Blockchain Papers

Alberto Maria Mongardini, Daniele Friolo, Giuseppe Ateniese

No abstract is available for this record.

Christian Cachin, David Lehnherr, Juan Villacis, François-Xavier Wicht

Sender untraceability hides the account spent by a cryptocurrency transfer among a set of candidates, its masking set. What a transfer does to that set separates two designs: classical schemes retain the whole set and append a nullifier marking the spent account, so the ledger grows with every transfer; constant-state schemes instead consume and replace the entire set. We ask how this choice affects synchronization. We formalize the two designs as the linear and constant untraceable asset transfer objects (LUAT and CUAT) and locate them in the consensus hierarchy. In LUAT, transfers from distinct accounts commute. Its consensus number is 2, compared with 1 for standard asset transfer, independently of the masking-set size and of the untraceability notion, and LUAT is starvation-free. Partitioning the accounts into fixed masking sets lets exhausted sets be garbage-collected without increasing that number. In CUAT, a transfer consumes and replaces every account of its masking set, so two transfers whose sets intersect cannot both take effect. We formalize this with the conflict graph on masking sets, whose edges join sets sharing an account. Under weak untraceability, which protects a transaction in isolation, the consensus number is unbounded already for one-round protocols. Under strong untraceability, which protects against an observer of the complete history, untraceability holds on a history exactly when any two accounts sharing a masking set occur in the same number of the masking sets in it. This uniform incidence bounds the conflict graph, and matching constructions attain it, so the consensus number is determined exactly and grows quadratically in the masking-set size. Finally, CUAT is not starvation-free. The two objects therefore pay for the same privacy differently: LUAT in storage, CUAT in synchronization and fairness.

Yuji Sakurai, Kanji Suzuki, Keiichi Goshima

Abstract We study comovement among major cryptocurrencies from a portfolio management perspective. To this end, we develop two new statistical tools. First, we propose a new measure called the portfolio-conditional correlation defined as the correlation conditional on the portfolio return being below or above a given threshold. Second, we develop a new multivariate model named the Common Autoregressive Jump Intensity Score-based (ComARJIS) model in which the time-varying intensity of a common jump in cryptocurrency returns is formulated under the Generalized Autoregressive Score (GAS) framework. Our main findings are as follows: First, we find an adverse downside correlation: the downside correlation is higher than the upside correlation. Second, the ComARJIS model successfully shows the correlation asymmetry of cryptocurrencies. Third, and most importantly, a market-timing strategy with the common jump intensity improves the Sharpe ratio. This result suggests that time diversification could be helpful for cryptocurrency investors even if asset diversification is impossible. Fourth, the meltdown risk represented by the common jump intensity is associated with the financial market stress in the U.S.

Priya S, Dakshayini M, Apsana S A, Anjana M R

One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transacted through the world financial system each and every year. The problem with this approach is that the criminals seldom use only one bank. They thread their way across five, ten, and sometimes dozens of institutions, all seeing merely a harmless nugget. In isolation, looking at his or her own transaction logs, no single bank will easily know that there is a problem. This paper is about a system, called AMLNet, which tackles this blind spot. Unlike the traditional approach, which would allow banks to share their customers' data with each other,AMLNet trains a detection model on customers' data within each bank, and shares only what the detection model learned from the data, not the data itself. All collaborative training is documented in a blockchain ledger, making it transparent and tamper-proof. With a Zero-Knowledge Proof, each bank is able to prove cryptographically that it is acting honestly, but not disclose anything private. A graph of transaction data (accounts as nodes, transfers as edges) is used to extract structural features, which are compressed by PCA before being input to a Multi-Layer Perceptron (MLP) risk-scoring classifier of each account. Together they increase fraud recall by approximately 20% over any single institution operating alone, while maintaining a low false positive rate, and that the overall computation time is less than 10 minutes on an average laptop.

