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In the United States, the FICO score has become the standard measure of consumer credit risk. There is so much weight on the score — 90 percent of consumer lending decisions are made using FICO — that you can rarely sit through a commercial break without seeing an ad for a credit report provider.
Yet FICO’s methodology is incredibly simplistic, and often illogical. FICO scoring lumps would-be borrowers into groups and treats everyone in that group the same. If you go strictly by FICO, you’d think that everyone with a 750 FICO score is the same; that’s obviously not true. The result is that many potential borrowers are cut out of this mix — without high enough scores they are denied access to credit or forced to pay higher rates. On the flip side, some borrowers are getting loans and they probably shouldn’t be.
However, FICO is the best we have in the States.
The story is completely different in Latin America. There, financial service providers have limited access to credit reports, making them nearly irrelevant. This has led lenders in countries like the Dominican Republic, Peru, Colombia, Chile, Mexico, and Bolivia to rely on other information. Consequently, Latin American financial service providers were forced to develop credit models based on behavior rather than relying on traditional binary credit models.
For decades, Latin America has served as testing ground for financial services. We’ve built models, tweaked what works, and come up with solutions that acknowledge the key to lending is analysis leveraging technology.
The result is a Latin American lending environment that is more conducive to financial inclusion, according to the Global Microscope 2014 — these countries have new ways of looking at risk enabling underserved populations to access money. This creates opportunity, spurring economic growth and giving more people the ability to improve their lives. At the same time, we find that the lenders get better loan performance with fewer write-offs.
By taking this idea of looking at behavior, rather than traditional credit models, it’s possible to provide millions of underserved consumers with access to money in the United States. Looking at the monumental amount of data being collected using big data analytics, today’s technology-savvy lender can understand a potential borrower based on a much wider range of criteria than what FICO measures, and make a better decision about that potential borrower. This approach creates a scalable, next-generation lending model.
In the current U.S. lending landscape, new rules and new types of lenders have created a vibrant place for borrowers, but many are still left out. These new lenders are still using the same model for risk as their traditional counterparts — the only difference is the way those lenders are regulated. To really make a difference in this space, we need to begin distinguishing the gray areas of risk. And today, we have the opportunity to use best practices from other countries and gain insights on how to serve people better in both traditional and nontraditional lending environments.
The next generation of nontraditional lending that takes a nod from the Latin American market, using behavior and big data to its advantage, will serve both the borrower and the lender better. Lenders will mitigate risk; borrowers will get the money they need.
Juan Tavares is cofounder of LendingPoint. He served as the Business Development Director for the Tavares Private Equity Group for over 12 years. He’s been responsible for spearheading the Tavares Group’s efforts in the progressive lending space, and in 2011, Juan cofounded Avanzame Latin America to introduce Merchant Cash Advance in the Dominican Republic.
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