What is the CFPB Saying?
In May of 2015, the CFPB Office of Research published Data Point: Credit Invisibles. This evidence-based research highlighted that the three NCRA’s face significant challenges in accessing most credit markets and that this lack of coverage is creating unintentional disparity when it comes to income and race.
Here is an excerpt from the conclusion of that report, “Our results also suggest that there is a strong relationship between income and having a credit record. Almost 30 percent of consumers in low-income neighborhoods are credit invisible andan additional 16 percent have unscored records. These percentages are notably lower in higher income neighborhoods. For example, in upper-income neighborhoods, only 4 percent of the population is credit invisible and another 5 percent has an unscored record. Additionally, our results suggest that there are significant differences in the incidence of having a limited credit history across racial and ethnic groups. While Whites and Asians are almost equally likely to be credit invisible or have an unscored record, the shares of Blacks and Hispanics with limited credit history are much larger. About 15 percent of Blacks and Hispanics are credit invisible (compared to 9 percent of Whites and Asians) and an additional 13 percent of Blacks and 12 percent of Hispanics have unscored records (compared to 7 percent of Whites).”
On February 16, 2017, the CFPB held a field hearing and issued a Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process. Comments for this request are due by May 19, 2017. In this field hearing, CFPB Director Richard Cordray discussed how the CFPB is working to ensure that consumers can gain access to financial products that are fairly priced. The CFPB wants to learn more about how alternative data can help borrowers that are outside the mainstream credit market to gain credit. They also want to understand if this data could potentially harm consumers.
The CFPBs interest in alternative data has risen out of the evolution of the credit markets. Before modern credit reporting, lenders relied on a relationship with and personal knowledge of a borrower. The banker knew how that borrower behaved, whom they hung out with and were related to, as well as their assets and spending habits. Many customers would have their entire banking relationship in one place offering the financial institution deep insight into their financial behavior. This knowledge was used to judge an applicant’s creditworthiness. Fast-forward to the present, we have automated decisioning engines with complex risk analysis models and companies that might only sell a single financial product. This scenario makes it impossible to personally know an applicant. Having a credit score in this automated environment is crucial if a person wants to borrow.
The CFPB stated that alternate data holds even further promise for those with low credit scores. Traditional credit scores look at past payment history to assess risk while alternative data looks at other predictive factors that help distinguish a responsible person who suffered misfortune from a chronically irresponsible person. These two people may currently have the same credit score, but the responsible person that has navigated out of their adverse situation is a much lower risk. Alternative data can help to differentiate these two borrowers. Additionally, consumers with no or very little trade line history can be disproportionately affected by collection activities on accounts that do not report to the bureaus. Payments like rent or medical obligations are not reported to the traditional bureaus however if the account becomes delinquent and goes to collections, the collection is reported. This means that the consumer gains no benefit for these types of payments but if they fall behind on these payments there is a negative impact on their credit file. The picture that is painted by their traditional credit profile is heavily weighted by that delinquent account. Alternative data can also offer a more holistic view on this borrower.
How does LexisNexis Risk Solutions fit into the solution?
LexisNexis Risk Solutions estimates that there are 50 million consumers without a traditional credit score. This group is often forced to gain credit at higher rates making them even more economically vulnerable. With its alternative data, LexisNexis Risk Solutions can score about 40 million of these people. This data gives the lender some of the insight they once had when they personally knew their customers. Products like RiskView™ can deliver attributes that help a financial institution understand who this borrower is and assign a score that helps to evaluate risk even when the borrower can’t be scored through traditional tradeline data. This opens a door to lend to this group of people and do it at fair and competitive rates. LexisNexis Risk Solutions is committed to financial inclusion through its credit risk decisioning products.
Do you have an opinion on the use of alternative data in credit decisioning? Click here to respond to the CFPBs RFI.
 Brevoort, Kenneth P., Philipp Grimm, and Michelle Kambara. Data Point: Credit Invisibles. Rep. The CFPB Office of Research, May 2015. Web. 24 Mar. 2017.