We had the opportunity to interview Douglas Merrill, the founder and CEO of ZestFinance. He started ZestFinance in 2009 with a team of [mostly] ex-Google folks who came together with a mission: “to make fair and transparent credit available to everyone.” It is a tech platform that applies “Google-like math” to credit decisions. 

Douglas Merrill, Founder & CEO of ZestFinance

Q: Can you tell us a bit about yourself and ZestFinance?

A: I earned my a Ph.D. from Princeton before going on to join Charles Schwab as a Senior VP and then Google (GOOGL), in 2003. I spent 5 years at Google as its CIO and VP of Engineering. I founded ZestFinance in 2009 with the mission to make fair and transparent credit available to everyone. At Google, we built an infrastructure to handle search errors, misspellings, and missing data, but still return highly predictive rankings. I took that same Google-style math and applied it to underwriting. Search and underwriting are both math problems. At ZestFinance, we apply data science and machine learning to help lenders effectively predict credit risk so they can increase revenues, reduce risk and ensure compliance.

Q: How did you get the idea of ZestFinance?

A: After 5 years at Google, I began looking for a new problem to solve that was both technically challenging and socially important. At Google we developed math that used thousands of pieces of information to rank web pages. I began to wonder, “why isn’t credit using these modern technological breakthroughs? It should be.” I found that traditional methods of credit appraisal do a very poor job assessing the creditworthiness of thin-file and hard-to-score customers in the U.S. and around the world. For example, if a recent college graduate doesn’t have any credit history, it can prevent them from financing their first major purchases or getting their first credit cards. I built a platform that uses Google-like math to ingest massive amounts of data, mine it for signals of creditworthiness and generate highly predictive assessments of a borrower’s ability and willingness to repay debts. 

Q: How will ZestFinance improve the lives of its users?

A: There are millions — if not billions — of consumers who go overlooked by financial institutions because they are considered thin-file or no-file borrowers. This is especially true with millennials in the U.S., and in emerging markets such as China, India and Latin America where systems for conducting credit appraisal are immature or non-existent. Traditional underwriting works well when evaluating borrowers with long credit histories, but when there is limited, missing or erroneous data, it has a harder time differentiating between creditworthy and high-risk applicants. Machine learning tools, like ZestFinance, fills those gaps by analyzing a vastly broader set of data to make sure accurate decisions about a potential borrower.