Blockchain Papers

Monica T. Whitty, Amin Sakzad

No abstract is available for this record.

Abhishree Sinha

Phishing attacks pose a significant security issue in Ethereum-based blockchain systems. Existing solutions, like TEGDetector, address these attacks by analysing how transactions evolve over time using Transaction Evolution Graphs (TEGs) constructed via time slicing, followed by a dynamic graph classifier that captures both spatial structure and temporal evolution with learned time coefficients. However, building and managing these graphs across multiple stages makes the overall approach complex and difficult to implement. In this work, we propose E2E-EmbedDetector, a lightweight end-to-end neural classification model that works directly with raw transaction data. The model learns embedding representations for important entities such as From, To, and ContractAddress, and also used two additional numeric features: transactional value and a derived input length. We train and evaluate the model on a balanced dataset of 50,000 Ethereum transaction using an 80/20 stratified split. The model achieves an accuracy of 95.63%, precision of 0.9265, recall of 0.9912, an F1 score of 0.9578, a ROC-AUC score of 0.9915 and a PR-AUC score of 0.9909. These results show that strong phishing can be achieved using a simpler and more practical tabular approach, without relying on complex temporal graph- based networks.

Brandon C. Dulisse, Jean Denis, Nathan T. Connealy

Purpose This systematic scoping review aims to map the peer-reviewed evidence on cryptocurrency-enabled money laundering to highlight significant gaps in knowledge, particularly regarding its role in the rapid expansion of Southeast Asia’s scam economy. It focuses on scam compounds, money mule networks and anti-money laundering/counter-terrorist financing strategies to inform policy responses. Design/methodology/approach Following systematic scoping review guidelines, 7,669 records from five databases (2009–2025) were screened, yielding 25 peer-reviewed studies. Dual-reviewer screening, standardized extraction and thematic synthesis were used, with quality appraisal emphasizing methodological rigor and theoretical depth. Findings Four themes emerged: (1) industrial scam ecosystems in Southeast Asia are linked to crypto-enabled wealth transfer and laundering, though peer-reviewed evidence on these specific regional operations remains limited; (2) offender rationales favor low-risk, high-reward techniques like mixing, privacy coins and high-volume, low-amount transfers; (3) blockchain forensics enable tracing and evidence gathering, but are challenged by evolving privacy tools; and (4) regulatory attempts often lag behind criminal innovation, necessitating financial reforms. However, the review reveals a critical gap: only a minority of studies directly address Southeast Asia’s scam compounds, underscoring the need for targeted research amid the region’s burgeoning scam economy. These themes illustrate that cryptocurrency has become essential financial infrastructure for organized crime, yet empirical insights specific to Southeast Asia remain limited. Originality/value To the best of the authors’ knowledge, this is the first systematic scoping review highlighting the shortage of peer-reviewed research and studies of Southeast Asia’s scam economy in cryptocurrency laundering research, synthesizing broader literature to advocate for criminologically informed interventions.

Scarlett Sieber, Ian Fong, Tina Lončarić, Dhanum Nursigadoo · 6 authors

Fraud is the financial world’s biggest headache. It doesn’t matter if you’re in traditional finance (TradFi) or decentralized finance (DeFi); you’re faced with an unrelenting wave of criminal behavior.

Ish Kumar, Vinay Maitri

Abstract The rapid growth of cryptocurrencies has redefined the global financial landscape, enabling decentralized and borderless transactions. While digital assets offer efficiency, innovation, and financial inclusion, they have also introduced new avenues for financial crime. This chapter critically examines the intersection of cryptocurrency and illicit financial activity, with a focus on typologies such as money laundering, terrorist financing, ransomware payments, investment fraud, and tax evasion. Through an analysis of real-world cases and peer-reviewed research, this chapter highlights how features such as pseudonymity, decentralized finance, and privacy-enhancing technologies have complicated regulatory enforcement and forensic tracking. This chapter also provides a comparative overview of global regulatory responses, including frameworks from the United States, European Union, Singapore, India, and China, as well as guidance from international institutions such as the Financial Action Task Force and the Organization for Economic Co-operation and Development. Key challenges such as legal ambiguity, technological complexity, and institutional fragmentation are explored in depth. In response, this chapter identifies emerging opportunities to strengthen oversight, including blockchain analytics, regulatory sandboxes, supervisory colleges, and capacity-building initiatives. It concludes by recommending a coordinated, adaptive, and risk-based regulatory approach that balances innovation with accountability.

