Purpose –this paper aims to examine the impact of the integration between Extensible Business Reporting Language (XBRL) and Smart Contracts, which represent the second generation of the decentralized ledger Blockchain, on the transition from continuous auditing to Real-time Auditing. Design/methodology/approach – Using Exploratory Study , this study examines the impact of the integration between XBRLand Smart Contracts on the transformation from continuous Auditing to Real-time Auditing. Findings – This paper finds that the XBRL- Smart Contracts is a good way to activate real-time Auditing because of the characteristics of XBRL and Smart contracts based on blockchain that can support real-time Auditing, including transparency, privacy, decentralization, and pre-validation of operations at the same time as they occur with no possibility of modification or fraud. Therefore, Smart Contracts is not a substitute for XBRL, as the Blockchain is a ledger through which transactions can be conducted, and XBRL is the standard that can standardize the terms and standards for the items that are exchanged in accounting in those transactions, which means that XBRL supports Smart contracts based on blockchain in transparency and trust in transactions. There for, this paper finds that the XBRL- Smart contracts based on blockchain integration affects significatively to transfer from continuous auditing to real time auditing. Originality/value – This paper contributes to the literature on Provide a proposed A Prospective framework for the integration between XBRL and Smart Contract to transfer from continuous Auditing to Real time Auditing.
Ethereum ist seit Jahren die größte Smart-Contract-Blockchain und nach Bitcoin die zweitgrößte Blockchain-Plattform. Smart-Contracts, die als dezentrale Anwendungen beschrieben werden können, laufen auf einer gemeinsamen Rechenplattform, auf der alle Teilnehmer auf einer geteilten Codebasis arbeiten. Zur Absicherung ist es nötig, dass ein Konsens über die Ein- und Ausgaben aller Smart-Contracts geschaffen wird. Die Ausführung von Smart-Contract-Code verbraucht sogenannte Gas-Einheiten, die als eine Art Treibstoff betrachtet werden können. Gas-Einheiten zeigen den erforderlichen Rechenaufwand an und haben direkte Auswirkungen auf den realen Energieverbrauch. Daher sollten idealerweise alle Smart-Contracts so implementiert sein, dass sie möglichst wenig Gas-Einheiten verbrauchen. Derartige Codeoptimierungsansätze sind nicht trivial. Zum Zeitpunkt des Verfassens dieser Diplomarbeit gibt es bereits solche Mechanismen, welche teilweise direkt in den gängigen Compilern integriert sind. Solche Mechanismen basieren in der Regel auf festen Mustern, welche manuell beschrieben werden müssen und dann auf Smart-Contracts angewendet werden können. In dieser Arbeit haben wir untersucht, ob klassische Verfahren zur Erkennung von Codeähnlichkeiten verwendet werden können, um Optimierungsmuster automatisch aus Quellcode-Repositories ableiten zu können. Zunächst haben wir einen Symbolic-Execution-Ansatz untersucht, welcher sich aufgrund von technischen Einschränkungen und der Abhängigkeit von veralteten Compiler-Versionen als ungeeignet erwies. Daraufhin haben wir einen Fingerprinting-Ansatz basierend auf Kontrollflussgraph-Blöcken gewählt. Mithilfe von Slither konnten wir Metriken wie Cyclomatic-Complexity, Fan-Out und Informationsfluss-Metriken extrahieren und anschließend Distanzen zwischen Codestücken berechnen, um mit den Ergebnissen potenzielle semantische Code-Klone zu erkennen. Wir haben die Evaluierung unseres Ansatzes auf 1.200 manuell markierten Smart-Contracts aus einem Datensatz mit 160.000 Einträgen durchgeführt, was zu 574 Vergleichen führte und konnten eine korrigierte Genauigkeit von 88% für die Erkennung von semantischen Code-Äquivalenzen auf Blockebene erzielen. Für 1.300 Code-Paare haben wir zusätzlich eine Gasverbrauchsmessung durchgeführt, indem wir die Blöcke in generierte Smart-Contracts verpackt und auf einer lokalen Blockchain ausgeführt haben. Dabei konnten wir tatsächliche gasreduzierende Codeänderungen identifizieren. Trotz einiger wesentlichen Einschränkungen zeigt das, dass das Mining gasoptimiertem Codes aus versionierten Source-Code-Repositories mittels Code-Metriken möglich ist.
