This comprehensive review examines the evolutionary trajectory of financial information systems from the 1670s to the present day, analyzing how technological innovations have fundamentally transformed financial reporting, auditing practices, and information accessibility. Through a bibliometric and conceptual analysis of seminal literature, this study identifies key technological inflection points including the emergence of structured bookkeeping systems, the institutionalization of financial publicity through the 1867 law, the development of sophisticated financial communication tools, and the recent integration of blockchain technology and data analysis capabilities. The review demonstrates that each technological wave has progressively enhanced data accuracy, real-time reporting capabilities, and audit efficiency while simultaneously introducing new challenges related to data security, regulatory compliance, and technological adoption barriers. Contemporary developments in distributed ledger technology and advanced analytics represent a paradigm shift toward autonomous financial reporting systems with unprecedented transparency and verification capabilities. The findings suggest that future financial information systems will be characterized by increased automation, enhanced predictive analytics, and seamless integration of blockchain-based audit trails. This evolution has profound implications for accounting professionals, regulatory frameworks, and corporate governance structures, necessitating adaptive strategies for stakeholder education and regulatory modernization.
Abstract Self-organized system (SOS) offers a compelling paradigm for enabling autonomous coordination in a multiagent system (MAS). By leveraging decentralized decision-making, these systems can dynamically adapt to evolving environments, making them highly suitable for complex engineering applications. While multiagent reinforcement learning (MARL) has advanced agents to perform collaboratively, the mechanisms driving such self-organization still remain largely unexplored. Thus, this paper aims to deepen the understanding how agents self-organize themselves into an intelligent team from the role specialization perspective. In the context of a precision assembly task with collision avoidance, agent teams are trained to collaborate without predefined role assignments. We apply physical motion analysis to quantify individual contributions to system dynamics as a team and utilize K-means clustering to further provide a data-driven view of emergent behaviors. By analyzing agent roleโs scores based on their contributions to the system, the mechanisms of role specialization have been uncovered. Results show that the effective role differentiation can be naturally emergent from the MARL training process. Furthermore, the training leads the role assignments into a hierarchical structure, where some agents take primary roles while others provide dynamic support. The findings offer practical guidelines for designing an adaptive, efficient, and robust multiagent systems for applications such as autonomous robotics and advanced manufacturing.
The existing methods do not effectively meet the security and performance demands for Internet of Vehicles (IoV) applications. They also do not provide low-latency, secure edge-computing solutions for end-users in vehicular environments. The study presented in this paper proposes a blockchain-based edge computing framework that utilises Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) consensus for simultaneously optimising latency, energy consumption, and security. For efficient microservice orchestration and task off-loading, the containerised architecture utilises Kubernetes with Hyperledger Fabric. The experiments conducted in urban, suburban, and highway scenarios confirmed that the proposed framework outperformed baseline algorithms with end-to-end latency reduction of 30โ45% while also lowering energy consumption by up to 55% under moderate-to-heavy loads. With less than 1.2 seconds per block on the blockchain consensus, the system also maintained task completion rates exceeding 95% during peak conditions. The framework demonstrates consistent performance across various vehicular densities and consumes zero-knowledge proofs with attribute-based encryption for data against cybersecurity threats. These results confirm that the integration of DDQN and blockchain technology effectively tackles primary obstacles IoV faces by providing secure edge computing for next generation vehicular networks.
Wazir Zada Khan, Ayesha Siddiqa, Faisal Alanazi, Muhammad Khurram Khan
The Metaverse creates a 3D virtual environment similar to the real world, enabling immersive interactions across diverse fields such as education, healthcare, and gaming. A critical aspect of these interactions is digital identity authentication, which ensures secure and trustworthy user experiences. This paper proposes a novel Non-Fungible Token (NFT) based digital identity authentication framework for the Metaverse, leveraging blockchain technology and Elliptic Curve Cryptography (ECC) to enhance security and user trust. The framework is tested using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, demonstrating its resilience against replay and man-in-the-middle (MITM) attacks. Our contributions include:(1)a secure NFT-based authentication mechanism,(2)a formal security analysis validating the frameworkโs robustness, and(3)a comprehensive discussion of practical implications. The proposed framework addresses key gaps in existing methods, offering a scalable and user-friendly solution for digital identity authentication in the Metaverse.
ABSTRACT This article explores the critical importance of traceability and accountability within modern supply chains, and how blockchain technology provides innovative solutions to enhance transparency and trust. Drawing upon literature, case studies, and emerging practices, the study identifies the transformative potential of distributed ledger technology (DLT) in tackling issues such as product authenticity, fraud, and ethical sourcing. As supply chains become increasingly complex, blockchain emerges as a decentralized mechanism to record, verify, and share immutable transaction data across all stakeholders, ensuring end-to-end visibility.
