Following the launch of investigations last year, the U.S. Department of Justice (DOJ) together with attorney generals from 11 U.S. states filed a lawsuit against Google on Tuesday alleging that the company maintains monopolies in online search and advertising, and violates laws prohibiting anticompetitive business practices.Read More
Landing AI is today launching LandingLens, a computer vision platform that enables manufacturers to train AI models.Read More
Anyscale, the startup behind the open source project Ray, today closed a $40 million funding round. A company spokesperson says the capital will be put toward growing the ecosystem around Ray and promoting Anyscale’s first commercial offering, a managed Ray platform.Read More
Zest AI, a company developing AI-powered loan decisioning products, today closed a $15 million funding round led by Insight Partners. A spokesperson says the capital will be used to accelerate Zest’s go-to-market efforts and product R&D.
About 1 out of every 9 loan applications (10.8%) for home buying — and more than 1 in 4 applications (26.4%) for refinancing — were denied in 2017, according to a nationwide analysis of lender data conducted by the U.S. Bureau of Consumer Financial Protection. Minorities were disproportionately rejected, with the overall denial rate for mortgage applications from Black Americans reaching 18.4% in 2018. (Hispanic and Asian applicants were rejected 13.5% and 10.6% of the time, respectively, compared with 8.8% for non-Hispanic white applicants.)
Zest, which was cofounded in 2009 by former Google CIO Douglas Merrill and ex-Sears VP Shawn Budde, claims its mission is to create “more rigorous” standards around debiasing algorithmic lending. To this end, the company helps banks, credit unions, and specialty lenders identify borrowers by taking into account more than credit scores. Zest claims institutions that lend using its models — including Discover, Akbank, and VyStar — have seen a 20% increase in approval rates on average and an up to 50% reduction in chargeoffs, or declarations that an amount of debt is unlikely to be collected.
Zest provides over 30 customers with resources to prep, build, iterate, and document machine-learning decisioning models for cards, auto loans, personal loans, mortgages, and student loans. Complementary tools help teams evaluate and validate the models for safety, stability, business impact, and compliance. Customers can deploy and monitor algorithms in production, or they can engage Zest’s team of service and machine learning experts for assistance with development and validation steps.
Zest claims to use a technique called adversarial debiasing to minimize potential model prejudice. The technique pits two machine learning models against each other, with one attempting to predict creditworthiness while the other second-guesses the race, gender, and other attributes of the applicant scored by the first model. Competition drives both to improve their methods until the predictor can no longer distinguish the race or gender outputs of the first model, resulting in a model that is ostensibly more accurate and fair.
Zest recently introduced ZAML Fair, which the company claims can reduce bias in loan portfolios with “little or no” impact on profitability. ZAML Fair leverages the transparency tools built into Zest’s solutions suite to rank a system’s variables by how much they lead to biased outcomes. It then attempts to mitigate the influence of those signals to produce a superior model.
Based on the mortgage lenders who tested ZAML Fair, Zest says the tool would eliminate 70% of the nation’s gap in approval rates between Hispanic and white mortgage applicants and cut the even larger gap between Black and white borrowers by more than 40%. In a blog post, Zest cited a survey conducted by the Harris Poll that found a majority of Americans would give up more personal data if it resulted in a fairer credit decision. With that in mind, Zest believes it can reduce bias by using “better math and more data to assess borrowers.”
Of course, it’s difficult — if not impossible — to completely rid algorithms of bias. Facial recognition models fail to recognize Black, Middle Eastern, and Latinx people more often than those with lighter skin. AI researchers from MIT, Intel, and Canadian AI initiative CIFAR have found high levels of bias from some of the most popular pretrained models. And algorithms developed by Facebook have proven to be 50% more likely to disable the accounts of Black users compared with white users.
But Zest claims the data proves its efforts are making a difference. Using Zest’s underwriting software platform, one lender says it was able to shrink the disparity in approval rates between white applicants and applicants of color by 30% on average, with no increase in portfolio risk. Separately, an auto lender was able to approve “thousands” more borrowers.
“The COVID-19 shock led many financial institutions to update and improve their systems for resilience and durability, which caused a significant increase in demand for our business,” CEO Mike de Vere told VentureBeat via email. “A big part of that included building new and improved underwriting systems with the latest math and software technology. This resulted in Zest’s best Q2 on record, with an eye on finishing the year with triple-digit growth.”
Los Angeles-based Zest has raised over $87 million in venture capital to date.
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