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

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.

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.

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.

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.

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.