Most mortgage industry leaders have made significant changes in their processes to keep current in the past few years and credit bureaus are no exception.  Lenders are seeking creative methods to evaluate potential borrowers who lack credit history, and in some cases, identify consumers who will likely soon apply for credit and reach out to them directly. In response to this challenge, credit bureaus are working on providing a dynamic blend of traditional, trended and alternative data.

Traditional, trended and alternative data

Traditionally, credit bureaus have scored consumers using a borrower’s payment history, credit utilization, credit age, types of credit such as revolving vs. installment or the number of inquiries on the borrower’s credit file. This “traditional data” provides a framework for understanding an applicant, but does not include enough data to paint a complete picture of a borrower’s financial patterns. Traditional data is like a silent film – viewers get the main idea but have to make inferences about the details of the plot.

Consumer protection groups complain that using traditional data alone is unfair to borrowers. While established borrowers have access to credit lines and are rewarded for their timely payments, millions of Americans may not have the same access to credit and are punished for a lack of credit history. Even those consumers that reliably pay everyday bills - such as phone or utility charges - are not able to build their credit report if they do not have access to credit. Looking ahead of this trend, some credit data companies were able to alleviate this systemic bias by including trended and alternative data.

“Trended data” supplements traditional data with a richer history of a borrower’s behavior.  Trended data includes up to 30 months of payment history, amount paid versus minimum payment due, and total amount owed.  By partnering with the right analytics data provider with new technology and insights, this information not only helps lenders identify consumers who will likely seek a new or refinanced loan in the near future, but it can play a significant factor in a lender’s ability to predict short-term risk. Thus unlike the silent film, trended data is like an in-color movie with sound. There is no depth to the action, but viewers suspend their disbelief and assume that the plot is taking place in a three-dimensional setting.

Credit bureaus are also looking at providing “alternative data” to help their customers better understand borrower behavior. Alternative data takes into account relevant information that trended and traditional data overlook, such as payday loans, club or magazine subscriptions, checking and debit account management, property, tax and deed records, assets and equity in a home and length and time of residency. This forward-thinking approach reflects an industry shift over the past four years towards embracing more comprehensive analytic models. Alternative data is like a three-dimensional movie.  It pulls back the curtain on the borrower and allows the lender to see the fine details of the borrower in a more intimate way.  The subtle nuances of a borrower are revealed and the lender gets an up-close inspection of the borrower more so than with just traditional data alone.

Opening up credit

Utilizing a blend of alternative, trended and traditional data allows financial institutions to remain competitive by tapping into underserved segments of the population. It could also permit smaller lenders to differentiate themselves through extending credit to those who are turned away from lenders who use more traditional data to evaluate their applications.  

Alternative and blended data has the potential to allow lenders and finance companies to more accurately evaluate under-served segments of the population, including millennials, minorities, immigrants and rural dwellers. Borrowers in these groups are often “thin-file” borrowers, meaning that they lack a long history of traditional data - which does not necessarily mean they are “risky” borrowers. When these thin-file consumers apply for loans, they are often denied because they have no visible track record of responsibility when evaluated with only traditional data. The cycle is hard to break:  borrowers need credit to get credit. Thus benefits that perhaps can be derived from using alternative data include approving more borrowers without increasing lender risk as well as building loyalty with consumers who are new to the credit market.  

According to TransUnion, a leading credit bureau and data analytics provider, 26.5 million previously unscorable thin-file borrowers can be scored in the prime and near-prime risk tiers using a combination of the three types of data. Borrowers in the prime or near-prime tiers are afforded better interest rates which, in turn, lower their monthly mortgage payments. This is in contrast to subprime borrowers who usually have weakened credit histories and come with greater risk of defaulting on their loans. TransUnion further promotes that through the fusion of traditional, trended and alternative data, 95% of the U.S. population can now be accurately scored. 

As with any new approach to a widespread service, some cautious government officials are holding out for more data before fully endorsing alternative data. Their concerns generally focus on the lack of a large-scale study of the potential impacts of alternative data. While the studies that have been conducted so far have reached optimistic outcomes, the size of such studies is limited and there has not yet been extensive independent analysis.

Another concern raised by consumer protection groups is that alternative data provides a comprehensive view of borrowers, which will inevitably harm some borrowers’ chances of being approved for loans. At the same time, allowing lenders to have access to more information will make the lending process more thoughtful, fair and efficient.

Alternative data and the crystal ball

Besides evaluating potential borrowers who have already applied for credit, another novel application of alternative data is to predict future borrowers – meaning those that will be seeking credit in the near future. TransUnion, an early adopter of utilizing alternative data, can identify other types of potential new applicants or borrowers looking to refinance – including new applicants that will be entering the market after a home equity draw period ends or those borrowers entering into their prepayment period. 

Leading the way

Early adapters of utilization of blended data include credit and debit card lenders, indirect auto lenders, regional banks and credit unions. According to a 2016 case study conducted by TransUnion, three of the top ten indirect auto lenders reported an average of a 25% increase in the size of their prime-borrower portfolios thanks to their use of alternative data. A bill currently under review in the House of Representatives, the Credit Score Competition Act of 2017, introduced by Rep. Edward Royce (R-Calif.), aims to mimic the benefits seen in the private-sector by requiring the Government Sponsored Entities (GSEs) to consider utilizing alternative data when evaluating loan applications. Even without a Congressional mandate, in 2016, one of the GSEs started including trended data and alternative data in its automated loan underwriting system.

While blended and alternative data are still garnering traction among credit agencies and lenders, it seems to be the direction the market is taking and it could result in transforming how lenders view potential borrowers. Within the next several years it is likely that most lenders will incorporate this type of data, in some form or fashion, as they update and stay current in lending practices. In fact, it is just as likely that the use of such alternative data will result in a revolution of credit reporting as we know it today.