Carrier Management: Machine Learning at an MGA

At Atlas, we decided that ownership and management of our data, machine learning and predictive analytics was something we wanted to retain control over. We invested in resources, and started down the path of answering simple questions first. Among them: Are we using our underwriters time wisely? Do we know which type of accounts we tend to bind at the price we want? Can we improve the operational handling of our claims? Do we know which submissions we don’t want to waste our time on?

But even with our team of budding data scientists, we had to juggle all the other work with maintaining data across an insurance company. I never had expectations around moving faster than the data science norm, at least not without a level of investment that would have killed our ROI. Then things changed, as they do from time to time. After attending an actuarial conference earlier in the year, we couldn’t stop talking about a data science tool that was demoed there. That vendor was DataRobot.

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