Ayoub Jadouli

We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs. Numerical results come from scripted fixed-seed model runs and deterministic simulators; human-supervised AI agents supported the July 20 evidence-integrity revision through literature retrieval, separately tasked critique, artifact reconciliation, documentation, and source packaging, not trading decisions. The strongest later-period evidence, conditional on extensive predecessor search, is negative: an unchanged ten-pair mandatory-daily selector lost 6.72\% over 19 July cycles at an assumed 31-bps completed-cycle cost, with 3 wins and 16 losses. In short model-specific July evaluations, the validation-selected local-minimum policy returned -1.79\%, while the local-maximum sell-to-cash/re-entry policy underperformed continuous holding by 2.80\%; their gross mean advantages of 11.11 and 12.21 bps were below even the 21-bps stress. A Gurgul-inspired, OHLCV-only daily adaptation attained minimum/maximum ROC AUC of 0.874/0.896 but average precision of only 0.134/0.116 and lost 44.30\% over seven cycles, versus -41.20\% for buy-and-hold. A forensic audit also downgraded an earlier One4All "30-day holdout": its dates had influenced prior architecture work, its four-hour outcome horizon was not purged at split boundaries, it used same-close entry, and its raw result directories were absent. Across the tested, mostly exploratory protocols, event-ranking performance did not establish positive executable policy value. Every operational decision remains NO\_TRADE.

Gouher Ahmed, Hamza Naim, Aqila Rafiuddin, Mohammed Nizamuddin · 5 authors

This study deals with the performance analysis and volatility estimation of conventional indices including Dow Jones, S&P 500, Brent Oil, Crude Oil and Gold and cryptocurrencies including Bitcoin and Ethereum for the period January 3, 2011 to November 26, 2021 for all of the indices except Ethereum for which the period chosen was from March 10, 2016 to November 26, 2021 due to late incorporation of the cryptocurrency. The stationarity, heteroscedasticity, and serial correlation of the data were considered. Time series regression using the GARCH model is applied for performance analysis and volatility estimation. GARCH (1, 1) estimates show the high performance of cryptocurrencies over the conventional indices, except Gold, which was insignificant, with Ethereum followed by Bitcoin being the most volatile among the different indices. However, Gold remains inert in response to the different indices. However, although the cryptocurrencies add to the country’s revenue, thus minimizing the deficits, there should still be proactive policies and practices to prevent the exploitation of stakeholders, especially for the sake of minority ones.

Diky Paramitha, Etik Ipda Riyani, Nadhira Hardiana, Kan Wen Huey

Bitcoin has a tendency of price volatility that is much higher than other cryptocurrency assets, this makes a very significant difference from other financial assets that can go beyond conventional market logic thus creating a major obstacle in risk management. This study aims to dissect the extreme anomalies of bitcoin trading volume against the volatility of Bitcoin returns. Using a quantitative time series approach, the study analyzed monthly data on bitcoin price and trading volume using Bitcoin prices in the period February 2015 to December 2025. We assess volatility using the GARCH-X model to introduce trading volume as an exogenous variable. The basic GARCH shows significant volatility persistence, indicating a clustering of high volatility in Bitcoin's returns. This finding results that trading volume is not just a static transaction number but reflects a very crucial information proxy. Every movement of trading activity generates new signals in which aggressive price react. Trading volume is also highly correlated with the volatility of returns, although the volatility of the model indicates the need for careful interpretation. Bitcoin's volatility is not solely due to historical volatility dynamics, but also the impetus from trading activity, highlighting the need to consider accurate volatility modeling in the digital asset market. This research adds value by embedding trading volumes into the GARCH model to evaluate its contribution in explaining Bitcoin's volatility through empirical insights for investment decisions and risk management in the cryptocurrency market

