Data Quality Automation Frameworks for Regulatory Reporting in Banking

The Basel III, BCBS 239 and IFRS 9 are some of the strenuous stipulations that have made the reporting of regulations in the banking sector complex. The problem of data quality assurance is a serious consideration in the broken systems especially when systems that can be easily corrupted, slow and inefficient are being utilized; this is the case with manual systems or the obsolete systems. The provided paper describes how data quality automation structures can solve these problems. By performing a structured review of the academic papers, regulatory reporting and practices in the industry, the paper assesses the effectiveness of automation in enhancing accuracy, efficiency and regulatory reporting and cleanliness. Specific attention is paid to the new technologies including artificial intelligence, natural language processing, cloud infrastructures, and the development of end-to-end automated controls, including the reconciliation, variance detection, and schema drift detection. The other positive aspect of the reduced manual labor, the augmented correspondence and readiness to audit as well as the issues pertaining to transparency are also confirmed in the results. The future applications would be explainable AI and blockchain audit trail.