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constrained by outdated input streams, static data assumptions,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.