A vast number of Americans fall into the category of the “underbanked”—those who are just starting out on their credit journey or who’ve had some sort of credit-related struggle. By some estimates, up to 100 million people fit that description. While they may not score high with the traditional credit bureaus, data that measures spending habits and bill payment history may offer insight into their true credit risk.
Banking institutions have access to the kind of data needed to create alternative credit scores, and advances in technology can help harness that data. Where once banks could only sample data, they can now process and analyze entire data sets to yield a more complete understanding of any individual borrower.
BAI recently spoke to Lisa Fischer, chief growth and lending officer at credit-focused fintech Mission Lane, about how data can be used to provide more financial access to the underbanked.
The interview has been edited for length and clarity.
How do you think about the data that goes into the traditional credit score, and how do you use it in your work?
It’s the foundation for the most depth of information for banking and lending, but it’s just one piece. And what we’ve grown to understand is it’s not enough to understand the nuances of a person. It’s not enough to have that one line or that three-digit number and the information associated with it to know everything about a customer so that you can understand their needs and deliver the right products to them.
What kinds of data should would-be lenders have that they don’t have?
It’s less about any individual data element and more about how they work together and how you can understand a population in substantially more detail. You need to look beyond the traditional data sources and look holistically at the customer. How do you create a full picture of the customer? If you try to say, “Oh, it’s these three elements,” then you’re looking much too narrowly. But if I were to mention one tool, it would be broad testing so that you can gain information on how people are going to perform with you on the various types of products you are offering.
Tell us more about the process of harnessing data and getting past the obstacles that banks are dealing with now.
With the advances in technology, you now are able to process so much information in a shorter period of time. You have the ability to add much more information about a customer so that you can understand them better. Back when I started my career in this area, you had to sample data. There was no possibility of using whole data sets, and you would have to pick and choose what should go into it. Now you have the ability, with machine learning and AI, to look across all of the data and look at it holistically.
How do you ensure that bringing in and analyzing more and more varied data will lead to fairer decision-making?
The goal is to have the inputs be as broad and as unbiased as possible. Therefore, we do what we refer to as “universe testing”—testing across the entire universe to remove any potential biases that goes into the model development sample. And then, on the production side, you must have rigorous monitoring controls in your production environment. You have to be looking at what is going on with the different variables and their transformations as you are using them in production, and you have to understand the choices the model is making and how it is affecting various different populations.
How do you sort out the categories of the underbanked in terms of the data needed to be able to reach each of these discrete segments?
We don’t look at people in categories, because the way you can actually get to a better place with customers is by providing products that will meet their individual needs. There’s a wide range of economic statuses and ways of interacting with products. If you stay at the segmentation level, you do not understand those nuances, and therefore you won’t be as refined at meeting their needs. It’s really about understanding the individuals, not the category.
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