Hiago Vinícius Benedito dos Santos, Raissa Rosa dos Santos Januario, Ravelly Carvalho Zanatta, Saulo Neves Matos · 5 authors
In recent years, blockchain technology has established itself as an effective, secure, and transparent data storage solution. In this context, smart contracts play a fundamental role by enabling the automated execution of agreements without intermediaries. With the advancement of language models, the opportunity to automatically generate these contracts has emerged, raising concerns about their reliability and potential vulnerabilities. This article proposes a comparative analysis of the available language models for developing smart contracts using Ethereum Virtual Machine’s contracts as a case study. Experiments were made using various Large language models using different metrics to evaluate the susceptibility to vulnerabilities and computational cost. After comparing various models, ChatGPT appears to be the most suitable for generating smart contracts due to its higher compilation rate and, consequently, a larger sample size, despite detecting more vulnerabilities.
The rise of social media has significantly influenced the cryptocurrency market, driving volatility through sentiment-driven trading. This study employs a bibliometric and content analysis approach to examine how social media, particularly Twitter, impacts cryptocurrency price movements. Using the bibliometric analysis, 151 peer-reviewed articles published between 2018 and 2024 were analyzed to identify key research trends, themes, and potential future research. This study finds that social media sentiment plays a crucial role in cryptocurrency price forecasting, with machine learning and natural language processing (NLP) techniques enhancing prediction accuracy. Thematic analysis reveals four primary areas of focus: sentiment analysis and market prediction, machine learning-driven algorithmic trading, blockchain investment risks, and influencer-driven market behavior. This study contributes to the field by consolidating existing social media sentiment and cryptocurrency valuation knowledge, offering insights to investors, regulators, and academics. It highlights the need for future research to integrate multi-platform sentiment analysis, regulatory considerations, and behavioral finance perspectives. These insights are vital for understanding the evolving landscape of digital asset markets and their susceptibility to sentiment-driven speculation.
Fábio R. Silva, Bruno B. Neves, Henrique Fan, Roben Castagna Lunardi · 6 authors
A rastreabilidade na cadeia produtiva de vegetais frescos tem ganhado relevância, especialmente no Brasil, onde a Instrução Normativa Conjunta (INC) 02/2018 estabelece a obrigatoriedade de rastreamento desses alimentos, influenciando também políticas como a destinação de 30% dos recursos da alimentação escolar para a agricultura familiar. Além disso, pesquisas de mercado indicam uma crescente preferência dos consumidores por produtos locais e sustentáveis. Neste contexto, este trabalho propõe uma solução baseada em smart contracts para rastrear a cadeia produtiva de vegetais frescos provenientes da agricultura familiar. O estudo inclui uma análise da INC 02/2018 e apresenta uma solução tecnológica, avaliando sua viabilidade econômica em diferentes redes de blockchains (públicas e privadas) para atender populações de múltiplas regiões geográficas.
Abstract This study offers a detailed literature review and bibliometric analysis of cryptocurrency, virtual digital assets (VDA), and distributed ledger technology (DLT)-based digital currencies. We analyze current research and publishing trends, particularly in forecasting cryptocurrency price volatility. The paper categorizes the development and maturity of various analytic methods employed across domains like centralized finance, decentralized finance, and blockchain. The review highlights both traditional econometric models and emerging machine learning algorithms in these areas, illustrating their evolution and application depth in the literature. Our research further explores national policies on VDA and DLT, focusing on India. We employ sentiment analysis to assess the tone and implications of Indian legislation, policies, and court orders concerning VDA. The analysis reveals a predominantly neutral stance, with a notable positive tilt, suggesting a favorable sentiment from Indian authorities towards these emerging technologies. The sentiment distribution also shows that the Indian authorities are anticipatory and expressing trust in their policies and regulations. Although policy frameworks are still evolving, efforts show a drive towards creating a safe, inclusive environment for VDA and DLT applications in India. Finally, we identify existing research gaps, propose theoretical questions, and recommend potential directions for national-level policies based on our findings.
Abstract: Blockchain is the kind of innovation that was presented to the world a long time ago with the offer assistance of a few cryptocurrencies, but as in the assist investigate done by individuals, world realized blockchain can be utilized in for diverse purposes to make framework more secure with the offer assistance of legitimate computer program and innovation we can make anything much secure and decentralized same can be utilized in house rental framework.
