Transform the web into your data source. Chapter 10 introduces methods for extracting financial data and downloadable files from websites using Python tools such as BeautifulSoup and requests. Learners will practise legal and ethical scraping through examples based on demonstration sites. The chapter includes a compliance checklist covering permissions, terms of service, and data protection. It also introduces the use of application programming interfaces for accessing financial data, including cryptocurrency prices from CoinGecko.
In this paper, they speak of the evidence protection system (EPS) that is a new approach to problem resolution involving contemporary legal and investigative procedures. The EPS uses the blockchain technology called Ethereum to ensure that under all the stages of the evidences life-cycle they are secured, authentic and comprehensive. Using timestamps, smart contracts, and cryptography sequencing, the system creates an evidence management platform, which is easy to read, decentralized, and cannot be hacked. The EPS stores evidence as a record that is not mutable through the use of distributed ledger technologies and digital timestamps. This is what makes it be safer than the centralized systems. smart contracts even the playing field of security and transparency by providing automation of functions such as chain of custody and access control. The integrity of data can be checked in two ways, encryption, and hashing, and keep the actual data safe. overall: the EPS provides the full solution to the issues of processing the evidence in legal environment of the current times, which is why confidence in the efficiency and credibility of evidence that is stored grows.
Nabeel Mahdi AlthabhawiďŞ, Raâed Fawzi Aburoub, Rizal Rahman, Faris Kamil Hasan Mihna ¡ 5 authors
Smart contracts raise persistent challenges regarding compliance with traditional contract formalities, including writing, signature, notarization, and in certain transactions, registration. These issues are particularly significant in high-value and public-facing transactions such as real estate, where formalities determine legal validity, evidentiary sufficiency and publicity effects. While existing scholarly work has examined these challenges from either doctrinal or technological perspectives, limited attention has been given to how the functional roles of formalities interact with blockchain architecture, practitioner perceptions and institutional legal frameworks. This study addresses this gap through a mixed-methods approach combining doctrinal legal analysis with qualitative socio-legal research based on 27 semi-structured interviews with legal professionals including attorneys, judges, and academic scholars. The analysis is grounded in a civil law framework, with particular reference to the Jordanian legal system, while references to the European Unionâs eIDAS Regulation are used illustratively to demonstrate regulatory approaches to digital authentication. The findings demonstrate that blockchain-based systems can effectively support the evidentiary and attribution functions of contractual formalities through cryptographic verification, consensus mechanisms, and automated execution. However, they do not independently satisfy formalities that perform cautionary, constitutive, protective or public order function, namely notarization and registration, which remain dependent on institutional validation and legal recognition. The analysis further shows that practitioner concerns reflect not only doctrinal constraints but also institutional roles and varying levels of technical familiarity. To address these limitations, the study proposes a function-based analytical framework for evaluating smart contract formalities and identifies two complementary pathways for legal adaptation: (i) institutional integration, including registry-linkage systems and hybrid contracts; and (ii) technological adaptation, including digital authentication frameworks and legal oracles that connect on-chain execution to off-chain legal conditions. The study concludes that smart contract formalitiesâ challenges arise not solely from technological limitations, but from the interaction between legal doctrine, institutional structures, and system design. It advances a functional framework for aligning automation with the evidentiary, protective, and publicity functions of contractual formalities.