Islam Abdeljawad, Ahmad Tina, M. Kabir Hassan, Mamunur Rashid
The aim of this comprehensive review of the papers published on the Scopus database is to gain insights into the various indicators that determine the level of market efficiency within the global cryptocurrency markets. We have employed a series of bibliometric and content analyses on 3,224 papers published during 2014–2024. Findings indicate that the scholarly literature on cryptocurrency exhibits a varied range of perspectives, frequently encompassing multiple academic disciplines such as economics, finance, accounting, technology, and engineering. We present three significant findings. First, despite a growing list of recent studies supporting some efficiency, cryptocurrency assets frequently deviate from conventional norms of market efficiency. Herding, co-movement, sentiment, and overconfidence are the major contributors behind the inefficient cryptocurrency market. Second, the intricate nature of these assets and their lack of connection to fundamental economic value contribute to inconsistencies, instability, and ambiguity. Third, regulators are expected to intervene with prudent and globally collaborative regulations to optimize the potential of this market. In this discourse, we analyze the potential consequences derived from various frameworks and methods, with the aim of informing forthcoming scholarly investigations.
The rapid development of financial technology has introduced cryptocurrency as a transformative innovation within the global financial system, raising fundamental questions regarding its compatibility with value-based financial models, particularly Islamic banking. Grounded in Sharīʿah principles that emphasize ethical finance, risk-sharing, asset-backed transactions, and the prohibition of ribā, gharar, and maysir, Islamic banking faces both opportunities and challenges in responding to the emergence of decentralized digital assets. This study aims to explore how Islamic banking can engage with cryptocurrency while maintaining its normative and ethical foundations, as well as to identify the key constraints that limit institutional adoption within global finance. Employing a qualitative research approach, the study conducts a systematic and interpretive review of scholarly literature, regulatory frameworks, and classical as well as contemporary Sharīʿah sources related to Islamic finance, blockchain technology, and cryptocurrency governance. The analysis reveals that blockchain technology demonstrates substantial alignment with Islamic banking principles through its transparency, traceability, and decentralized verification mechanisms, offering institutional potential in areas such as payments, trade finance, and smart contracts. However, the findings also indicate that cryptocurrency markets are characterized by high volatility, speculative behavior, weak real-sector linkage, and fragmented regulatory oversight, which raise significant ethical, financial, and governance concerns for Islamic banking institutions. Regulatory divergence and inconsistent Sharīʿah interpretations further complicate cross-border implementation and scalability. The study concludes that Islamic banking should adopt a selective and principle-oriented approach to cryptocurrency, distinguishing between permissible technological infrastructure and ethically problematic market practices.
K. Myrzabekkyzy, G. Lukhmanova, B. Dosanov, A. Bolganbayev · 5 authors
This article, within the context of modern financial technology development, analyzes the impact of decentralized finance (hereafter - DeFi) on the traditional financial system and its criminal-risk aspects. The study aims to describe DeFi operating mechanisms (decentralized architecture and smart contracts), systematize the directions of change in banking, lending, insurance, and investment services, and identify the main types of misconduct and fraud while proposing preventive measures. The paper clarifies DeFi’s operational features, the role of smart contracts, and the nature of decentralization, and examines DeFi’s position across traditional financial service segments. Types of offenses and fraudulent schemes occurring on DeFi platforms are identified, and prevention measures are proposed. A comparison between DeFi and traditional finance is provided, highlighting key advantages and disadvantages and offering recommendations to reduce criminal risks.
Abstract Decentralized Autonomous Organizations (DAOs) promise to transform governance through blockchain-enabled transparency and communal decision-making. However, unresolved legal responsibilities and speculative governance token dynamics complicate their ability to maintain trust, encourage participation, and secure legitimacy. Drawing primarily on Social Capital Theory (SCT), this study shows how bonding, bridging, and linking social capital intersect with liability ambiguities and token concentration to undermine institutional confidence and grassroots engagement. Public Goods Theory (PGT) clarifies how free-rider tendencies can deter infrastructural support, while Principal-Agent Theory (PAT) highlights incentive misalignments when whales prioritize short-term gains over collective welfare. Through a theoretical lens, this study illuminates how token-based power asymmetries, a lack of regulatory clarity, and conflicting motivations strain the viability of DAOs in fulfilling the promise of decentralized governance. In synthesizing these frameworks, this study advocates tailored governance strategies, ranging from reputation-based voting models to legally compliant organizational wrappers, to mitigate power imbalances, foster inclusive decision-making, and ultimately strengthen DAOs’ resilience in an evolving blockchain ecosystem. By bridging the sociological, economic, and legal perspectives, this study illustrates the interplay of trust, accountability, and incentives that shape DAO sustainability.