The study investigates how smart contracts work with artificial intelligence to modernize business process automation systems and describes their complex operational structures for creating independent automation systems.The paper explores architectural components that allow AI models to merge properly with smart contracts while focusing on how machine learning functions enhance smart contracts for complex decisions, predictive abilities, and environment-responsive features.The study investigates technical applications of decorated smart contracts across stock and healthcare industries together with financial services and decentralized autonomous systems.Artificial intelligence-integrated smart contracts lead businesses toward a new future by automatically handling business operations while providing users with stronger capabilities for enhanced operations efficiency improvements and better decision outcomes.The blockchain deployment of self-executing electronic agreements written in code as smart contracts transform business operations by holding automated processes and maintaining clear operations and secure computing environments.The main drawback of traditional smart contracts exists in their restricted ability to handle intricate operations along with their inability to adjust to new situations.These limitations in traditional smart contracts become obsolete when AI technology integrates with smart contracts because the result is an analytical system able to forecast and gain wisdom through experience.AI continues to automate business decision-making functions across multiple industries because it enhances both human and workplace operations.
Abstract: The FinTech landscape is undergoing a profound transformation driven by the convergence of Artificial Intelligence (AI), Decentralized Finance (DeFi), and evolving global regulatory paradigms. This paper explores how AI is revolutionizing financial services through intelligent automation, predictive analytics, and enhanced customer experience, while DeFi is redefining trust and transparency by eliminating traditional intermediaries using blockchain and smart contracts. Simultaneously, regulatory bodies are grappling with the rapid pace of innovation, necessitating dynamic, cross-border frameworks to ensure security, compliance, and ethical integrity. Through a critical synthesis of recent scholarly research and data-driven insights, this study identifies emerging synergies and tensions within the FinTech ecosystem. The findings highlight the need for proactive governance, technology-agnostic regulation, and collaborative innovation to shape an inclusive, resilient, and secure financial future. Keywords: FinTech, Artificial Intelligence, Decentralized Finance, Blockchain, Regulatory Transformation, Financial Innovation, Smart Contracts, Predictive Analytics, Cross-Border Compliance, Financial Ecosystem
Existing smart contract honeypot detection approaches exhibit high false negatives and positives due to (i) their inability to generate transaction sequences triggering order-dependent traps and (ii) their limited code coverage from traditional fuzzing’s random mutations. In this paper, we propose a hybrid fuzzing framework for smart contract honeypot detection based on taint analysis, SCH-Hunter. SCH-Hunter conducts source-code-level feature analysis of smart contracts and extracts data dependency relationships between variables from the generated Control Flow Graph to construct specific transaction sequences for fuzzing. A symbolic execution module is also introduced to resolve complex conditional branches that fuzzing alone fails to penetrate, enabling constraint solving. Furthermore, real-time dynamic taint propagation monitoring is implemented using taint analysis techniques, leveraging taint flow information to optimize seed mutation processes, thereby directing mutation resources toward high-value code regions. Finally, by integrating EVM (Ethereum Virtual Machine) code instrumentation with taint information flow analysis, the framework effectively identifies and detects security-sensitive operations, ultimately generating a comprehensive detection report. Empirical results are as follows. (i) For code coverage, SCH-Hunter performs better than the state-of-art tool, HoneyBadger, achieving higher average code coverage rates on both datasets, surpassing it by 4.79% and 17.41%, respectively. (ii) For detection capabilities, SCH-Hunter is not only roughly on par with HoneyBadger in terms of precision and recall rate but also capable of detecting a wider variety of smart contract honeypot techniques. (iii) For the evaluation of components, we conducted three ablation studies to demonstrate that the proposed modules in SCH-Hunter significantly improve the framework’s detection capability, code coverage, and detection efficiency, respectively.
