Cryptocurrency Transactions Research Articles

Anonymity Unmasked The Role of Cryptocurrencies in Global Money Laundering

This research paper examines the intersection of cryptocurrencies and financial crime, particularly focusing on money laundering. It examines how these digital assets are employed in illicit financial activities, investigates the methodologies used to obscure transaction trails, evaluates regulatory responses to these risks, and presents practical case studies highlighting the real-world criminal use of cryptocurrency. The study, covering data from 2020 to 2024, combines a thorough review of existing literature with empirical analysis of blockchain transactions and case studies. The novelty of this study is the use of blockchain analysis methods for monitoring cryptocurrency transactions in the blockchain and detecting criminal money laundering techniques. It also examines how the criminals use mixers to exponentially expand the transaction trail and hide the money flow. Our analysis reveals that cryptocurrencies are often viewed as tools for anonymity but the underlying technology provides tracking of transactions, albeit with challenges, particularly when advanced techniques such as coin mixers, privacy coins, and decentralized exchanges are used. Case studies, including high-profile hacks, highlight the complexities of tracing illicit funds and challenges faced by law enforcement agencies. Despite efforts by global regulatory bodies, regulations on digital assets vary significantly around the world, allowing criminals to exploit jurisdictions with lax regulations for their illicit activities. The study concludes that cryptocurrencies pose notable risks of money laundering, advancement in digital forensic tools, enhanced regulatory measures, and greater international cooperation are essential to address these risks. This research contributes to the understanding of money laundering through cryptocurrencies and underscores the need for continuous improvement of regulation and technology to ensure finance remains safe in the age of innovation.

Design of Cryptopol: A serious game for teaching cryptocurrency tracing techniques to Law Enforcement

Tracing cryptocurrency transactions is a far from trivial process. This likely explains the increasing utilization by criminal networks for a significant amount of criminal activity, not just cybercrime, but any crime requiring monetary transfers [1]. It is therefore vitally important that adequate training exists and is readily available for both new and experienced investigators to ensure that they are familiar with the latest techniques, tools and trends. Traditional training methods can be costly and resource intensive, requiring highly qualified trainers to give their time to conduct training sessions. To address this issue, a training resource, in the form of a serious game, has been created. The game aims to provide a platform to improve the skills and expertise of law enforcement officers whilst reducing the workload of experienced investigators. This paper describes the collaborative design and development of the serious game Cryptopol in a partnership between Europol and CENTRIC, which is a multi-disciplinary and end-user focused Center of Excellence, located within Sheffield Hallam University. Cryptopol is the first cryptocurrency-tracing training game of its kind, used by over 1,500 people representing over 550 law enforcement agencies from across the world.

Investigation of Fluctuations in Cryptocurrency Transactions with Sentiment Analysis

This study investigates public sentiment about popular cryptocurrencies listed on crypto exchanges in Turkey, using comments shared on social media platforms and online forums. The research seeks to enhance the existing body of knowledge by overcoming the shortcomings of sentiment analysis studies focused on Turkish texts. Data collected from social media and online forums were examined with sentiment analysis techniques. A total of 607,592 comments were analyzed, of which 89,986 were classified as negative, 72,655 as positive, and 444,951 as neutral. For binary classification, 89,986 negative and 72,655 positive examples were selected and machine-learning models were trained and tested on 162,641 examples. The study's methodology includes an in-depth examination of sentiment analysis results obtained using machine learning classifiers. The findings show how various cryptocurrencies are perceived on different social media platforms. For instance, BTC (Bitcoin) is generally perceived negatively on Investing.com and Telegram, while ETH (Ethereum) generally displays more negative views. These results help investors understand their perceptions and market expectations towards cryptocurrencies. This study deepens the role of social media sentiment analysis in cryptocurrency markets, contributing to the development of new methods and approaches for future research.

