Regional Dimension in European Union: Shaping Key Performance Indicators for Financial Institutions

The rise of fintech, or the combination of digital financial services and technology, represents a turning point in the evolution of financial institutions in an ever-changing financial environment. This Thesis explores the operational aspects and regulatory frameworks in the European Union. Fintechs are able to effectively provide services across the EU market due to the modern EU legislation, the passporting mechanism, in particular. These services involve complex cross-border and interregional operations, which are associated with risks and connected to KPIs. Chapter 1 presents the systematic literature review to determine the lacuna in the issue of KPIs formation for financial institutions. The genesis of fintech is also explored, with specific emphasis on the regional dimensions. The taxonomy of fintech is presented. The region of research application is identified. Special attention is paid to the operations of the European Central Bank (ECB) as a financial regulator in the EU region. Chapter 2 reviews and analyses the impact of EU regional and national policies on financial institutions. The conditions for the functioning of fintech are compared, highlighting the regional peculiarities. The taxonomy of digital financial products and their representation in different EU regions is presented. The comparative analysis between e-money products and crypto-based assets demonstrates their characteristics and operating environment. The taxonomy and life cycle of digital assets are shown. It also identifies the pricing components for digital and cryptocurrencies. Chapter 3 determines the KPIs and KRIs for financial institutions. This is done on the basis of the expert panel's estimates. The currently limited use of appropriate KPIS is discussed. In addition, the development of financial services in smart cities is shown and the regional aspect of fintech functioning in smart cities is specified. The opportunities for smart cities to use fintech and financial services as part of the sharing economy are also identified. In this chapter, two statistical models are presented. The PLS-SEM analysis is used to evaluate the relationships between the risks of internal processes and KPIs (Model 1) and the risk of compliance with regional legislation and KPIs (Model 2). At the end of the study, conclusions are drawn and recommendations formulated.