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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 decisionmaking 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 behav ior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences. [ABSTRACT FROM AUTHOR]

Research background: Globalization has had an impact not only on the Internet society, but also on the way in which digital solutions lead to the improvement of the business activity and optimal policies in simple decision-making. Big data, cloud computing and artificial intelligence may essentially improve the company management and the work of the accountants. The increase of sharing information by digital technologies and big data management are opening huge big opportunities and driving investors towards peak globalization. Purpose of the article: Our aim is to clarify and present the issues in question in terms of financial implications due to the influence of big data management and cloud computing in the digital world. Methods: Our method to conduct the research is a mix of combining quantitative and qualitative analysis in order to identify the benefits of implementing big data management with positively influences over firms’ performance directly. Findings & Value added: Our research presents the financial implications of using cloud computing and digital solution for businesses in the digital world, and also the impact study of adopting cloud technology in business development. There are many implications for implementing big data and cloud computing, but the most important one is increasing the productivity of the companies and improving the international economy.

Research background: Fintech companies should optimize banking sector performance in assisting enterprise financing as a result of firm digitalization. Artificial IoT-based fintechbased 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 datadriven technological and process innovation capabilities, AI system-based businesses can further automated services. [ABSTRACT FROM AUTHOR]

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Copyright of Quality Assurance Review is the property of Romanian Agency for Quality Assurance in Higher Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of career services for students, there has been identified a need for establishing some criteria, preferably some standards, for creating a quality assurance system or reference framework for these services in higher education institutions. The paper draws upon the results of an UE consortium that in the last two years has developed a reference framework as a potential tool for this area. There is a brief presentation of the research results in this specific area, including an outline of the proposed standards and guidelines for quality assurance of career services in higher education. [ABSTRACT FROM AUTHOR]

Airline industry is characterized by large quantities of complex, unstructured and rapid changing data that can be categorized as big data, requiring specialized analysis tools to explore it with the purpose of obtaining useful knowledge as decision support for companies that need to fundament their activities and improve the processes they are carrying on. In this context, business intelligence tools are valuable instruments that can optimally process airline related data so that the activities that are conducted can be optimized to maximize profits, while meeting customer requirements. An airline company that has access to large volumes of data (stored into conventional or big data repositories) has two options to extract useful decision support information: processing data by using general-purpose business intelligence systems or processing data by using industry specific business intelligence systems. Each of these two options has both advantages and disadvantages for the airline companies that intend to use them. The present paper presents a comparative study of a number of general-purpose and airline industry specific business intelligence systems, together with their main advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Lifecycle management of an informatics product necessarily involves following a framework were efficient modeling of specific processes occupy a central place. This paper presents some results of the research made by the authors in the field of informatics systems for Enterprise Resource Planning (ERP) emphasizing the framework of efficient processes modeling, in an integrated systemic approach. [ABSTRACT FROM AUTHOR]

Among the areas where the environment factors quality parameters are affected most there can be mentioned the mining fields, most often the surface ones, with consequences such as landslides, water pollution, air pollution, desertification, noise pollution and others. For this reason special care must be taken in the affected areas, monitoring permanently all the quality parameters of the environment factors. For remote monitoring of environment factors in the mining areas a network of wireless sensors must be used to gather data from the field. The gathered data must be analyzed using a hardware-software informatics infrastructure to determine in time any threats and to avoid any serious consequences as those mentioned above. [ABSTRACT FROM AUTHOR]