Baocheng Zeng, Jinhao Yang, Peilin Han, Kangnan He
Public cryptocurrency archives may appear usable when files exist, although factor research requires observations available and executable at each decision time. We audit public Binance BTCUSDT USD-M perpetual-futures data using event, publication, and availability times and separate proposal from deterministic auditing, evaluation, and holdout access. An initial gapless five-minute requirement for trade, mark, index, and open interest failed: the longest unrepaired intersection was 304.5729166666667 days. A disclosed revision made trade, mark, index, and realized funding the core streams and made open interest optional because its publication time was unverified. The revised mask retained 727 complete UTC days and supported a 436/145/146-day train, validation, and historical-holdout split. On 80 frozen known-rule templates, the auditor detected 40/40 violations and rejected 0/40 legal templates. Across ten null-signal paths, full auditing reduced mean false passes from 0.2910 to 0.0625. Under matched valid-candidate budgets, the audited adaptive agent tied random search and did not establish superiority. In the one-time historical holdout, all evaluated runs had positive IC but negative net Sharpe under primary costs. We therefore report a scoped negative result rather than a profitability or agent-superiority claim.
This review synthesizes theoretical and empirical insights from 1055 peer-reviewed articles on artificial intelligence (AI), corporate governance, and ethics. Situated in the corporate governance and accounting literature, it develops a computational framework to identify thematic patterns and conceptual links among AI, transparency, accounting, governance, and ESG. Using latent Dirichlet allocation, co-occurrence network analysis, sentence-level semantic similarity, and exploratory regression, the study identifies three recurring configurations of conceptual association: (1) Ethics, Governance, and Transparency; (2) Machine Learning, Finance, Blockchain, and Accounting; and (3) Corporate, ESG, and Accounting. The findings indicate that these themes are repeatedly connected within the scholarly literature.
The main objective of this study was to examine the effect of blockchain technology on future of external auditing in Nigeria. Decentralized network and consensus mechanism were the proxies for blockchain technology. Thus, two hypotheses were formulated to guide the investigation and the statistical test of parameter estimates was conducted using least squares regression model operated with E-Views.12. Survey design was adopted and data for the study was obtained through the use of e-questionnaire survey sent to the various Staff WhatsApp Group Platform of the selected audit firms in Anambra State Nigeria. The results of the study reveal that the use of decentralized network has positive and significant effect on the future of external auditing in Nigeria at 1% level of significance. Also, the use of consensus mechanism in auditing has positive and significant effect on the future of external auditing in Nigeria at 5% significant level. Based on this, the study concludes that blockchain technology ensure the future of external auditing in Nigeria. In lieu of the findings of the study, the study recommends for the continual use of decentralized network in auditing as the future of external auditing lies on it. Also, the use of consensus mechanism should also be encouraged as it ensures accuracy and reliability in audit reporting.
Blockchain technology records transactions on a distributed ledger that is cryptographically chained, replicated across independent nodes, and validated by consensus rather than by any single institution. Because the technology verifies that recorded transactions occurred and have not been altered, some commentators have concluded that it will make external auditors redundant. This article rejects that conclusion but takes the underlying disruption seriously. It argues that blockchain automates a narrow and historically labor-intensive slice of the audit, namely the verification of the existence, occurrence, and mathematical accuracy of recorded transactions, while leaving untouched the components of assurance that depend on professional judgment: valuation, accounting estimates, classification, completeness of off-chain events, related party identification, and going concern assessment. At the same time, the technology creates new objects that require assurance, including consensus protocols, cryptographic key management, smart contract code, and the oracles that connect ledgers to the physical world. The article examines the consequences for auditing standards, particularly the treatment of blockchain records as audit evidence, and for the education, skills, and business model of the profession. The external auditor’s future role, it concludes, lies not in verifying transactions but in assuring the systems that now verify them, and in exercising the judgment that no ledger can encode.
