In modern conditions of the development of the global economy and in connection with the emergence of new branches of economic activity in the field of IT, the phenomenon of Structured and Unstructured Big Data - the use of Data Science for advanced in-depth analysis of data and knowledge in all possible modes - leads to competitive advantages for corporations and institutions, both at the regional and interstate levels, which is especially relevant in the context of the current macroeconomic and military crisis [1].The following topical issues are systematically investigated in the article: current status and prospects for further development of Knowledge Discovery in Data Base (KDD), problems and critical issues of theory and practice of Data Mining, the specifics of effective use of Knowledge Discovery in DB (Data Base) in the current crisis in Ukraine.The above trends and features of the KDD market should be taken into account in further theoretical research and practical implementation or reengineering of KDD systems in Ukraine. The obtained results are relevant and applicable not only for local companies and organizations, but also for international applications in the context of global, regional macroeconomic and current national crisis phenomena.

Methodological approaches to investing in companies and reducing the negative impact of risks that are formed at the macro and micro levels are considered in the article. The algorithm for expressing investment risks through related risks and conducting an investment risk assessment as a group process is defined. It has been determined that the defining features of investment risks are the environment, duration, and scope of the project, risk position, profile, risk appetite, consequences, capacity, and results of the impact on the investment project. An investment risk accounting system is formed, which is represented by a set of organized structural elements that perform functions related to planning and implementation of a set of measures that identify, assess, monitor, and control risks to minimize negative consequences and enhance opportunities. A method of forming a real portfolio of investment projects considering the dynamic risk factor has been developed.

This publication presents the part of the research results and practical results obtained by the authors regarding the hybrid use of economic-mathematical modelling, knowledge-oriented decision support technology of an oil and gas production company using fuzzy logical inference. The purpose of this research is the development of theoretical provisions of modelling and knowledge-oriented decision support means at the macro level of oil and gas production companies. The purpose of the work determined the solution of the following tasks: -development of science-based recommendations regarding the architecture of a knowledge-oriented DSS of an oil and gas company, the basic model of knowledge presentation, features of the logical conclusion mechanism, etc.; -development of a complex system of economic and mathematical support for decision-making at the macro level of an oil and gas production company in modern economic conditions. The object of the study is the oil and gas production industry. The subject of the research is information processes, economic-mathematical models and knowledge-oriented methods and means of supporting the adoption of management decisions at the strategic level on economic and production issues of the domestic oil and gas production project. Methods/Approach: Economic and mathematical methods, methods of artificial intelligence, methods of logical generalization, expert evaluations and situational approach are used to solve the tasks set in the work. Results:The main scientific result of the work consists in the creation of the concept that allows creating a hybrid DSS of an oil and gas company on the basis of the developed systems of economic and mathematical decision-making support at the macro level of an oil and gas production company, focused on knowledge of technology and intelligent technologies. Conclusions: The scientific, theoretical and applied practical solutions proposed in this publication are universal for implementation by both state and private oil and gas resident and non-resident companies for emerging markets, however, in order for a specific oil and gas company to obtain special additional competitive advantages over others, additional industry-specific Big Data Analysis of collected and stored heuristics, expertise and project development are required.

The article proves that a necessary factor for increasing economic efficiency of an oil & gas company is the use of Internet technologies, in particular, in support of management decision-making. The advantages and disadvantages of using internet technologies in the oil & gas industry, its specificities, are studied. Recommendations for each possible direction of application of internet technologies in DSS in the oil & gas company are outlined. The trends, ways of improvement and practical recommendations identified by the authors should be taken into account during further theoretical research and practical implementation (or reengineering) of DSS systems in Ukraine for industrial corporations (ie, not only for oil and gas companies). The obtained results are relevant and applicable not only for local companies and organizations, but also for international applications in the context of global, regional macroeconomic and current national crisis phenomena.

The pandemic forced companies to rebuild business processes in an accelerated mode. Now they pay more attention to web products and work with customers in the virtual space [1].The financial technology market (FinTech) is getting bigger and more diverse every day. Financial news website Market Screener reports that the global FinTech market will be worth $26.5 trillion by 2022, with a compound annual growth rate of 6%. In Europe alone, the use of FinTech increased by 72% in 2020. The competition in this market segment is also growing. In the first eleven months of 2021, more than 26,300 startups have joined the fray, more than double the number of new entrants just three years earlier [2]. As the competition for customer engagement and loyalty heats up, FinTech players need to reach out to a much larger audience optimally distributed across ever-growing geographies. Monitoring and managing business operations is becoming increasingly complex as the number of customer accounts and financial transactions continues to grow. Therefore, more solutions are needed to address the challenges associated with financial IT. Therefore, the focus should be on algorithms and methods that help FinTech companies optimize all stages of their activities, from customer acquisition to payment processing and payout forecasting. In all aspects of a business, there is little room for errors, unexpected failures, or downtime. Performance optimization is the key to success in this industry. The explosion of activity caused by all these companies generates a huge amount of Structured and Unstructured Big Financial Data about customers and payments, as well as information about the underlying business processes [3]. The deep analytics hidden in this data can help companies optimize payment approval rates, transaction costs and reduce the risk of fraud, as well as customer retention and accelerate revenue growth. The above determines the acquisition of competitive advantages not only for FinTech corporations and companies, both regionally and globally, which is especially true in times of crisis. The article comprehensively explores the following topical issues: problems, features and prospects of effective optimization tasks in modern conditions, critical issues of theory and practice of Evolutionary Computations (including financial management), the specifics of effective use of Genetic Algorithms in information systems of FinTech companies. The above trends and peculiarities of the application of Evolutionary Computations in general and Genetic Algorithms in particular should be taken into account in further research and practical projects and real projects of effective implementation and use of Data Mining and Artificial Inelligence technologies in FinTech information systems. The obtained results are relevant and applicable not only for local companies та організацій, but also for international applications in the context of global, national and regional (not only economic, but also pandemic, military, natural disaster etc) crisis phenomena.