Chibuikem Michael Adilieme, Rotimi Boluwatife Abidoye, Chyi Lin Lee
Purpose Blockchain is an emerging digital technology proposed and trialled among different built environment professions. The technology has been proposed to introduce transparency, security and trust in property transactions. Despite this proposition, few studies have analysed the barriers and prospects in property valuation, especially in markets plagued by low transparency and a lack of stakeholder trust. Using Nigeria as a case study, this study assesses the barriers and prospects for adopting blockchain technology in property valuation. Design/methodology/approach Data was collected from 180 valuers practising in Nigeria through an online survey, and the data was analysed using mean score ranking and the chi-square (χ2) test of independence. Findings Firstly, there was a low awareness of the application of blockchain technology and an association between the number of valuation jobs executed annually and awareness of the application of blockchain technology. The most important barriers revolved around the knowledge, technical know-how of blockchain and the cost of implementing such technology. The prospects for blockchain are very high as all identified prospects were considered important, with transparency being the most crucial factor for its adoption, followed by the monitoring activities in real time and the permanence in storing records. Research limitations/implications This study's implications lie in the potential benefit of transparency identified for blockchain, which could act as a tool to introduce transparency into valuation industries that battle key issues surrounding transparency and trust. Furthermore, this study can be utilised by policymakers and property industry players in mapping strategies to adopt the beneficial use of blockchain as one among the suite of proptech tools disrupting the property valuation scene, in their practice. This also presents an opportunity to draw upon insights from this study to better prepare for using blockchain in property valuation. Originality/value This study appears to be the first to empirically assess barriers and prospects for blockchain in property valuation practice. It contributes to the literature by identifying key factors that will deter and/or promote the application of blockchain, an emerging and disruptive digital technology.
Blockchain provides a decentralised, tamper-proof and trustworthy distributed database technology that is widely used in finance and economics, IoT and big data. Artificial intelligence (AI) provides a technology that can mimic human intelligence, learn autonomously and automate decision-making, which plays a major role in enhancing productivity, solving complex problems and improving decision-making. The two represent two of the major driving forces in technology today, and their integration is redefining our digital world. The aim of this paper is to explore the integration of these two technologies and the innovations, challenges, and future prospects they bring. First, we trace their history and evolution, introduce the basic characteristics of blockchain and AI, and explain in detail how they work. We then delve into the integration of blockchain and AI, highlighting their importance and significance in areas such as finance, supply chain and healthcare. We analyse the applications and implications of this integration for these areas, as well as the challenges and dilemmas faced, including issues of security, privacy, data leakage, and technical feasibility. Finally, we explore future trends and related work, highlighting the importance of global community collaboration and innovation to realize the potential of blockchain and AI.
Great technological developments are taking place around the world. These developments are taking place in different areas of technology. While some of them take place within the framework of Industry 4.0, others are known by their own names. This study was prepared in this context.Blockchain is one of the emerging technologies. It draws attention with its features such as distributed ledger structure, unalterable and indelible records, low-cost high-speed processing. Blockchain technology is becoming widespread worldwide thanks to these features and more. As it becomes widespread, the areas it affects are also expanding. One of these areas is accounting. Blockchain affects the accounting field from different angles and shapes accounting as it is basically a ledger.Artificial intelligence is among the emerging technologies. Artificial intelligence is known for its ability to mimic human behaviour. The ability to imitate human behaviour in virtual or physical environments is important for accounting science. In this context, the roles and duties of accounting professionals shape the role of Artificial intelligence in accounting auditing.These technologies make it possible to perform activities such as budgeting, purchasing raw materials or finished goods, maintenance and repair scheduling of production machines and near real-time accounting audits at low cost and high speed.
Purpose This study investigates the factors that lead to the adoption of blockchain technology through payment transactions and how this not only affects real estate (RE) and blockchain transparency but also RE performance. Design/methodology/approach Data gathered across RE firms in the United Arab Emirates (UAE) were employed to test the model. The measurement model and structural equation modeling (SEM) were used to test the items and the hypotheses illustrated in the proposed model. Findings Perceived financial benefits, competitive pressure and top manager support were demonstrated to successfully influence blockchain adoption (BA). Despite blockchain’s early stages of development, its impact on RE operations cannot be ignored and should be more objectively examined in order to gain a better understanding of it. UAE blockchain-based companies could be seen as having a competitive advantage that maximizes resource consumption. Originality/value This study introduces the positive influence of blockchain technology on RE payment transactions and may advance information on how blockchain technology has the potential to change the RE sector. The paper finds its significance in exploring how RE payment systems must change to remain competitive in the market amid emerging digitalization trends.
