating in DeFi vs. CeFi platforms. Unlike CeFi, in which a central platform or party acts as an intermediary to facilitate crypto asset transactions, DeFi protocols and platforms rely on self-executing smart contracts to perform their functions and do not custody their users' assets. Accordingly, there are open questions as to how DeFi can be regulated. Indeed, some declare that DeFi should not be regulated. This paper analyses the key arguments in favour of and against regulation of DeFi in the context of the recent crypto asset market instability, including a breakdown of various theories of liability under which regulators seek to identify bad actors and penalise harmful activity. It then synthesises recent statements and enforcement activity by US regulators, including the Commodity Futures Trading Commission, Securities and Exchange Commission, Department of Justice, Financial Crimes Enforcement Network and legislative activity, including President Biden's recent executive order, and the Lummis–Gillibrand Responsive Financial Innovation Act. To date, US lawmakers and regulators have viewed the label ‘decentralised’ with scepticism, applying a functional analysis to identify participants exerting various degrees of control over DeFi protocols in order to enforce regulations.

A Knowledge Enforcement Network-Based Approach for Classifying a Photographer’s Images

Classification of photos captured by different photographers is an important and challenging problem in knowledge-based and image processing. Monitoring and authenticating images uploaded on social media are essential, and verifying the source is one key piece of evidence. We present a novel framework for classifying photos of different photographers based on the combination of local features and deep learning models. The proposed work uses focused and defocused information in the input images to extract contextual information. The model estimates the weighted gradient and calculates entropy to strengthen context features. The focused and defocused information is fused to estimate cross-covariance and define a linear relationship between them. This relationship results in a feature matrix fed to Knowledge Enforcement Network (KEN) for obtaining representative features. Due to the strong discriminative ability of deep learning models, we employ the lightweight and accurate MobileNetV2. The output of KEN and MobileNetV2 is sent to a classifier for photographer classification. Experimental results of the proposed model on our dataset of 46 photographer classes (46234 images) and publicly available datasets of 41 photographer classes (218303 images) show that the method outperforms the existing techniques by 5%–10% on average. The dataset created for the experimental purpose will be made available upon publication.

Effect of Weight in Motion Detection System on Road Enforcement Network of Hungary

After two years of legislative preparation, the introduction of National Dynamic Axle Weight Measurement System was done in 2018. More than 100 control points were deployed in Hungary. This paper aims to present a brief summary of the technological background of a large-scale integration of High Speed WIM systems, and their effect on road usage. Detailed statistical analyses of preliminary results were conducted. The introduction of vehicle in motion dynamic mass measurement system in Hungary had not caused significant changes in road usage in the investigated period on the investigated road segment. The ratio between nationality (domestic or foreign) of heavy road vehicles had not changed due to introduction of vehicle in motion dynamic mass measurement system.

Неформальная структура элитного пространства России (Опыт сетевого анализа)

This paper aims to examine the elite structure in Russia. Given the widely recognized dominance of personal relations over formal institutions in the political and economic spheres in Russia, to explore this question, the authors turn to the concept of the informal network and Social Network Analysis (SNA). Modelling an informal network requires operationalization of two elements: nodes and edges. The nodes are represented by the individuals who occupied top 50 positions in the “100 Most Influential Politicians of Russia” ranking in March-April, 2020. In contrast to the majority of the researchers who employ network approach to study elites, when deciding on edges (links) of the network, the authors take into account not only politicians’ ties that were formed during their previous career steps, but also other sources of their personal connections (outside work), and assess the relevance of the established connections in politicians’ biographies. The resulting informal network highlights the peculiarities of the organization of the Russian elite, which are quite curious in their multidirectionality. On the one hand, its structure has an obvious center in the person of Vladimir Putin, who is far superior to other players in terms of the number of connections, degree of closeness to all elite actors, and his intermediary role. On the other hand, the level of centralization of the network is rather moderate. The reason is the variety of horizontal ties between the rest of the elite. On the one hand, there are several clearly identifiable communities in the network. On the other hand, there are more connections between such communities than within them. Almost all representatives of law enforcement agencies belong to the same network community. However, there are practically no horizontal ties within the law enforcement network community. In general, the informal network of the Russian elite demonstrates a high level of coherence, with the dominance of complex, multicomponent ties that manifest themselves in different formats of personal relationships. This level of integration can play a significant role in minimizing intra-elite divisions — the “Achilles heel” of authoritarian regimes.