Abdelwahab Mansour Alenezi, Muaath Suleiman Almulla

<p>It seems that humans have gone too far in creating technological industries; it also seems that they have lost control over the ethics of developing their various tools. If the legal system in the past faced a major challenge in protecting the rights and interests of those dealing with the Internet from the misuse of information-technology tools, it now faces a new challenge after Mark Zuckerberg’s announcement (owner of Facebook, which changed its name to “Meta Platforms”) of his commitment to developing the virtual world he called “Metaverse” – a world that blends reality and fantasy, allowing users to interact with one another with real emotions in three-dimensional virtual environments completely similar to reality. This paper aims to define the nature of metaverse technology and study the extent to which criminal laws are prepared to face the challenges it poses, especially with the emergence of criminal activities through it. To this end, the research followed a descriptive and analytical approach in presenting the topic and was divided into three parts: Firstly, explaining the nature of metaverse technology. Secondly, Describing examples of attacks in this environment. Lastly, Addressing the extent to which criminal laws are prepared to face the challenges of metaverse technology. The research concluded that the metaverse is a recent phenomenon in the cyberspace environment, which began through the electronic-gaming portal and has become a business environment in various fields through blockchain platforms and non-fungible tokens (NFTs). Avatars are a form of personal data that identify users for access to the metaverse and may themselves become the subject or tool of criminal activities. The paper also found that the danger lies in the uncontrolled acceleration of technological development. The metaverse, without legal safeguards, is fertile ground for numerous crimes. Criminal activities in the metaverse are modeled on cybercrimes given their nature and characteristics. Consequently, the research recommends that legislators review substantive and procedural laws to ensure effective responses at both international and national levels, to adopt the idea of electronic legal personality, and to refrain from rushing to introduce practices and services in this environment until clear legal frameworks define obligations and responsibilities.</p>

Dr. G.V.Mahesh Naath

The quick proliferation of cryptocurrency markets has essentially transformed the financial frameworks of the globe in that it has made it possible to initiate the means of value transfer across borders that are decentralized, borderless, and technologically advanced. Cryptocurrencies are based on blockchain and cryptographic protocols and enable peer-to-peer transactions without use of traditional financial intermediaries, which improves efficiency, lowers the costs of transactions, and increases financial inclusion, especially in underserved areas. In addition to payments, the technologies have stimulated innovation in fields like decentralised finance, smart contracts, and systems of digital identity. But the very same characteristics which render cryptocurrencies appealing also pose serious threats to regulation and law enforcement. The anonymity of transactions combined with the decentralized and cross-border structure of blockchain networks make it difficult to determine who the users are and apply jurisdiction-specific legislation. As a result, cryptocurrencies have become more and more related to different types of financial crime such as money laundering, terrorist financing, tax evasion, ransomware attacks, and illegal trading in darknet markets. The paper discusses the principal types of crime in the context of cryptocurrency and evaluates the challenges encountered by regulatory bodies and law enforcement agencies that might need to overcome these challenges. It also discusses the international regulation reaction, including the involvement of the international standard-setting organizations and the development of compliance systems, including anti-money laundering (AML) and know-your-customer (KYC) systems. Furthermore, the paper also mentions that technological solutions, such as blockchain analytics, are increasingly gaining significance in enhancing investigative potential. The paper concludes that, regardless of the revolutionary potential of cryptocurrencies in terms of financial innovation, their productive regulation involves a moderate and coordinated strategy, incorporating legal and regulatory models, technological progress, and global collaboration.

