The auto finance industry should not only embrace data, but be willing to completely tear down the old ways of thinking to come up with innovative ways of analyzing that data, said Rod Arends, vice president of World Omni Financial Corp., at Used Car Week’s Subprime Forum.
Data has “mattered forever, but maybe we didn’t have a way to measure it with technology,” Arends said. “Hopefully we can get to those better, more finite measurements.”
In many ways, the struggles of the auto finance industry can be related to this year’s world series, said Jeff Haynes, national remarketing manager and vice president of risk mitigation and governance at BBVA Compass Bancshares Inc.
The Chicago Cubs and Cleveland Indians each came into the series with the longest World Series win droughts in the National League and American League, respectively. But by embracing sabermetrics — a statistical analytics methodology applied to the sport — both teams were able to beat the odds and win their divisions.
Arends agreed that the auto finance industry needs to approach their problems in the same way, because “it’s tough” to change the rules.
“You used the tried and true KPI (Key Performance Indicator) because that’s what we always used, and that’s what got us such success, but now the days are upon us where we can measure things differently,” Arends said. “We have to really embrace that change and let go of those tried and true metrics, leverage partnership technologies and really look at ‘what bits of data can I get out of that new technology and how can I put it into a formula or metric that truly measures what matters.’”
Social media is one area that could play a bigger role in vetting borrowers, said John Lewis, president of the recovery management software company Intellaegis. However, companies must be mindful of data security and compliance issues when, for example, analyzing public data from Facebook, he said.
Ultimately, Arends was not able to provide concrete answers for how to analyze the industry’s data, but encourages companies to reverse engineer their problems to come up with new data solutions.
