ZestFinance’s ZAML software uses a proprietary explanation method derived from game theory and multivariate calculus that works on the actual underlying model. ZAML explainability determines the relative importance of each variable to the final score by looking at how it interacts with other variables. ZestFinance was one of the first companies to deploy machine learning models for lending. ZestFinance's AI-based tools help lenders to increase revenue, reduce risk, and ensure compliance with complex federal regulations.
USP : ZAML is a machine learning credit and risk modeling solution with end-to-end explainability. It is fast and powerful and offers unique transparency with regards to fees and processes. Zest’s Automated Machine Learning (ZAML) enables lenders to analyze non-traditional data, including data they already have in-house, such as customer support data, payment histories, and purchase transactions.
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ZESTFINANCE ZAML suite Presence in AI in Fintech Solutions
ZestFinance made a strategic collaboration with Microsoft to deploy Zest’s machine learning (ML) software tools directly on Microsoft Azure and Machine Learning Server platforms to deliver the first fully explainable AI for highly regulated industries, starting with the financial sector. Financial institutions will now be able to use Zest’s ZAML suite of tools to build, deploy, and monitor transparent ML credit models on Azure and Machine Learning Server. The collaboration combines the intelligent capabilities of Microsoft’s technology with Zest’s deep focus on explainable ML. Zest has spent the last 10 years building comprehensive and thoughtful software tools that help lenders run powerful ML credit models with full transparency and compliance
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