Chaser Deploys AI to Predict Best Times for Payment Reminders

Chaser, a global accounts receivables (AR) platform and credit control service provider, has launched an artificial intelligence (AI)-powered “recommended chasing times” feature designed to help businesses optimize collections efforts and improve cash flow.

This new feature uses AI to predict the best times for businesses to reach their customers via email or text message for invoice payments, Chaser said in a Wednesday (Aug. 2) press release.

“Recommended chasing times empower businesses to reclaim valuable time spent on manual follow-ups and to optimize their collections efforts,” Chaser CEO Sonia Dorais said in the release.

The recommended chasing times feature is found in the Chaser software when businesses set up workflows and schedules to send out payment reminders, according to the press release.

By leveraging AI and machine learning, Chaser compiles a wealth of customer data, including payment patterns and historical behaviors, to generate tailored recommendations for the best times to send payment reminders, the release said.

This data allows businesses to reach their customers precisely when they are most likely to make payment. Payments reminders sent out at the optimal times and days have a higher chance of being received, leading to an increase in timely payments and improved cash flow.

The system also continuously learns and adapts based on customer data, ensuring that the recommended chasing times stay up-to-date, according to the press release.

Chasing down payments is a function that is ripe for automation, Dorais told PYMNTS in an interview posted in December 2021.

Companies are finding that it doesn’t make sense to hire full-time staff to take care of credit control, that employees have been overwhelmed by late payments, and that the adoption of a new solution is part of a larger strategy to digitize operations, Dorais said at the time.

“Credit controllers are coming into these roles, they’re entry-level roles typically, that are done by very young people who sit down at their desks and think, ‘Hey, there’s got to be a better way to do this; this is way too manual,’” Dorais said.