Overall, Polaris runs fairly lean and that aligns well with DataRobot because our data science team, frankly, feels a lot larger with DataRobot in the fold. We can train models faster and do it with more confidence that we’re following appropriate steps to get to an ML solution that’s going to deliver value.

Our role as data scientists or actuaries is not merely to write complex code or build dashboards. The key role that we play is to focus on solving complex business problems through data—and that’s the most critical factor that DataRobot enables.

When I think about how DataRobot has enabled us and supported us with our current use cases or ideas, it’s really tied to helping us with our objectives. From a customer perspective, I’ve found that’s rare in a partnership. It’s fantastic to bear the fruit of that relationship.

DataRobot allows us to try dozens of machine learning techniques in a short period of time. Then, we just need to pick the best-performing model, which is ready to be deployed and consumed by our decision engine to make more informed lending decisions.