ILLEGAL TRANSBOUNDARY MOVEMENT OF WASTE : INDONESIA EXPERIENCE

Southeast Asia, including Indonesia, is one of the destinations for global waste from Europe and America, Greenpeace reported (Greenpeace Southeast Asia, 2019). After China stopped importing plastic waste in July 2017, Indonesia handled more imported plastic waste, which impacted the environment by transforming the waste into toxic dump sites and contaminating the water. Furthermore, the local food industry also used the waste as a substitution for fuel scrap for fuel. Indonesia Customs participated in several enforcement tools from the World Customs Organization (WCO) under its Environment Programme, such as DEMETER operation, Regional Intelligence Liasion Officers (RILOs) network, the Customs Enforcement Network (CEN), and ENVIRONET. But, do WCO tools have some impacts on controlling the transboundary movement of waste in Indonesia? This paper investigates how far WCO tools help Indonesia in addressing the problem of the Illegal transboundary movements of waste and presents a global customs issue from the perspective of Southeast Asian countries. It will then give recommendations as to how the WCO can contribute more to addressing the issue of global waste transboundary movements.

The legal regime of competition in India

Abstract The study of the competition legislation in India has as justification the fact that the indian economy has one of the biggest growth rates in the world, India being the biggest democracy in the world. At the beginning, India had its own competition law, called the Monopolies and Restrictive Trade Practices Act 1969 (MRTP Act). After the initiation of economic liberalization in 1991, it became imperative to put in place a competition law regime that was more responsive to the economic realities of the nation and in accordance with international practices. In 2002, the Indian Parliament voted for a new law, Competition Act, to regulate business practices in India. The Competition Act has as its goal to regulate three types of conduct (anti-competitive agreements, abuse of a dominant position and combinations). The Competition Act was amended by the Competition (Amendment) Act in 2007 and 2009. The Competition Act has also created a new enforcement body, the Competition Commission of India (CCI), which is responsible for the enforcement of the Competition Act. According to the provisions of the Competition Act, is allowed to make an appeal to the Competition Appellate Tribunal (COMPAT) against the decisions of the CCI. A further appeal from the decision of the COMPAT may be submitted before the Supreme Court of India. In the same, the Competition Act is taking into consideration its enforcement with the aid of mutual international support and enforcement networks across the world.

Securing cross-border collaboration: transgovernmental enforcement networks, organized crime and illicit international political economy

ABSTRACT In a globalizing world, cross-border enforcement networks are rapidly emerging as important mechanisms to tackle illicit transnational markets. As a relatively recent mode of cross-border governance, both the IPE and public policy literatures have only just begun to explore the dynamics and implications of cross-border policy networks in general and security networks in particular. Cross-border enforcement networks are similar to current IPE conceptions of transgovernmental networks, yet the comparative analysis of such networks in this article shows that they extend, and differ, from transgovernmental networks. Instead, transgovernmental enforcement networks are emerging as a comparable but distinct transnational model and thus warrant emancipation as an object of study in their own right. By exploring two network cases concerned with US-Canada cross-border tobacco smuggling, the article discerns and describes factors and conditions that account for different outcomes among select U.S-Canada cross-border security networks: IBET/Shiprider and MYGALE. Data was collected by analyzing open primary sources and conducting interviews with subject participants in these policy networks. Based on these observations, the article generates insights that can subsequently be scrutinized using other cross-border policy case studies.