Anurag Maurya, Shruti Ranjan

The cryptocurrency market represents a decentralized, 24/7 arena that is to a great measure free from regulatory constraints. Although this favors innovation and inclusion, it puts the market in a very vulnerable place due to manipulations with the help of technology. Social media increasingly shapes flows of information and sentiment, disrupting crypto price dynamics. This chapter will review market manipulation in crypto and how social media fuels speculative activity. Manipulation types to be covered include pump-and-dump, wash sales, spoofing, and DeFi-related manipulation. These concepts have been integrated with the author's work on behavioral finance, market microstructure, and blockchain research.

Xihan Xiong, Junliang Luo

Money laundering enables malicious actors to integrate illegal profits into the legitimate economy and has long been a central concern in financial regulation. Blockchain systems introduce new channels for laundering through decentralized, pseudonymous, and cross-border asset transfers. In this context, blockchain exploiters often rely on laundering to conceal fund origins and enable cash-out.

Sreekanth Vp, Ronny Sebastian Roy, Surya Gopalakrishnan, Thanmaya S Krishna

The proliferation of cyber threats and the increasing digital vulnerability of students necessitate innovative educational approaches to cybersecurity awareness. Traditional methods of teaching cybersecurity often fail to engage learners effectively, resulting in poor knowledge retention and limited practical skills. This paper introduces Zyberix, an interactive educational game that leverages Web3 technologies to teach cybersecurity concepts through immersive gameplay and blockchain-based incentives. By integrating Unity game engine with Ethereum smart contracts, IPFS decentralized storage, and NFT rewards, Zyberix creates an engaging learning environment that simulates realistic cyber threats including phishing, malware, and social engineering attacks. The system rewards successful challenge completion with verifiable Non-Fungible Tokens (NFTs), providing both motivation and an introduction to blockchain technology. This research demonstrates how gamified experiential learning combined with Web3 technologies can significantly enhance cybersecurity education, addressing the critical gap between theoretical knowledge and practical application while fostering digital literacy among students.

Songyan Ji, Jin Wu, Wei Zhang, Ming Han · 6 authors

This paper centers on open-secret vulnerabilities (OSVs), a kind of smart contract vulnerability that allows attackers to exploit the natural transparency feature of blockchains to gain illegal monetary profits from problematic smart contracts. Attackers can easily launch OSV attacks by leveraging publicly visible information from a smart contract to issue a profitable transaction without violating its business logic. This poses significant challenges in detecting OSVs. Despite the severe impacts of OSVs, there is no prior research work that systematically discusses OSVs (to the best of our knowledge). To fill this knowledge gap, this paper presents a formal definition of OSVs, and OSVHunter, the first-ever tool aiming to detect OSVs in smart contracts. The detection results show that OSVs are prevalent in real-world smart contracts. Some of these vulnerabilities are even concealed within highly popular Ethereum contracts, with individual contract valuations exceeding five hundred thousand U.S. dollars. These vulnerabilities appear in finance, gaming, gambling, etc. We hope this paper can arouse our community’s attention to the significance of OSVs and lay the technical foundation for future research.

Oluwaseyi Ayodele Adedipe

Purpose: Cyber fraud and money laundering are growing threats to the integrity of operations in the Nigerian banking sector, which undercuts the confidence of customers. This study examined the influence of FinTech solutions specifically smart contracts and cryptographic security on fraud prevention in Nigerian deposit money banks (DMBs), in view of the increasing incidence of cyber fraud and money laundering in the sector. Methodology: The study adopted a quantitative research design, underpinned by the Technology Acceptance Model (TAM), agency theory, and control theory. A cross-sectional survey was conducted on 312 management and IT employees drawn from five selected DMBs in Lagos State. Data collected were analyzed using descriptive statistics and multiple regression analysis. Results and conclusion: The findings revealed that smart contracts have a positive and statistically significant effect on the prevention of cyber fraud (r = 0.408, p < 0.001), while cryptographic security exerts a strong and significant influence on the prevention of money laundering (r = 0.433, p < 0.001). The study concluded that these FinTech solutions are effective tools for enhancing fraud prevention and improving the security architecture of Nigerian banks. Implication of findings: The study implies that deposit money banks should prioritize investment in FinTech innovations, while regulatory authorities should establish supportive frameworks to facilitate their adoption, thereby strengthening financial security and restoring customer confidence in the banking system.