The rapid diffusion of digital technologies has fundamentally reshaped the way organizations generate and report financial and non-financial information, challenging traditional audit approaches that rely on manual and sample-based procedures. Building on this context, this paper aimed to provide a comprehensive synthesis of empirical evidence regarding the impact of digital technologies on auditing and to identify the key factors influencing their adoption across internal, external, and public sector audit functions during the 2015–2026 period. Using a qualitative descriptive design and a systematic literature review guided by the PICOC framework and PRISMA protocol, 33 relevant articles indexed in Scopus were selected from an initial pool of 959 publications. The findings showed that the use of various technologies, including computer-assisted audit techniques (CAATs), audit analytics, big data, artificial intelligence, robotic process automation, blockchain, and process mining, generally enhanced the effectiveness and efficiency of audit procedures, strengthened internal controls, and reduced errors and financial statement restatements, while simultaneously repositioning auditors as more strategic and data-driven partners. At the same time, the success of digital audit transformation was strongly influenced by technological infrastructure, data governance and security, organizational capabilities, leadership support, regulatory environments, and auditors’ individual competencies, indicating that digitalization was neither a neutral nor an automatic process. This study provides practical implications for audit firms, internal audit units, supreme audit institutions, and regulators in developing more targeted and sustainable digital audit strategies, while also proposing future research directions concerning the organizational and institutional dynamics of digital auditing.
This study examined blockchain technology's potential to enhance audit efficiency in selected service firms. Traditional auditing is often hindered by data manipulation, limited transparency, time-consuming verification, and high costs challenges that blockchain's decentralised, immutable, and transparent ledger system can plausibly address. The research assessed blockchain's role in improving audit efficiency, focusing on automation and realtime auditing, distributed ledger effects, and consensus mechanisms. Data was gathered through questionnaires, observation, and a technical readiness survey, and analysed using descriptive statistics, inferential statistics, and multiple regression. The study evaluated current auditing practices to identify the benefits and barriers of blockchain implementation, examining existing audit challenges, blockchain's capacity to resolve them, and the implications for service firms. Respondents were drawn from service firms with interest or prospects in adopting blockchain for audit activities. Findings showed marked improvements in audit accuracy, transparency, and overall efficiency, though adoption barriers, cost, and the need for regulatory structures were also identified. The study contributes to the growing body of knowledge on blockchain's practical application in auditing, offering guidance to service firms, audit practitioners, and policymakers on successful implementation. By addressing these challenges and leveraging blockchain's opportunities, service firms can achieve cleaner, safer, and more efficient audit practices. The research confirms that blockchain technology plays a significant role in enhancing audit efficiency within service firms.
Open access
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Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
The rapid advancement of digital technologies has significantly transformed auditing practices, leading to the emergence of audit analytics as an important research domain that integrates accounting, auditing, and data science. This study aims to examine the evolution, intellectual structure, influential contributions, and emerging research trends in audit analytics through a bibliometric analysis approach. Data were collected from the Scopus database using relevant keywords related to audit analytics and analyzed using VOSviewer to perform citation analysis, keyword co-occurrence analysis, density visualization, and collaboration network analysis. The findings indicate that audit analytics research has experienced substantial development, particularly with the increasing adoption of big data analytics, artificial intelligence, machine learning, predictive analytics, blockchain, and automation technologies. Citation analysis identifies key contributions focusing on the role of big data and artificial intelligence in improving audit quality, audit judgment, fraud detection, and decision-making processes. The keyword analysis reveals that recent research trends have shifted from traditional analytical methods toward intelligent and automated audit systems that support continuous auditing and risk-based decision-making. Furthermore, collaboration analysis demonstrates the global nature of audit analytics research, with the United States emerging as the most influential contributor and strong research connections among countries and institutions. This study contributes to the literature by providing a comprehensive understanding of the development trajectory of audit analytics and identifying future research opportunities related to generative artificial intelligence, explainable AI, cybersecurity, and digital audit transformation.
