SCHEMA-DRIVEN TESTING FRAMEWORKS FOR IFRS9 REGULATORY WAREHOUSES

International Financial Reporting Standard 9 (IFRS9) fundamentally transformed credit risk reporting by introducing
forward-looking Expected Credit Loss (ECL) models for financial institutions. The regulatory implementation of
IFRS9 requires large-scale data warehouses capable of integrating heterogeneous financial, risk, and macroeconomic
datasets while ensuring extreme levels of data quality, consistency, and auditability. Even minor schema mismatches,
referential breaks, or temporal inconsistencies in IFRS9 data pipelines can lead to material misstatement of ECL,
regulatory objections, and audit qualifications [2], [3].
Traditional testing and validation of IFRS9 warehouses rely heavily on manual rule execution, sample-based
verification, and post-aggregation reconciliations, which are operationally expensive, error-prone, and poorly
scalable for modern regulatory environments [4], [5]. This research introduces a schema-driven testing framework
for IFRS9 regulatory warehouses that automates structural, referential, temporal, and cross-table validation using
metadata-bound schema controls. The framework integrates a centralized schema registry, automated test
orchestration, exception governance, and audit-ready lineage tracking across the full IFRS9 reporting lifecycle.
Performance analysis demonstrates that schema-driven testing significantly improves defect detection coverage,
reduces test execution cycle time, strengthens audit defensibility, and enhances regulatory confidence in ECL
computations. By systematically enforcing schema integrity at every layer of the regulatory warehouse, the proposed
framework establishes a scalable and regulator-defensible foundation for IFRS9 data assurance in large banking
environments.