The traditional publicâprivate partnerships (PPP) contract is paper based, which makes contract documentation in PPP intensive, insecure, and prone to errors and unauthorized manipulations. These problems are in addition to many intermediates and associated negative impacts on productivity improvement and contractual compliance issues. Blockchain-enabled smart contracts (BSC) have unique features that suitably mitigate these issues when PPP projects are delivered. However, PPP stakeholders are confronted with certain barriers that impede the adoption of these technologies. Although interest in adopting blockchain-smart contracts in PPP is growing in the literature, a quantitative survey of global experts on the potential limiting factors to BSC adoption in the context of PPP is lacking. This study adopted a hybrid model of technology, organization, and environment (TOE) framework and institutional theory to critically examine the potential barriers to BSC adoption and implementation in infrastructure PPP projects. The structured questionnaire was used to gather data from 123 experts across countries, and descriptive analysis and partial least-square-structural equation modeling (PLS-SEM) were used for data analysis. The descriptive analysis emphasized the significant limiting impact of the barriers to smart contract adoption in PPP. The PLS-SEM revealed that the six constructs of the hybrid model have a statistically significant limiting impact on the stakeholdersâ decision to adopt and implement BSC in infrastructure PPP projects. This study provides a framework of barriers to guide industry stakeholders and policymakers in their quest to digitalize PPP contracts.
With the increasing demand for secure and scalable medical data sharing, blockchain-based solutions have attracted significant attention. This study proposes a novel data sharing framework that integrates Non-Fungible Tokens (NFTs), the InterPlanetary File System (IPFS), and the Lit Protocol to enable encrypted file storage, decentralized access control, and patient-driven data governance. The system adopts a dual-token structure, consisting of Ownership NFTs and Usage NFTs to separate and manage data ownership and usage rights. To verify the feasibility of the proposed method, we developed a prototype and measured the execution time of various processing steps using files ranging from 100 KiB to 100 MiB. The results showed that AES-based encryption and decryption were completed rapidly, with each operation taking less than one second. NFT issuance and key management via the Lit Protocol were unaffected by file size but exhibited variability likely due to network conditions. In contrast, file transfer processes involving S3 and IPFS were strongly dependent on file size, with significant delays observed for large files. In particular, IPFS uploads experienced delays of over 40 seconds. Furthermore, gas consumption remained constant across all file sizes, indicating stable blockchain-related costs. These findings suggest that the proposed system serves as a promising foundation for secure and scalable sharing of sensitive medical data.
ABSTRACT Smart contracts, integral to decentralized applications (dApps), depend heavily on the efficiency and scalability of underlying consensus mechanisms. This study evaluated the runtime scalability of two dominant protocolsâProofâofâWork (PoW) and ProofâofâStake (PoS). It proposes a novel hybrid consensus algorithm, ProofâofâActivityâandâDelegation (PoAD), to address performance and fairness limitations. PoAD combines validator activity scores, delegated stakes, and verifiable randomness into a composite eligibility function, with block finalization conducted via a PBFTâstyle mechanism. Experimental simulations were conducted across varying network sizes (10â60 nodes), where PoAD demonstrated significantly improved performance: execution time of 2.6 s at 50 nodes compared to 5.7 s for PoW and 4.3 s for PoS; transaction throughput reaching 125 tx/s; and finality latency reduced to 1.3 s, compared to 4.7 s in PoW. PoAD also maintained high proposer fairness (> 0.95), lower energy consumption (Ë45% less than PoW), and lower algorithmic complexity . These results were obtained using Pythonâbased simulations with controlled validator pools and standardized workloads. The findings suggest that PoAD offers a viable, scalable, and energyâefficient alternative to existing protocols, especially in latencyâsensitive and resourceâconstrained environments such as IoT and decentralized finance. Although promising, the effectiveness of PoAD under adversarial conditions and realâworld deployments remains to be validated. Future studies should explore resilience under Byzantine faults, adaptive parameter tuning, and integration with asynchronous BFT frameworks to enhance their trustworthiness and applicability.
