A New Approach to Early Warning Systems for Small European Banks

This paper describes a machine learning technique to timely identify cases of individual bank financial distress. Our work represents the first attempt in the literature to develop an early warning system specifically for small European banks. We employ a machine learning technique, and build a decision tree model using a dataset of official supervisory reporting, complemented with qualitative banking sector and macroeconomic variables. We propose a new and wider definition of financial distress, in order to capture bank distress cases at an earlier stage with respect to the existing literature on bank failures; by doing so, given the rarity of bank defaults in Europe we significantly increase the number of events on which to estimate the model, thus increasing the model precision; in this way we identify bank crises at an earlier stage with respect to the usual default definition, therefore leaving a time window for supervisory intervention. The Quinlan C5.0 algorithm we use to estimate the model also allows us to adopt a conservative approach to misclassification: as we deal with bank distress cases, we consider missing a distress event twice as costly as raising a false flag. Our final model comprises 12 variables in 19 nodes, and outperforms a logit model estimation, which we use to benchmark our analysis; validation and back testing also suggest that the good performance of our model is relatively stable and robust