Planning Processes play an important role in almost any business scenario. In particular, induced by the financial crisis, financial planning as a foundation for liquidity management is paid extraordinary attention to. Its quality and reliability is usually ensured by the use of information systems. Besides process efficiency, a key factor in liquidity management is the quality of the delivered planning data. More recently, business intelligence measures to increase data quality, for instance, realized through decision support services, find their way into the planning process. In this paper, we lay the foundation to include digital analyses of reported financial planning numbers into automated decision support services. In this vein, our contribution is twofold: First, based on a large and representative data set from a renowned, multinational enterprise, we empirically prove that financial planning numbers exhibit a certain, characteristic digit distribution, namely, Benford's Law. Second, we investigate whether decision support services that incorporate intelligence based on Benford's Law are appropriate to increase financial planning data quality. This question is tackled via analyses that relate detailed properties of the delivered data to Benford's Law as a prerequisite for the integration of automated decision support services into business intelligence systems.