Yangchun Xiong, Li Ding, Shu Guo, TsanâMing Choi · 5 authors
ABSTRACT Smart contracts, enabled by blockchain technology, are increasingly adopted by firms to automate the execution of agreements or contracts without the involvement of intermediaries. However, it is still unclear how smart contracts may affect firms' operational efficiency. We address this issue empirically by conducting a quasiânatural experiment in the United States in which certain states have enacted relevant laws that increase inâstate firms' propensity to adopt and use smart contracts. Our differenceâinâdifferences estimation suggests that compared with outâofâstate control firms, inâstate treatment firms' operational efficiency increases significantly after the enactment of smart contract laws. Our post hoc analysis further suggests that stateâlevel smart contract laws help increase inâstate firms' actual smart contract activities, which in turn lead to operational efficiency improvement. We also find that the operational efficiency improvement varies across firms with different supply chain complexities. While firms with a large number of supply chain partners (i.e., high horizontal complexity) gain more operational efficiency improvement, the improvement becomes less pronounced if firms' supply chain partners are distributed across different countries (i.e., high spatial complexity). Overall, our research not only demonstrates smart contracts' ability to improve operational efficiency but also reveals the critical role of supply chain complexity in affecting the operational efficiency improvement.
Otaxon Khayitboyev Shokirovich, Gulora Yuldasheva, Roxatoy Allayarova, R. Jayasudha
The fast digitalisation of commercial plans brings benefits and troubles to tax presidency, particularly when it comes to guaranteeing that taxes command a price of correctly and on time. This study proposes a new foundation utilizing blockchain electronics and smart contracts to simplify processes for tax agreement. Real-occasion tax calculation is approved by smart contracts in a permission-located blockchain design; auditability is determined by zero-information proofs (ZKPs) accompanying homomorphic encryption; solitude for secure data conversion is maintained. A numerical construction was grown to systematise tax forethoughts using variables like costs, revenue and supervisory necessities. Smart contracts' automatic deductions for taxes were encrypted into their sense for agreement accompanying regulations. Using a fake dataset of 10,000 economic undertakings, investigators conducted an exploratory imitation on a blockchain network established Hyperledger Fabric. Tax computations were proved to be exact to the insignificant value; human engrossment was diminished by 91.3%; and compliance accompanying organizing checks were seamlessly contained, producing nothing wrong a still picture taken with a camera. Average abeyance of 1.6 seconds and 280 TPS throughput verified the scalability of the design. ZKPs and encryption observed dossier secure from unlawful approach. This paper illustrates by what method smart contracts compelled by blockchain might improve tax structure adeptness, transparence and security. The projected model is having to do with various legal atmospheres and commercial environments, in addition to scalable for up-to-date tax presidency.
This research presents the design and implementation of a novel E-banking application based on blockchain technology and enhanced cryptographic practices to enable secure, transparent, and efficient financial transactions. The system deploys an integrated Python-based blockchain for distributed storage of user data, with SHA-256 hashing and Merkle root trees employed to secure transaction integrity and immutability. The sensitive user data which is also stored in local databases is also encrypted with Advanced Encryption Standard (AES), and user login security is reinforced with two-factor authentication from Google Authenticator. The application supports main functions such as user enrollment, loading cryptocurrency, peer-to-peer transactions, and monitoring transaction history, all backed by cryptographic protection such as salted hash password protection and PIN verification. A Solidity smart contract then extends the functionality of the system on the Ethereum blockchain, to enable secure deposit and transfer procedures with real-time balance updating. This integration employs local cryptographic protection with the benefits of blockchain technologyâs distributed ledger, to deliver a robust architecture for contemporary electronic banking. The results demonstrate the scalability and tamperproof platform, addressing the solution to privacy and trust in E-banking, and it promises to pave the way for decentralized financial systems.