Bharat Bhanushali

This study explores the integration of blockchain technology with anti-money laundering (AML) systems to enhance transaction transparency, ensure immutable audit trails, and reduce regulatory non-compliance. Through a mixed-methods approach, including a systematic literature review and hypothetical dataset analysis, the research examines blockchain’s potential to address AML challenges in financial institutions. Findings indicate that blockchain-enabled AML systems improve transaction traceability by 35%, reduce compliance costs by 20%, and enhance audit reliability through immutable ledgers. However, scalability and regulatory harmonization remain barriers. The study proposes a framework for blockchain-AML integration and offers policy recommendations for stakeholders. These results contribute to the discourse on leveraging distributed ledger technology for financial regulatory compliance, highlighting practical and theoretical implications for global banking systems.

Adegboyega Afolabi, Modupe M. Adesemowo, Olayemi O. Amosun, M. Olamide Otuyelu · 6 authors

As digital intermediation accelerates, Nigerian deposit money banks (DMBs) confront rising cyber-enabled fraud since the launch of Bitcoin in 2009, despite ongoing reforms. Most blockchain research still centres on cryptocurrencies, with relatively few studies examining their applications in other industries. This study investigates whether blockchain technology (smart contracts, permissioned distributed ledgers, and secure digital wallets) is associated with lower fraud in Nigerian DMBs.Using survey data from 120 bankers across five institutions spanning international, national, and regional licenses, we estimate Ordinary Least Squares (OLS) models relating each BCT dimension, and a composite index, to two outcomes: spread of fraud (SOF) and internet fraud activities (IFA). Reliability analysis shows strong internal consistency (α = 0.75–0.91). Models include robustness checks for multicollinearity and specification. Results indicate that higher perceived deployment of smart contracts, distributed ledger, and digital wallet capabilities is negatively and significantly associated with SOF and IFA; a composite BCT index positively predicts overall fraud-reduction assessments. These findings align with recent sectoral evidence that blockchain adoption lowers fraud-related costs and enhances transaction integrity in banking. Given Nigeria’s elevated incidence of electronic fraud in retail payments, the practical implication is that embedding programmable controls, tamper-evident shared records, and cryptographic authentication can harden high-risk processes. We recommend that regulators and DMBs advance permissioned BCT pilots integrated with Anti-Money Laundering (AML) and Know Your Customer (KYC) workflows, strengthen reporting standards, and build human-capital readiness. Beyond cryptocurrency, enterprise-grade BCT offers credible pathways to reduce fraud externalities and improve operational resilience in Nigeria’s banking sector. Keywords: Blockchain; Smart contracts; Distributed ledger; Digital wallet; Bank fraud; Nigeria.

