Skipping Credit Score, Using Machine Learning and Big Data to Offer Credit Lines to the Credit Invisible
Tech giants Google and Facebook rely on your data to customize your experience on their platforms to your preferences, habits, and circumstance. But all of the data being collected — on spending habits, search history, and location check-ins — is also being sold to advertisers, enabling them to more accurately target you and increase the likelihood of triggering your consumption of their products.
But several fintech startups are starting to use all of the data collected about your spending habits and use it in your favor — in a very tangible, actionable way.
Traditionally, when a consumer applies for a line of credit, their credit score, a compilation of credit information reported to the three major credit bureaus, is pulled. But, according to the Consumer Financial Protection Bureau, an estimated 20 percent of the adult American population either have no credit history or lack sufficient history to generate a credit score. This limits individuals new to credit to cards with low limits and high interest rates — or straight up denial.
Instead of relying on traditional credit scores, the team behind Petal card is using machine learning to assess consumer creditworthiness. Using bank account and debit card history, the technology accesses consumers’ past earning, spending, and saving patterns to identify those most likely to handle credit responsibly. For example, Petal founder Jason Gross told the New York Times, “regular savings behavior is a good sign.”
“These are young people who are working, managing their expenses and paying their bills, but by a debit card. So they aren’t really building a credit score, but because of the financial data we have access to, we know they are responsible and have good financial behavior,” Petal founder Jason Gross told Bank Innovation.
This means they can provide a credit line with higher limits and comparably lower rates, even to consumers without a formal credit history. Powered by Visa, the Petal card starts with a credit line of $500 and goes up to $10,000. APR ranges from 17.99% to 24.99%. Most novel: The card has no fees — overdraft, late, annual, or otherwise.
Beyond analyzing things that don’t factor into a credit score today, Petal’s digital product also strives to make saving and the real cost of — as in, interest that will accrue on — the decisions to delay or make lower payments on the credit card.
Without an extensive reward system, the card is primarily intended to service people new to credit, recent immigrants, and first time credit card owners. Prospective user James McNab spoke to his anticipation and the necessity of Petal’s services in a review on Product Hunt.
A recent transplant to San Francisco via Canada, McNab reported that he spent a month and a half struggling to set up his finances stateside. Without American credit history, even though he is making a decent salary in his new role, McNab said he has been rejected for lines of credit with Simple, Chime, Final, Capital One, and Chase.
“If this service works as advertised it will be one of the most useful and timely services I will have used in my life,” he wrote.
Petal’s application of data isn’t just used at the customer on-boarding stage, either. The company also monitors a customer’s spending patterns and can automatically increase credit limits if, say, a consumer receives a raise at work.
And Petal isn’t the only fintech banking on untraditional data to service customers without traditional credit histories. SelfScore targets international students, while Affirm looks at a variety of data sources, including your social media footprint, to determine creditworthiness. Meanwhile, pesonal loan financiers, like Earnest, SoFi, and Upstart, look at borrowers’ job and education histories, cash flow, and payment habits to offer loans at cheaper rates.
The Petal Visa card has yet to roll out universally, but customers can sign up online to receive an invitation to apply when it is available more widespread.
