  We introduce the needs for explainable AI that arise from Standard No. 239
from the Basel Committee on Banking Standards (BCBS 239), which outlines 11
principles for effective risk data aggregation and risk reporting for financial
institutions. Of these, explainableAI is necessary for compliance in two key
aspects: data quality, and appropriate reporting for multiple stakeholders. We
describe the implementation challenges for one specific regulatory
requirement:that of having a complete data taxonomy that is appropriate for
firmwide use. The constantly evolving nature of financial ontologies
necessitate a continuous updating process to ensure ongoing compliance.
