Business analytics promises faster and more accurate cash forecasting results, but implementation is not trivial as the underlying business drivers need to be modeled. Thus, in this article, we elaborate on the established CRISP-DM process model for the implementation of business analytics use cases and apply it to the management accounting and finance context. We show how CRISP-DM can be applied to build a machine learning-based cash forecasting model based on a real-world case study at a large European car retailer. This will help managers improve and structure their business analytics initiatives.