Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper introduces a novel random forests-based early warning system for predicting bank failures. We apply this method to the analysis of bank-level financial statements, in order to find patterns that identify banks in danger of failing. The experimental results show that our method outperforms conventional methods in terms of prediction accuracy.