PURPOSE – Financial technology, also known as “FinTech,” has evolved to disrupt nearly every aspect of traditional financial services and it has become increasingly important in the world’s economic system. The main purpose of the study is to explore the relationship between Financial Technology (Fintech) and Entrepreneurial Intentions. It focuses on the impact of specific Fintech innovations such as Crowdfunding, Mobile Payments, Blockchain, Cryptocurrency, and Artificial Intelligence (AI), on Entrepreneurial Finance. The study examines how these Fintech advancements have affected the overall entrepreneurial ecosystem, fostering innovation, supporting startups, and driving economic growth. Using mixed-methods, the research combines qualitative interviews and quantitative surveys to reveal key factors that have completely shaped the entrepreneurial ecosystem in the context of fintech. EXECUTIVE SUMMARY – Financial technology revolution unleashing a wave of technological innovations has transformed the entrepreneurial landscape. Crowdfunding, cryptocurrency, blockchain, mobile payments, and artificial intelligence (AI) play key roles in empowering aspiring entrepreneurs, fueling financial inclusion, and driving economic growth. This report examines the impact of these fintech advancements on entrepreneurial intentions, exploring their benefits, challenges, and future prospects.
This study explores the influence of financial technology (Fintech) innovations, environmental, social, and governance (ESG) reporting, and blockchain technology on financial transparency and accountability through a qualitative literature review. By examining a diverse range of academic papers, industry reports, and case studies, this research aims to provide a comprehensive understanding of how these factors contribute to enhancing financial transparency and accountability in the modern financial landscape. The literature review reveals that Fintech innovations, including mobile banking, peer-to-peer lending, and automated investment services, significantly improve financial transparency by providing more accessible and real-time financial information to stakeholders. These innovations enhance accountability by enabling more efficient and accurate tracking of financial transactions and performance. ESG reporting, which involves disclosing information related to a company's environmental impact, social practices, and governance structures, plays a crucial role in promoting financial transparency. It ensures that stakeholders are informed about the non-financial aspects of a company’s operations, thereby fostering greater accountability and ethical business practices. The integration of blockchain technology further enhances transparency and accountability by offering a decentralized and immutable ledger system that ensures the integrity and traceability of financial transactions. This technology reduces the risk of fraud and corruption, providing a transparent and accountable framework for financial reporting. Despite these benefits, the study also highlights challenges such as regulatory hurdles, the need for technological infrastructure, and concerns over data privacy and security. The findings suggest that the combined use of Fintech, ESG reporting, and blockchain technology has the potential to significantly improve financial transparency and accountability, provided that these challenges are addressed. This research offers valuable insights for financial institutions, policymakers, and technology developers aiming to enhance financial practices through innovative solutions.
Abdelatif Hafid, Maad Ebrahim, Ali Alfatemi, Mohamed Rahouti · 5 authors
The rapid growth of the stock market has attracted many investors due to its potential for significant profits. However, predicting stock prices accurately is difficult because financial markets are complex and constantly changing. This is especially true for the cryptocurrency market, which is known for its extreme volatility, making it challenging for traders and investors to make wise and profitable decisions. This study introduces a machine learning approach to predict cryptocurrency prices. Specifically, we make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model. We demonstrate our approach through an analysis focusing on the closing prices of Bitcoin cryptocurrency. We evaluate the model's performance through various simulations, showing promising results that suggest its usefulness in aiding/guiding cryptocurrency traders and investors in dynamic market conditions.
Raymond Wahyudi, Nanik Linawati, Farrell Ionwyn Eduardo
Cryptocurrency investment is a phenomenon that has gained popularity among Indonesian youth. However, the factors that influence their intention to invest in this digital asset class are not well understood. This study aims to identify and evaluate these factors using the Fuzzy Analytical Hierarchy Process (FAHP) method, which can handle uncertainty and ambiguity in decision making. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model as the theoretical framework, and considers six factors: social influence, financial literacy, facilitating condition, performance expectancy, effort expectancy, and hedonic motivation. The results show that social influence, financial literacy, and facilitating condition are the most important factors, while hedonic motivation is the least important. The study also ranks the sub-criteria within each factor according to their relative importance. The findings provide valuable insights for policymakers, investors, and educators in the field of cryptocurrency and blockchain technology.
