Q&A with Point Predictive Advisory Board Member Lauren Crossett
In June, Point Predictive announced that fintech veteran Lauren Crossett would be joining the firm’s advisory board. A few months later, we caught up with her to talk about her view of the industry and to see what insights she has to offer about the company.
Point Predictive: You’re a relative newcomer to Point Predictive, but you have been in the industry for some time now. What do you see as the main trends of the last few years?
Lauren Crossett: Yes, I’ve been involved in financial services or fintech for pretty much my entire career. One underlying trend I’ve noticed is this shift toward democratizing data to create better financial outcomes, or at least create the conditions for better financial outcomes.
Technologically, the capabilities are not exactly brand new, but it’s taken a little longer for the collective mindset to catch on, for people to realize that using data modeling or artificial intelligence isn’t reserved for just the massive legacy institutions.
Assuming they have the right talent and user-friendly products, companies like Point Predictive can tap into the huge amount of data out there and then offer their expertise to a local car dealership or a small credit union. It’s important work that can be done far more affordably than many realize.
PP: How do you see Point Predictive fitting into that shift?
LC: Point Predictive is a great example of how opening up all this data gives clients a competitive edge when it comes to making financial decisions. I like how their infrastructure can be a means for, say, an auto lender to simplify loan applications or provide better rates.
What specifically differentiates Point Predictive, in my view, is just how innovative they have been in the different ways they use and synthesize that data. Especially in the auto financing space, they’ve been able to turn it into products that touch on pretty much every aspect of the lending process, from validating income to evaluating and partnering with dealerships.
PP: As you know, one the services that Point Predictive offers is fraud detection technology. What interests you about that line of work?
LC: I think those without a financial background might consider defrauding a financial institution to be a mostly victimless crime. Of course, the fact is that it affects all of us, all of the other customers and borrowers and account holders, in the form of higher fees and more expensive credit. Working to prevent fraud, then, has positive ripple effects across the financial system. It also means that a financial institution can lend with more confidence to potential borrowers who might otherwise be deemed too risky.
PP: Right. Some of the stories we’ve been hearing involve lenders leveraging Point Predictive’s tools to look for diamonds in the rough – potential borrowers who have bright spots in their financial background that may not be reflected in a credit score.
LC: Exactly, and there’s so much happening in that space. Think about teachers or other government employees. Lenders have recognized for a while now that those populations have amazingly consistent incomes, which can be taken into account when making loan offers. What Point Predictive’s products help to do is expand that line of thinking and make it easier for lenders to consider things like employment history. They can also do so without worrying that they’re just looking at fake pay stubs.
PP: What excites you about the future?
LC: Another thing that sets Point Predictive apart is its leadership team. I consider them to be real visionaries in the fintech world in general, but specifically when it comes to fraud prevention. Some are the equivalent of household names for people in that field. So, it’s exciting collaborating with them and seeing what they’re going to come up with next. It’s also been interesting to see how different types of lenders respond to such a compelling line of products.
PP: Speaking of the future, this year’s Money20/20 conference is coming up.
LC: It is! Lenders have a lot to gain from considering a service like Point Predictive. As a rule, smaller lenders probably don’t have a team of data engineers to help inform decisions the way large banks do. With Point Predictive, they don’t need to. It makes for a pretty natural fit.