Cash Flow Forecasting in SAP ERP Enhanced by UiPath Automation: A Predictive Analytics Approach

Abstract

Maintaining liquidity, mitigating financial risks, and making strategic business decisions in today’s enterprises require accurate cash flow forecasting. Unfortunately, the native forecasting features of the SAP ERP are often constrained by outdated input streams, static data assumptions, and rigid model structures, severely impeding responsiveness and accuracy. This study proposes and evaluates the results-focused integration of UiPath robotic process automation (RPA) with predictive analytics to improve short and medium-term cash flow forecasting in SAP environments. We automated real-time data extraction from SAP FI, FI-CA, and bank interface modules, then employed machine learning and deep learning models (regression trees, LSTM networks, and ensemble methods) to demonstrate substantial gains in forecasting accuracy, cycle time, and exception handling. The framework was tested on large data sets from multi-currency, multi-business unit enterprises, achieving forecast accuracy improvement estimates of 15% to 28% compared to SAP’s baseline predictions. Aside from significantly reducing manual effort associated with forecast preparation, automation also expedited scenario-based liquidity analysis while enhancing governance through exception-based audit logging. These results provide a proven scaling architecture for intelligent real-time cash forecasting that is reliable and compliant, placing RPA and AI at the core of cash management operations of the future, and integrating deeply within ERP systems.