Efficiency of evolutionary algorithms in solving optimization problems on the example of the fintech industry
| dc.contributor.author | Kulynych, Yurii | |
| dc.contributor.author | Krasniuk, Maksym | |
| dc.contributor.author | Краснюк, Максим Тарасович | |
| dc.contributor.author | Krasniuk, Svitlana | |
| dc.date.accessioned | 2025-05-21T08:12:00Z | |
| dc.date.available | 2025-05-21T08:12:00Z | |
| dc.date.issued | 2022-05-27 | |
| dc.description.abstract | 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. 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. 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. 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, 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. | |
| dc.identifier.citation | Kulynych Yu. Efficiency of evolutionary algorithms in solving optimization problems on the example of the fintech industry / Yurii Kulynych, Maxim Krasnyuk, Svitlana Krasniuk // Grail of science : scientific journal III CISP Conference «Scientific researches and methods of their carrying out: world experience and domestic realities», 27.05.2022 / NGO European Scientific Platform, LLC International Centre Corporative Management ; [ed. chief: : M. Holdenblat]. – Vinnytsia, 2022. – № 14–15 (травень). – P. 77–84. | |
| dc.identifier.doi | 10.36074/grail-of-science.27.05.2022.010 | |
| dc.identifier.issn | 2710–3056 | |
| dc.identifier.uri | https://ir.kneu.edu.ua/handle/2010/50382 | |
| dc.language.iso | en | |
| dc.publisher | NGO European Scientific Platform | |
| dc.subject | FinTech | |
| dc.subject | optimization problem | |
| dc.subject | evolutionary algorithm | |
| dc.subject | Big Financial Data | |
| dc.subject | Data Mining | |
| dc.title | Efficiency of evolutionary algorithms in solving optimization problems on the example of the fintech industry | |
| dc.type | Article |