Risk modeling in banking is a complex task that requires tracing data connections across various investment baskets, holdings, financial instruments, and pricing data. The Financial Reform Transparency Bill (FRTB) requires banks to decompose risk models into their individual components and trace back through time to available pricing and position information. This process involves identifying relevant data, understanding data sources, and calculating risk factors that affect all upstream information dependencies. Effective internal risk models require a strong foundation in data governance, which is essential for risk aggregation, reserve calculations, and required reporting. Modern graph technology, such as Neo4j, provides a robust framework for building and testing internal risk models that can trace many layers of dependencies and adapt to changing market conditions.