Machine learning for financial stability

Abstract

What we learned from the global financial crisis is that to get information about the underlying financial risk dynamics, we need to fully understand the complex, nonlinear, time-varying, and multidimensional nature of the data. A strand of literature has shown that machine learning approaches can make more accurate data-driven predictions than standard empirical models, thus providing more and more timely information about the building up of financial risks. Advanced machine learning techniques provide several advantages over empirical models traditionally used to monitor and predict financial developments. First, they are able to deal with high-dimensional datasets. Second, machine learning algorithms allow to deal with unbalanced datasets and retain all of the information available. Third, these methods are purely data driven. All of these characteristics contribute to their often better predictive performance. However, as "black box" models, they are still much underutilized in financial stability, a field where interpretability and accountability are crucial.