Rhea Mirchandani and Steve Blaxland

Supervisors are responsible for ensuring the safety and soundness of firms and avoiding their disorderly failure which has systemic consequences, while managing increasingly voluminous data submitted by them. To achieve this, they analyse metrics including capital, liquidity, and other risk exposures for these organisations. Sudden peaks or troughs in these metrics may indicate underlying issues or reflect erroneous reporting. Supervisors investigate these anomalies to ascertain their root causes and determine an appropriate course of action. The advent of artificial intelligence techniques, including causal inference, could serve as an evolved approach to enhancing explainability and conducting root cause analyses. In this article, we explore a graphical approach to causal inference for enhancing the explainability of key measures in the financial sector.