Gaurav Kokane, Dr. Pratibha V. Kashid, Prathamesh Pandit, Hitesh Patil ¡ 5 authors
ABSTRACT Cryptocurrency trading has rapidly evolved into a highly dynamic and technology-driven financial domain, attracting significant attention from investors, researchers, and institutions worldwide. This paper presents a comprehensive review of modern cryptocurrency trading platforms by combining blockchain technology, artificial intelligence, and advanced trading mechanisms to create a secure, efficient, and scalable trading ecosystem. The study highlights the use of deep learning approaches such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and attention-based models for improving cryptocurrency price prediction. These techniques utilize technical indicators, trading patterns, and social media data to enhance prediction accuracy. In addition, reinforcement learning strategies are explored to optimize trading decisions and improve performance under highly volatile market conditions. Furthermore, the paper discusses real-time data integration using APIs, secure authentication mechanisms, and scalable system architectures required for continuous trading operations. It also examines the regulatory landscape of cryptocurrency, particularly in the Indian context, including taxation policies and emerging concepts like Central Bank Digital Currencies (CBDCs). Overall, this review provides insights into the development of intelligent, secure, and user-friendly cryptocurrency trading platforms such as CryptoVista. Keywords: Cryptocurrency, Blockchain, Deep Learning, Reinforcement Learning, Smart Contracts, Real-Time Data, Trading Platforms, Security, Scalability, CBDC
Abstract Money laundering is one of the most insidious and sophisticated threats to the integrity of worldwide financial systems. With criminals increasingly employing sophisticated methods to disguise the origin of ill-gotten gains, formal and informal financial structures are acutely exposed to abuse. This chapter discusses the underlying mechanisms and stages of the laundering processâplacement, layering, and integration. It discusses the profound interlinkages between money laundering and other criminal activities, such as drug trafficking, terrorist financing, and kleptocracy, to establish its status as the fulcrum of world illicit economies. Economic consequences of illicit financial flows are carefully weighed, citing their role in undermining market integrity, facilitating capital flight, undermining tax systems, and exacerbating social inequality. Case studies, such as the FinCEN Files and the Danske Bank case, expose structural weaknesses in regulation and enforcement. An assessment of international legal mechanismsâsuch as the FATF Recommendations, EU AML Directives, and US PATRIOT Actâexamines their efficacy, implementation, and cross-border cooperative frameworks. Key compliance tools such as know-your-customer (KYC), suspicious transaction reporting (STR), and beneficial ownership registers are evaluated in the context of the evolving role of financial intelligence units. Lastly, this chapter discusses the new challenges posed by digital finance, namely with regard to crypto-assets and decentralized finance platforms. It assesses the potential of regulatory technology, artificial intelligence, and blockchain analytics in strengthening enforcement measures. This chapter concludes with recommendations for reforms aimed at enhancing transparency, institutional coordination, and global financial resilience against laundering operations.
Crime, Illicit Activities, and Governance
Business and Economic Development
Legal, Health, Environmental and COVID-19 Challenges
Financial fraud across cross-border payment systems and blockchain-based transaction networks has grown in scale, sophistication, and velocity, driven by increased digitization, regulatory fragmentation, and the pseudonymous nature of decentralized infrastructures.This study presents a comprehensive examination of AI-driven anomaly detection techniques designed to address these evolving threats.From a broad perspective, the paper reviews the global financial ecosystem, highlighting vulnerabilities in traditional correspondent banking frameworks and emerging decentralized finance (DeFi) architectures.It then narrows to advanced machine learning and deep learning approaches, including supervised, unsupervised, and hybrid models such as autoencoders, graph neural networks, and reinforcement learning systems for real-time fraud detection.Particular emphasis is placed on transaction pattern analysis, behavioral profiling, and network topology modeling to uncover hidden relationships and detect anomalous activities across distributed ledgers and cross-border payment rails.The study further evaluates challenges such as data sparsity, class imbalance, adversarial manipulation, privacy constraints, and regulatory compliance, including AML and KYC requirements.By integrating AI with blockchain analytics and financial monitoring systems, the paper demonstrates how adaptive, scalable, and explainable detection frameworks can significantly enhance fraud prevention capabilities.The findings provide strategic insights for financial institutions, regulators, and fintech developers aiming to strengthen global financial security.
Donghan Chen, Zhihui Lu, Chenchi Luo, J I N Y I Lin ¡ 7 authors
The growth of decentralized finance (DeFi) has been accompanied by an increase in rug pull scams, in which developers misappropriate investorsâ funds, rendering the associated tokens worthless. Existing detection methods struggle to capture dynamic on-chain information and provide interpretable risk assessments. This paper presents RugKeeper, a multi-agent framework leveraging large language models for rug pull detection. RugKeeper constructs comprehensive token contexts via a two-step question-driven process and performs multi-path collaborative reasoning, with a Judger Agent validating results to reduce model hallucinations. Evaluations on historical datasets demonstrate that RugKeeper outperforms state-of-the-art methods, achieving 93.55% accuracy, 95.92% F1-score and robust generalization across model backbones. In a real-world sampled dataset from the BNB Chain, 638 previously undetected rug pull tokens were identified. These results highlight the effectiveness of RugKeeper in enhancing DeFi security and supporting risk mitigation.
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.