The objective of this research article is to consolidate existing literature in the field of sentiments and cryptocurrency through a systematic literature review (SLR) by adopting the SPAR-4-SLR protocol and establishing a structured foundation for future studies. The study includes 42 research articles from the SCOPUS and WoS databases. The SLR offers several findings. First, research in the field of sentiment and cryptocurrency has surged in the recent past and the area is still young to research. Second, the study sheds light on major contributors to the field of study. Third, sentiment has proved to be a major predictor of cryptocurrency due to its characteristics. Finally, the study’s findings reveal a range of sentiment measure indicators in the literature and shed light on the influential role of sentiments in cryptocurrency prices, returns, and volatility. In the absence of systematic reviews in the field, this article makes a significant contribution.
This paper presents SCareGPT, an advanced smart contract auditing tool that harnesses domain-specific GPT Assistant technology to enhance vulnerability detection and analysis. We created a standardized dataset featuring 100 labeled vulnera-ble smart contracts and performed comparative benchmarks between SCareGPT and ten traditional smart contract vulnerability detection tools. Our findings demonstrate that SCareGPT excels beyond these competitors in the majority of assessed vulnerability categories, affirming its superiority and utility in the rapidly evolving domain of smart contract security.
Cryptocurrency, a relatively new financial innovation, has sparked widespread interest in recent years, particularly among younger demographics such as college students. As digital currencies such as Bitcoin and Ethereum continue to dominate headlines, college students' familiarity and impression of cryptocurrency has become essential for assessing its future adoption and investment opportunities. This study investigates the level of investor awareness, interest, and perception of cryptocurrencies among college students, a demographic that represents the future of technical and financial advancement.College students are often seen as technologically adept and open to new technologies, making them an ideal group for researching cryptocurrency awareness. However, the complexity, volatility, and lack of legal structures surrounding cryptocurrencies have piqued interest while also raising concerns. This study looks at how students comprehend important cryptocurrency concepts such as blockchain technology, decentralized finance (DeFi), digital wallets, and the risks of investing in these digital assets.The findings indicate that, while a considerable majority of college students are aware of cryptocurrencies, their level of understanding differs. Many students have a shallow understanding based on media exposure or peer discussions, with a lesser fraction having participated in actual trading or investment. Factors such as field of study, access to financial education, and socioeconomic status all influence the level of awareness. Students in technology-related professions have a better understanding of the underlying blockchain technology, but those in finance and economics are more aware of the investment opportunities and hazards.Despite increased interest, many students are concerned about the volatility and unpredictability of the bitcoin market. Regulatory uncertainty and the possibility of fraud or hacking are highlighted as major causes for hesitation. Furthermore, the absence of formal financial instruction on bitcoin in college curricula inhibits students' capacity to make sound investment decisions.This study emphasizes the need for improved educational programs to give college students a thorough understanding of bitcoin. Colleges can help students navigate the evolving financial world with greater confidence by addressing knowledge gaps and concentrating on appropriate investment practices. The development of cryptocurrencies as a mainstream asset class may be heavily reliant on the understanding and preparedness of young investors, making it critical to cultivate a well-informed student body.
신원증명 기술의 발전에 따라 분산신원증명 기반의 모바일 운전면허증이 최근에 출시되었다. 본 연구는 이더리움 기반의 스마트 컨트랙트를 활용한 모바일 운전면허증 발급 시스템을 제안한다. 모바일 운전면허증 발급 과정에서 스마트 컨트랙트의 적절한 구현 및 운영 방식을 살펴보기 위해, 우선 Solidity 프로그래밍 언어를 사용하여 이름, 생년월일 등의 신원 속성을 블록체인에 저장하는 다양한 모델의 스마트 컨트랙트를 설계한다. 다음으로, 이더리움 테스트넷 및 배포 환경에서의 가스 사용량과 실행 시간을 측정하여 제안된 모델들의 성능을 비교 및 분석한다. 이 연구는 모바일 운전면허증 발급 시스템의 효율성 향상과 개인정보 보호 강화 방안을 제안함으로써, 디지털 신원증명 분야에 기여한다.
The exponential growth of digital banking transactions has intensified the demand for robust consensus mechanisms that can ensure transaction integrity while maintaining scalability and security in distributed ledger systems. Traditional Byzantine Fault Tolerant (BFT) consensus algorithms in banking blockchain networks suffer from limited throughput, high computational overhead, and vulnerability to sophisticated adversarial attacks in high-frequency trading environments. This paper introduces a novel Deep Learning-Enhanced Blockchain Consensus Mechanism (DL-EBCM) that integrates adaptive smart contracts with a hybrid Byzantine fault tolerance approach specifically designed for secure banking transaction processing. The proposed methodology employs a dual-layer consensus architecture combining Delegated Proof of Stake (DPoS) with Deep Reinforcement Learning (DRL) optimization for validator selection and transaction validation. The system incorporates Convolutional Neural Networks (CNN) for transaction pattern recognition, Long Short-Term Memory (LSTM) networks for fraud detection, and Generative Adversarial Networks (GAN) for synthetic transaction generation during stress testing. Experimental validation using real-world banking transaction datasets from multiple financial institutions demonstrates superior performance with 99.8% transaction validation accuracy, 2.3 seconds average consensus time, and 15,000 transactions per second throughput while maintaining Byzantine fault tolerance up to 33% malicious nodes. The framework achieves 45% reduction in energy consumption compared to traditional Proof of Work systems and 67% improvement in consensus finality compared to existing BFT implementations. The proposed approach successfully addresses scalability limitations while ensuring regulatory compliance and maintaining cryptographic security standards required for critical banking infrastructure.
