The collection of granular data from reporting entities could replace the need for authorities to request information using templates. It could also enable authorities to reuse those data for different use cases. Supervisory metrics could also be derived using granular data, as opposed to requiring reporting entities to aggregate the required data prior to submission.
Differences in the description of data for similar products and transactions across banks can be addressed using data standards and common data models. Granular reporting requires a common understanding by authorities and financial institutions of what those data are, so that financial institutions can map their operational data to a common “input” before the required data can be reported. Supervisory metrics could then be derived using programmable rules that reference machine readable and machine executable common data models.
Real-time insights using advanced analytics could be derived from large volumes of unstructured data that would supplement the granular reporting available. This would provide supervisors with additional indicators and early warnings of at-risk exposures of reporting entities.
Integration of Structured & Unstructured Data
Integrating granular data from reporting entities with other sources of unstructured information such as news and market data onto the same platform means supervisors would not have to spend time manually scanning for information. Advanced analytics such as artificial intelligence and machine learning could be used to make risk correlations and analyse sentiment, alerting supervisors in real time of issues that may need further investigation.