Personal Behavior Now Part of New Banks’ Loan Decisions


In the shifting world of big data and its use in business practices, new banks are beginning to use such personal behavior as whether potential borrowers use proper capitalization, spend time reading the terms and conditions of their loan, or their social network connections in an effort to find reliable correlations between borrowers and their creditworthiness.

Why, you might ask?

As many as 70 million Americans either have no credit score or a slender paper trail of credit history that lowers their score. This is particularly the case with students and immigrants.

Enter big data and predictive analytics that seek to connect the dots between behavior and credit risk.

The payoff for banks? If they can simply deduce from a few pieces of your data whether you’re a good credit risk or a bad one, they can save millions in either making the right loans or correctly pricing them to creditworthy borrowers and by disapproving high risk ones.  As the saying in carpentry goes: measure twice, cut once.  If it so happens that using proper capitalization, or a borrower who takes time to read the terms and conditions of the loans means they’re a good credit risk, it might be more accurate than looking a a number, which is all a credit score is.  And when someone doesn’t have a credit score, then what?

The downside for borrowers? If the data used in the algorithm predicting your creditworthiness is discriminatory, it both biases your ability to get a loan and violates your civil rights.

The trend here is unmistakable, though–more and more businesses are using big data to find reliable correlations in the data rather than trying to determine the why of things, such as behavior or even natural events.

And for banks and loan companies? According to Max Levechin, co-founder of PayPal and chief executive of Affirm, a start-up lender that offers alternatives to credit cards, “the long game is to use data and software to chew up and revolutionize the financial ecosystem.”

For the article in full, see:


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