Roy Kanavheti, Wellington Makondo, Wellington Simbarashe Manjoro
Academic qualification forgery poses a major concern for higher learning institutions, employers, and regulatory authorities throughout the world. In Zimbabwe, the increase in the level of fake degrees has greatly eroded trust in the education industry. Conventional verification processes are time-consuming, manual, and highly vulnerable to tampering. This paper introduces a hybrid blockchain-based and AI-enabled academic qualification verification platform to fight the problems. A prototype was implemented integrating various artificial intelligence algorithms including Convolutional Neural Networks (CNN), Autoencoder, Random Forest, and One-Class Support Vector Machines (SVM) with Algorand blockchain for secure, transparent, and decentralized record keeping. Zero-Knowledge Proofs (ZKPs) were utilized to ensure privacy. The system was tested based on a mixed-methods and Design Science Research (DSR) approach across many performance measures. Results show fraud detection accuracy, near-instantaneous verification speed, and satisfaction with privacy standards. The proposed system provides a sustainable and scalable framework for enhancing academic integrity in Zimbabwe's higher education system and primes the region for digital transformation of education.
Joel SepĂșlveda, Amanda Lemette, Karla Ohler-Martins
The rise of cryptocurrencies and decentralised finance (DeFi) has fuelled a fast-growing digital assets economy with major environmental and financial implications. Proof-of-work (PoW) systems like Bitcoin demand high energy and emit large volumes of COâ, while proof-of-stake (PoS) alternatives such as Ethereum and Cardano significantly reduce environmental costs. This paper analyses seven major crypto projects: Ethereum, Uniswap, Aave, Maker, Cardano, XRP, and Stellar. It focuses on their energy consumption, financial performance, and sustainability. The study proposes a novel sustainability scoring framework to support ESG-aligned investment and regulatory design. While PoW offers unmatched security, its environmental toll is unsustainable. PoS models show promise but face governance and scalability concerns. The study highlights the urgent need for sustainable innovation and regulatory differentiation to align crypto markets with climate goals, investor expectations, and long-term economic viability.
The present study aims to analyze the return and risk performance ofselected cryptocurrencies in orderto find out which cryptocurrencies have small risks and large returns. The research time period is 2017 to 2022. The objective of this research is to compute and compare the risk and return performance of the selected cryptos. The findings of this research are that the risk is very high in Bitcoin compared to Ethereum, as shown in the data analysis, and Ethereum has high returns. Before starting an investment, it is better to look at the ability of Cryptocurrency assets to minimize risks and make sure that the investment objectives are for the long and short term.
This chapter explains the transformative potential of Generative AI in enhancing financial decision-making and blockchain technology in reshaping FinTech processes. It examines Generative AI's applications in predictive analytics, portfolio optimization, and fraud detection, emphasizing its ability to improve accuracy, efficiency, and risk mitigation. The foundational concepts of blockchain, including decentralization, transparency, and smart contracts, are analysed in the context of regulatory compliance and operational automation. The chapter also addresses critical data privacy and cybersecurity issues in AI-driven FinTech, offering strategies for safeguarding sensitive information. Future trends such as decentralized finance (DeFi), robo-advisors, and blockchain-based lending are discussed, highlighting opportunities for collaboration between AI and blockchain to create innovative ecosystems. Supported by case studies, this chapter provides actionable insights for leveraging these technologies to drive innovation and build a secure, efficient, and inclusive financial landscape.
Kaharuddin Kaharuddin, Asep Saepudin Jahar, Arta Amaliah Nur Afifah
Blockchain technology introduces an innovative approach to waqf governance by incorporating smart contracts based on distributed ledger technology and encryption security. This advancement enhances administrative services and strengthens public trust in waqf management. This research uses a qualitative descriptive approach based on a literature review to explore the benefits of applying blockchain technology in waqf governance. The findings reveal that blockchain technology significantly improves the previously unstructured waqf governance system. By enabling encrypted and decentralised transaction recording, blockchain ensures transparent management of waqf funds, facilitates efficient tracking of fund status, and prevents data manipulation. Additionally, blockchain enhances the efficiency and security of transactions, paving the way for an innovative and digital waqf ecosystem. The technology also promotes financial inclusion by expanding public access to participate in waqf initiatives. This broader participation contributes to poverty alleviation (SDG 1) and reduces economic disparities (SDG 10) through waqf fundsâ transparent and accountable distribution. Integrating blockchain technology into waqf governance offers a transformative solution for advancing transparency, efficiency, and inclusivity in Sharia financial systems.
