Financial institutions

Artificial intelligence (AI) is rapidly transforming financial decision-making. To explore the implications for financial stability we ran simulation-based experiments on two different AI architectures. We found that Q-learning algorithms, a form of reinforcement learning, achieved a high degree of coordination, but were prone to bank run-like dynamics. In contrast, large language models , which rely on contextual reasoning, were less prone to such runs but generated heterogeneous and unpredictable behaviour.

Our new methodology builds an inter-country input-output table that distinguishes green products from the rest, allowing us to assess vulnerabilities in green value chains. In a multi-country, multi-sector model, our table reveals that a decoupling of green supply chains between a US-centric West and a China-centric East could globally cut trade in green products by up to 20%, lower welfare by up to 3% and raise yearly global greenhouse gas emissions by about 50 million tonnes.

The post-pandemic inflation surge is often attributed to pent-up demand andopportunistic price hikes. In fact, it is better explained by the effects of theeconomy’s production network and “state-dependent” pricing, where firmschange prices optimally when the reward justifies the effort. Firms are tightlylinked through supply chains so a surge in prices upstream can triggersimultaneous repricing by many layers of firms downstream, fuelling broad-based inflation.

Euro area insurers manage several trillion euro in assets and take a long‑term investment perspective. To counteract the long period of low interest rates, they have shifted towards holding more alternative and less liquid assets. As a result, their balance sheets have become less liquid and more sensitive to market conditions overall. Meanwhile, their holdings of sovereign bonds show a significant home bias, which may have even increased with quantitative easing.