Speaker
Description
The increasing digitalisation of financial services has changed the way banks compete, intermediate funds, and transmit monetary and financial shocks across countries. In this context, the role of FinTech in shaping the integration of banking markets has become an important research question, especially for the European Union, where financial integration remains uneven across member states. This paper aims to examine the relationship between FinTech development and the degree of banking market integration in the EU, using country-level banking interest rate data and a factor-based measure of integration.
The empirical analysis will focus on the 27 EU member states over a period determined by data availability, expected to cover approximately the last fifteen years. Banking integration will be measured using monthly MFI Interest Rate Statistics, with separate attention given to deposit rates and lending rates. The main methodological approach relies on Principal Component Analysis applied in a rolling-window framework. The degree of integration is proxied by the share of variance explained by the first principal component, interpreted as the strength of the common factor driving banking interest rates across countries. A higher contribution of the first principal component indicates stronger co-movement and, therefore, a higher degree of banking market integration. Alternative transformations of the data, including standardised interest rate levels, interest rate spreads, and monthly changes in basis points, may be used as robustness checks.
The second stage of the analysis investigates whether FinTech development helps explain differences in banking integration across EU countries. For this purpose, the paper will construct a panel dataset combining the integration measure with annual FinTech indicators, such as internet banking usage, card payments, e-money transactions, digital payment intensity, or other comparable indicators of financial digitalisation. The econometric framework will rely on panel data models with country and time fixed effects, while alternative specifications may account for cross-sectional dependence, heterogeneous slopes, and potential endogeneity. Macroeconomic, financial, and banking-sector controls—such as GDP per capita, inflation, unemployment, private credit, banking concentration, non-performing loans, or sovereign risk—will be considered depending on data availability and model parsimony.
