Treasurer's guidebook on how to overcome the challenges involved in forecasting A/R and A/P with Artificial Intelligence and help treasuries achieve accurate forecasts.
Cash is often regarded as the lifeblood of an organization as it enables the proper functioning of a company. However, when it comes to forecasting cash, most companies are lagging behind due to their inability to predict A/R and A/P accurately.
What Are the Top Challenges in Cash Forecasting?
Cash forecasting is a complex problem and trying to estimate what will happen on the A/R side of the equation is much more complicated than on the A/P side.
Both have a component of unpredictability, but
Why Is Accounts Receivable Difficult to Forecast?
Accounts receivable forecasting is particularly challenging as it is entirely in the client company’s hands. While payment terms are agreed upon, customers might not always adhere to them, adding an element of unpredictability to the process.
Further challenges that make forecasting A/R difficult include:
Factors Causing Unpredictability in A/R
Why Is Accounts Payable Difficult to Forecast?
In the case of A/P, the forecast is accurate in the short-term, up to the next 2 to 4 weeks.However, it is in the longer run that the accuracy takes a hit because of uncertainties revolving around payments.
Challenges when forecasting A/P are:
Factors Causing Unpredictability in A/P
Benefits of Using Artificial Intelligence for Cash Forecasting
Transformation for Digital Treasury Starts with the Following Steps
AI, when combined with TMS systems, Bank Systems and
ERP systems can substantially improve the quality of the forecasts produced.
Automate part or all of the process to reduce the risk of
human error, thereby improving confidence in the forecast.
Observe data from a high-level view across different
categories and regions.
Move from data entry and model creation to being strategic
contributors in the CFO’s office.
HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.