Everyone in lending is talking about alternative data. Many believe alternative data is the next natural evolution of credit decisioning, and that’s why we made it the focus of our recent webinar, “Streamlining Lending and Helping Prevent Fraud with Alternative Data.” You can watch this webinar on our website right now.
Here are a few highlights:
Jeff Hollander, Director of Strategic Consulting focusing on Credit at Envestnet® | Yodlee®, kicked off the webinar by highlighting how the world has been turned upside down by recent events such as the pandemic, supply chain issues, rising inflation, and more. This macroeconomic uncertainty has complicated the entire credit lifecycle, making reliance on traditional credit scores a riskier proposition.
For the last 100 years, the credit industry has been served amazingly well by traditional credit bureaus. The three main credit bureaus generate credit reports based on what is reported to them, and they’re good at measuring consumers’ intent to pay. What is reported to them, however, isn’t always enough to provide a complete picture of a household or what a household is trying to manage from day to day.
As a result, 59% of lenders are using some type of alternative data1 in their underwriting process to get a more holistic picture of a household and the individual that they’re trying to underwrite.
Consumers can be seriously impacted by the limitations of the traditional data that is fed into the credit scoring system—and some don’t know it.
“When you have one side of the picture, or a limited set of data inputs, meaning, what is reported to the credit bureaus, you’re going to get a score that is good for just that data. What’s not being taken into account is other pieces of information that could be useful,” says Hollander.
“Many consumers I’ve talked to believe that things like income is on their credit report, or all their utilities are on their credit report, when in fact they are not,” he says.
Today, 16 percent of the population has a very poor credit score, up to 580 through FICO, and another 18 percent has a fair score between 580 and 670. Some of these have thin files, or may be credit invisible immigrant populations, and others who may be more creditworthy than their scores suggest.
The lending industry has long tried to find good accounts by applying machine learning techniques to traditional credit data, but the new techniques that are out there now provide some additional benefits, Hollander says.
There are good accounts out there, but there’s a big population that is not being serviced today by the traditional credit market.
Certain generations are falling through the cracks, like the younger market that needs access to credit to keep growing their households and growing the economy.
The rise of the gig economy is yet another challenge, where many people are working two or three different jobs to replace or supplement a traditional two-paycheck-a-month job. These additional sources of income need to be included in credit underwriting processes.
So, what can lenders do to address these discrepancies and inequalities?
How can you capture a person’s actual circumstances in your underwriting so they’re eligible for credit? And how can you find sizable populations that have a high probability of being good consumers, that you can grow with and cross-sell to—but who haven’t qualified via traditional scoring models?
This is where alternative credit data comes into play.
What we’re recommending, and what a lot of the industry and regulators are recommending, is leveraging the consumer’s bank data. This bank data might be pure checking and savings data, but it can also be investment data, asset data for mortgages, and more. Checking and savings data in particular can add value to traditional credit data and augment traditional underwriting processes to transform the lending landscape.
At Envestnet | Yodlee, we designed Credit Accelerator to unlock cash flow, income deposit, and asset data for more intelligent and inclusive credit decisions.
This unique solution retrieves the data and delivers it in a report that breaks out income, expenses, and assets. It can pull up to two years of bank statement data for modeling purposes—which is more than what might be available on a financial institution’s site. And it provides an up-to-date view of cash flow, so lenders and underwriters have a better understanding of a consumer’s ability to pay.
Hollander mentioned that he previously worked as a risk manager in the consumer credit space and has seen that while the calculation of debt to income through the credit bureaus is good, it’s not always as accurate as it can be without all of the data. But by leveraging bank accounts for cash flow underwriting, you can get all the traditional credit data plus utilities, cell phone bills, and rent, which don’t show up on traditional credit reports. You also see all available sources of income—which is key in our gig economy. The end result is a more holistic view of a person’s finances and a more accurate picture of what they can afford.
To learn more, watch the full Streamlining Lending with Alternative Data webinar here, and for a demo or more information, contact our team.
