Auditors use predictive models to estimate the values of financial accounts in a financial statement. Previous studies suggest that incorporating organizational knowledge into these models results in better predictive accuracy. We propose a new method for building a network of financial statements to better understand the organizational structure of a company using actual financial transaction data from ten companies. We show that real data produces networks of financial statements of varying complexity. We introduce a method to aggregate the nodes and edges of the financial statement network, resulting in its visualization at the right level of traceability. We also show that this visualization allows the auditor to assess the complexity of a company’s organizational structure and use it as a risk indicator for the audit. In addition, the resulting network provides insight into the cash flow between financial accounts and business processes. We show that this information can be used to add organizational knowledge to predictive models for the purpose of obtaining evidence.