Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. We demonstrate our results which show the capability of each system for real-world graph applications, and hence, providing a quantitative understanding to select the system for our purpose. GraphX’s strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. These performance characteristics emphasize how the capabilities of graph processing systems can match the requirements for the performance of different emerging technology applications. Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains.

(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.

The accounting division plays a crucial role in providing accurate financial data and insights, which are instrumental to informed decision-making and strategic planning. Organizational financial transparency and effective support for resource management depend on accounting. This paper presents a deep neural network (DNN)-based accounting analytics, NeuralACT, to support the decision-making process using real-time predictions made from big data. It uses a Bidirectional Long Short-Term Memory (BiLSTM) network trained with Visegrad Group Companies (VGC) dataset customized and improved for accounting features prediction. The incorporation of bootstrapping while ensuring proper data variance significantly improved the performance of the system. Experimental results demonstrate the outstanding performance of NeuralACT, with an RMSE of 0.0181, MAE of 0.0145, MAPE of 5.14%, and an average R ² of 0.99, significantly outperforming existing methods. The experimental setup involves 10 categories of data from 2000 sources, sending data at a rate of 512 Mbps to 720 Mbps. The NeuralACT framework demonstrates consistent performance for big data, maintaining an average throughput of 0.1338 Gbps and latency of 0.039 s. In a nutshell, NeuralACT is a high-performing, innovative, and real-time accounting analytics framework capable of handling big data with exceptional performance.

This review synthesizes recent scholarships at the intersection of digital accounting and blockchain, examining how their integration reshapes financial reporting, auditing, and organizational performance. Following PRISMA 2020, we screen and analyze peer-reviewed studies published from 2023 through July 2025 across leading databases. Evidence indicates that digital accounting—driven by blockchain, artificial intelligence, cloud platforms, and robotic process automation—improves data accuracy, process efficiency, transparency, and fraud deterrence, enabling timelier, decision-useful information. At the same time, adoption is constrained by cybersecurity exposure, high implementation and maintenance costs, talent shortages, and change resistance. The literature also documents role reconfiguration: accountants are shifting from transactional data entry toward analytical, assurance, and advisory functions. Outcomes, however, are heterogeneous across regions and industries, reflecting differences in regulation, digital infrastructure, and organizational readiness. We conclude by outlining a research agenda on ethical governance and accountability, policy and standard-setting implications, curriculum and workforce development, and cross-cultural adoption dynamics to support sustainable, trustworthy digital transformation in accounting and auditing.

Type of the article: Research ArticleThis study proposes Agilance as a conceptual and technical framework for explainable strategic financial planning in data-intensive organizational environments. The framework is based on a custom transformer architecture that incorporates three sector-specific components: Financial Relevance Weighting, Context Shift Stabilization, and Output Compression. Because the evaluation was conducted on a confidential sector-specific dataset and through internal benchmarking procedures, the underlying source data and job-level operational records cannot be publicly released. Within these constraints, the internal evaluation yielded indicative results: 96.03% accuracy and 95.8% F1-score for priority classification on the held-out test set, 90.26% accuracy and 90.22% F1-score for implementation-duration classification, and an average 10-fold cross-validation accuracy of 91.7%. The expert explainability assessment produced mean scores of 4.67 for clarity, 4.53 for trustworthiness, and 4.48 for actionability, with inter-rater agreement ranging from 0.87 to 0.91. Internal operational benchmarks further suggested planning-cycle reductions and economic benefits, including 95.8% improvement in real-time data analysis and time-zone synchronization, 5.4% operational cost savings, and an illustrative first-year ROI of 46%. These results should be interpreted as preliminary internal evidence obtained under specific evaluation conditions, not as independently verified proof of broad organizational generalizability. The study contributes an auditable AI-supported framework and identifies the need for future validation using anonymized multi-organizational datasets, externally audited protocols, or independently reproducible benchmarks.Acknowledgments The publication fees of this manuscript have been financed by the MSc Tax and Financial Services Digital Transformation (DITAF), University of Patras.