RPA, AI and NLP For Finance and Order-to-Cash Teams

What you’ll learn


  • RPA, AI and NLP: Technology updates for finance teams
  • Meet Freeda: The HighRadius digital assistant powered by AI and NLP that could transform your A/R operations end to end
  • Intent and entity identification with NLP and its use cases for finance
  • Improving user-experience in finance: Happier employees make happy customers

Hunt For Technology In Finance

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.

RPA: Augmenting People With Process Automation In Finance

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:

  • Credit-data capture: Retrieves credit reports, risk alerts, and agency ratings from different sources such as credit agencies(D&B) and public financials (Yahoo) and collects this information into a single repository.
  • Auto-remittance aggregation: Aggregates remittance information from multiple sources like emails, customer portals, and EDI files.
  • Automated correspondence: Drafts and sends emails to customers to notify about payment reminders or provide missing remittance information.
  • Backup document aggregation: Aggregates all types of customer-related backup documents from different sources for deductions.
  • Template-based Optical Character Recognition(OCR): Reads multiple input formats but only if they’re available in a pre-defined structured templatized format.

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.

Artificial Intelligence: Reshaping The Future of Finance

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:

  • Payment date prediction: Predicts when a customer is most likely to pay using several data parameters based on the past experience and behavior of the customer
  • Blocked order prediction: Predicts blocked orders based on parameters like past-payment patterns, past-order behavior, risk class, etc. and suggest some recommended actions early to avoid any orders from getting blocked.
  • Dynamic worklist prioritization: Organizes a collector’s worklist with detailed information in a tabular format sorted by priority.
  • Non-standardized remittance capture: Identifies rows and reads every character using XML to create words to form legible sentences from the unstructured data.
  • Dispute validity prediction: Predicts the list of all valid and invalid deductions with a confidence score by matching them with the reason codes and prioritizes the worklist of a deductions analyst.

“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.

Why do we need Natural Language Processing in Finance?

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:

  • Intent Identification
  • Entity Identification

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.

How Does NLP Apply To Order-To-Cash?

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:

  1. Improved user experience
    • Freeda helps an analyst by organizing their worklist and arranging action items based on priority levels while also suggesting where to start the day from.
    • Freeda also manages the call dashboards and identifies the most critical customer/account based on the past-due date details along with the collection amount.
    • This enables the analyst to function faster and trims down their search time to look for different sources of data in order to begin the day.
  2. Customer interaction transcription
    • Freeda transcribes live customer calls along with highlighting and capturing the most important points. It then saves it in the call log.
    • This allows the analyst to quickly track all the essential details from the call and act accordingly.
  3. Intelligent suggestions
    • Freeda suggests intelligent action items based on the call with the customer and allows the analyst to make quick decisions.
    • It also notifies the analyst of the additional tasks done by the system, like saving the call notes, updating the correspondence history, etc.
    • Freeda can also draft emails, along with attaching the relevant documents, based upon the recent interaction with a customer. The analyst can then manually configure the email if required and send it out.
  4. Intent analysis
    • Freeda identifies keywords, phrases, letters, sentences, rows, and column headers in an email body.
    • For instance, the HighRadius Deduction cloud has an email inbox that enables an analyst to directly go through all the emails in a single place rather than navigating somewhere else for it. Freeda captures the data from it to provide a suggestive action item for the analyst.

How RPA, AI & NLP Sit Together: The Best of Three Worlds

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

To know more about Freeda, our AI & NLP Powered Digital Assistant – click here

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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.