PREDICTIVE ANALYSIS OF CRYPTOCURRENCY INCOME USING MACHINE LEARNING MODELS

This research explores the potential of various machine learning models to predict cryptocurrency income. With the increasing volatility and widespread use of cryptocurrencies, accurately forecasting income from these digital assets is crucial for investors and stakeholders. By examining a dataset of cryptocurrency transactions, this study addresses the challenge of outliers and assesses the correlations between different variables to ensure robust data analysis. We employ three distinct machine learning models—Linear Regression, Decision Tree Regressor, and Random Forest Regressor—to train and evaluate the dataset. Each model is analysed to calculate performance metrics, such as Mean Squared Error (MSE) and R2 Score, which provide insights into their predictive accuracy and reliability. The detailed analysis and discussion highlight the strengths and weaknesses of each approach, emphasizing the efficiency and accuracy of the models in forecasting cryptocurrency income. The Linear Regression model, known for its simplicity, is compared against more complex models like Decision Tree and Random Forest Regressors, which can capture nonlinear relationships and interactions between variables. The Random Forest Regressor, an ensemble learning method, is particularly noted for its superior performance in handling complex datasets and providing more accurate predictions. This study's findings offer valuable insights for developing more effective investment strategies and risk management practices in the rapidly evolving cryptocurrency market. Through this comprehensive evaluation, the research aims to contribute to the growing body of knowledge on financial forecasting using machine learning techniques, paving the way for future advancements in this field.

Cryptocurrency and its Nexus with Money Laundering and Terrorism Financing within the Framework of FATF Recommendations

Cryptocurrency has emerged as a viable alternative to conventional payment systems, offering its users notable advantages such as cost efficiency and rapid transaction processing. However, cryptocurrencies’ decentralized nature, anonymity, and susceptibility to cyber threats introduce the potential for their exploitation in illicit activities, notably money laundering and terrorism financing (ML/TF). This scholarly investigation seeks to investigate the roles played by international organizations actively combating such criminal activities. In this academic context, a meticulous consideration of the inherent risks associated with the aforementioned criminal endeavors is undertaken, aligning with the guidelines and recommendations set forth by the Financial Action Task Force (FATF). The study underscores a significant finding that cryptocurrency accounts can be opened anonymously, with no centralized registry to monitor cryptocurrency ownership. This characteristic poses a formidable challenge in confiscating funds linked to terrorist activities. Even in instances where cryptocurrency transactions may be traced, accessing this critical data necessitates the involvement of third-party entities, given that cryptocurrency transactions are recorded in decentralized ledgers spanning multiple jurisdictions. Furthermore, the study underscores the imperative of coordination and information exchange as indispensable elements in the ongoing battle against organized and transnational criminal activities, specifically ML/TF. Moreover, the study elucidates the incongruence between cryptocurrency and FATF regulations governing electronic fund transfers, as cryptocurrencies can make transactions through opaque and unregulated conduits, such as the deep web.

Leveraging tabular GANs for malicious address classification in ethereum network

The popularity of ethereum for cryptocurrency transactions attracts malicious actors to engage in illegal activities like phishing, ponzi, and gambling. Previous studies have focused mainly on phishing due to the large number of phishing addresses. However, there is no work done on ponzi or gambling classification due to the limited availability of these addresses, which makes their classification more challenging. In this paper, we propose a machine learning (ML) based method for classifying malicious addresses in ethereum, with a specific focus on phishing, ponzi, and gambling addresses. We use a selective upsampling technique through the tabular generative adversarial network (GAN) to solve limited data problems. We perform not only binary but also multiclass classification on various feature extraction methods, including Trans2Vec and Node2Vec, using Ethereum transactional data. We evaluate our method on F1 score, precision, recall, and accuracy. Our results show that the proposed method is effective in ponzi and gambling detection when compared with the state-of-the-art.

NEW AML TOOLS: ANALYZING ETHEREUM CRYPTOCURRENCY TRANSACTIONS USING A BAYESIAN CLASSIFIER

The emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a «critical» risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.

DBUP: Dynamic blockchain UTXO processing for storage efficiency optimization

In traditional blockchain systems, the Unsent Transaction Output (UTXO) dataset stores all generated UTXOs, which typically represent digital assets of the users. UTXOs can be used to track the output status of cryptocurrency transactions. However, with the development of blockchain networks, the UTXO dataset continuously increasing in scale. In the UTXO dataset, a large number of low-value UTXOs occupied huge storage space, making the blockchain dataset expanded. This impacts the running performance of the whole blockchain system. To address this issue, this paper proposes the Dynamic Blockchain UTXO Processing strategy (DBUP) for UTXO storage efficiency optimization. This strategy aims to rapidly consume the low-value UTXOs, effectively alleviating UTXO dataset expansion. Furthermore, Through improved simulated annealing (ISA) algorithm and a UTXO dynamic adjustment mechanism in DBUP, the strategy ensures that the transaction fee in the transaction process remain at a low level. The experimental evaluation indicates that our strategy can achieve the better performance against the Low Value First (LVF) and the First-In-First-Out (FIFO) strategies, enhancing the storage efficiency of blockchain systems.