Zahiduzzaman Zahid, Ruhul Amin, Ibrahim Khalil, Basharat Ali Khan Mohammed · 5 authors
This paper investigates the intersection of MiCA and Islamic finance by conducting a comparative regulatory analysis, focusing on core areas such as stablecoin structures, decentralized finance (DeFi), smart contracts, and ethical governance. It critically examines MiCAâs reserve and redemption frameworks for asset-referenced tokens (ARTs) and e-money tokens (EMTs) against Shariah mandates of asset-backing, risk-sharing, and prohibition of riba (interest) and gharar (excessive uncertainty). The analysis further explores the legal and ethical tensions between MiCAâs treatment of decentralized assets and Islamic jurisprudence, especially regarding profit-sharing models like Mudarabah and Musharakah. It highlights challenges Islamic fintechs operating within the EU face, including the lack of recognition for Shariah boards and faith-based audit systems under MiCA. The study concludes by proposing policy recommendations for greater inclusivity, including potential amendments to MiCA that accommodate ethical finance models and support Islamic digital financial innovation. The findings contribute to the global discourse on harmonizing digital asset regulation with diverse ethical and religious frameworks, offering valuable insights for regulators, scholars, and Islamic financial institutions
This chapter explores the transformative potential of blockchain technology in reshaping payment systems, focusing on its ability to address inefficiencies, reduce costs, and enhance financial inclusion. Blockchain's foundational features, including decentralization, transparency, immutability, and security, position it as a critical innovation in the payments industry. The chapter examines key blockchain applications, such as cross-border payments, central bank digital currencies (CBDCs), stablecoins, and decentralized finance (DeFi), highlighting their impact on global commerce and financial ecosystems. While blockchain offers significant opportunities, it also faces challenges related to regulatory compliance, scalability, environmental sustainability, and ethical considerations, such as privacy and accessibility. Emerging technologies, including interoperability solutions and artificial intelligence integration, are presented as promising avenues for overcoming these obstacles and advancing blockchain's adoption.
This chapter explores the role of Decentralized Finance (DeFi) in transforming the global financial ecosystem, focusing on its objectives, challenges, and potential. It highlights how DeFi platforms leverage blockchain technology to provide financial services without intermediaries like banks. The chapter identifies gaps in existing literature, particularly around DeFi's long-term sustainability, regulatory challenges, and scalability. Using a systematic literature review, the impact of DeFi on lending, borrowing, insurance, and asset management is analyzed. Findings show that DeFi enhances financial inclusion, transparency, and efficiency, but faces challenges such as regulatory uncertainties, legal issues, security risks, and user experience concerns. The chapter suggests that DeFi has the potential to revolutionize financial services, offering significant implications for academics, society, industry, and researchers.
This. dissertation examines the short-run effect of the January 2024 listing of spot Bitcoin Exchange-Traded Funds (ETFs) on the market dynamics of Bitcoin. Driven by increased institutional adoption and research in cryptocurrency markets, this study investigates whether the listing of funds like IBIT (iShares Bitcoin Trust), FBTC (Fidelity Wise Origin Bitcoin Fund), and GBTC (Grayscale Bitcoin Trust, on spot conversion) introduced quantifiable implications on the price returns, volume, and market capitalization of Bitcoin. To address this question, the study uses a hybrid event study approach through OLS and GARCH(1,1) model to identify abnormal returns and volatility patterns. Based on daily data gathered from September 2023 to March 2025, the study identifies an event window around the date of approval of the ETF and classifies expected and actual market behavior. No short-term statistically significant abnormal returns occur in any of the three measures investigated. Although GARCH models imply temporary volatility relationships and trading volume as a primary amplifier, the event itself also did not create quantifiable return anomalies. Such findings are also confirmed by robustness tests with an extended event and estimation windows and again reflect no significant influences. Overall, the research discovers that the ETF approval, while structurally important, was well absorbed by the market in the near term.
