S.N. Gomathi Balan, K. Thilagavathi, P. Raman, Sumit Pundir · 6 authors
By combining blockchain technology with federated learning, this study introduces a novel method of smart finance. The proposed method is designed to resolve the substantial challenges associated with data privacy, security, and operational efficiency that are prevalent in conventional banking systems. Federated learning enables numerous financial institutions to develop machine learning models that enhance privacy by cooperating rather than exchanging raw data. Furthermore, blockchain technology ensures the integrity and transparency of data by creating an immutable and distributed ledger. The federated learning process is automated by smart contracts, which ensures the safety and efficiency of participant participation. The system’s efficacy in enhancing model accuracy, operational efficiency, and trustworthiness, while simultaneously ensuring robust data privacy and security, is demonstrated by experiments. This integration is a substantial development in financial technology, as it provides a secure, efficient, and intelligent platform for the evaluation and decision-making process based on financial data. The model performed 95% accuracy.
Blockchain and smart contracts have emerged as transformative technologies for enhancing transparency, security, and automation in carbon credit management. This study explores the integration of these technologies within the context of Thailand’s carbon markets, focusing on system design, user interface development, and blockchain architecture. A UX testing phase was conducted to evaluate usability, security perception, and transaction efficiency. The results demonstrate strong stakeholder trust in blockchain’s security benefits while identifying areas for improvement in user guidance and error feedback. While blockchain-based carbon credit platforms exist globally, this study examines their feasibility in Thailand and highlights potential challenges, including policy considerations and stakeholder engagement. The findings suggest that blockchain and smart contracts can enhance market transparency and compliance mechanisms, paving the way for further research into their broader adoption in sustainability initiatives.
Divyanshu Singh, Atharv Mishra, R. Brindha, P. Renukadevi · 6 authors
Particularly after the Covid-19 outbreak, crowdfunding has become a prominent way to raise money for a variety of initiatives, causes, and people. In order to solve important issues like security, transparency, and worldwide accessibility, this study investigates how blockchain technology might be integrated into crowdfunding platforms. Through the use of Ethereum blockchain smart contracts, Crowd Fund generates an unchangeable record of transactions that documents each stage of the fundraising process, from the start of the campaign and contributions to the distribution of funds. Contributors' trust is increased by this method, which guarantees data transparency and integrity. Furthermore, blockchain technology makes it easier for people from all around the world to participate, enabling seamless contribution. In addition to reducing the possibility of fraud, blockchain technology and open fundraising procedures enable real-time tracking of fund utilization. Better accountability and quicker resolution of any issues result from this, which eventually creates a trustworthy atmosphere for crowdfunding projects.
This thesis examines how blockchain-based fundraising mechanisms like ICOs and IEOs reshape startup finance by offering decentralized access to capital. Analyzing 100 projects from 2019–2025, it identifies key success drivers using regression analysis. Findings show that strong community presence, top-tier investor backing, and compliance measures (e.g., KYC) significantly influence fundraising success. ICOs raise more than IEOs, despite looser oversight, highlighting a trade-off between decentralization and trust. Interaction effects reveal that credibility signals are especially effective in fragmented regions like APAC, and that compliance enhances ICO outcomes, while offering minimal added value in IEOs due to existing exchange-level due diligence.
Abstract: This e-commerce platform is specifically designed for agriculture-based trade, leveraging advanced blockchain technology and decentralized file storage to create a transparent, secure, and efficient marketplace for farmers, buyers, and suppliers. The platform utilizes Ganache, a simulation of Ethereum transactions, to ensure that all transactions are secure, immutable, and verifiable on the blockchain. The decentralized architecture is further enhanced with IPFS (Interplanetary File System), enabling farmers to securely store their product information, including images and descriptions, in a way that prevents alteration or loss. This ensures that the product listings are transparent and tamper-proof. The platform also incorporates cryptocurrency payments, enabling fast, secure, and borderless transactions between buyers and sellers. Utilizing smart contracts, the system automates payment flows based on predefined conditions, reducing the reliance on intermediaries and minimizing fraud risks. This fosters a trusted environment for agricultural trade, where both buyers and sellers can engage in transparent, efficient, and secure transactions. In addition to these core features, the platform includes a staking mechanism, allowing users to lock tokens to gain transaction privileges, influence governance decisions, and access premium features. This incentivizes long-term commitment and creates a sense of ownership within the platform. Active participants, such as those verifying transactions or maintaining data integrity, are rewarded with tokens, further promoting continuous engagement. The system also supports multilingual user interfaces, making it accessible to a global audience, and includes real-time updates for seamless interaction. Through transparent governance, decentralized voting, and economic incentives, the platform ensures a resilient and future-ready ecosystem for agro-commerce, empowering stakeholders to participate in decision-making and market dynamics, while addressing the challenges faced by farmers in accessing reliable markets and efficient payment systems.
