In a world of fully automated credit decisioning for consumer lending, it has become difficult to gain a complete view of the applicant’s financial situation. More thorough application processes such as mortgage or business loan applications not only look at the borrower’s ability to repay on a monthly basis but also look at the borrower’s financial margin. We know that cash reserves give the borrower the ability to pay even when adverse circumstances arise. It is difficult to assess financial margin during the consumer loan application process due to the very streamlined process lenders have put in place to reduce friction and increase the likelihood that the consumer will complete the application. Many consumer lending programs like store cards offer up small credit lines and thus rely on high volumes to be profitable. Some lenders are not even gathering all of the applicant personal information at application. They may just gather partial address and a few other pieces of personally identifiable information.
This is where alternative data enters in to assess a consumer’s financial margin and foundational credit worthiness. Attributes like property ownership, level of education and professional licenses can give a lender insight into margin and depth of financial strength. The presence of records like criminal history and liens or judgements points towards less financial strength. Alternative data has been shown across the industry to be very predictive by itself as well as in combination with traditional tradeline data. This gives the lender the ability to approve applicants with no credit tradeline history and also more accurately assess risk on borrowers with credit tradeline history.
Assessing the ability to repay based solely from past payment performance makes the assumption that a borrower’s circumstances outside of their payment history will remain consistent in the future. This can be a costly assumption to make. Introducing other data into the decision gives insight into what will happen if the borrower’s circumstances change. If the borrow has a strong financial foundation, they will most likely continue making payments. If the borrower has little financial margin, their consumer debt will be the first payments they stop making.
Check out this white paper for more information on how to effectively model with blended tradeline and alternative data: Modeling Blended Alternative and Traditional Data.
LexisNexis® Risk Solution’s RiskView™ is an FCRA compliant consumer credit scoring tool with a full suite of scores and attributes. LexisNexis Risk Solutions has a long history of building robust consumer scoring models and can custom fit a model to any lenders needs or can work alongside a lender on their own internal modeling efforts. Creating models that blend tradeline history and alternative data can be difficult due to the mutually exclusive nature of the data sets.