Speaker anonymization protects against speaker identity inference, yet third parties cannot verify that released speech is authenticated and anonymized as predefined without revealing the original. We propose Verifiable Speaker Anonymization (VSA), a paradigm that enables public verification that a predefined anonymization has been applied while the original remains hidden. We instantiate this paradigm as ZK-VSA using zero-knowledge succinct non-interactive arguments of knowledge (ZK-SNARKs): we encode phase vocoder with time-scale modification (PV-TSM) as arithmetic constraints suitable for succinct proofs, complemented by SNARK-friendly phase handling, and integrate cryptographic commitments with digital signatures for authentication. We evaluate ZK-VSA on LibriSpeech, using automatic speech recognition (ASR) for intelligibility and automatic speaker verification (ASV) for anonymity. Our proof-constrained anonymization closely matches floating-point PV-TSM, while proofs add only a slight overhead and verify in milliseconds. These results demonstrate the practicality of VSA and open a path to proof-based guarantees for broader speech transformations.
Australia's Anti-Money Laundering and Counter-Terrorism Financing Amendment Act 2024 (Cth), effective 1 July 2026, extends the national AML/CTF regime to designated non-financial businesses and professions. While art dealers are not expressly targeted by the reforms, the statutory definitions give rise to significant interpretive challenges at the intersection of art law and the regulatory framework, with potentially far-reaching consequences for the art market. This article critically examines three such challenges: the historical artist versus artisan distinction embedded in the legislation's reference to "goldsmith's or silversmith's wares"; the functional classification of objects that straddle the boundary between fine art and decorative art; and the degree of physical attachment required for an artwork to constitute a "precious product" by virtue of its material composition. The article further considers the extension of the regime to virtual assets, including non-fungible tokens, and its implications for digital art transactions. It concludes that the current definitional framework risks producing arbitrary regulatory outcomes, capturing certain art objects while excluding others of comparable money laundering risk, and recommends that art market participants adopt a precautionary compliance approach (including robust know your client procedures and readiness to satisfy designated services obligations) pending further regulatory guidance from AUSTRAC.
Ethereumâs active financial ecosystem makes itself become a hotbed of phishing scams. Existing studies construct transaction subgraphs and employ GNNs to identify potential phishing accounts. However, existing detection methods rely on complete historical transaction data, making it difficult to detect scams at an early stage. To address this issue, we propose Ethereum Phishing Scams Early Detection (called EPED) method. First, we combine GCN and GRU to capture accountsâ local structural relations and the temporal evolution of their transactions. Second, we introduce deep reinforcement learning for adaptive optimization of the detection time. The two strategies jointly enable early detection with limited data. Experimental results demonstrate that by using only 4.6 days of transaction data, our method achieves a Recall 4.09% higher than existing methods that rely on the full dataset. This result demonstrates the methodâs timeliness and effectiveness.
The rapid growth of decentralized finance on Ethereum has facilitated the rise of fraudulent Ponzi schemes, which exploit blockchain immutability and pseudonymity to deceive investors. Existing detection methods, often fail to generalize to evolving attack strategies, while current multimodal approaches suffer from high computational overhead. To address these challenges, we propose LightPonzi, a lightweight multimodal framework that integrates transaction graphs, abstract syntax trees, and textual semantics of smart contracts. By leveraging GraphSAGE and DistilBERT, LightPonzi efficiently encodes structural, behavioral, and semantic features, which are fused for accurate classification. Extensive experiments on a curated dataset of Ethereum contracts demonstrate that LightPonzi achieves a balanced F1 score of 0.911 while processing each contract in 80.11 ms on average, outperforming state-of-the-art baselines in both effectiveness and efficiency. Our framework provides a practical solution for real-time Ponzi scheme detection.
Ethereum's EIP-1559 fee mechanism was designed under the assumption of homogeneous, myopic agents responding to a single congestion signal. We examine how this assumption interacts with the heterogeneous demand structure of real-world Ethereum users. Analyzing 62,142 confirmed transactions from seven operational firms across seven industries (January--March 2026), we document significant intraday gas-fee variation: fees peak at hour~12 UTC (7\,AM ET, $\hatβ_{12}=\$0.054$ above the U.S.\ evening baseline, $p<0.001$) and are associated with periods of elevated speculative-arbitrage activity. Operational firms exhibit heterogeneous scheduling responses moderated by transaction deferrability and gas intensity. Residual cost floors, i.e. the gap between observed expenditure and the counterfactual under perfect off-peak scheduling, range from 40.7\% to 92.5\% of actual expenditure, and persist even during the lowest-cost hours ($h\in\{20,21,22,23\}$ UTC, 3--6\,PM ET). We introduce an On-Chain Scheduling Matrix that maps firms to four scheduling regimes as a practical framework for managing gas-fee exposure under the current mechanism.