The Ethereum blockchain has transformed decentralized finance (DeFi) and is widely used to issue ERC20 tokens. However, many of these tokens rely on unverified smart contracts, which pose serious security risks. Hackers can take advantage of vulnerabilities in these unverified ERC20 tokens, leading to scams, financial losses, and a decline in user trust. Although several tools are available to audit smart contracts, their effectiveness in analyzing unverified ERC20 tokens remains uncertain. This study examines three auditing tools HoneyBadger, Maian, and Mythril by testing how well they detect security issues in unverified ERC20 tokens. The SmartBugs framework was used to support the auditing process, enabling parallel execution, standardized reports, and bulk auditing of contracts. For a thorough evaluation, two datasets were used: one from 50,581 Ethereum blockchain blocks and another from the DappRadar list of blacklisted ERC20 tokens. These datasets were chosen to provide a broad and realistic view of how the tools perform on both typical and high-risk contracts. The tools were compared based on their ability to detect issues, their execution speed, and their overall effectiveness. The results revealed clear differences in performance: some tools were better at finding vulnerabilities accurately, while others focused more on speed than depth. This study emphasizes the need to improve smart contract auditing methods and highlights the importance of developing more effective security tools to strengthen the Ethereum blockchain.
Smart contract classification holds significant application value in the field of blockchain. However, existing methods suffer from inefficiencies and high computational complexity when dealing with smart contract data. To address these issues, this paper proposes a Cluster-BERT model based on neural clustering techniques. The model reduces the computational burden of self-attention mechanisms by clustering attention heads, thereby improving training efficiency. The Cluster-BERT model comprises multiple modules. Module 1 preprocesses smart contract data, converting abstract syntax trees and graph structure features into text representations suitable for BERT models. Module 2 serves as the core of the model, introducing neural clustering methods to reduce computational complexity. Module 3 further optimizes the model by finding the optimal number of centroids, achieving a balance between training efficiency and classification accuracy. Experimental results show that our proposed Cluster-BERT achieved an accuracy of 91.42%, a recall of 91.44%, and an F1 score of 91.43%, which indicates a noticeable improvement over the baseline model. Our model reduces computational complexity from quadratic to linear, resulting in an average reduction of 8.48% in training time and 7.88% in prediction time compared to the baseline model. On the smart contract dataset, the accuracy and precision of our model outperformed other models proposed in recent years by 1% to 2% points on average.
This article presents a comprehensive overview of smart contract implementation for automating compensation processes within Workday systems. It explores how blockchain-based smart contracts can transform human resources management by codifying compensation rules and policies into self-executing agreements. The integration enables organizations to automate performance-based bonuses, stock option vesting, and salary adjustments while ensuring transparency, accuracy, and compliance. Through detailed examination of technical requirements, integration architectures, and governance frameworks, the article demonstrates how these implementations deliver substantial benefits across operational efficiency, error reduction, and employee satisfaction. Both quantitative returns on investment and qualitative advantages like increased trust and fairness perception are addressed. The material offers practical insights for organizations considering smart contract adoption for modernizing compensation management.
Flavio Corradini, Alessandro Marcelletti, Andrea Morichetta, Barbara Re
Blockchain technology has been widely adopted to enhance the security and the decentralisation of smart applications in large-scale pervasive systems. In such a context, data extraction is crucial as it provides a better understanding of the system’s behaviours. However, several challenges arise in automatically extracting data, due to the variety of data sources, such as transactions, events, contract storage, and the complexity of the blockchain structure. In particular, retrieving smart contract state changes remains unexplored despite its potential usage for discovering unexpected behaviour. For such reasons, in this work, we propose a novel methodology and a supporting application for extracting smart contract state changes and other execution-related data. The obtained data is then decoded and offered in a standard format to be easily reused. The methodology provides additional functionalities such as transaction filtering and capabilities for querying over extracted data. The effectiveness and the performance of the methodology were evaluated on three real-world projects from different EVM-based blockchains.
Non-Fungible Token (NFT) merupakan salah satu inovasi dalam dunia ekonomi berupa aset digital yang memiliki keunikan dalam aspek identitas dan kepemilikan yang menggunakan teknologi blockchain untuk menyimpan dan memverifikasi data. Penelitian ini bertujuan untuk melihat peran NFT dalam ekonomi digital, apakah NFT benar-benar menjembatani kesenjangan dalam digitalisasi ekonomi atau justru menambah lapisan kompleksitas yang kontraproduktif. Penelitian ini menggunakan metode Systematic Literature Review (SLR) untuk mengumpulkan data dari artikel-artikel yang berkaitan dengan topik NFT. Hasil penelitian ini menunjukkan bahwa peran NFT dapat memiliki potensi ganda sebagai jembatan kesenjangan sekaligus sumber kompleksitas kontraproduktif. Teknologi NFT mampu meningkatkan transparansi, memperluas akses ke pasar global, melindungi hak kekayaan intelektual, dan mendukung inovasi di bidang pendidikan, administrasi pertanahan, dan pembiayaan program SDGs. Namun, NFT juga menghadapi tantangan multidimensi seperti ketidakjelasan regulasi yang berpotensi memicu penipuan, pencucian uang, dan ketimpangan akses yang diperburuk oleh volatilitas pasar, spekulasi berlebihan, dan kesenjangan infrastruktur digital di negara-negara berkembang.