Ali Shiri, Mikaeil Mayeli Feridani, Samira Keivanpour
This paper presents a hybrid chatbot for sustainable cryptocurrency investment, powered by small-scale Large Language Models (LLMs). We evaluate the performance of various lightweight LLMs (under 10B parameters) on blockchain and sustainability-related tasks, demonstrating that carefully orchestrated smaller models can effectively match or exceed the performance of larger models in domain-specific applications. Our multi-agent Retrieval-Augmented Generation (RAG) pipeline, incorporating real-time sustainability metrics from the Crypto Carbon Ratings Institute, achieved 87.09% accuracy in providing investment guidance, improving upon the 72.58% baseline of individual models. The results show that refined instruction engineering and specialized pipeline architecture can significantly enhance model performance without requiring larger, more energy-intensive models. This work contributes to both the practical implementation of sustainable cryptocurrency investment tools and the broader discussion of environmental considerations in AI system design and deployment.
Emanuele Antonio Napoli, Lorenzo Gangemi, Silvio Meneguzzo, Noemi Romani · 5 authors
This paper introduces a user-friendly tool designed to simplify smart contract analysis through a microservice architecture implemented in Golang and Flutter. The system combines four primary components: a Codec for contract encoding/decoding, an Auditor powered by Slither for security assessment, a Database for version control, and an AI Assistant leveraging gpt-3.5-turbo from OpenAI for automated annotations. User interaction is facilitated through UML-based visual representations that allow intuitive comprehension of the smart contract implementation. An evaluation through the NASA-TLX questionnaire demonstrated that the proposed tool could significantly reduce the overall workload (52.62 versus 71.74) compared to Remix IDE, with notable improvements in mental demand, temporal demand, and frustration levels.
This paper presents Wrapless -- a lending protocol that enables the collateralization of bitcoins without requiring a trusted wrapping mechanism. The protocol facilitates a "loan channel" on the Bitcoin blockchain, allowing bitcoins to be locked as collateral for loans issued on any blockchain that supports Turing-complete smart contracts. The protocol is designed in a way that makes it economically irrational for each involved party to manipulate the loan rules. There is still a significant research area to bring the protocol closer to traditional AMM financial instruments.
This research examines the impact of Bitcoin adoption as a cryptocurrency on financial stability in Indonesia. It analyses potential systemic hazards and how banks might effectively handle these risks. Bank Indonesia, the central bank, has articulated apprehensions over the volatility of Bitcoin and its potential ramifications on the nation's monetary aggregates and financial stability. The study seeks to elucidate the implications of cryptocurrency integration into the Indonesian financial system. The research utilises a quantitative approach, integrating a data analysis method. We gather data from financial institutions, regulatory authorities, and Bitcoin users in Indonesia to assess the impact of Bitcoin on financial stability. The findings suggest that, although Bitcoin presents potential advantages like enhanced financial inclusion and innovation, it also entails considerable dangers. These encompass market volatility, cybersecurity concerns, and regulatory obstacles. The study identifies critical areas where banks must improve risk management practices to mitigate them. A significant component of this research is the identification of specialised risk management solutions especially adapted to the Indonesian setting, such as the integration of local regulatory frameworks with international standards and best practices.
ABSTRACT Using a textâbased measure of peer opinions constructed from cryptocurrencyârelated social media posts, we find that peer opinions contain valuable information about the prices of cryptocurrency options. Bitcoin options exhibit a volatility smile, which becomes steeper when peer opinions become bearish. The riskâneutral skewness of Bitcoin returns implied by options prices becomes more negative in times of bearish opinions. The predictability of peer opinions for Bitcoin option prices remains robust after controlling for momentum, volatility, demand pressures, news effects, and other sentiment measures, and exhibits no evidence of reversal over time. This effect is pronounced when Bitcoin attracts high investor attention, more diverse opinions about Bitcoin are expressed on social media, and Bitcoin options are more actively traded. We find similar results for Ethereum options.
