Ontology drift is a challenge for explainable data governance

TL;DR

This paper discusses the challenge of ontology drift in financial data governance, emphasizing the need for explainable AI to maintain compliance with evolving regulatory standards and data taxonomies.

Contribution

It highlights the importance of continuous ontology updates for compliance and explains the implementation challenges of regulatory data taxonomy requirements.

Findings

Ontology drift impacts compliance in financial data governance.

Explainable AI is essential for regulatory reporting and data quality.

Continuous updating of financial ontologies is necessary for ongoing compliance.

Abstract

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.