Cash & Working CapitalTechnology

Treasurers Educate HighRadius on Cash Forecasting Needs

By February 11, 2020No Comments

NeuGroup members give to get by providing feedback on AI-based cash forecasting solutions.

Treasurers at a NeuGroup meeting in Texas sponsored by HighRadius provided feedback to the Houston-based technology company on what they’d like to see from cash forecasting solutions that use artificial intelligence (AI) and machine learning (ML) to improve accuracy, reduce treasury’s need to input data and allow a wider variety of pertinent data.

  • HighRadius has long provided forecasting services for accounts receivable (AR), the biggest component of cash forecasting, that make use of ML. It now is applying the methodology to accounts payable and other cash forecasting components.

Input wanted. HighRadius executives eagerly sought input on the company’s cash forecasting solution now being developed, and here’s some of what they heard from NeuGroup members:

  • Drill down to the invoice level. The HighRadius app enables companies to explore which of dozens of variables—business line, currency, country—generate most of the variance between forecasted and actual cash. A NeuGroup member suggested going deeper still, to reveal which customers generate the variance.
    • A HighRadius representative said the firm’s technology already predicts payments by individual customers on the collection side, and “we’ve been debating how far to take it” with cash forecasting.
  • A longer tail. Orders for goods and services would be another helpful variable, one member said, because they look out further than invoices.
  • Size matters. Another member suggested that HighRadius include the ability to drill down to customers responsible for 80% to 90% of a company’s AR and provide details on those accounts.
  • Override. The app allows for manual overrides when, for example, a major customer wants to pay early, prompting one meeting participant to say it should identify the customer and explain the reason for early payment.
    • “We’ve heard similar requests, so it’s on the roadmap,” a HighRadius rep responded.
  • Cross-company learnings. A corporate customer may historically pay its annual invoice on time but face challenges this year that aren’t captured by historical data. A member asked whether HighRadius’ app incorporates that company’s more recent payment history with other clients, indicating potential troubles ahead.
    • That will come as High Radius’ customer base grows, a rep said, since “learnings from one company could apply to others.”
  • Sales forecasts? Yes, said a HighRadius rep, sales forecasts provided by a company’s FP&A group could be included in the model to generate long-term forecasts.
Antony Michels

Author Antony Michels

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