Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber–physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi-sensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber–physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber–physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber–physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

Research background: In creative accounting, the primary goal of every enterprise is to increase and strengthen its market position. Over the years, manipulation of financial statements has also reached the territory of Central European countries, including the Slovak Republic. Therefore, an analysis was conducted to identify enterprises that handle accounting. This article focuses specifically on Sector A: agriculture, forestry, and fisheries. Purpose of the article: The aim of the article was to reveal the creative accounting practices of a sample of enterprises operating in the Slovak business environment in a sector using the Beneish model. Methods: The Beneish model was used to calculate the manipulation of enterprises? financial statements. Both variants, that is, the 5-parameter model and 8-parameter model, were used for the calculation. The results of these models were plotted using graphs and receiver operating characteristic (ROC) curves. Findings & value added: Based on the use of both variants of the Beneish model, it was proven that enterprises in the analyzed sector use the possibility of manipulating financial statements. The added value of the article is the detection of the use of creative accounting in a specific sector, which makes the study original in its application and space-time orientation.

The economic results of a company are an important tool for many entities, e.g., for internal entities as well as for external entities. As the economic results of a company are often the only source of information that informs the company’s partners about the managerial activities of their company, it is necessary to present these economic results using real numbers. However, companies prefer to achieve better results by applying the principles of creative accounting, which leads to improved economic values being shown to be achieved during an accounting period. The purpose of this article is to apply models that have been developed to detect creative accounting, which occurs under conditions that help enterprises to adjust their financial statements and tax bases and involves using creative accounting techniques to become competitive or to be able to take advantage of deductions. These models were applied to the Slovak Republic’s agriculture, forestry, and fishing sector (sector A), which is highly affected by earnings manipulation. This article provides a numerical expression of companies, which were previously, with some probability level, involved in conducting financial statement manipulation. Subsequently, the results that were obtained have been displayed using receiver operating characteristic (ROC) curves. The outputs of the analysis show that a large proportion of the companies in this sector tend to use creative accounting, which is not only harmful for entrepreneurs and their business partners in sector A, but also for the Slovak Republic at large, as the Slovak government cannot determine whether the reported accounting results reflect a company’s real financial situation.

Capital structure is one of the most frequently discussed issues within Corporate Finance Theory. Optimizing the capital structure and value of the tax shield through the evaluation of its interests can lead to the increasing value of the enterprise, followed by the rising competitiveness and flexibility. The aim of this study is to provide a novel look at the value of the interest tax shield and its determinants in the emerging economies of the Visegrad Four. The model was created on a net sample of nearly 7,000 profitable enterprises between 2015 and 2019 using a one-way fixed effects model of panel data. Regional model results show five main determinants of the debt tax shield (Tangibility, Current Ratio, Gearing, Cost of debt and Size). Others, such as non-debt tax shield, business growth or profitability, are regional, and their impact varies depending on the economic conditions of the countries. The direction of influence of the main determinants indicates that, contrary to the assumed trade-off theory, profitable companies manage the capital structure and the value of tax shield according to the pecking order or modified pecking order theory. The tax shield is made up mostly of interest on short-term loans, which increases the risk of financial distress. There is a hierarchy of funding sources, from trade credit, through shortterm loans, to long-term loans, which are used in the analysed firms to the smallest extent. The structure of liabilities may be considered another determinant of the debt tax shield.

The COVID-19 outbreak has rapidly affected global economies and the parties involved. There was a need to ensure the sustainability of corporate finance and avoid bankruptcy. The reactions of individuals were not routine, but covered a wide range of approaches to surviving the crisis. A creative way of accounting was also adopted. This study is primarily concerned with the behavior of businesses in the Visegrad Four countries between 2019 and 2021. The pandemic era was the driving force behind the renaissance of manipulation. Thus, the purpose of the article is to explore how the behavior of enterprises changed during the ongoing pandemic. The Beneish model was applied to reveal creative manipulation in the analyzed samples. Its M-score was calculated for 6113 Slovak, 153 Czech, 585 Polish, and 155 Hungarian enterprises. Increasing numbers of handling enterprises were confirmed in the V4 region. The dependency between the size of the enterprise and the occurrence of creative accounting was also proven. However, the structure of manipulators has been changing. Correspondence analysis specifically showed behavioral changes over time. Correspondence maps demonstrate which enterprises already used creative accounting before the pandemic in 2019. Then, it was noted that enterprises were influenced to modify their patterns in 2020 and 2021. The coronavirus pandemic had a significant potency on the use of creative accounting, not only for individual units, but for businesses of all sizes. In addition, the methodology may be applied for the investigation of individual sectors post-COVID.