Web3 grant programs are evolving mechanisms aimed at supporting innovation within the blockchain ecosystem, yet little is known on about their effectiveness. This paper proposes the concept of maturity to fill this gap and introduces the Grant Maturity Framework (GMF), a mixed-methods model for evaluating the maturity of Web3 grant programs. The GMF provides a systematic approach to assessing the structure, governance, and impact of Web3 grants, applied here to four prominent Ethereum layer-two (L2) grant programs: Arbitrum, Optimism, Mantle, and Taiko. By evaluating these programs using the GMF, the study categorizes them into four maturity stages, ranging from experimental to advanced. The findings reveal that Arbitrum's Long-Term Incentive Pilot Program (LTIPP) and Optimism's Mission Rounds show higher maturity, while Mantle and Taiko are still in their early stages. The research concludes by discussing the user-centric development of a Web3 grant management platform aimed at improving the maturity and effectiveness of Web3 grant management processes based on the findings from the GMF. This work contributes to both practical and theoretical knowledge on Web3 grant program evaluation and tooling, providing a valuable resource for Web3 grant operators and stakeholders.
Emerging technologies, such as artificial intelligence (AI), blockchain, and fintech, have profoundly reshaped the financial sector driving unprecedented innovation and creating transformative opportunities for development. However, they also pose significant challenges to long-term sustainability. While the existing literature provides valuable insights into their influence, a broader scope is necessary to reflect their role in advancing sustainable finance. This study conducts a bibliometric analysis of 2,446 publications from the Web of Science (1996–2024) to map the evolving nexus between emerging technologies and finance. Our findings reveal an expanding research landscape, with key themes including the application of emerging technologies in solving financial problems, the integration of technologies with behavioural and regulatory frameworks, financial innovation for promoting development, risk management and financial stability, digital currencies and blockchain, digital transformation challenges, and sustainable finance. The analysis highlights the dual nature of emerging technologies: while they enhance financial efficiency, transparency, and inclusion, and offer significant opportunities to advance sustainable finance, they also introduce risks such as cybersecurity threats, algorithmic bias, regulatory challenges, and critical barriers to long-term sustainability. To address these challenges, we propose a research agenda prioritizing ethical governance, stress-testing AI models under economic crises, securing decentralized systems, mitigating greenwashing risks, and fostering globally aligned regulatory standards. Interdisciplinary collaboration is essential to tackle ethical, security, and inclusivity concerns. It is imperative for policymakers, regulators, and financial institutions to align technological innovation with sustainability objectives to ensure that advancements contribute to the development of an equitable, resilient, and inclusive financial ecosystem.
The article argues for the sociocultural contextualization of Web3 affordances by examining play-to-earn gaming in the Philippines. It first outlines how socioeconomic factors promote blockchain technology and cryptocurrency. Against this background, and based on scholarship in cultural communication, anthropology, and critical platform studies, the article illustrates how sociocultural frames shape the interpretation and enactment of blockchain-based gameplay affordances. A Grounded Theory analysis of interviews and documents reveals that players identify persistent access and ownership as technical affordances, performing them through the cultural frame of cockfighting and its digital economy version, the side hustle. The study challenges universalist notions of Web3 adoption, highlighting how technical affordances both support and disrupt sociocultural and economic reproduction through narratives of family, competition, and inclusivity. The research calls for comparative studies on how platform corporations structure societies in emerging economies, how platforms exploit culture as use value, and how adopters strategically utilize Web3 technologies.
