Conference Agenda

We exploit cyber incidents to examine the value of supervisory strictness for banking stability. During the 2023 banking crisis, stocks of banks that were subject to cyber incidents before the crisis performed better, consistent with their lower exposure to the vulnerabilities that amplified the banking turmoil. Our evidence suggests that financial supervisors are stricter with banks affected by cyber events. In turn, these banks adopt more prudent strategies before the crisis, making them more resilient during the 2023 episode. The novelty of our setting lies in linking increased supervisory strictness induced by cyber risk to its value during a crisis. Consistent with our interpretation, we find no similar effect during the COVID-19 crisis or the 2008 Global Financial Crisis, as the former was unrelated to bank fundamentals and the latter preceded the rise of cybersecurity as a regulatory priority. Overall, our results are consistent with higher supervisory strictness induced by cyber incidents strengthening bank stability in times of distress.

We define a county as underbanked if it has so few bank branches per capita that shocks to its banks have an enhanced impact on the county’s credit, house price, income, and unemployment outcomes. Our two-step estimation method first identifies the cutoff level of bank branches per capita which best separates underbanked from normally-banked counties. In the second step, we use differences-in-differences estimation to test how shocks to bank financial conditions affect underbanked counties differently from better-banked counties. Using the 2008 financial crisis, we find that underbanked counties experience substantially larger credit contractions, steeper house price declines, reduced income, and elevated unemployment. We confirm these findings using the March 2023 Silicon Valley Bank collapse. We further find that while traditional market concentration is associated with fewer small business loans on average, it actually attenuates the effects of bank shocks on lending outcomes. These findings suggest important trade-offs between banking competition and financial stability at the local level.

This paper develops a house price-at-risk framework for the UK. The model allows us to track and decompose different parts of the distribution around house price growth. We focus both on the national level, and nine English regions, Wales, Scotland, and Northern Ireland. We utilise a comprehensive list of possible variables and indicators that could contribute to house price dynamics. Our main findings are that since the 1970s, the most important predictors for the tail of the distribution have been transaction growth, mortgage rate change, credit to GDP gap, financial stress, and exuberance. We utilise several forecasting horizons and find that our framework can be used to forecast downside risks to house price growth and probability of negative growth up to two years ahead. At the regional level, our analysis reveals considerable variation in the estimated coefficients

for mortgage interest rates, with supply-inelastic regions showing higher values than other areas. Finally, we find that an increase in the housing supply in most regions is related to future alleviation of price pressures in regional markets.

This paper examines the impact of FinTech and Artificial Intelligence (AI) on the profitability of banks in the European Union (EU). Recent developments in the European financial system, driven by digitalization, AI, and FinTech companies, have reshaped traditional banking models and introduced new risks. In addition to technological progress, financial risks associated with digitalization, climate change, and the use of energy resources have become increasingly relevant.

To assess these dynamics, we conduct a critical review of the latest literature and reports from financial institutions. Furthermore, using a sample of the EU banking sector over the period 2014–2024 and applying two econometric panel data models, our findings indicate a positive short-term impact of AI and FinTech on bank profitability. However, we also identify risks related to AI adoption in the financial sector, as well as to the use of energy resources. Finally, the paper concludes by presenting both the positive and negative implications of FinTech and AI for traditional banking performance.