Using Data to Expand the Creditworthy

Financial institutions are opening up a wider customer base by using big data to drive alternative credit scoring.

As the global recession has dragged on, more and more consumers have dropped out of the standard definition of “creditworthy.” On the other hand, banks are pushing to expand their customer base, going back to their efforts to include the “underbanked,” which they abandoned in 2008. This time, the banks are arming themselves with three important advantages related to big data, and the resulting analytic procedures that weren’t previously available.

Volumes of alternative data
Brand new types of data are being used to assess the risk of borrowers with bad traditional credit, or thin credit files. Instead of using third-party credit scores, credit issuers are going down to the attribute level to build custom credit models. The tens of thousands of attributes contained in every credit report, along with the data from utilities, rent, and mobile phone payments, create volumes of data for each potential borrower.

Big data platforms are becoming critical for managing this new alternative data.

Alternative data providers such as ID Analytics are creating databases from mobile phones and other providers. From this data, banks can establish a broader base of attributes, and build more robust models. All of this depends on the ability to store and process large volumes of information.

Velocity of credit approval
Traditionally, credit givers would have to engage in manual underwriting to handle borderline candidates. However, many credit offers are made at the time of sale. If banks cannot underwrite the new borrower at the cash register, they may lose the borrower. Or worse, they may lose the borrower to an alternative financial services provider that can underwrite the risk.

Companies like Zoot Enterprises are building extensible, real-time decisioning engines that allow banks to process credit applications using a varied set of input data elements at the point of sale. These systems also allow credit departments to test new attributes, and deploy models in a matter of weeks, instead of months or even years.

Variety of social media
One of the most interesting developments in big data is the use of social networks to provide insight into consumer behavior. Recent research has shown that consumers who know someone who has defaulted on a mortgage are more likely to do so themselves. Therefore, there is promise in the use of social networks to assess consumer risk.

Clearly, there’s a slippery slope as “guilt by association” isn’t good underwriting policy. But in the future, our social networks will likely be used to understand us better. How they will be used, and what analytic techniques will be required to make good policy, remain the work of quantitative and credit policy analysts.

Big data holds the promise of extending financial products to a larger segment of the population. Further, it promises to do it in a way that is more responsible. A more diverse product offering, and a larger base of “banked” consumers, could be a shot in the arm to an ailing industry.

Related posts:

• Stick to the Facts
• Big Data Tackles Financial Fraud
• 3 Ways Big Data Retains Profitable Banking Customers

— Frank Bria, Co-Founder, Bria Analytics Group