Blockchain is revolutionizing the field of financial services by presenting a secure and decentralized framework that enhances efficiency and trust. This framework spans the entire spectrum of finance and financial services, from simple transfers to complex management and regulation. This technology has the potential to reduce the need for intermediaries while also lowering the cost of doing business and potential fraud avenues and fostering greater express transactions. The very design of this open, shared record ensures that all networks have access to the same document, which cannot be altered. Blocks validate transactions, and users have a say over it. Smart contracts provide the most convenient transaction process by eliminating human error and spike earnings. Furthermore, the integration of blockchain in the financial sector confronts several problems. Some of those are dependency, variation, and stability. Nevertheless, this technology makes financial markets more secure, effective, and open which results in new goods and business environments.
The dynamic progression of technology has induced a profound metamorphosis within the realm of commerce, ushering in novel prospects and trials for enterprises spanning diverse sectors. In contemporary times, the rise in non-fungible tokens (NFTs) and the conception of the Metaverse have ensnared the focus of corporate entities and visionary proprietors alike. This article explores the transformation of business frameworks during the era of NFTs and the Metaverse. It delves into traditional paradigms, clarifies the unique characteristics of NFTs, and examines their potential impacts on commerce. This article investigates the convergence of virtual reality (VR), augmented reality (AR), and blockchain technology within the Metaverse. To investigate these transformations, this study undertakes a comprehensive literature evaluation. The findings highlight how NFTs and the Metaverse have introduced new avenues for generating revenue and creating value. These advancements are achieved through the utilization of smart contracts and adaptable strategies that cater to evolving consumer behaviors. This article also addresses significant challenges in this landscape and provides a forward-looking perspective on the anticipated trajectory.
The article addresses the inefficiencies in the mechanisms for forecasting cash flows and managing liquidity in local budget boiler accounts, necessitating a revision of liquidity management practices. Financial decentralization has posed numerous challenges for local financial authorities, including the need to maintain sufficient cash balances in local budget accounts to ensure financing and payment of obligations with minimal costs. Effective management of cash reserves and forecasting the revenue base of local budgets are also critical. The authors emphasize the importance of applying modern forecasting methods, such as machine learning and neural networks, which enable faster and more accurate financial data analysis and forecasting. Special attention is given to data preprocessing and automation, algorithm selection, model training, and performance evaluation. The article also examines the liquidity management concept developed by the Ministry of Finance of Ukraine for 2020-2023 and its impact on improving public finance management practices. This article underscores the need to implement effective forecasting and liquidity management methods to ensure the stability and efficiency of the financial system at both local and state levels.
Maryna Sadovenko, Olga Kondratyuk, Nataliia Suprun, Maxim Tarverdiev
In today's digital age, technical engineering plays an important role in using artificial intelligence and cryptocurrency to optimize tax systems and improve financial efficiency in fintech businesses. Artificial intelligence helps automate business processes, especially in taxation, which reduces the cost of tax administration. Cryptocurrencies open up new opportunities for optimizing tax systems, providing greater transparency and efficiency in financial transactions.. The use of AI in tax administration can streamline processes, reduce human error, and improve compliance. AI algorithms can analyze large amounts of data, identify patterns, and detect potential tax evasion or fraud, leading to more accurate tax assessments and improved revenue collection. Additionally, AI-powered chatbots and virtual assistants can provide taxpayers with personalized support and guidance, enhancing the overall experience. Cryptocurrencies, on the other hand, offer a transparent and secure way to conduct financial transactions. By leveraging blockchain technology, cryptocurrencies enable immutable and auditable records of transactions, which can facilitate tax reporting and compliance. Furthermore, the decentralized nature of cryptocurrencies eliminates the need for intermediaries, reducing transaction costs and increasing efficiency. However, the implementation of these technologies in tax systems requires significant investments in infrastructure, software, and personnel training. Tax authorities ought to allocate substantial budgets to modernize their systems and integrate AI and blockchain solutions seamlessly. Additionally, concerns over data privacy and the potential for cyber threats pose challenges in ensuring the confidentiality and security of taxpayer information.
