Finance leaders exploring technology usually get lost among a plethora of terminologies like RPA, AI, Machine Learning, Natural Language Processing, etc. As a result, they might end up with something that doesn’t quite fit their requirements. With increased scrutiny on budgets and resources, order-to-cash teams need to effectively evaluate every business decision- including the one to leverage technology.
This blog would give finance and order-to-cash teams specific examples of each one of these technologies and how they apply in the finance and the A/R space.
Robotic Process Automation(RPA) mimics human actions to perform a well-defined business function. Its comprehensive algorithm is based on rules and repetitions and processes only predefined input formats.
Some of the key use cases of RPA in order-to-cash are:
Since RPA follows repetitions and processes only predefined input formats, hence it can only read structured and templatized information. But that is not how we receive data in the real world and this is where AI comes in.
In the real world, we receive data in multiple formats, with multiple complexities. And this is why finance operations require artificial intelligence(AI). Unlike RPA, AI mimics human intelligence, which allows it to learn, reason, think and perform complex tasks. This enables it to handle unstructured data as well with human-like decision making.
Some of the key use cases of AI in Order-to-cash are:
“Artificial Intelligence Can Process Unstructured & Non-Templatized Data”
To learn more about how AI can assist order-to-cash teams and make work smarter, we feel this resource called “The Non-Nerdy Guide to Artificial Intelligence in Order to Cash” would be very helpful.
Natural Language Processing(NLP) is a branch of artificial intelligence that is concerned with the interactions between an intelligent system and human languages.
Before getting into how it can help in day-to-day finance operations, let’s first try to understand how does it work. On a high-level, NLP does two things:
Let’s take an example of a question to understand this better: “What is the number of open invoices for ABC Products”?
Here the algorithm identifies the ‘number’ of open invoices as the intent. It then identifies whether ‘open invoices’ falls under ‘ABC Products’ or vice versa to recognize the entity. Based on these factors, the comprehensive NLP algorithm provides a suitable answer to the question.
A lot of time is lost in finding reports to measure team performance and other data mining activities to gain meaningful insights. AI-enabled digital assistants leveraging NLP algorithms enables order-to-cash teams to get quick customer information, real-time data analysis and have future predictions for better customer experience.
HighRadius has its own NLP powered digital assistant named Freeda, which is backed up by the HighRadius AI engine named Rivana. Let us take a look at how Freeda helps finance and A/R professionals to manage time and effort with the help of the following:
RPA and AI are used nowadays in many fields in the order-to-cash processes, from financial analysis and reporting to risk management, with a variety of roles, from assessing the customer behavior, workflow optimization and collaboration, to implementing cash forecasting and treasury management. On the top of RPA & AI, advanced tools and technologies in NLP like Freeda are evolving to become the future of finance. The incorporation of these technologies combined together in financial strategy and forecasting would help to push finance & A/R processes into a new era of speed and accessibility- saving hours of manual work. Digital Transformation, when implemented successfully, could not only benefit the organization but also help boost its employees’ workflow
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.