This paper presents the application of Cognitive Work Analysis (CWA) to create an Abstraction Hierarchy (AH) model that helps users to identify key functional relationships for managing financial systemic risk. Users may include investors, government agencies, policymakers, and financial institutions. The AH model will ultimately lead to an artefact that embeds visual analytics (the science of analytical reasoning facilitated by interactive interfaces) and combines automated analysis with dynamic interaction with the data. Based on the notion that companies with high leverage (total debt/equity) are more likely to become financially distressed than those with low leverage, our approach demonstrates how the CWA approach can be incorporated into a visual analytics system development methodology, and how the resultant prototype can be successfully applied to visualise macroprudential risk.