Sony Warsono, Fitri Amalia, Muhammad Roy Aziz Haryana, Rudi Prasetya Timur
This study examines how blockchain technology (BT) can extend the conventional double-entry accounting (DEA) framework to improve financial information transparency. The study revisits the duality concept underlying DEA and explores its development toward a triple-entry accounting (TEA) structure supported by blockchain infrastructure. Using a design science approach in information systems, the study proceeds through problem identification, artefact definition, and conceptual system design. Drawing on the resource-event-agent (REA) framework, the study develops a conceptual architecture that integrates a third ledger into the accounting entry system. The proposed model positions message type (MT) as a navigational mechanism that coordinates transaction validation within a blockchain-enabled TEA environment. This structure supports improved tracking, verification, and transparency of financial information, particularly in external transactional relationships. The findings contribute to the ongoing discussion on blockchain-based accounting systems by clarifying how the duality principle can evolve within a distributed ledger environment. The study also outlines potential directions for future research on the development of triple-entry accounting and third-ledger mechanisms in accounting information systems.
Masdar Ryketeng, Samsinar, Hariany Idris, Anni Suryani · 5 authors
This study examines the emergence of double-entry bookkeeping, memoranda, journals, and ledgers as the foundations of modern accounting from an accounting historiography perspective. Using a qualitative approach, the research employs a non-systematic literature review (non-SLR) of 26 national and international journal articles, supported by primary historical sources on accounting record systems. Data were analyzed through identification, classification, literature synthesis, and thematic analysis. The findings show that double-entry bookkeeping evolved gradually from medieval Italian trade through the development of memoranda, journals, and ledgers as tools for transaction recording, economic control, and trade documentation. This evolution was driven not only by commercial and technical needs but also by social, cultural, political, legal, and economic factors. The study also identifies a historical continuity between traditional ledger systems and contemporary accounting developments, including blockchain-based distributed ledger technology. It concludes that memoranda, journals, and ledgers form part of the multidimensional evolution of accounting knowledge that has shaped accounting practices from the medieval period to the modern digital era.
High-quality smart contract auditing datasets are crucial for evaluating security tools and advancing smart contract security research. Two major limitations of existing datasets are the manual-induced scalability bottleneck and the deficiency in data granularity and diversity. To address these limitations, we propose GiANT, an automated framework designed to curate smart contract auditing datasets by distilling vulnerability insights from real-world auditing reports. GiANT employs a divide-and-conquer strategy coupled with the Chain-of-Thought technique to extract structured vulnerability information from Code4rena reports, followed by an LLM-as-a-judge mechanism to perform rigorous quality assurance. To evaluate GiANT's effectiveness, we run it on 388 real-world audit reports and generate the GiAnt Corpus comprising 7,711 vulnerability findings across five severity levels. Manual assessment of the dataset demonstrates exceptional reliability in information extraction, achieving a mean quality score of $4.76\pm0.37$ (out of 5) with inter-rater agreement $κ$ of 0.88. We further validate the practicality of our dataset by benchmarking 4 state-of-the-art LLMs on vulnerability detection, code summarization, mitigation recommendation, and automated gas optimization tasks, to establish performance baselines, thereby providing a valuable data foundation for future research in automated smart contract auditing.
Traditional auditing processes are inefficient and produce low-quality audit reports due to human intervention. This research project constructs a novel automated auditing architecture based on smart contracts, comprising four functional modules: (i) data acquisition, (ii) rule encoding, (iii) execution verification, and (iv) report output. This paper demonstrates how to achieve a high-throughput, low-latency, and verifiable automated auditing system by utilizing technologies such as multi-source data cross-validation, formal encoding of audit rules, privacy protection based on zero-knowledge proofs, and cross-chain communication. The developed novel auditing process can shorten the traditional audit cycle to 8 to 15 days, reduce manual operation costs by 37.5% to 44.4%, reduce the error rate to 0.2% to 0.5%, and exhibit high fault tolerance during disaster recovery, making it an effective approach to achieve digital transformation of auditing processes.
