When a Finn talks to an AI helper like ChatGPT, they often get the sense that something is subtly wrong. "You really feel that this conversation is not the way that you would have a discussion in Finland," says Peter Sarlin. For a start, Finnish people are known for a blunt approach to dialog and chatbots are usually tuned to be overly courteous. But there's also the fact that most leading chatbots and the large language models behind them are developed in the US and trained on mostly US data. Cutting-edge AI products often come with a tonality that is essentially American.

We create a tool for visual surveillance of the European banking system from a macroprudential perspective. The tool performs visual dynamic clustering with the self-organizing time map (SOTM) to visualize evolving multivariate data from two viewpoints: (i) multivariate cluster structures, and (ii) univariate drivers of changes in structures. In assessing the European banking system, the main tasks the SOTM can be used for are (i) identifying structural changes and breaking points in a large number of risk indicators, and their specific location in the cross-section, and (ii) identifying the build-up of, or generally changes in, individual risk indicators in the banking system as a whole. While the former view provides indications of changes in the banking system, the latter describes the sources of these changes.