New Credit Risk Models For The Unbanked
For years, members of the unbanked population, or those that don’t have access to credit, were forced to turn to payday lenders for quick cash. Part of this is a result of the riskiness that comes with lending to someone with little credibility – high interest rates were needed because lenders had no way of knowing if a loan would be paid back.
Many banks also steered clear of offering small extensions or microloans, since the same amount of work would be needed for a larger loan, but without the high profits. But with technology and access to alternative data, financial institutions are running out of excuses to not lend to the underbanked, and other companies are filling in the gap.
An American Banker article recently detailed new options that unbanked members have to get a loan – ThinkFinance uses data from social media as a risk assessment tool, and BillFloat announced it would be offering an additional $21 million worth of short-term loans. Another similar-minded startup, ZestFinance, was originally started when a family member of the creator needed quick funds for an emergency, but without any credit, had few options.
As the article explains, all of these have the same idea in mind: “use some form of big data to drive down the cost of a loan so underserved customers can get credit without paying an exorbitant price.”
Though these companies are using new technology to make small loans, banks don’t seem to be expanding their services. This past year, some experts said they believe that payday lenders made more than $17 to $30 billion in profits. In 2008 and 2009, an FDIC experiment required 28 different banks to offer small-dollar loans. Despite the low default rates, banks still noted the smaller profits that come with the program.
However, as these tech startups continue to grow, financial institutions may be forced to look more into lending software to expand their clientele and attract the now-overlooked customers.
Related Data Analytics Articles
Alternative Data for Credit Scoring
Alternative Data in Finance
The importance of using non-traditional data
The growth and evolution of the lending data source library
With technology and alternative data, the unbanked population may have more options