Abstract Corporate treasury departments face growing challenges created by liquidity fragmentation, inefficient cash management, and delayed cross-border settlements-a perfect storm for increased financial risks for the firms and for operational difficulties. The present-day treasury systems rely on centralized banking and manual processes. A traditional one thus lacks the flexibility and the transparency needed in today’s very uncertain global environment.Decentralized finance (DeFi) is presented in this paper as an essential infrastructure layer that has the potential to transform how businesses handle liquidity. DeFi offers programmable, real-time, and international financial execution through the use of smart contracts, algorithmic liquidity pools, decentralized exchanges, and tokenized assets. Conceptual modeling links DeFi mechanics to essential treasury functions, comparative analysis examines DeFi and traditional systems, and scenario simulations explore practical examples of corporate use cases.It is found that DeFi can enhance access to liquidity, reduce transaction costs, and automate treasury operations, especially with respect to intercompany fund flows, short-term financing, and FX execution. However, adoption needs strong governance frameworks, regulatory agreement, and technical compatibility with existing systems. This study offers a practical framework for CFOs, fintech developers, and policymakers to evaluate DeFi’s role in corporate treasury environments. It positions decentralized infrastructure as a useful tool for next-generation liquidity strategies.
Decentralized finance (DeFi) lending platforms have rapidly evolved within financial markets, with stablecoins playing a pivotal role in these ecosystems by providing price stability and enhancing capital efficiency. However, DeFi lending platforms still face challenges including market volatility, smart contract security, regulatory uncertainty, and liquidity constraints. This paper analyzes the function of stablecoins within DeFi lending platforms, explores their advantages and challenges, and forecasts future development trends. The article first introduces the fundamental concepts and classifications of stablecoins, then analyzes their advantages and challenges within lending markets, and finally looks ahead to innovations and future developments for stablecoins. Through this analysis, the paper provides in-depth insights for researching stablecoin applications in DeFi lending.
ABSTRACT Cryptocurrency, a decentralized digital asset enabled by blockchain technology, has transformed global finance by introducing novel mechanisms for value exchange, security, and governance. This comprehensive academic review synthesizes current knowledge across multiple dimensions: the technical foundations of cryptocurrencies (including distributed ledger technologies, cryptographic primitives, and consensus mechanisms), economic and financial implications (market behavior, monetary policy interactions, speculation, and investment risk), legal and regulatory frameworks (jurisdictional approaches, taxation, anti-money laundering measures, and consumer protection), as well as societal and ethical concerns (environmental impact, privacy, financial inclusion, and potential for illicit use). Drawing on recent empirical studies, case analyses, and theoretical models, the review highlights both the transformative potential of cryptocurrencies to democratize access to financial services and foster innovation, and the significant challenges—such as scalability, volatility, regulatory uncertainty, and energy consumption—that could inhibit or slow their integration. The paper concludes with a discussion of future research directions, including evolving consensus innovations (e.g. proof-of-stake, sharding), central bank digital currencies (CBDCs), and frameworks for balancing innovation with systemic risk mitigation. KEYWORDS Cryptocurrency, probabilistic forecasting, value-at-risk, expected shortfall, volatility, risk management, threat modeling, fintech, blockchain
ABSTRACT Research Question/Issue Blockchain technology promises to revolutionize governance through strong commitments, trustlessness, and transparency. This paper examines how these promises have failed to materialize in practice. Research Findings/Insights Drawing on case evidence from major blockchains, including Bitcoin and Ethereum, I argue that blockchains have evolved into technocracies where developers, foundations, and companies exercise disproportionate control. Rather than being exceptional, blockchain governance suffers from the same coordination problems, collective action failures, and centralization tendencies that plague traditional governance systems. Theoretical/Academic Implications The paper concludes that while blockchains offer valuable experiments in governance design, their alleged advantages over traditional institutions remain largely mythical. Practitioner/Policy Implications Blockchain organizations should acknowledge their reliance on off‐chain coordination and informal authority. Investors must understand that blockchain governance depends on trusting technical elites, while regulators should recognize that decentralization claims often mask concentrated power structures requiring traditional oversight.