Qing Fang, Hong Su, Xi Wu, Haichuan Zhang ยท 5 authors
Smart contracts are essential tools for enabling interaction between blockchain and Internet of Things (IoT) systems. For example, in cold chain logistics, the blockchain can obtain the states of the logistics system through smart contracts. However, direct interactions between smart contracts and these systems introduce uncertainties, potentially leading to network forks or state inconsistencies, which can compromise the security and reliability of the blockchain. To address these challenges, a novel smart contract variable, ExState, is proposed, specifically designed to track and store the dynamic states of IoT systems. Additionally, a corresponding operational logic is defined to organize these states into sequential records, ensuring that the state sequences obtained by each node remain consistent, effectively mitigating state conflicts. In addition, a formal model is developed, accompanied by a theoretical analysis of its determinacy. Experimental results demonstrate that, in cross-chain scenarios, this method achieves a performance improvement of up to 50.41% compared to the traditional Oracle method.
With the increasingly turbulent political situation and the outbreak of public health events without warning, it will not only affect peopleโs physical health, but also affect the global financial market, causing the market to fall into a huge crisis, thus leading to a continued decline in the worldwide economy. During periods of financial market turmoil, many investors fall into panic and urgently need a โhavenโ to protect their assets. With the rise of the digital economy, gold no longer seems to be the only safe-haven option. Bitcoin has gradually entered the investorsโ field of vision. Some investors believe that Bitcoin can become an emerging safe-haven asset that is as important as or surpasses gold. Based on an analysis of the safe-haven properties of Bitcoin and gold during major political and historical events and public health events, this article will clarify which of the two is more suitable as a reliable contemporary safe-haven asset and provide advice to investors.
The rise of digital payments enhances global internet and mobile usage. However, there are still issues with customer satisfaction in mobile e-banking. This study examines how mobile banking service quality impacts customer satisfaction, detects hackers, and offers solutions for improvement through blockchain integration. This study compares artificial neural network performance with ML models like naive Bayes and XGBoost. The validated data is first sent to cloud for verification, and then securely stored on blockchain to protect customer information. The study uses ANN, a DL model to reduce hacking and ensure secure transactions for enhanced security. The proposed approach is implemented using Python platform and Ethereum tool. The study shows that the ANN model outperforms the ML models in terms of security, achieving an accuracy rate of 99.44%, making the proposed model ideal for e-banking applications. This approach not only enhances security against hacking but also builds customer trust and satisfaction.
Cryptocurrencies are subject to thorough examination and discourse by numerous media outlets, venture capitalists, financial institutions, banking organizations, market stakeholders, and political entities worldwide. Cryptocurrencies are currently emerging as a new investment class, and this presents an opportunity to explore historically revealed properties of cryptocurrencies. Consumers or investors may use online wallets to buy, store, and trade cryptocurrencies. Cryptocurrencies are not regulated by any government or bank and are designed to replace fiat money. The cryptocurrency market is highly volatile due to its emergent stage. Understanding the dynamics of cryptocurrency โmarket volatilityโ is crucial for investors and formulating investment strategies. Volatility is essentially attached to risk and return; as volatility rises, the cryptocurrency market faces greater instability. The volatility inherent in the Bitcoin and Litecoin market is analyzed through a daily return series comprising 3865 observations from January 2014 to July 2024. This study uses symmetric and asymmetric โGeneralized Autoregressive Conditional Heteroskedasticity (GARCH)โ models to evaluate Bitcoin and Litecoin returns and volatility. The study found a positive โrisk premiumโ in both markets, supporting the hypothesis that volatility correlates with predicted returns. Furthermore, our findings suggest that cryptocurrency return has a โleverage effect,โ and the effect of news (information) is asymmetric. Negative news has a larger influence on volatility than positive news in Bitcoin returns and has an effect of the same magnitude in Litecoin returns.