Open access
Impact of AI and Big Data on Business and Society
Digitalization and Economic Development in Agriculture
Development of Agriculture 4.0 brings higher demands for managing agricultural products. Blockchain technology can enhance the transparency, traceability, and security of the supply chain. Traditional single-blockchain model faces the scalability limitations. Therefore, we propose multilayer blockchain system for agricultural data management to enable the precise management in various aspects, such as supply chain tracing, land management, market transactions, and sustainability tracking. Interplanetary file system helps to alleviate the burden on data storage. Based on the characteristics of system data flow, we devise a post-quantum cross-blockchain data exchange approach using attribute-based encryption (ABE-PQCBCDEA). It solves the scalability problem and realizes fine-grained access control of different blockchain data, and it significantly reduces the size of ciphertext sets for ciphertext reuse. ABE-PQCBCDEA obtains the 42.6% improvement in the encryption and decryption efficiency compared with previous approaches, and its communication costs have been reduced by 48.5%, thereby it reduces the burden of cross-chain data exchange.
Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling.
Gurpreet Singh, Ashok Kumar Sahoo, G. S. Yadav, Ajay Gairola · 6 authors
Crypto currencies represent a digital form of currency, entirely reliant on electronic transactions and lacking a physical counterpart in the form of traditional banknotes. Unlike fiat currencies, they operate in a decentralized manner, free from third-party intervention, enabling users to access services directly. Nevertheless, the volatility in cryptocurrency prices significantly impacts international relations and trade, contributing to economic inequalities on a global scale. This research concentrates on Bitcoin price prediction, a highly popular cryptocurrency widely accepted by various stakeholders, including investors, researchers, traders, and policymakers. Therefore, in this research, it is proposed to compare the performance of two machine learning models (a RNN model and a LSTM model) for bitcoin price prediction. Open and close pricing are the main requirements for implementation of the model. In addition, the research compares the accuracy values of both models for closed prices, contributing to Sustainable Cities and Communities. The results prove that LSTM model is a better choice than RNN model with a very low error of 0.196%.
A capacity of foreseeing price fluctuations in bitcoin with exceptionally precise is very worthwhile to investigators and funding sources. However, as the cryptocurrency market is nonlinear, it can be challenging to determine the distinctive features of time-series data, which renders it challenging to forecast accurate price estimations. Massive oscillations in non-stationary cryptocurrency values underscore the pressing necessity to precise forecasting models. The most effective methodology for cryptocurrency price forecasting is machine learning, foremost ensemble and deep learning. Traditional statistical methods are difficult to execute accurately because to the lack of seasonal variations and the need to meet a number of naive requirements. The suggested methodology builds upon the random walk theory, commonly utilized in financial markets to model stock values. To simulate market volatility, this methodology utilizes randomization into the observed feature activations of neural networks at a layer-wise level. Moreover, a mechanism to assess the market’s reaction pattern is incorporated into the prediction model. Training was conducted on ARIMA and LSTM, short for Long Short-Term Memory models using Ripple, Ethereum, and Tron as illustrative examples.
This chapter provides an overview of the crucial role played by blockchain technology in securing healthcare metaverse by ensuring the privacy and integrity of sensitive medical data. Techniques like zero-knowledge proofs and commitment schemes empower patients to control their data while enabling secure data sharing. Secure multi-party computation further enhances security by facilitating joint computations without revealing individual data fragments. However, challenges remain in implementing these technologies effectively. Scalability issues arise from the vast amount of data generated in the metaverse, requiring efficient processing and storage solutions. Existing cryptographic techniques, while robust, might not be future-proof against advancements like quantum computing. By prioritizing data security and privacy, the metaverse can evolve into a reliable and accessible digital environment for healthcare delivery. By embracing innovation, fostering collaboration, and upholding patient autonomy, we can create a future where healthcare is personalized and accessible, for all.
Finance, decision-making, and AI interaction have been transferred to the digital economy due to the reduction of transaction costs and increase in security through blockchain technology. Integration of artificial intelligence (AI) and big data analytics with decision support systems (DSS), with a focus on risk assessment, predictive analytics, and strategic planning, has been explored. AI and DSS collaborated to deliver responsiveness and flexibility across several industries, leading to improved, data-driven decision-making. The current and future paths of AI with a focus on finance, healthcare, and customer service, in addition to ethical problems, have also been discussed. Future developments in the digital economy, such as cybersecurity, decentralized banking, and quantum computing, have been explored to optimize benefits and reduce risks.