Banks come in different shapes and sizes. Do prudential regulations that work well for big banks work as well for small ones? To help us find out, we measure the effectiveness of some key regulatory ratios as predictors of bank failure. We do so using ‘receiver operating characteristic’ – or ‘ROC’ – analysis of simple threshold rules. When we do this, we find that we can use the ratios we test to make better predictions for large banks than for small ones. This provides evidence that an efficient set of regulations for large banks might not be as efficient for small ones.
The great American baseball sage, Yogi Berra, is thought to have once remarked: ‘It’s tough to make predictions, especially about the future’. That is certainly true, but thankfully the accelerating development and deployment of machine learning methodologies in recent years is making prediction easier and easier. That is good news for many sectors and activities, including microprudential regulation. In this post, we show how machine learning can be applied to help regulators. In particular, we outline our recent research that develops an early warning system of bank distress, demonstrating the improved performance of machine learning techniques relative to traditional approaches.