Michael Neubert, Wolfgang Rams, Patrick Gruhn, Marcel Lötscher

Perpetual futures (often called perpetual swaps) are the dominant crypto-derivatives instrument. They replicate the economic exposure of a futures contract without an expiry date. They replace maturity-based convergence with a funding mechanism that transfers cash flows between longs and shorts, typically every eight hours. This paper explains how perpetuals evolved from early proposals for non-maturing futures into a standardized crypto market instrument, and why key design choices changed over time. It synthesizes recent theoretical and empirical research on funding design, pricing, and arbitrage intuition, market microstructure, liquidation risk, and regulation. Finally, this study proposes a research agenda organized around funding design, constrained arbitrage, transparency, decentralized exchange design, policy, and legal classification, because recent U.S. and EU developments show that the same economic structure may be characterized as a futures contract, swap, CFD-type instrument, or other derivative depending on statutory definitions, venue design, and supervisory interpretation. This paper proposes the following definition: a cryptocurrency perpetual is an open-ended, margin-based derivative that gives synthetic long or short exposure to an underlying crypto asset and replaces expiry-based settlement with periodic funding payments that anchor the contract price to a reference spot price.

Muhammad Habibi, Mirza Agung Rahmatullah, S Huda, Achmad Alif Nurbani

Perkembangan ekonomi digital telah melahirkan berbagai bentuk aset digital, seperti cryptocurrency dan Non-Fungible Token (NFT), yang memiliki nilai ekonomi tinggi dan berpotensi dimanfaatkan dalam hubungan keperdataan. Penelitian ini bertujuan untuk menganalisis kedudukan hukum cryptocurrency dan NFT dalam perspektif hukum perdata Indonesia sebagai objek hak kebendaan serta mengkaji pengaturan dan perlindungan hukum terhadap penggunaannya sebagai objek jaminan utang. Penelitian menggunakan metode hukum normatif dengan pendekatan perundang-undangan, konseptual, dan perbandingan. Data yang digunakan berupa bahan hukum primer, sekunder, dan tersier yang dianalisis secara kualitatif melalui metode yuridis normatif. Hasil penelitian menunjukkan bahwa cryptocurrency dan NFT secara konseptual memenuhi unsur sebagai benda bergerak tidak berwujud karena memiliki nilai ekonomi, dapat dimiliki, dikuasai, dialihkan, dan menjadi objek hubungan hukum. Kedua aset digital tersebut juga memenuhi persyaratan dasar sebagai objek jaminan utang. Akan tetapi, sistem hukum kebendaan dan hukum jaminan di Indonesia belum memberikan pengakuan dan pengaturan yang tegas mengenai kedudukan cryptocurrency dan NFT sebagai objek jaminan kebendaan. Ketiadaan regulasi khusus menimbulkan ketidakpastian hukum terkait mekanisme pengikatan, pendaftaran, penilaian, penguasaan, dan eksekusi aset digital. Oleh karena itu, diperlukan pembaruan hukum yang mampu mengakomodasi perkembangan teknologi digital guna memberikan kepastian hukum, perlindungan hukum, dan kemanfaatan bagi para pihak.

Brannon Nickles

Cryptocurrency is no longer that of a topic solely for traders and tech enthusiasts, as crypto ETFs have worked their way into mainstream retirement accounts, bringing with them many questions to financial planners. The question this study addresses is whether small Bitcoin and Ethereum ETF allocations actually improve the risk-adjusted performance of a traditional balanced retirement portfolio. To find out, five different portfolio constructions were tested using real ETF return data, with performance measured across Sharpe ratio, Sortino ratio, maximum drawdown, and correlation, all with quarterly rebalancing built in. Every portfolio that included cryptocurrency outperformed the standard baseline on risk-adjusted return metrics, though drawdown did increase as the allocation grew. What this tells us is that small, structured cryptocurrency allocations have the potential to improve retirement portfolio performance for the right investor, but suitability still needs to be worked out on an individual basis, something financial planners can take directly into their practice.