This research explores the developments and challenges in decentralized finance (DeFi) since 2015 and the increasing use of blockchain technology. DeFi provides access to financial services without traditional intermediaries, improving the economic system's efficiency through automated and transparent smart contracts. The bibliometric analysis shows a significant growth in DeFi-related publications, with 1,909 articles identified between 2015 and 2025. The research also highlights the importance of collaboration between authors and stakeholders to build a more secure and sustainable financial ecosystem. Analysis results using VOSviewer identified 173 keywords in 18 clusters, focusing on "digital twin" and "artificial intelligence." This research recommends further exploration into DeFi adoption, blockchain technology innovation, and the application of smart contracts to support the development of an inclusive, efficient, and innovative DeFi ecosystem in the future. In addition, this research aims to bridge existing research gaps and provide deeper insights into the potential of DeFi in the global financial system.
Jiazhen Gan, Jianzhong Su, Kaixin Lin, Zibin Zheng
Smart contracts are Turing-complete programs that run on blockchain technology, capable of managing on-chain assets according to predefined logic, and become immutable once deployed on the blockchain. In recent years, the value of smart contracts on blockchains, notably Ethereum, has been on the rise. However, the hiding vulnerabilities made the substantial value of smart contracts a target of many hackers, leading to numerous attack incidents. Therefore, vulnerability detection in smart contracts before deployment is essential. Currently, many fuzzers for detecting smart contract vulnerabilities can only identify vulnerabilities based on the execution patterns of the underlying opcodes, overlooking the financial semantic properties of the contracts, which leads to many vulnerabilities being difficult to detect or resulting in a high rate of false positives. To this end, we focus on the financial characteristics of contracts, define contract vulnerability patterns starting from the high-level semantic properties of contracts, and combine fuzzers using evolutionary algorithms and symbolic constraint solving to detect vulnerabilities, culminating in the development of FinanceFuzz . Specifically, FinanceFuzz defines invariant and equivalence properties of finance that contracts should satisfy. Utilizing these properties, FinanceFuzz can generate transaction sequences for testing and identify vulnerable contracts that violate the properties. We conducted experiments on a dataset containing 437 smart contracts from the real world, the experimental results demonstrating that our tool outperforms other state-of-the-art tools in detecting vulnerabilities, achieving higher recall rate without false positive.
Marco Bellucci, Damiano Cesa Bianchi, Luca Bagnoli, Giacomo Manetti
Purpose This study aims to understand the impacts of nonfungible tokens (NFTs) on business models (BMs), particularly in terms of enabling decentralization and digitalization through innovations in products, customer interfaces, infrastructure management and financial aspects. Design/methodology/approach By adopting a conceptual approach based on the BM framework proposed by Osterwalder and Pigneur, this study adopts a qualitative methodology based on multiple case studies such as those of Christie’s, OpenSea, Uffizi Gallery and Ticketmaster. Findings Despite the bursting of the speculative bubble, the exploratory findings suggest that NFTs can foster digitalization and decentralization within existing BMs while also presenting opportunities for new BMs that focus on simplifying and securing technology for customers to serve as intermediaries. Originality/value This study contributes to the specialized literature on the relationship between digital NFT innovation and related BM changes in different market niches within the digital marketplace ecosystem. Furthermore, this study of NFTs also contributes to the growing body of research on accounting and finance related to cryptoassets and digital innovation.
Efficient contract management is essential for ensuring sustainable and reliable supply chains; yet, traditional methods remain manual, error-prone, and inefficient, leading to delays, financial risks, and compliance challenges. AI and blockchain technology offer a transformative alternative, enabling the establishment of automated, transparent, and self-executing smart contracts that enhance efficiency and sustainability. As part of AI-driven smart contract automation, we previously implemented contractual clause extraction using question answering (QA) and named entity recognition (NER). This paper presents the next step in the information extraction process, relation extraction (RE), which aims to identify relationships between key legal entities and convert them into structured business rules for smart contract execution. To address RE in legal contracts, we present a novel hierarchical transformer model that captures sentence- and document-level dependencies. It incorporates global and segment-based attention mechanisms to extract complex legal relationships spanning multiple sentences. Given the scarcity of publicly available contractual datasets, we also introduce the contractual relation extraction (ContRE) dataset, specifically curated to support relation extraction tasks in legal contracts, that we use to evaluate the proposed model. Together, these contributions enable the structured automation of legal rules from unstructured contract text, advancing the development of AI-powered smart contracts.