Patterns in nascent, ascendant and mature border security: regional comparisons in transgovernmental coordination, cooperation, and collaboration

ABSTRACT This special issue raises the prospect of trust-based determinants of security communities other than cultural similarity. The case studies in this special issue document the emergence of cross-border and transgovernmental policy and enforcement networks that facilitate policy development, implementation and alignment through coordination, cooperation, and collaboration: nascent communities coordinate, ascendant communities coordinate and cooperate but struggle to collaborate, while mature communities coordinate, cooperate, and collaborate. Specifically, pluralistic forms of communication and interactions away from the actual borderline seem to play a key role in the emergence of friendly and trustful relationships among border dyads that need not necessarily be contiguous.

The anti-money laundering act of 2020

Purpose The purpose of this article is to inform stakeholders about the Anti-Money Laundering Act of 2020 (AMLA), explain its impact on the U.S. anti-money laundering (AML) regime, and highlight critical updates for financial institutions. Design/methodology/approach The article provides an overview of the AMLA, and specifically addresses three key components: (1) the development of uniform beneficial ownership requirements and the creation of a beneficial ownership registry at the Financial Crimes Enforcement Network (FinCEN); (2) the expansion of FinCEN’s powers and the Bank Secrecy Act/AML program requirements; and (3) new subpoena powers with potential extraterritorial effect granted to the U.S. Secretary of the Treasury and the U.S. Attorney General for documents located at foreign banks that have a correspondent banking account in the U.S. The article also notes the purpose and goals of the AMLA. Findings The AMLA is the first major overhaul of the U.S. AML regime since the 2001 USA PATRIOT Act, and is designed to strengthen national security, protect the financial system, and simplify compliance obligations. The beneficial ownership reporting requirements represent an effort to combat illicit financial activity conducted by shell companies formed and registered in the U.S. Originality/value The article provides financial institutions with a brief overview of a lengthy new law that will impact their AML compliance obligations. Financial institutions should be on alert for forthcoming regulations.

The Federal Corporate Transparency Act - Waiting for Answers

The federal Corporate Transparency Act, adopted on January 1, 2021 over President Trump's veto, is the product of two decades of proposed federal legislation to combat money-laundering and terrorist financing through anonymous business organizations. When rules are finalized, newly-formed and existing business entities will have to file reports with the Department of the Treasury's Financial Crimes Enforcement Network identifying control persons and beneficial owners of more than 25% of the entity. Unfortunately, and despite the very long gestation period for the Corporate Transparency Act, the act itself raises a large number of questions for which we are awaiting answers. Hopefully the answers will come in rulemaking which must be completed by December 31, 2021. This paper describes a number of the questions for which we are awaiting answers.

Why Business and Real Estate Lawyers Need to Worry About the 2021 National Defense Authorization Act

Colorado, specifically, and the United States, generally, allow free formation of for business and non-business purposes, with few and many times no obligation to disclose the underlying beneficial owners. This purportedly allows these to be used for money-laundering and terrorist financing operations. The William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 is another step to change this, following international attention focused from the international Financial Action Task Force, almost twenty years of effort in Congress, and the geographical targeting orders issued by the Department of the Treasury’s Financial Crimes Enforcement Network. This paper discusses the Federal Corporate Transparency Act requiring that the Department of Treasury develop disclosure obligations for existing and newly-formed businesses in the United States. While the Corporate Transparency Act is not effective immediately but must wait for rule-making, it will change the method that business lawyers and real estate lawyers advice their clients when the rules are adopted and involves ethical implications for the lawyers who intentionally or inadvertently assume their client's obligation to make the reports required under the Corporate Transparency Act. While waiting for rulemaking, those who form are waiting for answers. - Do you want to risk being an applicant as defined in the Corporate Transparency Act and therefore possibly responsible for the entity's filing obligations at the potential risk of criminal and civil liability? - What fundamentally will be companies? We know that it includes corporations, LLCs, and similar entities - but what will be defined as a similar entity? - How will the reporting companies gather the necessary information? Should there be language in articles of incorporation, articles of organization, bylaws, operating agreements, partnership agreements, and buy-sell agreements (among others) requiring the control persons and owners provide the necessary information? This paper gives a detailed background on the history that went into the Corporate Transparency Act. This paper does not answer those questions, but raises and discusses those questions and a number of other questions, including the attorney's ethical obligations that arise from the Corporate Transparency Act.