Cryptocurrencies have changed the financial industry and have the potential to make a considerable social impact. Since cryptocurrencies are a new phenomenon in the financial market, research on the social impact of cryptocurrencies remains in the initial stage. This chapter aims to present the current research stage on the social impact of cryptocurrencies. The findings from the review of the literature are structured to positive (financial inclusion, provision of access to credit, remittances, empowerment of individuals and communities, and philanthropic initiatives) and negative (high risk, illicit activities, and environmental harm). The chapter shows most of the findings are inconsistent and the conclusions vary. It is argued in the chapter that the negative impact of cryptocurrencies on the social fabric prevails due to the very meaning (nature) of cryptocurrencies.
Purpose This study addresses the challenges of accessibility to financial technology (FinTech) services in emerging markets, focusing on Palestine as a case study. The purpose of this study is to examine the determinants influencing access to and usage of FinTech services in this context, amidst the unprecedented disruptions faced by the conventional financial system due to decentralization and the removal of physical barriers in the dynamic landscape of FinTech. Design/methodology/approach To investigate the determinants shaping FinTech access and usage, the study uses a robust multinomial generalized linear model regression analysis. The analysis is based on a nationally representative secondary survey dataset on financial inclusion from the year 2022. The study uses the theory of planned behavior (TPB) to dissect the factors influencing FinTech usage, adding a distinctive layer to existing literature, particularly within the specific context of a developing country like Palestine. Findings The study reveals significant insights into the challenges of financial inclusion in Palestine. Economic conditions and individual income levels emerge as formidable obstacles impacting both the accessibility and utilization of FinTech services. These factors intertwine with motivational and attitudinal behaviors among the surveyed population. A noteworthy finding is the nuanced revelation that individual motivation plays a more potent role than attitude in steering the decision-making process surrounding FinTech adoption. The findings contribute to a deeper understanding of FinTech dynamics in emerging markets. Research limitations/implications The paper not only contributes valuable insights to academic discourse but also formulates pragmatic policy recommendations tailored for both policymakers and FinTech service providers. While providing valuable insights, this study is not without limitations. The study focuses on Palestine, and the extent to which findings can be extrapolated to other emerging markets requires careful consideration. Furthermore, the research acknowledges the complexity of individual motivations, and the TPB may not capture all relevant psychological factors influencing FinTech adoption. These limitations should be taken into account when interpreting the results in the field of FinTech in emerging markets. Practical implications The paper not only contributes valuable insights to academic discourse but also formulates pragmatic policy recommendations tailored for both policymakers and FinTech service providers, mainly in creating and/or improving an enabling environment for FinTech-based microfinance services, such as alternative financing products tailored to specific underserved segments that cannot afford the costs of traditional financial services. Originality/value This research introduces original contributions to the existing literature by applying the TPB to dissect the determinants of FinTech usage within the specific context of a developing country, Palestine. The study goes beyond conventional analyses by exploring the intricate interplay between economic conditions, individual income levels, motivational factors and attitudinal behaviors in shaping FinTech access and usage. This innovative approach adds a distinctive layer to current research on FinTech dynamics in emerging markets.
Kevin Darmawan, Lastuti Abubakar, Tri Handayani, Dewi Kania Sugiharti
The development of financial technology has driven the emergence of digital assets such as Bitcoin, which challenge the conventional legal framework of property law. Although not recognized as legal tender, Bitcoin has been acknowledged as a legal asset by Indonesia's Commodity Futures Trading Regulatory Agency (Bappebti). This study aims to examine the legal status of Bitcoin as collateral in Indonesia’s property law framework, as well as assess the adequacy of current regulations in accommodating this function. This research employs a normative juridical method with a qualitative approach and deductive reasoning. The findings indicate that Bitcoin fulfills the legal criteria of an intangible object and, in theory, can be used as an object of fiduciary security. However, the absence of explicit legal recognition and a digitally integrated collateral registration system has led to legal uncertainty. Although regulations in Indonesia have provided a degree of legality and oversight, systemic institutional integration remains lacking. Therefore, legal reform is needed in the area of digital asset-based collateral, along with the establishment of a comprehensive administrative registration system to ensure the validity of security interests in digital assets.
