Abstract
This paper analyzes the transmission of macroeconomic shocks (inflation, interest rates, GDP) to technical provisions (TP) and liquidity (cash demand) of insurers in Poland, Czechia, and Hungary during 2016–2024. Comparing Ridge, Random Forest, Gradient Boosting, and Panel Fixed Effects models, we show that tree-based ensembles outperform linear baselines, confirming the non-linear nature of reserve dynamics. We find a significant strengthening of the “Inflation-Liquidity Nexus” during the 2022–2023 inflation peak as insurers expanded cash buffers. K-Means clustering reveals that business models, not geography, drive solvency behavior. Forward-looking forecasts (2025–2027) under monetary easing project reserve stabilization. These findings highlight the value of machine learning for macroprudential risk monitoring in CEE markets.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.77
Machine Learning-Driven Assessment of Systemic Risk and Macroeconomic Shocks in CEE Insurance
This paper analyzes the transmission of macroeconomic shocks (inflation, interest rates, GDP) to technical provisions (TP) and liquidity (cash demand) of insurers in Poland, Czechia, and Hungary during 2016–2024. Comparing Ridge, Random Forest, Gradient Boosting, and Panel Fixed Effects models, we show that tree-based ensembles outperform linear baselines, confirming the non-linear nature of reserve dynamics. We find a significant strengthening of the “Inflation-Liquidity Nexus” during the 2022–2023 inflation peak as insurers expanded cash buffers. K-Means clustering reveals that business models, not geography, drive solvency behavior. Forward-looking forecasts (2025–2027) under monetary easing project reserve stabilization. These findings highlight the value of machine learning for macroprudential risk monitoring in CEE markets.
Recommended Citation
Wojtowicz, A., Rak, E., Denkowska, A. & Wanat, S.(2026). Machine Learning-Driven Assessment of Systemic Risk and Macroeconomic Shocks in CEE Insurance. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.77