The explosion of multimodal healthcare data such as medical images, physiological signals, and electronic health records has posed major security storage problems, credible data sharing, and correct disease diagnosis. Traditional healthcare is usually vulnerable to privacy concerns, unauthorized access, and poor analytic abilities. To handle the above challenges, this paper suggests STMD-BTNet, an intelligent and secure architecture that combines blockchain-based trust management and deep neural networks to process multimodal healthcare data reliably. The proposed system secures patient data with a dynamic hash-based session key generation system and secures the transmission with a verified blockchain bridge that uses a trust-conscious Proof-of-Stake consensus system. Access control with smart contracts can be used to provide access to sensitive records by authorized medical professionals. A multimodal deep learning model that incorporates convolutional neural networks to process medical images, long short-term memory networks to process physiological signals, and fusion layer to combine features of clinical attributes allows patients to receive the correct diagnosis. Experimental results on publicly accessible healthcare datasets show better performance with accuracy of 98.62, precision of 98.45, recall of 98.30 and AUC of 99.12 in the combined application of the multimodal, and low latency and improved data security. Comparative analysis has proved that STMD-BTNet is more reliable to diagnose, scale, and trust well than current deep learning and blockchain-based methods and is therefore applicable in next-generation intelligent healthcare infrastructures.
In client-server applications such as copyright protection and content moderation, learning-based perceptual hashing compresses images into compact binary codes whose Hamming distances approximate perceptual similarity. Clients then transmit these codes to servers for comparison. However, this approach faces dual challenges: algorithmically, how to effectively balance robustness and discriminability while mitigating bit imbalance issues; protocol-wise, transmitting these hashes compromises client privacy through content inference and cross-platform user tracking. To address these challenges, we propose a trustworthy privacy-preserving framework that integrates deep hashing with zero-knowledge proofs. The framework comprises: (1) A robust deep hashing module that generates discriminative binary codes by optimizing a composite objective function composed of the Angular Triplet and quantization losses, while using a multi-scale strategy to correct bit imbalance. (2) A privacy-preserving similarity comparison protocol based on Sumcheck and Logarithmic Lookup, which enables clients to locally prove batch Hamming distance relationships against public dataset entries without disclosing their hash values. We conducted comprehensive evaluations to demonstrate the practicality and efficiency of our design compared to existing schemes. Source code is available at https://github.com/mengdehong/zkph.
Advanced Steganography and Watermarking Techniques
Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load.
This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.
Pradipta Agung Wahyu Pratama, Hujjatullah Fazlurrahman, Fresha Kharisma, Muhammad Fajar
This study analyzes the influence of Digital Leadership, Digital Literacy, and Digital Competency on Innovative W This study examines the influence of Digital Leadership, Digital Literacy, and Digital Competency on Innovative Work Behavior (IWB) among Web3 remote workers in Surabaya City. The research is grounded in the transformation of remote work within the Web3 ecosystem, which presents innovation challenges due to limited spontaneous social interaction. This study addresses both a contextual gap, focusing on the underexplored population of Web3 workers, and a methodological gap by integrating three key digital factors into a single analytical framework. A quantitative approach was employed using purposive sampling, involving 70 Web3 remote workers in Surabaya. Data were analyzed using multiple linear regression with SPSS software. The results show that Digital Leadership (β = 0.330), Digital Literacy (β = 0.322), and Digital Competency (β = 0.301) have a positive and significant effect on Innovative Work Behavior (p < 0.05). Simultaneously, the variables significantly influence IWB (F = 45.646), with a coefficient of determination (R²) of 0.675, indicating that 67.5% of the variation in IWB is explained by the model. These findings confirm that the integration of digital leadership, strong digital literacy, and well-developed digital competency plays a critical role in enhancing innovative work behavior in Web3 remote work environments. The study contributes to the development of Upper Echelons Theory in the context of decentralized digital work and provides practical insights for organizations in designing effective digital talent strategies.
