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Know your customer (KYC), accounting standards, issuance, clearing, and trade settlement became the major barrier to implement accounting, accountability and assurance process in supply chain finance (SCF). Blockchain technology features have the potential to solve accounting problems. This research focuses on exploring how blockchain technology provides solutions to overcome the barriers of accounting process in SCF. The benefits, opportunities, costs and risks related to blockchain adoption are also explored.

Multi-case study and qualitative methods are used with a framework based on blockchain role to overcome the accounting process barriers. Ten blockchain projects in SCF and 29 interviews of participants as a unit of analysis are considered.

The findings indicate that blockchain technology offers solutions to solve accounting, accountability and assurance problems in SCF. Validity, verification, smart contracts, automation and enduring data on trade transactions potentially solve those barriers. However, it is also necessary to consider costs such as implementation, technology, education and integration costs. Then there are possible risks such as regulatory compliance, operational, code development and scalability risk. This finding reflects the current status of blockchain technology roles in SCF.

This study unveils blockchain's SCF accounting potential, emphasizing multi-case method limitations and future research prospects. Diverse contexts challenge findings' applicability, warranting cross-industry studies for deeper insights. Addressing selection bias and integrating quantitative measures can enhance understanding of blockchain's accounting impact.

Accounting professionals can get an idea of the future direction and impact of blockchain technology on accounting, accountability and assurance processes.

This study provides initial findings on the potential, costs and risks of blockchain that is beneficial for parties involved in SCF, especially for banks and insurance underwriters. In addition, the findings also provide direction for the contribution of blockchain technology to accounting theory in the future.

Challenges of property data in developing markets have been reported by several authors. However, a deep understanding of the actual nature of this phenomenon in developing markets is largely lacking as in-depth studies into the actual nature of data challenge in such markets are scarce in literature. Specifically, the available literature lacks clarity about the actual nature of data challenges that developing markets pose to valuers and how this affects valuation practice. This study provides this understanding with focus on the Lagos property market.

This study utilises a qualitative research approach. A total of 24 valuers were selected using snowballing sampling technique, and in-depth semi-structured interviews were conducted. Data collected were analysed using thematic analysis with the aid of NVivo 12 software.

The study finds that the main data-related challenge in the Lagos property market is the lack of database of market property transactions and not the lack or absence of transaction data as it has been emphasised in previous studies. Other data-related challenges identified include weak property rights institution with attendant transaction costs, underhand dealings among professionals, undocumented charges, undisclosed information, scarcity of data relating to specialised assets and limited access to the subject property and required documents during valuation. Also, the study unbundles the factors responsible for these challenges and how they affect valuation practice.

The study has implication for practice in the sense that the deeper knowledge of data challenges could provide insight into strategy to tackle the challenges.

This study contributes to the body of knowledge by offering a fresh and in-depth perspective to the issue of data challenges in developing markets and how the peculiar nature of the real estate market affects the nature of data challenges. The qualitative approach adopted in this study allowed for a deep enquiry into the phenomenon and resulted into an extended insight into the peculiar nature of data challenges in a typical developing property market.

The purpose of this study is to explore and use artificial intelligence (AI) techniques for identifying the relevant attributes necessary to file a suspicious activity report (SAR) using historical customer transactions. This method is known as predictive modeling, a statistical approach which uses machine learning algorithm to predict outcomes by using historical data. The models are applied to a modified data set designed to mimic transactions of retail banking within the USA.

Machine learning classifiers, as a subset of AI, are trained using transactions that meet or exceed the minimum threshold amount that could generate an alert and report a SAR to the government authorities. The predictive models are developed to use customer transactional data to predict the probability that a transaction is reportable.

The performance of the machine learning classifiers is determined in terms of accuracy, misclassification, true positive rate, false positive rate and false negative rate. The decision tree model provided insight in terms of the attributes relevant for SAR filing based on the rule-based criteria of the algorithm.

This research is part of emerging studies in the field of compliance where AI/machine learning technology is used for transaction monitoring to identify relevant attributes for suspicious activity reporting. The research methodology may be replicated by other researchers, Bank Secrecy Act/anti-money laundering (BSA/AML) officers and model validation analysts for BSA/AML compliance models.

In 2019, FIU-the Netherlands celebrated its 25th anniversary. This study takes the occasion to reflect on the role of the FIU in financial surveillance and to describe its core practices of collecting, analysing and disseminating financial intelligence.

Because FIU practices are often secret and its transaction data classified as state secrets, the FIU’s daily operational activities remain obscure. Drawing on interviews, public reports and an online training course, this study encircles secrecy and offers a fine-grained analysis of the FIU's core activities.

