Big data management algorithms in artificial Internet of Things-based fintech

Oeconomia Copernicana, Journal Year: 2023, Volume and Issue: 14(3), P. 769 - 793

Published: Sept. 30, 2023

Research background: Fintech companies should optimize banking sector performance in assisting enterprise financing as a result of firm digitalization. Artificial IoT-based fintech-based digital transformation can relevantly reverse credit resource misdistribution brought about by corrupt relationship chains. Purpose the article: We aim to show that fintech decrease transaction expenses and consolidates stock liquidity, enabling excess leverage cutting down information asymmetry across capital markets. AI- fintechs enable immersive collaborative financial transactions, purchases, investments relation payment tokens metaverse wallets, managing data, infrastructure, value exchange shared interactive virtual 3D simulated environments. Methods: AMSTAR is comprehensive critical measurement tool harnessed systematic review methodological quality evaluation, DistillerSR producing accurate transparent evidence-based research through literature stage automation, MMAT appraises describes study checklist mixed studies reviews terms content validity predictors, Rayyan responsive intuitive knowledge synthesis cloud-based architecture for article inclusion exclusion suggestions, ROBIS bias risk relevance concerns. As reporting assessment tool, PRISMA flow diagram, generated Shiny App, was used. bibliometric visualization construction tools large datasets networks, Dimensions VOSviewer were leveraged. Search “fintech” + “artificial intelligence”, “big data management algorithms”, “Internet Things”, search period June 2023, published inspected selected sources 35 out 188. Findings & added: The growing volume products optimized operational industries provide firms with multifarious options quickly. Big data-driven innovations are pivotal markets institution efficiency. Through technological process innovation capabilities, AI system-based businesses further automated services.

Language: Английский

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Machine Learning Ensemble Modelling for Predicting Unemployment Duration

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(18), P. 10146 - 10146

Published: Sept. 8, 2023

Predictions of the unemployment duration economically active population play a crucial assisting role for policymakers and employment agencies in well-organised allocation resources (tied to solving problems unemployed, whether on labour supply or demand side) providing targeted support jobseekers their job search. This study aimed develop an ensemble model that can serve as reliable tool predicting among Slovakia. The was developed using real data from database (those registered unemployed actively searching through Local Labour Office, Social Affairs, Family) stacking method, incorporating predictions three individual models: CART, CHAID, discriminant analysis. final meta-model created logistic regression indicates overall accuracy prediction almost 78%. demonstrated high precision identifying at risk long-term exceeding 12 months. presented model, working with robust nature, represents operational be used check functionality current market policy solve problem individuals Slovakia, well creation future government measures unemployment. state are financed budget funds, by applying appropriate it is possible arrive rationalization financing these measures, specifically determine means intended Slovakia (this, together regional disproportion unemployment, considered one most prominent Slovakia). also has potential adapted other economies, taking into account country-specific conditions variables, which due data-mining approach used.

Language: Английский

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Impact of Direct Taxation on Economic Growth: Empirical Evidence Based on Panel Data Regression Analysis at the Level of Eu Countries

Sustainability, Journal Year: 2023, Volume and Issue: 15(9), P. 7146 - 7146

Published: April 25, 2023

Through fiscal policy, the government can influence businesses and individuals in order to regulate their behaviour. The research used panel data from all 27 EU countries covering period 2008–2020 investigate impact of direct taxation on economic growth at level two main clusters concerning efficiency. Therefore, analysis employed cluster methods classify both groups with a high efficiency those rather limited study employs fixed effect models dynamic GMM components (personal corporate income taxes) growth. also considers informal economy’s role relation official economy. empirical results revealed that taxes significantly negatively for high- countries. Additionally, personal tax was associated lower group. Thus, perspective policymakers, lowering increase disposable income, stimulate consumption growth, encourage investment leading job creation, competitiveness, reduce evasion avoidance, thereby more efficient system.

Language: Английский

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Has the COVID-19 pandemic affected the corporate financial performance? A case study of Slovak enterprises

Equilibrium Quarterly Journal of Economics and Economic Policy, Journal Year: 2023, Volume and Issue: 18(4), P. 1133 - 1178

Published: Dec. 30, 2023

Research background: The corporate debt situation can be considered a crucial factor influencing the future development of financial performance firm. It is essential for every business entity to know its health, strengths and weaknesses, how has been affected by COVID-19 pandemic all changes it brought. Purpose article: main aim this paper explain quantify consequences pandemic, analyze in growth determinants, identify new trends Slovak enterprises throughout monitored period 2018‒2021. Methods: Hence, statistically significant difference between individual indicators due which firms achieved these values was determined using Friedman test. whether average remained constant over under review (the years 2018 2019 are pre- years, while 2020 2021 when globe already being impacted outbreak pandemic) or differed significantly. Findings & value added: Considering that there differences self-financing ratio, current indebtedness equity leverage ratio periods except 2021, where same, results indicate also negatively enterprises. Although research paper, focusing on post-pandemic period, pioneering Slovakia, biggest contribution study application latest information, could help more precise monitoring stability policy during challenging period. obtained provide important universal guidelines building strategies improving long-term resilience

Language: Английский

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Human Error Analysis and Fatality Prediction in Maritime Accidents

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(12), P. 2287 - 2287

Published: Dec. 1, 2023

The main objective of this paper is to underscore the significance human error as a dominant cause maritime accidents. research based on comprehensive analysis 247 accidents, with aim being identify failures occurring during onboard and port activities, well supervision process. first step was facilitating Human Factor Analysis Classification System (HFACS) an advanced analytical tool for identification categorisation factors. Based coding process, most critical areas are identified, process risk evaluation assessment. Furthermore, prediction model developed predicting probability fatality in accident. This constructed using logistic regression, considering predominant causal factors their interplay. Lastly, set preventive measures aimed at enhancing efficiency safety transport provided.