Tomi Setiawan, Muhammad Farras Samith, Muhammad Hammam Mughits, Aulia Fitrah Uswatun Hasanah ¡ 5 authors
This study synthesizes Web3-based governance strategies in closed supply chains for electric vehicle batteries in Indonesia, focusing on reducing environmental impacts and improving resource efficiency. It includes a theoretical framework and case studies to address the overall issues in empirical validation and contextual adaptation. This paper aims to expand the governance framework, compare digital product passport implementations, identify incentive mechanisms including CSR integration, quantify regulatory models, and analyze practical case studies. An integrated analysis of multidisciplinary literature, using theoretical modeling, game theory, and empirical case studies, reveals that blockchain-based digital product passports improve transparency and lifecycle traceability but face challenges in large-scale implementation and data standardization. Incentive mechanisms based on game-theoretic frameworks and CSR promote resource efficiency, although empirical evidence in Indonesia remains limited and its social dimensions remain underexplored. A comparative regulatory analysis highlights the relevance of EU policies as a benchmark while also highlighting regulatory gaps and enforcement challenges in Indonesia. The case studies demonstrate the feasibility of Web3 technologies in improving environmental outcomes and operational efficiency, but scalability and stakeholder coordination require further investigation. These findings highlight the need for an integrated, multi-stakeholder governance model that bridges theoretical innovation with practical implementation in Indonesia's unique socio-regulatory context. This study informs future research and policy development to optimize sustainable battery supply chains through digital governance and circular-economy principles.
Sahri Ramadan, Sawali Wahyu, Budi Tjahjono, Riya Widayanti
The increasing adoption of electronic certificates in academic and professional environments raises critical challenges related to authenticity, data integrity, and verification reliability. Conventional certificate management systems commonly rely on centralized architectures and manual validation procedures, which are vulnerable to manipulation, duplication, and single points of failure (SPoF). This study proposes a blockchain-based electronic certificate verification system implemented on a private Hyperledger Fabric network using smart contracts. The system records certificate verification metadata on a distributed ledger to ensure integrity and traceability while maintaining storage efficiency. Smart contracts automate the issuance and validation lifecycle, enabling transparent and tamper-resistant certificate management. The verification process is conducted by comparing document authentication data with records stored on the blockchain. Experimental evaluation demonstrates that the proposed system can accurately identify document alterations and consistently distinguish between valid and invalid certificates. The results indicate that the integration of blockchain and smart contracts as an active validation mechanism enhances transparency, reduces dependence on centralized authorities, and improves trust in mobile-based digital credential systems. Therefore, the proposed approach provides a secure and reliable framework for electronic certificate verification in academic environments.
Introduction The proliferation of dApps is increasing the attack surface for exploitable vulnerabilities in smart contracts, and thus there is a need for verifiable detection methodologies. Methods In this work, we propose a machine learning framework with blockchain integration for explainable and note that âexplainableâ implies âverifiableâ smart contract vulnerability detection. The SmartBugs-curated data was systematically pre-processed with metadata filtering, feature correlation analysis and encoding for model evaluation. Four ensemble learning methods, Random Forest, XGBoost, LightGBM and CatBoost were tested under identical experimental settings for comparison. Results The Random Forest classifier initially achieved the best balance in terms of stability and performance with an accuracy of 87.67%, successfully detecting important vulnerability classes such as re-entrancy, unchecked low-level calls, etc. To enhance the applicability of our blockchain-based machine learning framework for vulnerable smart contract analysis we extend it from the initial 143-contract dataset SmartBugs-Curated to evaluate it on on large-scale set, namely, SmartBugs-Wild which contains 47,398 real-world Ethereum contracts. Based on 29 static contract-level features, unsupervised clustering (k = 4, silhouette score = 0.3735) identifies discrete structural archetypes present in the dataset. Ensemble classifiers (such as XGBoost, CatBoost, Random Forest and LightGBM) can get excellent discriminative performance on these cluster labels: LightGBM achieves 99% accuracy and 0.98918 macro-F1. Discussion The additional results show that the approach scales, is robust and leads to stable models, even if interpretable. After injecting SHAP-based explainability, the interpretability and predictive power of CatBoost became similar to those of Random Forest. In order to guarantee end-to-end trust and traceability of our optimised classifier, this was linked to a blockchain oracle that independently store the outcomes as well as confidence scores for predictions directly onto an Ethereum-compatible ledger through a Vulnerability Registry smart contract. This integration provides the data is immutable, auditable and transparent in reporting.