May 29, 2017 3:16 PM ET

Internet Software and Services

Company Overview of ZestFinance, Inc.

Company Overview

ZestFinance, Inc. develops a technology platform that applies Google-like math to make credit decisions. Its platform helps companies make credit decisions to expand the availability of fair and transparent credit. ZestFinance, Inc. was formerly known as ZestCash, Inc. and changed its name to ZestFinance, Inc. in July 2012. The company was founded in 2009 and is based in Los Angeles, California.

6636 Hollywood Boulevard

Suite 210

Los Angeles, CA 90028

United States

Founded in 2009





Key Executives for ZestFinance, Inc.

Founder and Chief Executive Officer
Co-Founder and Chief Operating Officer
Head of Product Management
Executive of People Operations
Compensation as of Fiscal Year 2016.

ZestFinance, Inc. Key Developments

ZestFinance Introduces Machine Learning Platform to Underwrite Millennials and Other Consumers with Limited Credit History

ZestFinance announced the Zest Automated Machine Learning (ZAML™) Platform for credit underwriting. ZAML enables lenders to analyze vast amounts of non-traditional credit data to increase approval rates and reduce the risk of credit decisions, particularly for thin-file and no-file borrowers like millennials. The platform also provides the ability to explain data modeling results to measure business impact and comply with regulatory requirements. The ZAML Platform is available today. Financial institutions, including banks, credit card issuers, auto financiers, and others, are under competitive pressure to increase revenues while managing risk and ensuring compliance. Expanding credit to new borrowers, particularly to millennials who are financing their first major purchases or looking for their first credit cards, is an effective way to build new markets. Traditional underwriting systems make credit decisions using a limited number of data points, primarily from credit bureaus. However, many millennials have no credit history or have missing and inaccurate data in their credit files. As a result, they are denied credit because they cannot be underwritten by traditional systems. However, many of those applicants would actually perform as well as prime borrowers. Unlike traditional underwriting methods, ZAML uses machine learning to analyze tens of thousands of nontraditional and traditional variables to more accurately score borrowers, including thin-file and no-file borrowers. ZAML can analyze vast amounts of data they already have in-house, such as customer support data, payment histories, and purchase transactions. The platform can also add traditional credit information and nontraditional credit variables, such as how a customer fills out a form, how they navigate a lender’s site, and more.

ZestFinance, Inc. Presents at Stifel 2016 Technology, Internet and Media Conference, Jun-07-2016 11:30 AM

ZestFinance, Inc. Presents at Stifel 2016 Technology, Internet and Media Conference, Jun-07-2016 11:30 AM. Venue: The Fairmont Hotel, 950 Mason Street, San Francisco, California, United States.

ZestFinance, Inc. Presents at Needham Emerging Technology Conference 2016, May-19-2016 02:00 PM

ZestFinance, Inc. Presents at Needham Emerging Technology Conference 2016, May-19-2016 02:00 PM. Venue: Westin Grand Central, 212 E 42nd St., New York, New York, United States.

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Recent Private Companies Transactions

Private Placement
July 18, 2016

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