How Acquirers Prepare for AI’s Utility in Retention and More

Artificial intelligence tools may still be in development, but their utility for acquirers is quickly becoming apparent. “A lot of people think it’s a ‘nice to have,’ but it’s no longer a ‘nice to have,’” Sebastian Builes, cofounder and chief executive at Arcum, a merchant-retention firm that uses AI, told attendees at the Midwest Acquirers Association conference this week in Chicago.

Atlanta-based Arcum uses algorithms to incorporate AI to help independent sales organizations find merchants that might be susceptible to switching acquirers. In one example on its Web site, Arcum analyzed 5,000 merchants in a portfolio that had an annual attrition rate of 20%. From that group, it provided a list of 40 merchants that were at risk of leaving. The ISO was ultimately able to reduce its churn rate from 20% to 18%, or a 10% reduction in its overall attrition rate.

The first step in using AI with merchant data is to collect the data, said Jeff Sanders, chief revenue officer at Union, an Austin, Texas-based hospitality-engagement  platform. One of the crucial bits is to extrapolate detailed residual files and pull daily settlement files, he said. Do that while keeping in mind what problem needs to be solved, he added, and start with a simple database. “That’s ultimately the first piece,” Sanders said. “From this point, you can make better decisions.”

The early decisions, before an AI tool is even employed are crucial. “The input on the front end matters,” said Jeff Shea, cofounder and chief executive at Under, a Chicago-based merchant-onboarding service. “If you’re not writing clean data on the front end, you can’t expect clean data on the back end.” Builes echoed that advice. “These models are almost the last leg; this is the end result,” he said. “To get there, you really need to focus on the inputs. What am I feeding the machine?”