H. Wu, Haijun Wang, Shiteng Li, Yin Wu · 7 authors

With the rapid advancement of decentralized finance (DeFi), security incidents related to cryptocurrency have become increasingly prevalent. After such incidents, attackers typically attempt to rapidly move stolen assets, concealing the origin of illicit funds and ultimately converting them into fiat currency. However, existing anti-money laundering (AML) methods struggle to cope with the semantic complexity of DeFi transactions. They either rely heavily on low-level token transfers, or perform protocol-agnostic money flow analysis, failing to capture the high-level intent of transactions. In this paper, we propose AMLGuard, a semantic-aware AML framework for account-based blockchains. AMLGuard tracks illicit fund flows from known malicious addresses by performing semantic analysis on complex DeFi transactions, enabling accurate and continuous laundering tracking. Given a complex transaction, AMLGuard combines static rule-based analysis with retrieval-augmented large language model (LLM) reasoning to infer implicit DeFi semantics, transforming raw transaction data into high-level semantic representations. Furthermore, for cross-chain transactions where laundering intent is not explicitly exposed, AMLGuard parses transaction parameters and performs argument parsing to recover cross-chain semantics, enabling seamless tracking across ledgers. Based on the inferred semantics, AMLGuard abstracts each transaction into a DeFi Semantic Unit (DSU). We evaluate the effectiveness of AMLGuard on 82 real-world laundering cases, involving illicit assets worth over $1 billion. Specifically, AMLGuard reconstructs compact illicit fund-flow topologies with destination precision of 94.4% and 87.6%, while achieving the highest address recall of 98.4% and 95.8% and destination recall of 94.1% and 93.8% on single-chain and cross-chain datasets.

Maorufa Zaman, Haris Md Sahed

Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance. In particular, rare but impactful events are often difficult to detect using conventional modeling approaches, which typically treat extreme movements as isolated observations. This study proposes a volatility-aware approach for extreme event detection using high-frequency Bitcoin limit order book (LOB) data. Motivated by empirical evidence of volatility clustering, the target formulation is extended to incorporate both large future returns and high-volatility regimes. This redefinition increases the proportion of informative samples and aligns the learning objective with the underlying market dynamics. Using a tree-based model (XGBoost) with time-series cross-validation and imbalance-aware evaluation, the proposed method achieves a Precision-Recall AUC of approximately 0.40, significantly outperforming the baseline formulation with a PR-AUC of around 0.06. This represents more than a sixfold improvement in detecting rare events. The results highlight that target design plays a critical role in financial machine learning, often exceeding the impact of model complexity. By incorporating volatility structure into the labeling process, the proposed approach provides a more effective and realistic framework for extreme event detection in high-frequency cryptocurrency markets.

Chloe Ahn, Nina Obermeier

National currencies have long been associated with nation-state building and the expansion of state control. The rise of cryptocurrencies has the potential to disrupt state-society relations traditionally mediated through state-issued currencies. However, unregulated cryptocurrencies may be perceived as too unsafe to act as a true alternative to government-regulated currencies or investment vehicles. Cryptocurrency’s failures may instead lead people to appreciate the role of government more. Using the case of South Korea, we show that public discourse on cryptocurrencies has been more negative than positive in recent years. A demographically representative survey experiment reveals that exposing South Koreans to information about the volatility of cryptocurrencies increases their trust in government, as hypothesized. At the same time, exposure to positive information about cryptocurrencies does not undermine trust in government or support for government regulation. These results point to limitations of unregulated cryptocurrencies when it comes to eroding state-society relations.

Bofeng Pan, Andrei Natadze, Enrico Branca, Jadyn Kimber · 5 authors

Similar to all other cryptocurrency platforms, Ethereum is constantly confronted with malicious activities. In recent years, research efforts have targeted the detection and mitigation of malicious activities and the associated accounts within the Ethereum ecosystem. Yet, the malicious accounts represent only a small visible part of the substantial collaborative network enabling these activities. In this work, we offer the first analysis of this collaborative network and the corresponding affiliate accounts that often remain hidden from detection. We present enEtherShield, an enhanced framework for detecting affiliate accounts that assist malicious accounts in the related Ethereum scams. Our research findings lay the foundation for the detection of the collaborative network enabling Ethereum scams.