Language: Английский

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The Relevance of Sectoral Clustering in Corporate Debt Policy: The Case Study of Slovak Enterprises

Administrative Sciences, Journal Year: 2024, Volume and Issue: 14(2), P. 26 - 26

Published: Jan. 30, 2024

The processing and transformation of natural resources into completed semi-finished products is the primary function industry in each nation’s economy. There no denying significance sectoral classification economy, but slow development extension one could have resulted advancement other sectors that are now a part contemporary communities. Since there statistically significant differences between various industries, numerous authors currently investigating impact on financial structure firms, revealing as crucial determinant corporate indebtedness. Thus, main aim this study to determine debt level sample 4237 enterprises operating market period 2018–2021 from using eight indicators, well identify relationships them, which may help reveal with homogeneous patterns indebtedness (using cluster analysis) thus understand most stable independent. Kruskal–Wallis test then used if calculated ratios related economic sector. Based results, it can be concluded choice significantly influenced by industry. Financial performance indicators quantitative statistics assess, monitor, forecast company or health. They act instruments for business insiders outsiders assess company’s performance, particularly comparison competitors, pinpoint its strengths weaknesses, making outputs important all types stakeholders.

Language: Английский

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The role of knowledge and interpersonal competences in the development of civic and public engagement and entrepreneurial intention

International Entrepreneurship and Management Journal, Journal Year: 2023, Volume and Issue: 20(1), P. 189 - 213

Published: Oct. 14, 2023

Language: Английский

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Industry 4.0: Marvels in Profitability in the Transport Sector

Mathematics, Journal Year: 2023, Volume and Issue: 11(17), P. 3647 - 3647

Published: Aug. 23, 2023

Despite the COVID-19 pandemic, current era offers ultimate possibility for prosperous corporate life, especially in transport sector. Industry 4.0 covers artificial intelligence, big data, or industrial IoT, and thus spatial cognition algorithms, traffic flow prediction, autonomous vehicles, smart sustainable mobility are not far away. The mentioned tools have already been implemented by enterprises emerging countries. This exploration focused on transportation within V4 region from 2016–2021. article aims to confirm positive sequel of applying chosen indicators profitability. positive, negative, no shift development 534 businesses was based Pettitt’s test. Pearson chi-square test disclosed significant dependency between shifts profitability ratios. Then, more than 25% involved had ROA, ROC, ROS, ROR. research proved only its balanced effect but also augmented force through z-test proportion. investigation may provide multiple proofs connected sectors with adapt deliver call governments make this tool achievable.

Language: Английский

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Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?

International Journal of Financial Studies, Journal Year: 2024, Volume and Issue: 12(1), P. 8 - 8

Published: Jan. 18, 2024

Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success rates classification tasks, it remains uncertain whether its use significantly enhances for early warning impending problems. The following question arises: will classical methods eventually replace effectiveness these advanced techniques? This paper sheds light on fact that even continue to achieve results are not far behind, highlighting their enduring importance financial analysis. aims develop prediction models chemical industry Slovakia compare effectiveness. Predictions generated using logistic regression (LR) method well AI techniques, neural networks (ANNs), support vector machines (SVMs), decision trees (DTs). analysis determine which employed most efficient. research sample consists circa 600 enterprises operating Slovak industry. selection eleven indicators used was grounded prior existing literature. show all explored yielded highly similar outcomes. Therefore, determining clear superiority any single difficult task. might be partially due potentially reduced quality input data. In addition statistical econometrics, there an ongoing development AI-based hybrid forms. what extent can newer approaches enhance accuracy effectiveness?

Language: Английский

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Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(1), P. 32 - 46

Published: May 6, 2024

Research background: Many authors are currently exploring the impact of industry on financial structure enterprises since there statistically significant differences across various sectors, exposing as a critical factor influencing corporate indebtedness. Clusters sectors with homogeneous patterns indebtedness and comparable levels debt in economic conditions may be determined, and, therefore, firms their should systematically examined evaluated. Purpose article: The main aim this paper is to evaluate level Slovak environment sample 19,480 from identify relationships among them consequently, comprehend which most stable independent. Methods: Because NACE classification provides framework for gathering presenting statistical data based many number input was reduced cluster analysis. Using Ward's hierarchical clustering method using squared Euclidean distance, selected ratios were used define levels. To determine if between calculated related sector, Kruskal-Wallis test performed. Subsequently, results indicated ratios, post hoc analysis Findings & Value added: A group activities sufficiently that it appropriate chosen considered C, F, G H, included tertiary while K, R S also grouped one cluster, form secondary sector. key relevance our findings benchmarking about indebtedness, further examine growth each V4 nations, an essential area evolution European economy whole. Studies considering relatively amount capital determinants beneficial owners managers, regulators, institutions policy affects firm performance, value, survival.

Language: Английский

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