Blockchain technology has reshaped digital finance, enabling decentralized applications (DApps) on platforms like Ethereum. However, these innovations have also facilitated fraudulent schemes such as Ponzi schemes, which deceive users with false promises of high returns. These schemes cause financial losses and weaken trust in blockchain systems. Existing detection methods face key challenges, including limited labeled data, over-reliance on transaction history, and failure to identify scams early. To address these issues, we propose a framework that combines static and dynamic features of smart contracts for early Ponzi detection. Our feature set includes opcode patterns, developer behavior, temporal trends, and metadata, crafted to work independently of transaction data. We enhance feature representation using TF-IDF, CountVectorizer, and Word2Vec for deeper semantic understanding. These features are used to train multiple machine learning and deep learning models such as Random Forest, XGBoost, CNNs, and BiGRUs. A stacking ensemble with a neural meta-learner integrates predictions for improved performance. The model achieves 99% accuracy and an AUC of 0.9522 on a curated Ethereum dataset, handling class imbalance through oversampling and synthetic data generation. We also employ SHAP for model explainability, offering insights into feature importance and promoting transparency. Our framework is scalable and supports real-time monitoring of contracts, helping prevent financial damage by detecting fraud at deployment. This solution enhances the security and reliability of decentralized finance platforms.
Survival modeling predicts the time until an event occurs and is widely used in risk analysis; for example, it's used in medicine to predict the survival of a patient based on censored data. There is a need for large-scale, realistic, and freely available datasets for benchmarking artificial intelligence (AI) survival models. In this paper, we derive a suite of 16 survival modeling tasks from publicly available transaction data generated by lending of cryptocurrencies in Decentralized Finance (DeFi). Each task was constructed using an automated pipeline based on choices of index and outcome events. For example, the model predicts the time from when a user borrows cryptocurrency coins (index event) until their first repayment (outcome event). We formulate a survival benchmark consisting of a suite of 16 survival-time prediction tasks (FinSurvival). We also automatically create 16 corresponding classification problems for each task by thresholding the survival time using the restricted mean survival time. With over 7.5 million records, FinSurvival provides a suite of realistic financial modeling tasks that will spur future AI survival modeling research. Our evaluation indicated that these are challenging tasks that are not well addressed by existing methods. FinSurvival enables the evaluation of AI survival models applicable to traditional finance, industry, medicine, and commerce, which is currently hindered by the lack of large public datasets. Our benchmark demonstrates how AI models could assess opportunities and risks in DeFi. In the future, the FinSurvival benchmark pipeline can be used to create new benchmarks by incorporating more DeFi transactions and protocols as the use of cryptocurrency grows.
Wallets are access points for the digital economys value creation. Wallets for blockchains store the end-users cryptographic keys for administrating their digital assets and enable access to blockchain Web3 systems. Web3 delivers new service opportunities. This chapter focuses on the Web3 enabled release of value through the lens of wallets. Wallets may be implemented as software apps on smartphones, web apps on desktops, or hardware devices. Wallet users request high security, ease of use, and access of relevance from their wallets. Increasing connectivity, functionality, autonomy, personal support, and offline capability make the wallet into the user's Universal Access Device for any digital asset. Through wallet based services, the owner obtains enhanced digital empowerment. The new Web3 solutionareas, Identity and Decentralisation, enable considerable societal effects, and wallets are an integral part of these. One example is self sovereign identity solutions combined with wallet borne AI for personalised support, empowering the enduser beyond anything previously known. Improved welfare is foreseen globally through enlarged markets with collaborative services with drastically lowered transaction costs compared to today, the expected vastly increased levels of automation in society necessitate enhanced enduser protection. As wallets are considered a weak spot for security, improving overall security through blockchains is essential.
Ferdous Ahmmed, Boakye Yam Boadi, Michael Guillemette
This study examined the relationship between margin trading and cryptocurrency investment using data from the 2018 and 2021 waves of the National Financial Capability Study (NFCS) Investor Survey. Guided by behavioral finance theory, which suggests that cognitive biases may influence risk-taking, the study explored whether margin loan use and margin calls are associated with higher cryptocurrency participation. Margin loans are inherently risky, as they must be repaid regardless of investment outcomes, and margin calls are triggered when an investorâs equity falls below a required threshold. The results showed a positive and statistically significant association between margin activity and cryptocurrency investment. Specifically, individuals with a margin loan were 17 percentage points more likely to invest in cryptocurrency, while those who have experienced a margin call were 23 percentage points more likely. Given the extreme volatility of cryptocurrencies, these results highlight the increased risks investors face when using leverage in speculative markets. The analysis is based on cross-sectional data from U.S. investors; therefore, the findings should be interpreted as correlational rather than causal.