Ádám Bereczk, Zoltán Musinszki, Erika Szilágyiné Fülöp, Bettina Hódiné Hernádi

This study investigates the allocation of pre-sale capital by blockchain technology-based startup ventures, with a specific focus on the Play-to-Earn (P2E) segment within the Web3 ecosystem, and its impact on token price performance. Our aim is to determine the proportion of initial capital that P2E startups, according to their business plan (whitepaper), allocated to key areas such as team and advisor expenses, marketing activities, and product development. Subsequently, this research centers on the question of how the focal areas of pre-sale capital utilization (team, marketing, development) correlate with the subsequent price performance of the tokens issued by these startups. The timeliness and relevance of this topic are underscored by the dynamic evolution of blockchain technology and the P2E model, as well as the critical role of startups' capital allocation decisions. Understanding how the utilization of initial funding influences long-term value is also of paramount importance for investors. Based on the results, while excessive marketing expenditures may offer a project short-term benefits, this strategy can potentially have negative long-term consequences. A project's financial viability is contingent upon competent human resources and the insights of external experts; nevertheless, these elements alone are not definitively sufficient. The significance of product development was only evident when the effect was measured in Bitcoin terms; no correlation was found when measured in Dollars.

Varshitha C.N, Leela M.H

ABSTRACT The rise of cryptocurrency has transformed the way individuals perceive and participate in investment activities. As digital assets continue to gain global recognition, major digital assets, including Bitcoin, Ethereum, Ripple (XRP), and Tether, have gained significant interest among investors seeking alternative avenues for wealth creation. The growing adoption of blockchain technology, expansion of digital financial services, and increasing accessibility of cryptocurrency trading platforms have contributed to the rising popularity of crypto investments in India. The present study explores the emerging cryptocurrency investment trends in India through the analysis of secondary information collected from scholarly articles, industry reports, government documents, and other credible sources. The research focuses on important areas including cryptocurrency adoption, market growth, investor demographics, regulatory developments, and investment behaviour. The findings indicate that investments in cryptocurrencies have experienced notable growth in India, particularly among younger investors, despite challenges related to market volatility and regulatory uncertainty. The study concludes that digital assets are gradually becoming an important part of the Indian investment environment and may continue to influence future investment patterns. Keywords:, Digital Assets, Investment Trends, Blockchain Technology, Investor Behaviour.

İsmail Cem Özkurt, Deniz ÖZYAKIŞIR, Yunus Kutval

This paper seeks to assess the feasibility of utilizing Bitcoin as a currency within Türkiye. To achieve this, the research analyzes long-term cointegration relationships between Bitcoin and both the US Dollar and Euro, employing monthly data from November 2017 to February 2025 and utilizing the Fourier Shin cointegration test. The results of the cointegration tests, bolstered by Fourier series analysis, reveal significant long-term cointegration relationships between Bitcoin and both the USD and Euro. The DOLS analysis indicates that a 1% rise in Bitcoin leads to a 14% decrease in the USD price and a 17% increase in the Euro. These results imply that Bitcoin exhibits a high sensitivity to ex-change rates, positioning it as a speculative investment in the short term. The pronounced inverse correlation between the US Dollar and Bitcoin raises the possibility of Bitcoin serving as a substitute for the US Dollar.