Guilherme Martins Soares, João L. D. S. Filho, Nicholas P. Fontanini, Bruno Evaristo
Contratos inteligentes gerenciam ativos digitais de alto valor, mas falhas de segurança frequentemente causam perdas financeiras irreversÃveis. Embora existam diversas ferramentas de auditoria automatizada, seu uso isolado gera altas taxas de falsos positivos e falsos negativos. Este trabalho propõe e avalia um framework unificado para auditoria de contratos inteligentes em Solidity, orquestrando análise estática (Slither), execução simbólica (Mythril) e testes dinâmicos (Foundry). A arquitetura unifica os resultados heterogêneos utilizando o padrão SARIF e aplica um Modelo de Linguagem de Grande Escala (LLM) para traduzir logs brutos em relatórios contextuais explicáveis. Avaliado em um dataset curado de 53 contratos do repositório SmartBugs, o framework alcançou um F1-Score de 92,93%, superando substancialmente o desempenho isolado do Slither (72,28%) e do Mythril (88,42%). Os resultados demonstram que a orquestração hÃbrida mitiga as limitações estruturais de cada motor, reduz a carga cognitiva do auditor e consolida-se como uma plataforma robusta e eficaz para o desenvolvimento seguro no ecossistema Web3.
This study investigates how information transparency affects organizational value in the Chinese institutional setting, where firms operate under a heavily regulated disclosure regime while increasingly referencing Decentralized Autonomous Organization (DAO) or blockchain-based decentralized governance concepts. Using a panel of 10,029 firm-year observations from 1368 Shenzhen A-share listed firms over the period 2012–2022, we employ two-way fixed effects regressions and robustness tests, with information transparency proxied by Shenzhen Stock Exchange disclosure ratings. We find that higher transparency is positively and significantly associated with organizational value (measured by Tobin’s Q). Heterogeneity analyses show that this positive relationship is stronger among state-owned enterprises, firms with lower digital maturity, and firms led by innovation-oriented executives. Comparative tests further reveal that the transparency–value link holds primarily among DAO-referencing firms, whereas it turns negative (though marginally significant) for non-referencing firms. These results suggest that signaling interest in decentralized governance mechanisms can enhance the value relevance of disclosure in regulated emerging markets. Practical implications for managers and policymakers are discussed, along with limitations and directions for future research.
This research examines the structural and paradigmatic mutation of the internal audit function in the face of the massive integration of distributed ledger technologies (Blockchain) and cognitive artificial intelligence (AI). Through an in-depth empirical analysis conducted on a targeted sample of n=156 decision-making professionals in the financial sector, this article highlights the legitimacy crisis currently faced by traditional control bodies, which are now confronted with the intrinsic opacity of automated systems. The quantitative results demonstrate that the obsolescence of auditors' technical skills is no longer merely an operational lag, but constitutes a direct systemic threat to bank governance and risk management. Drawing on continuous auditing and agency theories, this study conceptualizes and advocates for a radical hybridization of skills, coupled with an architectural overhaul of control frameworks, as a sine qua non condition to ensure the sustainability and ethics of institutions in an irreversible dematerialized financial ecosystem.
Declaration PI. Les DOIs comme actifs financiers verifiables. Un portefeuille de DOIs = capital garanti par timestamp CERN. ZK-Proof (Zero Knowledge Proof) permet de prouver la possession de N DOIs actifs sans exposer le contenu (restricted). Impossible a falsifier: timestamp CERN, ORCID, Zenodo. Zero faux documents, zero faux comptes, zero fraude documentaire. Le DOI remplace le releve bancaire. La banque verifie sans voir. Le createur prouve sans exposer. Living Key appliquee a la finance. All Rights Reserved.
