Ontology drift is a challenge for explainable data governance
This work tackles regulatory compliance issues for financial institutions, but it is incremental as it focuses on a specific implementation challenge without introducing new methods.
The paper addresses the challenge of maintaining explainable AI for regulatory compliance in finance, specifically under BCBS 239, by highlighting the need for continuous updates to data taxonomies due to evolving financial ontologies.
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.