Countering money laundering and terrorist financing: A case for bitcoin regulation

Bitcoin was created in 2008 to serve as an alternative payment mechanism for both the under-banked and un-banked, or those in regions where the formal financial system suffers from broad corruption and efficient regulation. However, criminals and terrorists quickly exploited Bitcoin's unique properties, namely its peer-to-peer nature and pseudo-anonymity, to facilitate extensive terrorist financing and money laundering schemes. Government reactions to safeguard national security interests have been extremely varied, ranging from outright bans to passive tolerance. This inconsistency stems from how to effectively classify Bitcoin. On one side are those who argue Bitcoin is a currency, and on the other are those who claim it is a type of asset. In the US alone, these discrepancies have led to a bureaucratic turf war between different regulatory bodies, namely the Financial Crimes Enforcement Network, the Commodity Futures Trading Association, the Securities and Exchange Commission, and the Internal Revenue Service. This study seeks to move beyond the existing legal frameworks, arguing that Bitcoin should be classified as a technology and regulation should rest with private sector technology companies.

A Safe Harbor for Ransomware Payments: Protecting Stakeholders, Hardening Targets, and Defending National Security

The United States is under ransomware siege. Victims range from small municipalities to non-profits to multi-national corporations and governments. The law is struggling to respond. Few entities, crippled by a ransomware attack, can refuse to pay. Not paying the ransom may result in significant harm, including financial ruin or even loss of life. Paying a ransom, however, is likely to generate attacks on other targets. Paying may not even lead to recovery of the data as promised. By definition, paying ransoms transfers value to criminals, and that is against many laws. But more than simple illegality is at issue. While ransomware hackers may be lone criminals or infamous cyber-gangs, they may also be hostile foreign countries, or non-state actors such as terrorist groups. Ransomware and other digital threats have the potential to compromise U.S. critical infrastructure. Strategically significant economic transactions have long been prohibited or highly regulated. In the wake of the September 11th attacks, the discovery and prevention of terrorist financing became a key pillar of U.S. security architecture. Under this regime, paying a ransom, thereby aiding the “enemy,” may trigger liability. Regulators have threatened enforcement of sanctions and anti-money laundering laws not only against ransomware victims who pay, but also against third-party service providers who facilitate payment. Both the Office of Foreign Assets Control (OFAC) and the Financial Crimes Enforcement Network (FinCEN) have issued advisories emphasizing their strict policies against paying prohibited persons or transmitting funds without required procedures. How to steer between the Scylla of legal liability and the Charybdis of a cyberattack? Sometimes ransoms should be paid as the lesser evil. Confronted with the potential damages of a ransomware attack, people may rightly choose to pay. On the other hand, society cannot allow itself to be held hostage. Those who endanger individual lives, enterprises, and core social functions must be resisted. That is, the status quo, in which many enterprises simply pay off cybercriminals, thereby incentivizing more cyberattacks, is unsustainable. This article argues that the threat of legal liability for ransomware payments, with no positive incentive for potential victims, is unlikely to spur adoption of sound security measures or even to stop payments, and may be counterproductive if it leads victims to conceal attacks. Instead, this article suggests the creation of a safe harbor for ransomware payment that (i) enables the victim and those who assist the victim to pay when necessary (protecting stakeholders), but that also (ii) deters attacks (hardening targets) and (iii) facilitates interdiction of attacks that do occur (defending national security).