Secure Storage of Crypto Wallet Seed Phrase

The Expense Tracker Application is a user-friendly and efficient tool designed to help individuals manage their personal finances with ease. In the ever-changing landscape of daily expenses, this application offers a structured method for tracking income, categorizing expenditures, and generating insightful financial reports. Its intuitive and visually appealing interface ensures that users of all technological skill levels can navigate it effortlessly. The application features visual representations to help users understand their budgetary trends and set limits for different expense categories. It provides timely alerts and notifications when users approach or exceed these budget limits. With robust security measures in place, the application ensures the confidentiality of users' financial data, enhancing their awareness of spending habits and enabling informed financial decisions. Additionally, this system proposes a novel approach to cryptocurrency transactions. Instead of relying on complex public and private keys, it simplifies key management by binding these keys to the unique IP addresses of the nodes involved. This method minimizes the need for users to manage large cryptographic keys, offering a more secure and user-friendly alternative for cryptocurrency wallets. By addressing the limitations of traditional cryptocurrency systems, this approach enhances key management and overall security

Investigating various cryptocurrency research trends: an analysis employing text mining and topic modeling

Purpose The trend among the financial investors to integrate cryptocurrencies, the very first completely digital assets, in their investment portfolio, has increased during the last decade. Even though cryptocurrencies share certain common characteristics with other investment products, they have their own distinct characteristic features, and the behavior of this asset class is currently being studied by the research scholars interested in this domain. Design/methodology/approach Using the text mining approach, this article examines research trends in the field of cryptocurrencies to identify prospective research needs. To narrow down to ten topics, the abstracts and the indexed keywords of 1,387 research publications on cryptocurrency, blockchain and Bitcoins published between 2013 and 2022 were analyzed using the topic modeling technique and Latent Dirichlet allocation (LDA). Findings The findings show a wide range of study trends on various aspects of cryptocurrencies. In the recent years, there have been lots of research and publications on the topics such as cryptocurrency markets, cryptocurrency transactions and use of blockchain in transactions and security of Bitcoin. In comparison, topics such as use of blockchain in fintech, cryptocurrency regulations, blockchain smart contract protocols and legal issues in cryptocurrency have remained relatively underexplored. After using the LDA, this paper further analyzes the significance of each topic, future directions of individual topics and its popularity among researchers in the discussion section. Originality/value While similar studies exist, no other work has used topic modeling to comprehensively analyze the cryptocurrencies literature by considering diverse fields and domains.

Suspicious transaction alert and blocking system for cryptocurrency exchanges in metaverse’s social media universes: RG-guard

In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to contribute to the development of such studies.

Economic Behavioral Anomalies In Cryptocurrency Transactions During The Covid-19 Pandemic

The COVID-19 pandemic has had a significant impact on various aspects of life, including financial markets. The cryptocurrency market, already renowned for its volatility, experienced a surge in activity and significant changes in investor behavior during this period. This research aims to analyze various economic behavioral anomalies that emerged in cryptocurrency transactions during the COVID-19 pandemic. This research uses a literature study method to identify several dominant behavioral anomalies, such as FOMO (Fear of Missing Out), Herding Behavior, Noise Trading, Overconfidence, and Anchoring Bias. These behavioral anomalies trigger extreme market volatility, asset bubbles, and financial losses for investors. This research highlights the importance of investor education, market regulation, and technology development to minimize the impact of behavioral anomalies and protect investors in the cryptocurrency market.