The article finds that the FIU plays a pivotal role in financial surveillance because it can operate at various intersections. An FIU operates at the intersection of finance and security, in between the public and private sector and at the national and international domain. This pivotal role makes the FIU indispensable in the surveillance of payment systems and spending behavior.

The article poses that the desirability and effectiveness of financial surveillance has to date not received sufficient consideration, while it affects (the privacy of) anyone with a bank account. The article asks: is it ethically justifiable that transaction information is declared suspect, investigated, and shared nationally and internationally, without the individual or entity concerned officially being notified and legally named a suspect?

This case-study is not only relevant for the study of finance/security, AML/CFT and financial surveillance, but also to policy makers and the broader public who merit an understanding of how their financial behaviour is being surveilled.

The purpose of this study is to propose a decentralized multi-party cross-trading scheme based on a certificate transaction mechanism for the transaction of excess consumption certificates (ECCs) of renewable energy. The aim is to address the problems associated with the existing centralized transaction mode and to promote the development of the green electricity industry.

The proposed scheme involves calculating the quotation difference for the same type of certificate transaction based on the quotations of all users of both buyers and sellers. The transaction volume is then determined based on the order of quotation difference from large to small, and the total interests of cooperation are calculated. The nucleolus method is adopted to allocate the total interests to each member of the alliance and calculate the final transaction price. The blockchain technology is used for the transaction to achieve accurate traceability and efficient supervision, and a corresponding smart contract is designed and simulated in the Ethereum consortium chain.

The results of the simulation show the rationality and effectiveness of the proposed scheme. The decentralized multi-party cross-trading scheme can overcome the problems associated with the existing centralized transaction mode, such as low transaction efficiency, difficulty in obtaining the optimal transaction strategy and efficient supervision. The proposed scheme can promote the development of the green electricity industry by stimulating users' demand potential for green electricity.

The proposed scheme is original in its use of a certificate transaction mechanism to facilitate the trading of ECCs of renewable energy. The scheme adopts a decentralized multi-party cross-trading approach that overcomes the problems associated with the existing centralized transaction mode. The use of the nucleolus method for the allocation of total interests to each member of the alliance is also original. Finally, the use of blockchain technology for accurate traceability and efficient supervision of the transaction is an original contribution to the field.

The purpose of this paper is to ensure the anonymity and security of health data and improve the integrity and authenticity among patients, doctors and insurance providers. Simulation and validation algorithms are proposed in this work to ensure the proper implementation of the distributed system to secure and manage healthcare data. The author also aims to examine the methodology of Wireless Body Area Networks and how it contributes to the health monitoring system.

Wireless Body Area Network (WBAN) plays an important role in patient health data monitoring. In this paper, a novel framework is designed and proposed to generate data by the sensor machines and be stored in the cloud, and the transactions can be secured by blockchain. DNA cryptography is used in the framework to encrypt the hashes of the blocks. The proposed framework will ensure the anonymity and security of the health data and improve the integrity and authenticity among the patients, doctors and insurance providers.

Cloud Computing and Distributed Networking have transformed the IT industry and their amalgamation with intelligent systems would revolutionize the Healthcare Industry. The data being generated by devices is huge and storing it in the cloud environment would be a better decision. However, the privacy and security of healthcare data are still a concern because medical data is very confidential and desires to be safe and secure. The blockchain is a promising distributed network that ensures the security aspect of the data and makes the transactions authentic and transparent. In this work, the data is collected using various sensor devices and is transmitted to the cloud through the WBAN via the blockchain network.

In this paper, a framework for securing and managing the healthcare data generated by intelligent systems is proposed. As the data generated by these devices are heterogeneous and huge in nature, the cloud environment is chosen for its storage and analysis. Therefore, the transactions to and from the cloud are secured by using the blockchain-based distributed network.

The target end-users of our system are the patients to keep themselves informed and healthy, healthcare providers to monitor the conditions of their patients virtually, and the health insurance providers to have a track of the history of the patients, so that no fraudulent claims can be made.

The target end-users of our system are the patients for keeping themselves informed and healthy, healthcare providers for monitoring the conditions of their patients virtually and the health insurance providers to have a track of the history of the patients, so that no fraudulent claims can be made.

This study examines the cognitive factors of adopting blockchain technology in various supply chain scenarios and its role in reframing the distinctive values of supply chain financing. Based on expectancy theory, this study explores the different profiles underlying the components of expectancy, valence and instrumentality.

This is a multiple-case study of four Fintech companies using blockchain technology to promote the performance of supply chain operations and financing.