The rapid expansion of algorithmic decision systems, blockchain infrastructures, and autonomous operational technologies has begun to transform the structural organization of corporations. Increasingly, firms rely on automated governance mechanisms, decentralized ledgers, and smart contract frameworks that allow economic transactions and corporate decisions to occur with minimal direct human intervention. This transformation raises serious ethical and professional questions for accounting, a discipline historically grounded in human judgment, fiduciary responsibility, and professional oversight. Ethical accounting within autonomous corporate structures therefore emerges as a critical field of inquiry. The study examines how accounting ethics must evolve when financial reporting, asset transfers, contractual obligations, and performance measurement are executed through autonomous computational systems rather than traditional managerial decision chains. Particular attention is given to the implications for accountability, transparency, auditability, and stakeholder trust when algorithmic processes replace or supplement human managerial authority. Drawing upon ethical theories, accounting governance principles, and recent technological developments such as blockchain based corporate systems and algorithmic management, the study explores how ethical safeguards may be preserved in environments where corporate operations become partially or fully self executing. The analysis also considers the responsibilities of accountants, auditors, regulators, and system designers in ensuring that autonomous corporate structures remain aligned with principles of fairness, transparency, and societal responsibility. Ultimately, the discussion highlights the need for expanded ethical frameworks capable of addressing emerging technological realities within modern corporate governance systems.
Abstract Blockchain technology is emerging as a transformative innovation in the field of accounting by enhancing transparency, accuracy, and reliability of financial information. Traditional accounting systems often face challenges such as data manipulation, lack of real-time reporting, and dependence on centralized control. Blockchain, with its decentralized and immutable nature, provides a secure platform for recording financial transactions in a transparent and verifiable manner. Each transaction is recorded in a distributed ledger that cannot be altered without consensus, reducing the chances of fraud and errors. This technology also supports real-time data sharing among stakeholders, improving trust and accountability in financial reporting. The study explores how blockchain can improve accounting practices, auditing processes, and financial decision-making while highlighting its benefits, challenges, and future potential in the accounting profession.
(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support.
Many systems map governance and execution power directly to purchasable capital (stake, tokens, shares). This creates structurally unsafe paths to power: influence can be bought, short-window manipulation can become long-lived authority, and low-integrity applications can contaminate system-level decision making. This paper defines Proof of Contribution (POC) as a parent-layer execution-weight reference and constraint layer for contribution-generated assets (ABUE / CGA). POC converts finalized contribution-derived claims into execution weights under strict constraints: Source purity (external purchases do not mint influence), verifiable value caps (weights cannot exceed auditable backing), decay (power requires continued contribution), downward-only normalization (anti-compounding), local negative contributions (risk isolation), and delayed activation (audit windows). Crucially, POC is specified as an audit-executable closed loop: versioned policy bundles with timelocks, deterministic recomputation, public commitments (roots), challenge windows, and automatic consequences (freeze/down-weight/remove; Only-Down). We provide falsifiable hypotheses (H0–H4), trigger playbooks (TRW1–TRW3), Minimum Qualifying Implementation (MQI) boundaries, a parameter ledger, and a reproducible toy simulator framework (ReproPackW/MVDW) intended to validate invariants—not to claim economic optimality. A consensus instantiation is treated as a conditional subset and fully developed in a companion paper.