Blockchain Innovation: Enhancing Security and Clarity in Digital Exchanges

Blockchain technology, which was originally intended for cryptocurrency transactions, has grown into a transformative tool for improving security and transparency in a variety of industries. This research paper examines blockchain mechanisms such as decentralisation, cryptographic security, and consensus algorithms, emphasising their importance in ensuring data integrity and transparency. The broad impact of blockchain is demonstrated through applications in financial services, supply chain management, healthcare, digital identity verification, real estate, and public services. Despite challenges such as scalability, regulatory issues, and energy consumption, blockchain offers significant benefits in terms of fraud prevention, cost savings, and efficiency. Future possibilities include combining blockchain with emerging technologies such as AI and IoT, central bank digital currencies, and more energy-efficient consensus mechanisms. This study emphasises blockchain's crucial role in improving digital exchanges and its potential to revolutionise them. Key Words: Blockchain technology, security, transparency, digital transactions, decentralization, cryptographic security, smart contracts

Study of Crypto Currency in Indonesia

Currency as a legal means of payment is needed in domestic and international economic activities to achieve the social welfare of Indonesian society as a whole. Regulations regarding the nature and price of currency are contained in Article 23 B of the 1945 Constitution of the Republic of Indonesia. To ensure legal certainty and legal protection, another law was enacted, namely Law Number 7 of 2011 concerning currency. The aim of this research is to analyze the use of crypto money as a means of payment in Indonesia. This research uses a statutory approach and a conceptual approach. Data analysis uses qualitative analysis techniques to test the validity of the data in this research by looking for existing reference sources. Based on the research results, it shows that a series of legal regulations in Indonesia regarding digital crypto money show the government's awareness in creating the rule of law within the scope of the economic ecosystem in the digital era. So this is something special for Indonesia as a country that is serious about protecting its people in cryptocurrency transactions as assets or commodities (crypto assets). However, there are conflicting perspectives in viewing the position of digital crypto money. Bank Indonesia places it as digital money (virtual currency) so its use is prohibited as a means of payment. As for the Ministry of Trade, through Bappebti, it places crypto digital money (cryptocurrency) as a digital asset or commodity so that based on normative provisions it is allowed to be traded on the Futures Exchange. Therefore, the government needs to harmonize the law in positioning crypto digital money.

Криптовалюты: учет и аудит в цифровой экономике

The article examines the impact of cryptocurrencies on the field of accounting and auditing, emphasizing the need to update existing methodologies in light of the digitalization of the economy. The development of the digital economy leads to the widespread use of electronic currencies. In the modern world, cryptocurrency is not only relevant as a form of electronic payments, but also promising for investments in the international market. In recent years, there has been an unprecedented increase in interest in cryptocurrencies as an innovative financial instrument, which has caused significant changes in traditional accounting and auditing approaches. The development of blockchain technology and an increase in the volume of transactions using cryptocurrencies stimulate the need to adapt accounting systems and audit procedures to new economic conditions. The article is dedicated to analyzing the advantages and disadvantages of cryptocurrencies, as well as developing new audit procedures adapted to the specifics of cryptocurrency transactions. The relevance of the topic is determined by the rapid penetration of crypto assets into the financial world and the challenges they pose for traditional accounting systems. It also necessitates the adaptation of accounting standards and auditing practices to new economic realities. The work uses a comprehensive approach, including the analysis of academic sources, regulatory documents, as well as expert opinions and practical case studies. The results show that the integration of cryptocurrencies requires standardization of accounting and updating of audit approaches, as well as raising the level of specialists' knowledge. The article offers recommendations that contribute to increasing the transparency of financial operations and strengthening investor confidence. This research is of interest to professionals in the field of financial accounting and auditing, as well as researchers studying the impact of digital technologies on economic processes.

Tax Regulations On Cryptocurrency Transactions In Indonesia

This study explores the evolving landscape of cryptocurrency taxation in Indonesia, focusing on regulatory developments and challenges. Tracing the trajectory from the introduction of Bitcoin in 2009 to the establishment of a formal crypto exchange in 2023, this research emphasizes the government's commitment to regulate and facilitate crypto asset trading. Despite this progress, challenges such as regulatory uncertainties, security concerns, necessitating a profound understanding of cryptocurrency dynamics to navigate the global financial landscape. This research focuses on exploring Indonesia's regulatory response, particularly in taxation, employing a qualitative descriptive methodology that integrates primary data from legal frameworks and secondary data through literature reviews, document analyses, and interviews with cryptocurrency traders and tax officials. This study contributes not only to academic discourse but also provides insights for fostering the growth of the cryptocurrency ecosystem while managing potential tax-related risks.

Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model.

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.