The results show that blockchain-enabled supply chain finance (BSCF) can be classified into four scenarios based on the scope and purpose of blockchain technology applications. The success of BSCF depends on the profiles of BSCF expectancy (the recognized purpose and scope of BSCF), instrumentality (identified blockchain attributes and other technology combinations) and valence (the perceived distinctive value of BSCF). Blockchain attributes help solve information asymmetry problems and enhance financing performance in two ways: one is supporting transparency, traceability and verification of transmissions and the other entails facilitating a transformation to new business models.

This research applies a new perspective based on expectancy theory to study how cognitive factors affect Fintech companies' blockchain solutions under a given supply chain operation or financing activity. It explains the behavioral antecedents for applying blockchain technology, the situations appropriate for the different roles of blockchain technology and the profiles for realizing the value of blockchain technology.

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the corresponding address by transactions. The deployed smart contracts are immutable, even if there are bugs or vulnerabilities. Therefore, it is critical to verify smart contracts before deployment. This paper aims to help developers effectively and efficiently locate potential defects in smart contracts.

GethReplayer, a smart contract testing method based on transaction replay, is proposed. It constructs a parallel transaction execution environment with two virtual machines to compare the execution results. It uses the real existing transaction data on Ethereum and the source code of the tested smart contacts as inputs, conditionally substitutes the bytecode of the tested smart contract input into the testing EVM, and then monitors the environmental information to check the correctness of the contract.

Experiments verified that the proposed method is effective in smart contract testing. Virtual environmental information has a significant effect on the success of transaction replay, which is the basis for the performance of the method. The efficiency of error locating was approximately 14 times faster with the proposed method than without. In addition, the proposed method supports gas consumption analysis.

This paper addresses the difficulty that developers encounter in testing smart contracts before deployment and focuses on helping develop smart contracts with as few defects as possible. GethReplayer is expected to be an alternative solution for smart contract testing and provide inspiration for further research.

Intelligent real-time systems are significantly impacting several of the UN Sustainable Development Goals (SDGs) by revolutionising processes in several areas such as Industry 4.0, smart cities, transportation, agriculture, renewable energy, climate change and other economic activities. Given that much of the work to achieve the SDGs relies on information and communication technology, cybersecurity has a potentially immense role to play towards achieving these outcomes. Moreover, cyberattacks have emerged as a new functional threat for interconnected, smart manufacturers and digital supply networks, employed in intelligent real-time systems for the Fourth Industrial Revolution. The effects of cyberattacks can be much more widespread than ever before due to the interconnected nature of Industry 4.0-driven operations. Blockchain can be really useful in such situations as it provides edge protection and allows authentication of the machine-to-machine and human–machine operations, stable data share, life cycle management, access control compliance of devices and self-sustaining operations. Moreover, blockchain can be applied for tracking and tracing transactions through devices, which are performed during the operation, as well as to encrypt and transmit data securely. It is vital to establish complete trust in a technology that is being adopted so that its full potential can be exploited. It is consequently critical that the organisational and information technology strategy fully integrates secure, vigilant and resilient cybersecurity strategies such as blockchain. This will ensure that cyber risks are properly managed in the age of Industry 4.0. This chapter, therefore, analyses the application of blockchain in intelligent real-time systems such as Industry 4.0 so that the opportunities these systems present for the SDGs can be exploited safely with minimum risks to society.

Blockchain is a technical solution integrating multiple technologies, with the potential to overcome the drawbacks of organizational governance. Given the emergence and prevalence of blockchain, its importance for organizational governance has progressively increased. Therefore, this study aims to analyze how blockchain restructure organizational governance.

This study presents a structured systematic literature review of blockchain-enabled applications across diverse organizational governance models and several case studies to illustrate them. Based on the above analysis, governance mechanisms, transaction upgrading and challenges are proposed.

Based on the literature review and typical applications, the authors summarize the advances in the research on the theoretical and practical applications of blockchain in organizational governance. We also identify the influence mechanisms of organizational governance and investigate transaction upgrading based on blockchain. Finally, the authors discuss three types of challenges (i.e. administrative, technical and environmental) to the relationship between blockchain and organizational governance. Along with the development of blockchain applications, the impact of blockchain on organizational governance has progressed in both theory and practice. Therefore, these findings will have significant implications for both academics and practitioners.

This paper makes three key theoretical contributions: we review the literature on the impact of blockchain on organizational governance and present typical cases to illustrate it; propose four governance mechanisms for the application of blockchain in organizational governance; and propose an innovating-and-matching-oriented model of transaction upgrading based on blockchain.