Ahod Alghuried, Qasem Abu Al‐Haija

This chapter explores the role of blockchain and cryptocurrency forensics in investigating Darknet-enabled cybercrime. Cryptocurrencies such as Bitcoin and privacy-focused coins are widely used in Darknet marketplaces because they support pseudonymous transactions that complicate tracing and attribution. The chapter examines forensic techniques for blockchain analysis, including address clustering, transaction graph analysis, and heuristic-based tracing. It also explains how illicit financial flows are concealed through mixers, tumblers, and chain-hopping strategies. In addition, the chapter reviews analytics tools used by law enforcement and cybersecurity professionals to detect suspicious patterns and link wallets to entities. Challenges related to privacy-enhancing cryptocurrencies, blockchain scalability, and legal considerations are discussed. Finally, emerging threats involving decentralized finance (DeFi) and cross-chain transactions are explored to provide researchers, forensic analysts, and policymakers with insights into illicit financial activity in the Darknet ecosystem.

Foued Saâdaoui, Othman Ben Messaoud

Abstract This paper analyzes the electricity consumption of Bitcoin mining as a component of blockchain-based financial infrastructure and develops a hybrid forecasting framework that combines a Neural Network Autoregressive model with Exogenous Inputs (NARX) and Mixed Data Sampling (MIDAS). The specification embeds nonlinear state dependence within a feedforward neural network structured as a NARX and exploits mixed-frequency information from daily and monthly indicators to forecast weekly electricity consumption. A key methodological contribution lies in reframing exogenous variable selection as a ranking-based optimization problem grounded in individual explanatory power. To support this, a large language model (LLM)-assisted screening procedure is used to construct a theory-consistent pool of candidate predictors drawn from the finance, energy and cryptocurrency literature. From this pool, an optimization-based strategy identifies a parsimonious subset of variables that minimizes forecast error within the NARX–MIDAS framework. Empirical results demonstrate that the optimized model significantly outperforms benchmark specifications, achieving reductions of 15–20% in root mean squared error and 10–12% in mean absolute error. Beyond predictive performance, the proposed framework yields interpretable insights into how macroeconomic conditions, policy-related uncertainty and financial market dynamics influence Bitcoin mining activity. These findings have direct implications for risk management, energy planning and regulatory oversight in blockchain-based financial systems, highlighting the value of integrating LLM-assisted knowledge extraction with rigorous optimization-driven forecasting methodologies.

Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca, Rafael A. Berri · 6 authors

The growing use of Bitcoin as a decentralized digital asset and investment tool has sparked strong interest in understanding its market behavior. This study presents a new approach to analyze Bitcoin market sentiment by combining on-chain and financial data with social media posts. Unlike models that aim to predict prices, this work focuses on explaining market sentiment using blockchain transactions, historical price data of Bitcoin, and daily Twitter sentiment classifications. The method merges sentiment trends with on-chain and financial metrics, normalized into a dataset for detailed market analysis. Multiple machine learning models were tested using cross-validation, with Gradient Boosting (XGBoost) emerging as the most reliable model for classifying sentiment, achieving an average F1-score of about 0.84. SHAP (SHapley Additive exPlanations), a game theory-based method for model interpretability, was used to quantify the contribution of on-chain features to the model's predictions, improving transparency. The results indicate that this data combination yields meaningful predictive signals and insights, supporting data-driven cryptocurrency analysis and future improvements with deep learning.

Francesco Cesarone, Gianna Figà‐Talamanca, Francesca Luciani

Abstract This study develops a large-scale framework to evaluate whether, and under what conditions, adding cryptocurrencies to equity investment universes improves portfolio performance.We apply four long-only portfolio strategies, Global Minimum Variance, Risk Parity, Most Diversified Portfolio, and Equally Weighted, to 10,000 randomly generated investment universes. These universes consist of baskets containing either only equities or varying combinations of equities and cryptocurrencies. We conduct an out-of-sample analysis on real-world data from 2018 to 2023 to assess the influence of cryptocurrencies on portfolio outcomes. The empirical findings reveal that portfolios constructed from mixed equity and cryptocurrency universes provide a better risk-return profile compared to purely equity-based portfolios, particularly for Risk Parity, Most Diversified, and Equally Weighted.

