Cashflow planning, forecasting is done through state-of-the art algorithms and automated data discovery techniques that analyze your historical data, find patterns, outliers, anomalies, relationship between various variables.
A machine learning driven engine gives you a precise vision on what will affect your Cash Flow position and by how much. The traditional way to build a forecast is labor intensive, and it doesn’t fit in today’s business culture where teams are expected to do more with fewer resources.
Predictive analytics solves this problem by digitizing and speeding up many of the tasks that are involved in planning your company’s cash requirements. We analyze trends in overall cashflow as well as patterns behind counterparty behavior.
Discovery questions –
The output of the model is the time period in which an open invoice is expected to be paid, such as before the invoice due date or within 15 days after due date. Users specify custom time periods as predefined input. The figure below shows an example of aggregated output of the model.
Integrating enterprise systems to pull relevant data for predictive analytics and superior financial planning is the next step forward. Machine learning models have made predictions simpler and improved overall effectiveness of business processes. It has brought a deeper understanding of the drivers in your business model and helps to incorporate a driver based forecasting for further granularity.
Vinay is a data scientist with far fledged experience in transportation, industrial engineering and B2B marketing. He works with teams to identify the inherent need of the business and how data analytics can help, particularly in maintenance (predictive and preventive).