Ehi’s Predictions for the Anti-Money Laundering Sector in 2021

The year 2020 witnessed the introduction of new rules and regulations by the United Kingdom for virtual asset service providers, and for the first time, a Bitcoin “Mixer” was Penalized by the United States Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) for Violating Anti-Money Laundering Laws. The anti-money laundering landscape also witnessed a shift from documentation based procedures to electronic evidence. This was as a result of the COVID-19 pandemic. The year 2021 will likely continue from where the year 2020 stopped with more regulations and enforcement actions.This paper provides an in-depth review and analysis of the topics that are likely to trend on the anti-money laundering space in 2021. This paper predicts that countries around the world will introduce more rules and regulations for virtual asset service providers along with the adoption of an appropriate mix of on-site and off-site supervision of dealers in precious metals, precious stones or jewels, including the introduction of corporate transparency and register reforms. This paper also predicts that financial institutions will enhance their anti-money laundering systems and controls to accommodate application programming interfaces during the customer onboarding process, and upgrade their anti-money laundering surveillance monitoring systems to a new system that can detect highly suspicious transaction patterns including possible layering schemes, transactions not commensurate with the business’s purpose, and commingling of funds between two independent check cashing entities. This paper concludes that the combined power of robotic process automation, machine learning technology and human intelligence will help to deter and detect money laundering.

Dirty Money: How Banks Influence Financial Crime

On September 21st, 2020, a consortium of international journalists leaked nearly 2,500 suspicious activity reports (SAR) obtained from the U.S. Financial Crimes Enforcement Network, exposing nearly $2 trillion of money laundering activity. The event raises important questions regarding what role banks play in facilitating financial crime and the effectiveness of SAR reporting. In this study, we examine the incentives that banks face to report money laundering activity via SAR reports, and the implications of a bank’s reporting strategy for criminal activity. We first analyze banks' SAR reporting decisions using a stylized model, which predicts that banks facing depressed revenues from their routine business lines and more profit-seeking pressure adopt more lax reporting policies. These reporting policies help to attract criminals, thus increasing the underlying amount of suspicious activities that banks need to examine and report. Empirically, we test the relation between risk-taking incentives and SAR volume at the county level. We find that counties in which banks face higher competition and lower profitability generate higher volumes of SAR activity. To provide further support for the underlying mechanism, we incorporate a maximum likelihood estimation that uncovers a highly elastic demand from criminal customers with respect to banks' reporting stringency. We establish causality using shale gas expansion in unrelated states. Consistent with risk-taking incentives influencing SARs, we find that banks experiencing higher (lower) shale growth generate fewer (more) SAR reports. Overall, our results provide important insights regarding the role of banks in influencing financial crime, and suggest that a bank’s reporting policy has indirect implications for local criminal activity.

Cryptocurrency Regulation: Countering Money Laundering and Terrorist Financing

Bitcoin was created in 2008 to serve as an alternative payment mechanism for both the under-banked and un-banked, or those in regions where the formal financial system suffers from broad corruption and efficient regulation. However, criminals and terrorists quickly exploited Bitcoin’s unique properties, namely its peer-to-peer nature and pseudo-anonymity, to facilitate extensive terrorist financing and money laundering schemes. Government reactions to safeguard national security interests have been extremely varied, ranging from outright bans to passive tolerance. This inconsistency stems from how to effectively classify Bitcoin. On one side are those who argue Bitcoin is a currency, and on the other are those who claim it is a type of asset. In the US alone, these discrepancies have led to a bureaucratic turf war between different regulatory bodies, namely the Financial Crimes Enforcement Network, the Commodity Futures Trading Association, the Securities and Exchange Commission, and the Internal Revenue Service. This study seeks to move beyond the existing legal frameworks, arguing that Bitcoin should be classified as a technology and regulation should rest with private sector technology companies.