The rise of decentralized finance (DeFi) has driven the demand for secure and efficient cross-chain transfers, enabling assets to seamlessly flow across different blockchain ecosystems. At the core of these innovations lie smart contracts, which facilitate trustless trading by automating transactions without relying on intermediaries. This paper explores the pivotal role that smart contracts play in enabling secure and transparent cross-chain transfers. We examine how these self-executing contracts eliminate counterparty risks, ensuring that transactions are immutable, verifiable, and executed only when predefined conditions are met. Furthermore, we investigate the challenges associated with cross-chain interoperability, including the complexities of maintaining security across different blockchain protocols. Through case studies of existing cross-chain protocols such as Polkadot, Cosmos, and Layer 2 solutions, we demonstrate how smart contracts are utilized to bridge disparate blockchains, fostering a more inclusive and accessible financial ecosystem. By providing a decentralized and trustless environment for asset transfer, smart contracts not only enhance security but also promote broader adoption of blockchain technology.
The convergence of blockchain technology and cloud computing has transformed traditional financial infrastructures, addressing critical scalability challenges that previously hindered widespread adoption. Cloud-native distributed ledger technology (DLT) emerges as a promising solution that leverages elastic computing resources, scalable storage solutions, and serverless architectures while preserving immutability and transparency. This integration creates hybrid architectures that balance decentralization benefits with cloud computing performance advantages, enabling financial institutions to process high-volume transactions efficiently. Major cloud providers have developed specialized blockchain-as-a-service offerings that reduce implementation barriers while ensuring regulatory compliance. These technologies revolutionize multiple financial domains: cross-border payments become faster and less expensive, trade finance processes transition from paper-based to digital workflows, and traditional assets gain unprecedented liquidity through tokenization. Despite remaining challenges in data privacy, regulatory compliance, and interoperability, cloud-native DLT represents a significant evolutionary step in financial infrastructure development, creating more inclusive, efficient systems that reshape global financial services and expand access beyond traditional banking relationships.
Blockchain technology represents a revolutionary concept in the field of digital economy, enabling security, transparency, and decentralization of data and transactions. This paper explores the fundamental principles of blockchain technology, the role of cryptocurrencies, and the development of decentralized finance (DeFi). It analyzes key differences between centralized and decentralized exchanges, as well as the significance of DeFi applications in the modern financial ecosystem. Additionally, through a case study, the paper presents the process of creating a new cryptocurrency—ELAB—including the implementation of a smart contract on the Binance Smart Chain network and token distribution. The main goal of the paper is to provide both theoretical and practical insights into the fundamental concepts of blockchain and its application in real-world systems. The study’s results demonstrate how cryptocurrencies can be utilized in various contexts, including the educational system, where gamification through digital tokens can enhance student engagement.
Ichsan Kurniawan, Made Sudiarta, Luh Mei Wahyuni, Ida Ayu Ketut Sumawidari · 7 authors
The rise of Decentralized Finance (DeFi) represents a transformative shift in the global financial landscape, challenging traditional financial models and offering new possibilities for a more inclusive and efficient financial system. This study aims to explore the opportunities and challenges that DeFi poses to the conventional financial sector, focusing on its impact on banking, credit systems, investments, and payment systems. A mixed-methods approach was employed, including secondary data analysis, expert interviews, and first-hand experience with DeFi protocols such as lending, borrowing, and arbitrage. The findings highlight the significant potential of DeFi in creating alternative financial models that can increase financial inclusion, enhance access to capital, and reduce transaction costs. Recent data indicates that the Total Value Locked (TVL) in DeFi platforms has surged to over $50 billion as of January 2025, reflecting growing adoption. Additionally, daily transaction volumes across major DeFi platforms have reached approximately $10 billion, with active users exceeding 4 million globally. However, the research also identifies critical challenges, including regulatory uncertainty, security vulnerabilities, and the complexity of DeFi platforms, which pose barriers to mainstream adoption. This paper contributes to the understanding of how DeFi can reshape the financial ecosystem, offering insights into its future potential, the risks involved, and the steps required to address the existing challenges. Furthermore, it underscores the need for ongoing research into the regulatory aspects of DeFi and its collaboration with traditional financial institutions.