Visual Exploration of Financial Data with Incremental Domain Knowledge

Area of Science:

• Financial Data Analysis
• Visual Analytics
• Economic Modeling

Background:

• Financial environments are complex, requiring integration of heterogeneous data for holistic understanding.
• Scattered information across scales hinders comprehension of firm relationships and economic landscapes.

Purpose of the Study:

• To present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data.
• To develop a pipeline for generating firm-to-firm financial transaction networks.
• To facilitate a holistic understanding of national economies by integrating data across scales.

Main Methods:

• Developed Sabrina 2.0, a Visual Analytics (VA) solution for financial data exploration.
• Created a pipeline to generate firm-to-firm financial transaction networks by fusing firm-level data, sector transactions, and economic domain knowledge.
• Enabled multi-instance network generation for scenario comparison.

Main Results:

• Sabrina 2.0 facilitates the generation of insights into financial data.
• The approach successfully integrates information from individual firms to nation-wide aggregates.
• Incorporation of transaction models enhances user exploration of national economies.

Conclusions:

• Sabrina 2.0 provides a robust VA approach for analyzing complex financial environments.
• The system effectively bridges micro (firm-level) and macro (national) economic data.
• Expert evaluation confirmed the utility of Sabrina 2.0 in generating economic insights.