We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

This paper assesses the value of multiple requirements in bank regulation using a novel empirical rule‑based methodology. Exploiting a dataset of capital and liquidity ratios for a sample of global banks in 2005 and 2006, we apply simple threshold-based rules to assess how different...

Is human or artificial intelligence more conducive to a stable financial system? To answer this question, we compare human and artificial intelligence with respect to several facets of their decision-making behaviour. On that basis, we characterise possibilities and challenges in designing...

We propose a generic workflow for the use of machine learning models to inform decision making and to communicate modelling results with stakeholders. It involves three steps: (1) a comparative model evaluation, (2) a feature importance analysis and (3) statistical inference based on Shapley...