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 Smart contracts are self‐executing programs that operate on a blockchain network. They are designed to automate transaction execution without the need for intermediaries. Once deployed in the blockchain network, smart contracts cannot be altered. However, this immutability can also lead to security risks. If a smart contract contains a vulnerability upon deployment, it cannot be corrected, leaving the code vulnerable. Therefore, incorporating security considerations during the design phase of smart contract development is crucial. Access control is a key security concept that must be integrated into the design of smart contracts to prevent unauthorized access to critical functions or data. In this paper, we introduce a Model‐Driven Architecture (MDA) approach to design access control for smart contracts, and we validate, using Ethereum, the proposed approach using Smart Contract Security Verification Standard (SCSVS) rules.
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
This paper presents a novel cash-on-delivery method for e-commerce, combining blockchain-based smart contracts and parcel lockers to enhance transparency, trust, and efficiency in last-mile delivery. The system reduces payment risks for suppliers while ensuring timely deliveries and tracking customers. By integrating the Internet of Things (IoT) and blockchain, this approach addresses key challenges like fraud and delivery inefficiencies, paving the way for more secure and sustainable e-commerce logistics. Furthermore, this study serves as a roadmap for other academics and blockchain professionals, encouraging further experimentation and research to refine and validate this approach through practical applications and real-world testing.
This paper explores the application of blockchain enabled smart contracts to automate financial transactions across a decentralized network. We discuss performance measurements including transaction throughput, gas consumption, security, scalability, and the interoperability of cross-chain. We simulate traditional financial systems and blockchains and compare them in terms of transaction throughput, cost ratios and susceptibility of a smart contract to attacks. Furthermore, we devise the performance of a few types of blockchain platforms related to Ethereum and Binance Smart Chain under different network settings. Security issues, including integer overflow and re-entrancy, are mitigated using formal verification techniques. We also evaluate the impact of cross-chain interoperability in terms of bridges and oracles, with a specific emphasis on the latency and failure rate of the asset swaps. The results illustrate the capability of blockchain-based smart contracts to disrupt financial systems by providing a more efficient, secure, and scalable alternative.
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.
Security and trust are two critical challenges of IoE, which refers to the interconnection of electronic devices providing intelligent services. The IoE ecosystem works using smart contracts based on pre-specified rules and dynamic automated transactions that self-eject contracts, permitting the removal of intermediaries from the process. This dynamism reduces human error and fraud while enhancing assurance and transparency. The chapter offers insights into the data integrity, better authenticity, and efficiency of identified communications provided by smart contracts in IoE architectures. Smart contracts are essentially platforms through which security and public confidence in IoE are reinforced by alleviating risks. Because they depend on tamper-free immutable blockchain ledgers, event information is recorded safely, creating immense efficiency, command over fraud, and been further improved with standards. They scale, interoperate, and comply with regulation—three key points—when constructing a secure and trusted IoE ecosystem.
Arpit Jain, Prince Kumar, Padma Naresh Vardhineedi, Jay Shah · 6 authors
A smart contract is the code that controls such digital assets and runs them on a blockchain network. It is used to try and default contract terms, remove intermediaries within the network, and ensure a trustless environment with more efficiency. However, smart contracts are of great concern as they handle sensitive and valuable assets. Smart contracts & Bloom hash function: An essential element in blockchain technology with smart contracts enabling safety and security through Bloom hash functions. With the help of hash functions, every data block generates a specific cryptographic hash, making it almost immutable and ensuring that nobody can tamper with your information. In the realm of smart contracts, hash functions can be used to generate a digital signature of the contract code, and then all parties involved can verify that by doing dust authentication. Since tampering with the code changes its hash, each side could easily detect if someone had altered a document. You may use hash functions with other security layers (e.g. multi-factor authentication, encryption) to set up a strong protection for your smart contracts. While smart contracts stay true to the core principles of immutability and transparency in order, using hash functions can further strengthen security against potential threats.