Enhancing cash management using machine learning

Cash management is a complicated task since it is the interaction between multiple monetary activities including collections, disbursements, concentration, investments and funding [1], moreover, it can be easily influenced by several unpredictable internal and external factors from different areas. A misuse or underestimation may lead to devastating financial consequences. To manage the cash requires painstaking approaches, to approximate its size requires advanced and meticulous tools.By relying on machine learning concepts, we attempt to build an intelligent tool adapted to the public expenditure management sector. Giving a set of payment orders in progress, the model is conceived to allow the cash managers to predict the amounts to be drawn in a period of time, thus, to have a clear vision over cash trend.The experiments demonstrate the applicability of the model and exhibit encouraging prediction results. Yet, we believe that still there are unexplored features to be considered and leveraged to enhance the model performance and specially the accuracy which is valuable for a crucial financial decision.