EYYÜP ENSARİ ŞAHİN

Money laundering in cryptocurrency networks poses persistent challenges for financial intelligence units due to the pseudo-anonymous architecture of blockchain systems and the limited effectiveness of conventional rule-based detection methods. This study introduces chaos theory and recurrence quantification analysis (RQA) as a novel framework for characterizing temporal behavioral dynamics in Bitcoin money laundering transactions. Analyzing 46,564 labeled transactions from the Elliptic Bitcoin Dataset spanning 2009-2018, we construct aggregate time series for illicit and licit transaction volumes across 49 discrete temporal steps, corresponding to the dataset’s inherent graph-based snapshot structure, and apply phase space reconstruction techniques to compute three RQA metrics: determinism (DET), laminarity (LAM), and entropy (ENTR). Results reveal paradoxically higher determinism in illicit transactions (38.24% vs. 16.67% for licit), substantially elevated laminarity (35.80% vs. 0.00%), and greater entropy (0.45 vs. 0.00%), indicating that sophisticated obfuscation strategies inadvertently introduce detectable deterministic signatures. Augmenting conventional graph-based features with RQA metrics significantly enhances Random Forest classification performance, reaching near-optimal levels (F1 = 1.000, AUC = 1.000) within the evaluated dataset environment, with entropy emerging as the single most discriminative predictor. While these exceptional results reflect the high fidelity of chaos-based features in capturing structured laundering patterns from this period, they serve as a benchmark for the theoretical potential of nonlinear analysis in blockchain forensics. These findings demonstrate that temporal complexity features offer a powerful diagnostic tool for real-time monitoring and detection of systemic financial crime in evolving cryptocurrency ecosystems.

David Wood

This working paper examines a structural shift in search behaviour affecting crypto and Web3 brands: the divergence between AI-summarised retrieval (zero-click) and deep-intent click-through traffic. Drawing on published zero-click search data and observed AI citation patterns, it identifies why high-volume content strategies are failing in AI-mediated search environments and outlines three asset classes - statistics hubs, original industry research, and high-utility tools - that generate the editorial backlink profiles AI models use to select citation sources. The paper defines the concept of "authority infrastructure" as a capital investment in linkable assets with compounding residual value, contrasting this with recurring spend on keyword-optimised content with no durable equity. Intended for crypto protocol marketing teams, Web3 founders, and DeFi growth leads evaluating content strategy for AI search visibility. Published by David Wood, CryptoContent.dev.

Semaria Eva Elita Girsang, Shaumiwaty, Muhammad Noval Aryansah, Mario Putra Sanjaya · 5 authors

The development of blockchain technology has driven the emergence of Non Fungible Tokens (NFTs) as unique digital assets traded through specialized marketplaces, forming a new digital economic ecosystem. Despite the rapid growth of the NFTs market, issues such as price volatility, the dominance of speculative activities, and uncertainty regarding long-term economic value remain insufficiently understood in academic studies. This research aims to analyze the role of NFTs marketplaces in shaping the economic value of digital assets, identify the factors influencing NFTs price dynamics, and evaluate the economic implications of the NFTs market for creators, investors, and marketplace platforms. This study employs an empirical quantitative approach by utilizing NFTs transaction data obtained from the OpenSea API, NonFungible.com, and CryptoSlam. The variables analyzed include NFTs prices, trading volume, liquidity, creator reputation, rarity score, and asset category. Data analysis is conducted using statistical and econometric methods to identify price determinants and market dynamics. The results indicate that NFTs values are significantly influenced by scarcity levels, creator reputation, asset utility, and the visibility provided by marketplaces. Marketplaces play a crucial role in shaping liquidity and market expectations, but they also contribute to increased volatility and speculative tendencies. This study concludes that the NFTs market has the potential to generate real economic value, yet it continues to face risks related to speculation and instability. These findings contribute theoretically to the digital economics literature and provide practical implications for the development of a more sustainable NFTs ecosystem.

Zhixiang Zhou, Chang Feng, Huiqin Wang, Huaqing Wu

No abstract is available for this record.