Edward Lee, Andrew Moshirnia

As cryptocurrency is increasingly adopted, regulators must consider whether regulations are needed to protect investors and consumers. In prior research involving a behavioral experiment, we identified the existence of a face value effect when people use cryptocurrency in transactions. Just as prior researchers have found a face value effect when people use foreign cryptocurrency,we found a face value effect with the use of cryptocurrency. People predictably anchor on the nominal amount indicated by the cryptocurrency and fail to accurately convert the amount into their home currency. This cognitive bias results in significant overspending when the cryptocurrency is stronger than the U.S. dollar (USD). This Article examined whether different interventions could reduce this cognitive bias. Based on the results of another behavioral experiment we conducted, we found two interventions did so. First, when prices for a transaction are displayed in both USD and cryptocurrency values, the face value effect and overspending were mitigated. Second, in situations involving bidding on an item with no fixed price, requiring people to write out their bid or payment in USD before bidding in cryptocurrency was even more effective in reducing the face value effect and overspending. Accordingly, we propose the adoption of (1) domestic currency pricing (DCP) for items sold in cryptocurrency that requires the corresponding USD amount to be included for any price in cryptocurrency; and (2) for bidding on items in cryptocurrency, a simple requirement for people to “type out the price” of their bid first in USD, or the “TOP price” for short. These interventions are modest, but they may help reduce unintended overspending due to the face value effect.

Cesar Osvaldo Alcantar, Gaby Dagher, Steven Cutchin

Wash Trading remains a large concern for cryptocurrencies. Wash Trades happen when a buyer trades an asset with himself or with a trusted associate to artificially inflate the value of the asset. This market manipulation means that the victim buys the asset at a higher price than the actual value. It is important to detect Wash Trading because researchers have shown that wash trading is very common in today’s cryptocurrencies and it has been reported that millions of dollars have been lost because of wash trading. However, detecting wash trading is difficult because of the pseudo-anonymality of the buyer and the seller of the block chain for that cryptocurrency. It is imperative that we create algorithms to detect Wash Trading. In this paper, we introduce a framework and algorithms to quantify the characteristics of wash trading. Then visualize those characteristics in the context of the Non-Fungible Token Market as well as the Ethereum blockchain to illustrate suspicious events of wash trading.

Owolabi Babatunde Akinsanya, Jacob Bethel Obeng

The rapid expansion of U.S. financial technology platforms has created new vectors for money laundering, terrorist financing and financial crime that traditional anti-money laundering frameworks were not designed to address. This article presents a systematic literature review of 78 peer-reviewed studies published between 2015 and 2025 to examine the design, performance and policy implications of advanced anti-money laundering frameworks for U.S. fintech platforms. This study draws on evidence from financial criminology, regulatory law, computer science and organizational studies; the review finds that machine learning-based transaction monitoring systems reduce false positive alert rates by 40 to 70 percent compared to rule-based systems, as well as improving detection of sophisticated layering schemes. Blockchain analytics tools partially de-anonymize cryptocurrency transaction flows and have been used to identify illicit financial activity on major blockchain networks. Regulatory technology platforms automate suspicious activity reporting, beneficial ownership identification and customer due diligence workflows in ways that reduce compliance costs as well as improve regulatory data quality. However, the reviewed literature also documents persistent challenges, including algorithmic disparate impact in AML monitoring systems, beneficial ownership opacity through shell company structures, regulatory arbitrage between licensed exchanges and decentralized finance protocols and the systemic underutilization of suspicious activity report intelligence by law enforcement agencies. The article concludes with six evidence-based policy recommendations and a research agenda for advancing AML framework effectiveness in the rapidly evolving U.S. fintech sector. Keywords: Anti-Money Laundering, Fintech, AML Compliance, Machine Learning, Transaction Monitoring, Know Your Customer, Cryptocurrency Regulation, Regulatory Technology, Suspicious Activity Reporting, Financial Crime.