Auditors use predictive models to estimate financial account values in a financial statement. Prior studies suggest that incorporating organizational knowledge into these models yields better predictive accuracy. We propose a novel method to con- struct a financial statement network in order to gain insight into the organizational structure of a company using real financial transaction data from ten companies. We show that real data yields financial statement networks of varying complexity. We introduce a method to aggregate the nodes and edges of the financial statement net- work, which results in its visualization at the right level of tractability. We also show that this visualization enables the auditor to assess the complexity of the organizational structure of a company and to use it as a risk indicator for the audit. Further, the obtained network yields insights into the monetary 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 audit evidence.