Shabnam Kumari, Amit Kumar Tyagi, Shrikant Tiwari

Ethereum and other prominent cryptocurrencies have revolutionized decentralized finance (DeFi) and blockchain technology. Introduced in 2015 by Vitalik Buterin, Ethereum expanded blockchain&s;s capabilities beyond Bitcoin by enabling smart contracts and decentralized applications (dApps). Operating on a public, permissionless network, Ethereum uses its native cryptocurrency, Ether (ETH), to facilitate transactions and computational services. Smart contracts—self-executing agreements encoded directly into the blockchain are central to Ethereum, enabling automated, trustless interactions without intermediaries. This innovation has fostered a thriving ecosystem of dApps, DeFi protocols, and non-fungible tokens (NFTs), driving adoption across industries. Cryptocurrencies like Binance Coin (BNB), Cardano (ADA), and Solana (SOL) each contribute unique innovations to the blockchain ecosystem. Binance Coin, initially launched as an ERC-20 token on Ethereum, now operates on the Binance Smart Chain (BSC). It offers faster and cheaper transactions, supports decentralized applications, and enables reduced trading fees within the Binance ecosystem. Additionally, BNB facilitates staking, governance participation, and cross-chain compatibility for broader usability. Cardano, founded by Charles Hoskinson, emphasizes a Ethereum and other prominent cryptocurrencies have revolutionized decentralized finance (DeFi) and blockchain technology. Introduced in 2015 by Vitalik Buterin, Ethereum expanded blockchain&s;s capabilities beyond Bitcoin by enabling smart contracts and decentralized applications (dApps). Operating on a public, permissionless network, Ethereum uses its native cryptocurrency, Ether (ETH), to facilitate transactions and computational services. Smart contracts—self-executing agreements encoded directly into the blockchain are central to Ethereum, enabling automated, trustless interactions without intermediaries. This innovation has fostered a thriving ecosystem of dApps, DeFi protocols, and non-fungible tokens (NFTs), driving adoption across industries. Cryptocurrencies like Binance Coin (BNB), Cardano (ADA), and Solana (SOL) each contribute unique innovations to the blockchain ecosystem. Binance Coin, initially launched as an ERC-20 token on Ethereum, now operates on the Binance Smart Chain (BSC). It offers faster and cheaper transactions, supports decentralized applications, and enables reduced trading fees within the Binance ecosystem. Additionally, BNB facilitates staking, governance participation, and cross-chain compatibility for broader usability. Cardano, founded by Charles Hoskinson, emphasizes a research-driven approach with peer-reviewed development. It employs a proof-of-stake consensus mechanism called Ouroboros to enhance scalability and security while maintaining energy efficiency. Cardano’s layered architecture enables seamless upgrades and supports smart contract functionality for decentralized applications.

Udai Bhan Trivedi, Bhagwan Jagwani, Shashi Kant Dikshit

Blockchain technology is a somewhat new approach to finding the integrity and chain of digital evidence in various industries, including law enforcement, forensic investigations, supply chain management, and judicial proceedings. Although traditional evidence-keeping systems are prone to manipulation, loss, and inefficiency, blockchain offers an immutable, transparent, and decentralized ledger that securely records and validates every evidence-related transaction. Blockchain technology increases reliability in handling both physical and digital evidence. It uses distributed consensus, intelligent contracts, and cryptographic hashing to eliminate human error and backdoor intervention by assuring immutability, accountability, and automation. This study offers a model blockchain (Chain of Digital Evidence) based on the Ethereum blockchain to guarantee integrity and authenticity in the chain of digital evidence. Ethereum&s;s decentralization ensures that digital evidence is free from manipulation, transparent, and easily verifiable. The study discusses other challenges and prospects for integrating the Ethereum blockchain into the digital evidence chain.

Dare Adegoriolu

Web3, the notion of a decentralised internet powered by blockchain technology, has introduced new scams that are masked in legitimacy and perpetrated through social media. Drawing on interviews and social media data, the study reveals that Web3 fraud thrives among African youth due to economic hardship and weak regulatory oversight. It contends that Web3's ethos feeds a population embroiled in the quest for survival, creating an avenue for manipulation in a largely unregulated space. Here, two kinds of fraud thrive: the use of Web3 as a smokescreen by fraudsters and 'community as bailout' coupled with the 'fear of missing out' (FOMO) as an entrapment, thus revealing how 'communities' become exploitative tools within digital economies of trust. It stresses the need for increased Web3 literacy and clearer oversight as essential to addressing fraud, and situates 'Satoshi-Pablo' as a framework for understanding how innovation and exploitation co-exist in Nigeria's digital landscape.

