Andy’s car transmission unexpectedly goes out. He knows it’ll cost him anywhere between $1,000 and $4,000 to replace it, but unfortunately, it’s an extra expense that will strain his budget.

He searches “best loan rates” in his web browser and clicks on the first relevant search result. Although Andy is looking for a direct lender, it’s very likely that he’ll find a lead broker in the top results.

Andy searches for "best loan rates".

The lead broker’s site will ask Andy for some personal information and promise to match him with the best loan options.

A lead generator asks Andy to fill out a form.

What Andy may or may not know is that as soon as he hits the submit button, a fierce competition ensues.

The bidding wars begin.

We know that Andy is looking for the “best loan rates”, but lenders are also looking for the best customers at the best price.

Andy will wait seconds for a match, but a lot is happening in the background in those seconds. Dozens of lenders are evaluating his creditworthiness with the data the lead broker has funneled them. They’ll then compete with other lenders to get the lead broker to route Andy their way if he turns out to be a good fit.

It’s actually a lot like day trading. Decisions have to be made in real time.

And the fintech companies that come out on top have leveraged data science to make those decisions.

Continue reading this article on Medium to learn how data science can help fintech companies get the best leads at the best prices.