Use Machine Learning to remove the guesswork.
Whether you prefer to use spreadsheets or software to produce reports and forecasts doesn’t really matter, it’s the accuracy of the data that counts.
The main reason why forecasts are unreliable is because the Accounts Receivable data used (when cash will arrive from customers) is not realistic. Most forecasts assume customers pay on time but, in reality, up to 60% of customers pay late (some pay months after the due date) so this needs to be factored into any reliable forecast.
Machine learning solves the problem. By plugging into your accounts package, learning the payment patterns, cycles and payment policies of each customer, and then producing intelligent predictive data to make forecasts reliable.
Monitoring customer payment patterns will also provide valuable, actionable insights on customers and invoices, showing financial departments when invoices should be paid, the order in which to chase them, which invoices pose a genuine risk and which customers need watching – whether they’ve paid or not.
It’s too time consuming for humans to learn and monitor the payment patterns of every customer, but Machine Learning software can produce the data and insights in seconds.