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

Abstract

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

Leasing from the perspective of environmental management and its influence on business performance

“…In today´s globalized world, the approach of companies that have decided to apply not only EMA, but also other options of environmental behavior, is judged positively. These activities are associated with the concept of "Corporate Social Responsibility", or CSR, which means a voluntary form of integration, social and environmental practices, and responsibilities within their business model (Andronie et al, 2019). However, CSR is not only about adhering to established rules, but also about willingness and practical investment in human and environmental capital within the civic, political, and social engagement that will benefit the entire community in the broadest sense (Dinu, 2011).…”

Credit risk assessment of small and micro enterprise based on machine learning

“…[ 48 ], demonstrating its supremacy in prediction accuracy, notably outpacing traditional methodologies like discriminant analysis and logistic regression. With the deepening application of machine learning models, a growing body of scholarly work is turning attention towards integrative research that combines these models with cutting-edge technologies such as the Internet of Things (IoT) and cloud computing [ 49 , 50 ]. This trend benefits innovative research in credit assessment for small and micro enterprises.…”

“…Additionally, the continuous drive to bolster the precision of assessment models remains salient. Previous researches [ [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] ] have crafted predictive models through various methodologies, including expert analysis, statistical techniques, and machine learning. Even though these efforts have achieved commendable success in predictive fidelity, there is ample room for further enhancements and pioneering breakthroughs in the field.…”

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

“…Using these methods and procedures, firms are able to obtain and subsequently evaluate data that reflect not only the dynamics of corporate development but also quantify the impact of the economic crisis on changes in the structure of financial indicators over the years and formulate proposals and recommendations for the future 2 of 30 (Stratone 2023). The corporate financial situation is constantly influenced by many factors that must be identified for the firm to maintain its long-term development, particularly considering integrating cutting-edge technologies such as artificial-blockchain-and Internet of Things-based fintech (Andronie et al 2023;Barbu et al 2021;. The assessment of the financial situation of the enterprise helps in identifying problems and critical circumstances in time, thus preventing bankruptcy.…”