The importance of data for decision making by a software can be observed when you try searching for directions on Google Maps in off-line mode. In the absence of live data, it only proposes a list of possible routes but can’t tell you which is your best route available at that time. The AI-engine of Google Maps uses live information collected through Internet to make decisions which simplify your life.
Can Artificial Intelligence (AI) in Order to Cash (OTC) also make such useful predictions when equipped with the right data?
After all, there is no dearth of data on customer behavior, payment patterns and ordering history. Could ML start making sense of all this information to make useful predictions such as likely delay in payments, the validity of customer claims, missing remittance values or increased customer risk? The answer is YES.
Join Gwyn Roberts, Vice President, EMEA, as he shares his insights and experience to gain a comprehensive yet fundamental understanding of Machine Learning in order to cash.
HighRadius Autonomous 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. Autonomous 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.