Phase 1 of our project illustrates the potential for process efficiencies that may be gained when adopting machine executable reporting using common data models. It could also increase the volume of granular data available to supervisors, which is needed to enable the use of advanced analytics. Further exploration between regulatory authorities would be needed to validate these findings and to see whether the exercise could be extended to other reporting use cases.
Building on this first use case, Phase 2 will explore the integration of granular data sets with unstructured data, using AI and ML to extract insights from these data sources to highlight correlations between current events and supervisory metrics. Insights extracted from the mined data would be displayed as early warnings for supervisory attention via dashboards.