Henrique Yassuyuki Tsuboi, Rafael Sousa Lima, Kleber Vasconcellos de Oliveira

Purpose This study aims to provide an alternative machine learning model to more quickly and efficiently detect addresses on the Ethereum network suspected of involvement in fraudulent activities. Design/methodology/approach This study performed a machine learning technique known as LightGBM. The machine learning model is trained by using a dataset that identifies licit or illicit addresses on the Ethereum network. This study then applies the trained model to predict the probability that a new transaction should be classified as suspicious for money laundering. Findings Through a set of performance metrics, we show that our model outperforms machine learning models from previous studies, better predicting suspicious money laundering activities. The most relevant attributes in identifying an illicit transaction are: (i) the time difference between the first and last activity of the crypto wallet (a short “lifetime” of the address); (ii) the total number of transactions (accounts used only once or a few times) and (iii) the difference in the distribution of values between the crypto wallets (low values). Research limitations/implications Machine learning techniques have great potential to contribute to the activities of government agents, regulatory authorities and accounting professionals. Practical implications This study adds another tool to combat money laundering, which could lead to improvements in auditing and forensic accounting procedures. This study may be of special interest to regulators and policymakers in their anti-money-laundering roles. Originality/value This study adopts a modern technique that can be considered a valuable tool in identifying and combating fraudulent activities on blockchain networks.

Kyung-Shick Choi, Mohamed Chawki, Subhajit Basu

Sextortion has rapidly expanded into a global cyber-enabled crime that leverages anonymous digital communication and decentralized payment systems. This study examines the financial infrastructures underlying contemporary sextortion by conducting a two-phase analysis of 87 confirmed cases involving cryptocurrency payments. Using blockchain forensic tools and open-source intelligence, the research traces fund movements across perpetrator-controlled wallets, identifies laundering techniques such as mixers, peel-chain transfers, and exchange-based cash-outs, and links these behaviors to narrative patterns within victim reports. The results reveal a dual-tier ecosystem in which mass-produced, multilingual extortion scripts coexist with divergent laundering typologies that differentiate lower-value, high-volume scams from more organized and higher-yield operations. By integrating qualitative and quantitative evidence, this study provides a forensic framework for detecting illicit cryptocurrency activity, improving threat classification, and strengthening investigative and regulatory responses to sextortion and related crypto-enabled interpersonal crimes.

Brandon Dulisse, Chivon H. Fitch, Nathan T. Connealy

Cryptocurrency fraud represents one of the fastest-growing financial crimes worldwide, yet the psychological mechanisms that enable these scams remain understudied. Drawing on 282 verified victim narratives from California and Wisconsin state crypto scam trackers (2023–2024), this study systematically coded the use of seven psychological tactics (PTacs) and seven psychological techniques (PTechs) previously validated in cyber social engineering research. Fraudulent trading platforms (51.5%) and pig-butchering schemes (33.7%) dominated the sample. Across all cases, scammers relied overwhelmingly on impersonation and persuasion techniques paired with fit-and-form and familiarity tactics. On average, 1.77 tactics and 1.86 techniques were deployed per incident; higher psychological complexity (4–6 combined elements) was significantly associated with greater financial losses in fraudulent trading platform scams ($135,346 vs. $63,034, p =.029). These findings demonstrate that cryptocurrency fraud resembles more of a repeatable, psychologically-engineered “playbook” rather than random opportunism by unorganized actors. By revealing consistent patterns of manipulation that scale harm, our study provides an evidence-based roadmap for prevention: psychologically informed user education, platform-level disruption of scripted interaction sequences, standardized narrative reporting in complaint systems, and proactive regulatory alerts keyed to emerging PTac/PTech signatures. Implementing these targeted interventions can materially reduce both victimization rates and aggregate financial losses in digital asset markets.