In todayโs digital landscape, safeguarding sensitive information during transactions has become increasingly critical. This project focuses on developing a secure data exchange framework by integrating cryptographic techniques, blockchain technology, and steganography. Inspired by blockchain-based asset management systems and decentralized transaction models, the study employs cryptographic methods like SHA-256 hashing, Proof of Work (PoW), and Proof of Stake (PoS) to ensure data integrity, authenticity, and confidentiality. To further enhance security, the project incorporates multimodal steganography and Least Significant Bit (LSB) techniques for embedding sensitive information within digital media. The combined strengths of blockchainโs immutable ledger and the covert nature of steganography create a system that prioritizes end-to-end security, privacy, and resistance to tampering. The methodology involves creating a decentralized peer-to-peer network for validating transactions, using advanced encryption techniques, and applying steganographic methods to conceal data within multimedia formats. Testing demonstrates the systemโs ability to prevent data breaches, support secure transactions, and withstand cyberattacks. This innovative hybrid model offers a reliable solution for secured digital communications and transactions across finance, healthcare, and e-commerce domains. Future advancements could explore quantum-resistant cryptographic approaches and AI-driven mechanisms to detect and mitigate fraud.
The insurance claim process is quite cumbersome; it is time-consuming, with high personnel costs from manual review. It may even take several months to complete the entire process. Therefore, how implementing insurance claim settlement automation to reduce costs, improve efficiency, reduce claim processing time, and increase client satisfaction is a common issue the insurance industry must face. This study explores the application of smart contracts in the casualty insurance settlement process to achieve the effect of automatic claim settlement and double protection for special accidents. When the insurance industry conducts insurance claim reviews through the characteristics of blockchain and smart contracts, such as openness and transparency, anonymity, and automation, the review process can be curtailed, and the premium can be directly transferred to the bank account of the insured. Thus, the purpose of automating casualty insurance claims is achieved through smart contracts.
The process of rendering authenticity to the Degree Certificate (DC) is known as Degree Attestation (DA). None of the prevailing works have focused on zero trust-based DA, verification, and traceability for secured DA. So, zero trust-based secured DA, verification, and traceability of degree credentials are presented in the paper. Primarily, to upload the DC of the student, the university registers and logs in to the Blockchain (BC). Subsequently, by utilizing radioactive decay-based elliptic curve cryptography (RD-ECC), the DC is secured. Next, by utilizing Glorot initialization-based Proof-of-Stake (GPoS), the data is stored in the BC. Further, to verify the traceability of the data, a Smart Contract (SC) is created. In the meantime, the student registers and logs in to the BC and gives attestation requests to the university. By utilizing rail fence cipher (RFC) RD-ECC hash-based message authentication code (RFCR-HMAC), the university authenticates the request. By utilizing a quadratic probing-based digital signature algorithm (QP-DSA), the university attests the DC after authentication. Lastly, by utilizing RD-ECC, the attested certificate is encrypted and sent to the student. Hence, the certificate is secured with an encryption time (ET) of 5971ms and DA is performed with a Signature Generation Time (SGT) of 6637ms.
Vincent W.J. van Gerven Oei, Pieter Effendy, Lili Ayu Wulandhari, Islam Nur Alam
Cryptocurrencies have recently become popular among many people, young and old, with various backgrounds. The popularity of cryptocurrency shines due to several things, one of which is Bitcoin, the largest cryptocurrency ever to exist. However, even though Bitcoin is well-known and considered as the largest cryptocurrency, Bitcoin still experienced major price fluctuations over the years, seen in daily trades and yearly valuations. It is because in this digital era, the whole market can be said to be vulnerable because news and social media posts can easily be accessed on the internet. Therefore, it can create sentiments and trends across society. Due to the possibility that sentiment and trends can influence the volatility movements of cryptocurrencies such as Bitcoin, this research wants to see whether the use of sentiment analysis and trends in one of Machine Learning algorithms, namely Random Forest, can predict the Bitcoin price action well.
Jie Yang, Yuta Kodera, Samsul Huda, Yasuyuki Nogami
In the distributed medical information management system, blockchain technology has more obvious advantages in terms of data tampering data traceability. However, considering the scalability issues of current Layer-1 blockchain, uploading massive medical information data onto the blockchain will add non-negligible space storage pressure. Patients are also unable to have a comprehensive grasp of their personal privacy information retained in these data, which can lead to concern about the systemโs privacy and security. To cope with these challenges, this paper introduced Layer-2 network to reduce the data space occupation in a single block. By storing unnecessary information in the off-chain channel, the transaction speed and throughput of the system would be improved. In addition, to strengthen the security within the process of data communication and storage, this paper also deployed the system based on consortium blockchain utilizing AWS cloud service. And the system integrated with the zero-knowledge proof algorithm zk-SNARKs to protect personal information privacy. According to the experimental simulation and analysis, the proposed scheme can reduce data storage space in the Lay-1 blockchain. Whatโs more, it also allows quick verification of on-chain data without disclosing personal privacy information.