How do you measure risk for consumers without long credit histories in the U.S.?
That’s the question Amitay Kalmar confronted when he founded Boston-based Lendbuzz in 2016. Three years later, Lendbuzz is carving out a share of potential auto loan customers by leveraging machine learning and data analytics. The startup lender targets foreign-born residents, such as ex-patriots and international students, with limited or no credit histories. Rather than use a traditional credit score as a basis to score risk, Lendbuzz uses machine learning algorithms that analyze other indicators such as a borrowers’ educational and employment background, earning potential and cash flow.
According to 2018 Census Bureau data, there are about 45 million foreign-born nationals living in the U.S., with Kalmar himself included. When he moved to the U.S. to complete his MBA at MIT, he opened a bank account at Bank of America and wired $100,000 for tuition and living experiences, only to be surprised when he was declined from a credit card application.
“It didn’t make much sense to me at the time, and I knew this problem existed in the U.S. consumer credit system,” Kalmar said. “When I started thinking about starting my own business, I built on my own experience and the experiences of friends who relocated and realized it’s a much bigger problem than I initially thought.”
So far, the startup auto lender secured $150 million in debt and equity financing and has grown originations to $100 million since the company’s founding. Kalmar said Lendbuzz is on track to increase originations 150% in 2019, after tripling volume between 2017 and 2018. In this episode of The Roadmap, Auto Finance Excellence chats with Kalmar about plans for the fresh capital, geographic growth and tech enhancements expected to propel the startup onto the main stage.
