Increased data availability, advanced analytics and new data aggregation technology is improving the ability to evaluate credit worthiness of consumers who were previously ‘credit invisible.’ The industry is finally moving beyond traditional credit scores.
Subscribe to The Financial Brand via email for FREE!More than 25 million Americans are considered “credit invisible,” meaning they have no credit history with any of the three main credit reporting bureaus. Another 19 million don’t have enough data to produce a score. When it comes to purchasing a car or home, the absence of credit history can put consumers in quite a precarious position. A general lack of trust, coupled with high interest rates, makes it difficult, if not impossible, for many consumers to get access to loans.
This is especially true among Millennials, who might be more likely to not have the credit history needed to go ahead with big purchases. Since parents or guardians may have covered Millennials’ expenses for most of their life, there might not be much of a credit history to fall back on. Unfortunately for consumers, no credit usually means no loan – even for those capable of paying it back.
For years, loan decisions have almost exclusively hinged upon credit scores. While such data can help inform an initial assessment, there’s more to a loan candidate than just credit history. Discover how data aggregation technology can transform the credit decisioning process for both borrowers and lenders.
The Power of Expanded Data Points
Credit cards don’t always tell the whole story. From phone to utility bills, there are plenty of common data points to consider when evaluating a borrower’s ability to pay back a loan. In fact, almost every American owns a cellphone of some kind. Carefully reviewing mobile plan payments can help lenders gauge whether loan candidates keep a close eye on their expenses.
Better yet, they can hone in on cash flow and transaction data. Although credit history isn’t always a guarantee, it’s safe to assume most consumers have access to checking and savings accounts. More than 90 percent of U.S. households have access to a bank account.
Leveraging data aggregation technology can digitally gather up to two years of data across accounts. The more insight lenders have into a consumer’s transaction history, the greater chance there is to assess their propensity and ability to repay a loan. As use of machine learning increases, the power of the data and advanced analytics can set financial services organizations apart from competitors who still use outdated and narrow loan decisioning criteria.
As a result, consumers that were once deemed ineligible for loans might now be classified as potential customers. Instead of borrowing from friends or family, consumers may use loans that were once out of reach to purchase big-ticket items.
Streamlining the Application Process
Qualification isn’t the only thing standing in between borrowers and the funds they require. In many cases, a detailed application process can often create further complications.
Volumes of paperwork tend to take hours to chase down and complete. And even then, potential customers must wait up to 70 days for approval. Lenders can reduce the friction currently frustrating consumers with insights driven by data aggregation technology.
By digitizing the application process, data aggregation can cut down on the amount of paperwork required. Copies of sensitive documents could be replaced by a single online experience, improving both security and convenience.
Looking to fight fraud? Financial data aggregation technology can help do just that. Whether it’s for checking, savings or something else entirely, consumers typically have accounts across a few different financial institutions. As the number of accounts increases, so too does the difficulty associated with keeping track of them.
We’re heading towards an increasingly digital, more streamlined process using consumer-permissioned data aggregation. Rather than flipping through dozens of documents, now it’s possible to gain a complete understanding of a loan candidate’s financial situation from just one digitally generated report. In addition, the loan approval period will be shortened – up to 11 days saved with verification of income and assets.
The Future of Consumer Lending
Slowly but surely, the credit decisioning process is evolving. Having focused entirely on credit histories and scores in the past, major credit bureaus such as Experian are now considering other sources of data. And the rise of data aggregation technology is a big reason why. Financial transaction data that may have once been scattered across several different accounts is quickly becoming available at the touch of a button.
Alternative lending organizations have found that added ease and simplicity can go a long way toward enhancing the income and asset verification process for loan candidates. Time previously spent searching for financial information can instead be used to evaluate which type of loan works best. Lending processes that once depended on a credit score or history could also see a spike in speed, accuracy and efficiency with the use of expanded criteria and machine learning.
With more information at lender’s fingertips, financial services organizations stand to gain a better idea of who a loan candidate is and whether or not they can be trusted. From bill payment to bank account history (and even social media insights), data aggregation and advanced analytics uncovers plenty of insights that might have previously gone unnoticed.
While credit history is sure to stick around, that doesn’t mean it will always be the status quo in decisioning. Complementary forms of data are gaining traction throughout the U.S., opening the door for loan candidates that lack a traditional credit score. Alternative data points, such as a customer’s payment history, promise to help pave the way for a credit decisioning process that not only welcomes the credit invisible but also boasts improved experiences for all.
More importantly, the use of expanded credit criteria and machine learning expands the potential loan universe significantly, providing much needed loan volume and spread potential, positioning a traditional financial organization in a positive position compared to new fintech competitors.
The Guide to Digital Lending
The 63-page Guide to Digital Lending Digital Banking Report sponsored by CRIF Lending Solutions, provides insight into the progress being made by financial institutions globally in the area of digital consumer lending. Beyond a review of goals and investments, this report delves deeply into the strategies, effectiveness, challenges and gaps in delivering an exception consumer lending experience.
The report includes the results of a survey of more than 200 financial services organizations worldwide. The report has 63 pages of analysis and 33 charts/graphs. There are also guest articles from market leaders on digital consumer lending.
Subscribers to The Digital Banking Report and those wishing to purchase the complete report can access it immediately by clicking here.
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