Decentralized finance (DeFi) protocols are becoming increasingly targeted by cyber threats, such as liquidity drain attacks, smart contracts flaws that leverage instant loans, and increasingly sophisticated threats that include DarkGate ransomware. We develop a hybrid framework that integrates CTI and predictive analytics to facilitate improving consensus mechanisms in a blockchain network. The proposed framework is centered on three layers , a data collection and processing layer, a security oracle layer that engages to mitigate intervention, and a dynamic adaptive mechanism to reach consensus. A 250-node testbed was built and deployed with the Hyperledger Besu and Geth deployments of Ethereum incorporating hybrid GRU-BiLSTM which utilize GNN's for predicting attacks. The results reveal improvements of transaction processing TPS of up to +236%, settlement latency improved -75%, fork rate improved to less than 3%, and downtime improved from 15% to 1.5%. Statistical tests T-Test and ANOVA also reveal these were of high statistically significance at p < 0.01. This study emphasizes that bridging functional aspects of AI with adaptive consensus mechanisms will be an effective approach at combating advanced cyber-attacks while maintaining reliability and resilience in DeFi systems.
The convergence of blockchain and Zero Trust Architecture (ZTA) offers a transformative pathway for enhancing security and resilience in financial infrastructures. Traditional network perimeter models are increasingly inadequate for safeguarding decentralized finance (DeFi), payment systems, and digital asset platforms that operate across distributed environments. This study explores how Zero Trust principles rooted in continuous verification, least privilege access, and micro-segmentation can be effectively integrated into blockchain ecosystems to mitigate identity spoofing, insider threats, and data tampering. By examining hybrid frameworks that combine permissioned blockchains with Zero Trust access controls, the research highlights a paradigm shift toward adaptive, identity-centric security postures in financial networks. The proposed model emphasizes dynamic authentication, real-time monitoring, and cryptographic assurance to ensure trustless yet verifiable interactions among nodes and participants. This integration not only fortifies compliance with emerging regulatory standards but also enhances interoperability and transparency across multi-chain financial systems. The findings suggest that embedding Zero Trust principles within blockchain-oriented infrastructures can create a self-healing, auditable, and future-ready digital finance ecosystem.
The evolution of the technical architecture of digital currencies is profoundly reshaping the global monetary system. This article starts from the core dimensions of technical architecture selection, systematically analyzes the technical characteristics and applicable scenarios of blockchain, distributed ledgers, and hybrid architectures, and combines the two-tier operation system design of central bank digital currencies (CBDC) to explore their sustainable development paths in areas such as payment efficiency, privacy protection, and regulatory compliance. Research shows that the modular reconfiguration of the technical architecture, the improvement of cross-chain interoperability, and the application of quantum-secure encryption technology are the keys to promoting the realization of "controllable anonymity" and global deployment of CBDCS. This article puts forward policy suggestions such as driving technological iteration through a regulatory sandbox mechanism and building a multilateral central bank digital currency bridge, providing theoretical support for the maintenance of monetary sovereignty and the upgrading of financial infrastructure in the digital currency era.
The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these halvings, then the neoclassical economic theory posits that the price of Bitcoin should have increased due to the halving. But did it, in fact, increase for that reason, or is this a post hoc fallacy? This paper uses synthetic control to construct a weighted Bitcoin that is different from its counterpart in one aspect - it did not undergo halving. Comparing the price trajectory of the actual and the simulated Bitcoins, I find evidence of a positive effect of the 2024 Bitcoin halving on its price three months later. The magnitude of this effect is one fifth of the total percentage change in the price of Bitcoin during the study period - from April 2, 2023, to July 21, 2024 (17 months). The second part of the study fails to obtain a statistically significant and robust causal estimate of the effect of the 2020 Bitcoin halving on Bitcoin's price. This is the first paper analyzing the effect of halving causally, building on the existing body of correlational research.
This paper investigates how the evolution of interbank payments towards central bank settlement, and thus central bank money as a settlement asset, has affected the dynamics of bank crises.We take the cluster of bank defaults in the United States in 2023 as a starting example and show how, alongside fractional reserves and fast digital communication, centralized settlement in central bank money played a critical role in triggering swift bank failures.We argue that technical centralization has amplified banks' fragility in the development of confidence crises, making bank runs easier and expanding the role of central banks to a point where conflict of interest becomes nearly inevitable.While previous literature has emphasized the effects of fast news spread and online banking, the role of settlement technology in recent bank runs has been largely overlooked.Thus we describe the stability consequences of different settlement architectures in detail, and also discuss potential improvements to the current architecture, particularly decentralized approaches built on distributed ledgers, to mitigate financial instability and reduce the negative effects of centralization without reverting to inefficient legacy systems.
