Abstract: This disclosure relates to a method and system for creating a dynamic canonical data model. The method includes creating staging tables to analyze regulatory data collected from a plurality of heterogeneous sources. The method further includes creating a dynamic canonical ontology based on the staging tables representing the regulatory data. The dynamic canonical ontology determines a plurality of attributes associated with the regulatory data and relationships amongst the plurality of attributes. The method includes identifying automatically at least one modification associated with at least one of the plurality of attributes by applying machine learning techniques on the staging tables. The method further includes updating the dynamic canonical ontology by adding the at least one modification to create the dynamic canonical data model.

Abstract: Apparatuses, methods, and non-transitory computer readable media for complying with solvency regulations of one or more jurisdictions are described herein. In one example, the method, for complying with solvency regulations of one or more jurisdictions, comprises classifying financial data based on type of risk associated with the data and performing solvency gateway checks, based on solvency rules, to determine validity of the classified data for determination of risk. The method further comprises aggregating the validated data based on at least one aggregation level and computing the solvency risk, based on risk computation rules, associated with the aggregated data.