AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness

Abstract

Data governance strategies based on artificial intelligence are urgently needed to meet financial regulations that require authorities to track and control transactions and flows. Recent events have demonstrated the severe consequences when such regulations are not fulfilled. AI technologies can help close the gap between the high level of regulatory risk and the maturity of compliance and audit governance. Implementing AI techniques in data governance to improve regulatory-ready reporting, audit, and compliance can be mapped to various frameworks. The necessary components and connections can be expressed through the lenses of data lineage and provenance, metadata management and regulation language adapters, natural language processing systems for extracting governance policies from natural language documents, and machine-learning models for monitoring data quality.

New regulatory imperatives are piling pressure on institutions that already run overloaded production systems. High levels of regulatory risk require institutions to secure and map their governance processes and controls. Most of the recent regulatory breaches can be traced to poor data quality, weak data flows, and insufficient controls. Data sources and flows are seldom documented, and no system manages data quality in an integrated way. Regulatory stress tests have also led supervisory authorities to seek holistic risk evaluations instead of using the usual isolated pillar assessments. The aim is to have a clear view of the institution's credit risk in order to assess capital needs in extreme scenarios. For many institutions, such assessments require a monumental amount of additional infrastructure and resources because the required reporting does not flow naturally from their business-as-usual processes.