Alexandru Ursu, Petru Lucian Curșeu, Sabina Trif, Alina Maria Fleştea
Cryptocurrencies are rapidly transforming digital finance and entrepreneurship, yet their adoption by entrepreneurs remains rather poorly understood. Drawing on the Threat-Rigidity Model (TRM) and the opportunity recognition literature, this study examines how entrepreneurial experience, financial literacy, perceived opportunities, and perceived threats influence entrepreneurial intention to use cryptocurrencies. We tested a moderated mediation model in which the association between financial literacy and experience, on the one hand, and intention to use cryptocurrencies, on the other, was mediated by perceived opportunities. In this model, perceived threats served as a moderator on the relationship between financial literacy and intention, as well as between perceived opportunities and adoption intention. Data were collected from a sample of 133 Romanian entrepreneurs across diverse industries. The results supported the mediating role of perceived opportunities in the relationship between financial literacy and intention to use cryptocurrencies in business and showed that the positive association between financial literacy and intention was attenuated by perceived threats. Entrepreneurial experience did not significantly influence perceived opportunities, while women entrepreneurs reported lower intention to adopt cryptocurrencies in business. This study is among the first to use the TRM to explore how the interplay of perceived opportunities and threats shapes cryptocurrency adoption in entrepreneurship. Other implications, limitations, and directions for future research are also discussed.
Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios.
AI text-to-video systems, such as OpenAI’s Sora, promise substantial efficiency gains in media production but also pose risks of biased outputs, opaque optimization, and deceptive content. Using the Orientation–Stimulus–Orientation–Response (O-S-O-R) model, we conduct an empirical study with 209 Chinese new media professionals and employ structural equation modeling to examine how information elaboration relates to AI knowledge, perceptions, and adoption intentions. Our findings reveal a knowledge paradox: higher objective AI knowledge negatively moderates elaboration, suggesting that centralized information ecosystems can misguide even well-informed practitioners. Building on these behavioral insights, we propose a blockchain-based governance framework that operationalizes five mechanisms to enhance oversight and trust while maintaining efficiency: Expert Assessment DAOs, Community Validation DAOs, real-time algorithm monitoring, professional integrity protection, and cross-border coordination. While our study focuses on China’s substantial new media market, the observed patterns and design principles generalize to global contexts. This work contributes empirical grounding for Web3-enabled AI governance, specifies implementable smart-contract patterns for multi-stakeholder validation and incentives, and outlines a research agenda spanning longitudinal, cross-cultural, and implementation studies.
I‐Fang Su, Shun-Ming Wang, Yu-Chi Chung, Yi-Hsien Tsai
Abstract In this research, we introduce an advanced approach for the detection of smart contract vulnerabilities leveraging Large Language Models (LLMs). Smart contracts are pivotal in the ecosystem of decentralized finance (DeFi), functioning as automated protocols for data management and transaction execution. The foundation of numerous blockchain-based applications lies in smart contract technology. Nevertheless, these contracts’ code vulnerabilities can become targets for malicious exploitation, leading to substantial financial damages, exemplified by the 2016 Dao smart contract incident which incurred a loss of 55 million USD. In response to such challenges, detection mechanisms for smart contract vulnerabilities have been devised, drawing upon conventional static analysis, fuzzy testing, and machine learning methodologies. Owing to the swift progression of LLMs, such as GPT, a broad spectrum of entities has adopted these models for routine operational management. By recognizing LLMs’ inherent capability to comprehend programming code, we investigate their aptitude for identifying smart contract vulnerabilities. We have integrated prompt engineering techniques, including the Chain of Thought (CoT), Plan-and-Solve, and few-shot learning, to augment the LLMs’ vulnerability detection efficacy. Furthermore, a sequence of empirical studies has been orchestrated to validate the effectiveness of our proposed prompt engineering strategies against diverse smart contract vulnerabilities.
