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How is AI Future-Proofing Order to Cash for Global Business Services

What you’ll learn

  • What are RPA & AI and their use cases in Order To Cash process
  • How combining these technologies in the right ratio can help A/R teams deliver the right results

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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, it can only read structured and templatized information. However, RPA does not have cognitive capabilities – which processes within the order to cash function rely heavily on. Be it predicting the payment dates of a customer or finding out if a deduction is valid or not, there are various levels of complexities associated with how we receive data in the real world. This is why finance functions, especially order to cash, need AI.

AI: Reshaping The Future of Finance

Unlike RPA, AI mimics human intelligence, which allows it to learn, reason, think and perform complex tasks. This enables it to handle unstructured data with precision and human-like cognitive abilities. One of the major advantages of investing in an AI-powered accounts receivables solution over RPA is that the AI’s ability to learn and reason will improve scalability of A/R teams. The various suggestions and action items an AI-powered A/R solution can provide, helps the analysts to focus more on qualitative analysis.

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 based on past experience and payment behavior.
  • Blocked order prediction: Predicts blocked orders and recommends actions to avoid the same based on relevant parameters.
  • Dynamic worklist prioritization: Organizes a collector’s and a deduction analyst’s worklist sorted by priority.
  • Non-standardized remittance capture: Learns every character using XML to create words to form coherent sentences from unstructured data.
  • Dispute validity prediction: Predicts the list of all valid and invalid deductions with a confidence score and matches with reason codes.

To learn more about how AI can assist order to cash teams and make work more intelligent, we feel this resource called “The Non-Nerdy Guide to Artificial Intelligence in Order to Cash” would be beneficial.

What is NLP and Why Do We Need NLP in Finance?

NLP is a branch of artificial intelligence 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 understand how it works. 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 determines 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?

Much 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 enable order to cash teams to get quick customer information, real-time data analysis, and future predictions for a better customer experience.

How to Best Leverage RPA, AI & NLP: The Best of Three Worlds

An accounts receivables solution powered by RPA, AI & NLP can radically change how an order to cash team functions in the real world. Some of the use cases that set apart an A/R team leveraging the full potential of these three technologies can positively impact the working capital optimization of an organization, which is required more than ever in the current economic climate.

1. Improved user experience

  • Analysts can organize their worklist and arrange action items based on priority levels while also suggesting where to start the day.
  • The system manages call dashboards and identifies the most critical customer/account based on the past-due date details and 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

  • Live customer calls are transcribed, while the system highlights and captures the most critical points, which get saved in the call log.
  • This allows the analyst to quickly track all the essential details from the call and act accordingly.

3. Intelligent suggestions

  • An analyst can make quick and correct decisions based on the intelligent action items populated by the system from the call with the customer.
  • It also notifies the analyst of the additional tasks done by the system, like saving the call notes, updating the correspondence history, etc.
  • The system can also draft emails, attaching 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

  • Keywords, phrases, letters, sentences, rows, and column headers are identified in an email body.
  • In certain instances, the system collates all the emails into a single repository to easily access the same. The captured data from those emails provide a suggestive action item for the analyst.

What role does RPA & AI play in the future of A/R?

RPA and AI have several real-world use cases in the order to cash process, 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. Incorporating advanced technologies such as RPA & AI in finance strategy and forecasting would help push finance & A/R processes into a new era of speed and accessibility – saving hours of manual work. Digital transformation, when implemented successfully, not only benefits the organization but also helps boost its employees’ productivity.

Meet Freeda, HighRadius’s Digital Assistant for Order to Cash and Treasury Departments. Freeda is capable of answering questions and helping with work just like a knowledgeable colleague, or reliable resource – click here to know more.

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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.