Context. Machine learning approaches for smart contract vulnerability detection are typically evaluated on synthetic benchmarks of programmatically generated code snippets. Practitioner reports and recent independent evaluations indicate that automated tools continue to miss critical vulnerabilities in production audits, yet the contribution of benchmark selection to this gap remains under-examined.Objectives. This study investigates whether surface-level Solidity features that correlate with vulnerability severity in synthetic benchmarks retain their predictive validity on professionally audited contracts, and proposes a quantitative metric for assessing benchmark suitability for severity prediction research.Methods. Fifteen features were extracted identically from a 10,448-sample synthetic Solidity benchmark and DAppSCAN, a corpus of 1,646 findings from 1,199 audit reports authored by 29 firms. Feature-severity correlations were compared using Fisher r-to-z, Kolmogorov-Smirnov, and Levene tests. Logistic Regression, Random Forest, and Gradient Boosted classifiers were trained in three conditions: in-distribution synthetic, in-distribution real, and cross-distribution.Results. Mean absolute correlation was 0.228 on synthetic data versus 0.057 on real data, a fourfold gap (all p
In early November 2025 the yield-bearing stablecoin sector experienced its first systemic run: over roughly seventy-two hours, three synthetic dollar tokens lost between 94 and 99 percent of their value, set off by the disclosure of an external-manager loss at Stream Finance. Using hourly on-chain data we reconstruct the cascade and show that survival was not determined by on-chain exposure or scale-the largest instrument, sUSDe, held its peg while absorbing several hundred million dollars of redemptions-but by the quality of the backing and whether redemptions were honored under stress. We further show that the contagion did not travel through observable decentralized-finance composability: public lending exposure to the failed collateral was negligible. Transmission ran instead through off-chain reserve relationships and, in the single material public exposure, through a price oracle that remained frozen at the pre-crash value, implying a roughly 119-fold overvaluation weeks into the collapse, so that no liquidation fired and approximately $7.5 million of bad debt accrued without a single onchain bad-debt event. We read the episode as evidence that opacity in valuation, rather than composability, was the systemic channel, and draw implications for the disclosure, redemption, and oracle requirements that govern tokenized dollars. The paper is a descriptive and structural anatomy of one systemic episode; we make no causal-identification claim.
<b><i>Traditional financial audits</i></b> have long served as the primary instruments for oversight, disclosure assurance, and risk assessment in regulated financial systems. These mechanisms, however, were designed for centralized institutions, periodic reporting cycles, and human-paced transaction environments. In blockchain-based systems—particularly those supporting stablecoins, tokenized real-world assets (RWAs), and decentralized finance (DeFi)—risk materializes continuously and often irreversibly. This paper presents a structural comparison between traditional audit models and the Crystal Validator™ (CV), a pre-execution enforcement architecture designed for real-time regulatory compliance. We demonstrate that post-fact auditing is structurally incapable of preventing modern on-chain failures, regulatory breaches, and systemic collapses. We argue that effective blockchain regulation requires a shift from retrospective verification to deterministic, pre-transaction authorization enforced at the protocol level.
Double-entry bookkeeping ensures internal balance but offers limited independent evidence that reported state follows from complete, unaltered records under a stated accounting policy. Ian Grigg's operational triple-entry model—cryptographically linked inter-firm receipts—and subsequent advances in hash commitments, zero-knowledge proofs, and payment-layer compliance attestations motivate ledger-native assurance: verification artifacts produced during accounting close, not assembled from exports afterward. This working paper presents a design-science framework with four assurance layers (source, posting, record, disclosure); a taxonomy of source-anchoring paths including on-chain settlement, TLS-attested ingestion, and bilateral finalization; an analysis of payment-layer versus ledger-layer zero-knowledge statements; and a close-cadence model distinguishing continuous, partial, and batch close under different capture postures. We state explicit scope limits aligned with audit evidence theory and outline an empirical and regulatory research agenda.
We construct a protocol-native valuation signal for Ethereum based on demand-side fees expressed as a share of token supply. The signal measures the log deviation of current fee intensity from its trailing median, a dimensionless ratio denominated entirely in ETH. It predicts subsequent token returns at 10 to 60 day horizons with in-sample R-squared up to 22.8% and expanding-window out-of-sample R-squared of 14.4% at 45 days. The signal retains predictive power after macroeconomic controls, standard crypto risk factors, and momentum controls, and predicts ETH-specific relative returns. Predictability emerges only after the Dencun hard fork (March 2024), which separated execution fees from data availability fees, making the demand signal empirically detectable. Our findings demonstrate that demand-side economic flows are capitalized into token prices in the absence of firms, contracts, or residual cash flow rights, extending valuation logic to rule-based economic systems.