The rapid digitization of financial services has resulted in a staggering increase in sophisticated fraud, endangering global economies and damaging public trust. The dynamic nature of current fraud is outpacing classic fraud detection systems, which frequently rely on static, rule-based methods. This study reveals a new hybrid framework that pairs distributed ledger technology for immutable transaction avoidance with Machine Learning (ML) for real-time fraud detection. The fundamental driving force is to address the inherent shortcomings of centralized systems, as well as the lack of an unchangeable audit trail in ML-only solutions. Using a range of classification algorithms, our methodology entails creating separate machine learning pathways for three important financial domains: credit card, UPI, and loan applications. A fraud verdict is subsequently produced using the top-performing model for each domain, which is determined by a thorough analysis of metrics. Through a smart contract, this decision is safely and irrevocably documented on a private blockchain. This study shows how a strong security architecture may be produced by fusing the decentralized trust and immutability of blockchain technology with the predictive performance of machine learning. The findings demonstrate that this integrated approach strengthens the integrity and dependability of digital financial transactions by achieving high performance in fraud detection as well as creating a transparent and impenetrable record.
Since more users are moving to cloud computing, keeping our data safe and private now matters more. Since most cloud storage is run from just one location, it is easier for attackers and causes issues if the system fails. We come up with a new way to secure cloud storage by using blockchain technology and smart contracts. Blockchain mainly allows us to have a safe and distributed record that ensures data access can be trusted. By using smart contracts, we enable people to safely access data without any help from a middleman. It means that you can see every use of data access in the system, and these actions cannot be deleted or changed. Using blockchain technology with distributed storage, we ensure that everything happening is recorded and access is regulated correctly. We also discuss a case study to illustrate how our idea helps with transparency and trust, while at the same time mentioning concerns such as how far it can be used and problems with regulations, and our solutions for these issues.
Son, Do Hai, Hieu, Le Vu, Khoa, Tran Viet, Alem, Yibeltal F. · 8 authors
Blockchain technology has experienced rapid growth and has been widely adopted across various sectors, including healthcare, finance, and energy. However, blockchain platforms remain vulnerable to a broad range of cyberattacks, particularly those aimed at exploiting transactions and smart contracts (SCs) to steal digital assets or compromise system integrity. To address this issue, we propose a novel and effective framework for detecting cyberattacks within blockchain systems. Our framework begins with a preprocessing tool that uses Natural Language Processing (NLP) techniques to transform key features of blockchain transactions into image representations. These images are then analyzed through vision-based analysis using Vision Transformers (ViT), a recent advancement in computer vision known for its superior ability to capture complex patterns and semantic relationships. By integrating NLP-based preprocessing with vision-based learning, our framework can detect a wide variety of attack types. Experimental evaluations on benchmark datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in terms of both accuracy (achieving 99.5%) and robustness in cyberattack detection for blockchain transactions and SCs.
New technologies, such as blockchain, are designed to address various system weaknesses, particularly those related to security. Blockchain can enhance numerous aspects of traditional banking systems by transforming them into digital, immutable, secure, and anonymous ledger. This paper proposes a new banking application ALBank, which is based on blockchain and smart contract technologies. Its functionality relies on invoking functions within smart contracts deployed on the Ethereum blockchain. This approach enables decentralization and enhances both security and trust. In this context, the paper first presents a critical analysis of existing research on blockchain and traditional banking systems, with a focus on their respective challenges. It then examines the Know Your Customer (KYC) process and its various models. Finally, it introduces the design and development of ALBank, a decentralized banking application built on the Ethereum blockchain using smart contracts. The results show that the integration of blockchain and smart contracts effectively addresses key issues in traditional banking systems, including centralization, inefficiency, and security vulnerabilities by storing critical data on a decentralized, immutable ledger, managing processes autonomously, and making transactions transparent to all users.
The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), and zero-knowledge proofs (ZKPs). By eliminating reliance on centralized authorities, our system enhances data privacy, reducing personally identifiable information (PII) disclosure by 80% while ensuring compliance with AML and GDPR regulations. The integration of zk-SNARKs enables trustless identity verification with an average proof generation time of 12.5 seconds, significantly reducing the 3–5 day verification period required by traditional systems. Smart contract-based KYC automation eliminates intermediaries, cutting compliance costs by 40% and reducing fraud risk by 60%. Through comparative analysis, we highlight that decentralized KYC improves security, cost-effectiveness, and scalability compared to traditional models. Performance evaluation confirms that transaction throughput remains within acceptable blockchain limits, with gas costs stabilized at 35,000–55,000 Gwei per verification request. Despite challenges in regulatory adaptation and zk-SNARK scalability, the proposed model demonstrates the feasibility of Web3-driven identity management for trustless, privacy-preserving, and compliant financial ecosystems.