Analytical Study for Prjice Prediction of Bitcoin Using Machine Learning and Artificial Intelligence

Abstract: Cryptocurrency transactions rely on encryption algorithms for security and decentralization. However, traditional machine learning algorithms often struggle with the dynamic cryptocurrency market, leading to inaccuracies. To address this, integrating Integrating sentiment analysis of Twitter data alongside Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) models enriches the analysis of cryptocurrency market dynamics. Twitter provides dataset from kaggle open source and the sentiment expressed by users on cryptocurrencies, enabling the prediction of tweet sentiment (positive, negative, or neutral) through Natural Language Processing (NLP). This integration enhances understanding of market sentiment's impact on cryptocurrency prices: positive sentiment may drive prices up, negative sentiment may lead to declines, and neutral sentiment may indicate stability. By analyzing sentiment alongside historical trends and emerging patterns, users gain a holistic view of cryptocurrency markets. This approach aids decision-making, improving transaction accuracy and efficiency. Ultimately, the combination of sentiment analysis with BiLSTM and BiGRU models advances the comprehension of cryptocurrency market dynamics, enhancing user insights and facilitating informed decisions in the volatile cryptocurrency ecosystem.

Enhancing consumer protection in cryptocurrency transactions: Legal strategies and policy recommendations

Enhancing consumer protection in cryptocurrency transactions presents a critical challenge due to the decentralized and often opaque nature of the cryptocurrency market. This abstract explores the legal frameworks and mechanisms aimed at safeguarding consumers engaging in cryptocurrency transactions, focusing on key issues, challenges, and recommendations for improvement. Consumer protection in cryptocurrency transactions is a pressing concern due to the prevalence of fraud, hacking, and market manipulation. The lack of regulatory oversight and the pseudonymous nature of transactions make it challenging for consumers to seek recourse in cases of fraud or misconduct. To address these challenges, legal frameworks have been developed at both national and international levels. At the national level, some countries have implemented consumer protection laws that apply to cryptocurrency transactions, such as requiring exchanges to comply with anti-money laundering (AML) and know-your-customer (KYC) regulations. However, the effectiveness of these laws is limited by the global nature of the cryptocurrency market and the difficulty in enforcing regulations across borders. Internationally, organizations like the Financial Action Task Force (FATF) have issued guidelines to combat money laundering and terrorist financing in the cryptocurrency sector. These guidelines recommend that countries implement AML and KYC measures for cryptocurrency exchanges and wallet providers. While these recommendations are a step in the right direction, implementation remains a challenge, particularly in countries with limited regulatory capacity or political will. To enhance consumer protection in cryptocurrency transactions, several recommendations can be considered. These include increased collaboration between regulators and industry stakeholders, the development of international standards for consumer protection in cryptocurrencies, and the establishment of a regulatory framework that balances innovation with investor protection. Additionally, consumer education and awareness campaigns can help individuals make informed decisions when engaging in cryptocurrency transactions. In conclusion, enhancing consumer protection in cryptocurrency transactions requires a multi-faceted approach that addresses regulatory gaps, promotes international cooperation, and empowers consumers through education and awareness. By implementing these recommendations, policymakers and industry stakeholders can work together to create a safer and more transparent cryptocurrency market.

Text Analytics on Regulation of Cryptocurrency

This paper presented the views of Malaysians regarding their perception of cryptocurrency. Data were collected through interviews with 59 existing and potential users of cryptocurrency. Text analytics through keyword extraction and content analysis on the responses from the survey were then performed. Keywords on the question related to the regulation of cryptocurrency included “Government should”, “Should regulate”, and “Money laundering”. Keywords such as “Government should”, “Should issue”, and “Regulatory regime” appeared for the question related to whether the government should develop a cryptocurrency regulatory regime. On issuing their own cryptocurrency, 49% of the respondents supported it, while 42% were against such a move. 88% of the respondents felt that the government should not ban cryptocurrency transactions. For the question related to suggestions to overcome issues associated with cryptocurrency, keywords such as “financial system”, “privacy seriously”, and “enhance financial” were reported. This paper adds to the body of knowledge on cryptocurrency by shedding light on the regulation landscape in an emerging market. In view of the increased usage of cryptocurrency, the public should be educated, and relevant bodies should establish regulations to minimise possible related illegal activities while developing a cryptocurrency-friendly regulatory regime. Keywords: cryptocurrency, text analytics, regulation, Malaysia, fintech