“To be able to set the right course for the future, finance functions must get better at processing—and extracting forward-looking insights from—large amounts of data.”
-E&Y | The DNA of the CFO, 2016 Even by normal metrics, using technology in day to day data analytics gives huge returns in the processing time used overall. The first step for implementing a better analytics strategy is to introduce measures that promote the use of a single database. This could help with convenient tracking of information and enabling software to integrate into the database can further support it. However, constant improvement also plays a key part. Growing from just business intelligence to implementing machine learning into analytics turns that step into a leap, cutting down manual efforts and tasks exponentially. Because at the end of the day, being the best in the game, requires speed and accuracy. The point is that leveraging technology and machine learning algorithms in the field of finance and centralizing more processes like revenue forecasting could help improve effectiveness.“By 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality.”
-IDC ML is used nowadays in many fields in the order to cash cycle, from financial analysis and reporting to risk management, with a variety of roles, from assessing the health of a company and revenue planning, to implementing blockchain in treasury management. The incorporation of technology in financial strategy and forecasting has helped usher it into a new era of speed and accessibility. Saving hours of manual work and the ability to adapt to different needs make it ideal for this fast economy.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.