Use Machine Learning Explainability to Your Advantage

Banks and lenders could make a lot more money using machine learning to find better borrowers and reject more bad ones.

Advanced machine learning (ML) is a subset of AI that uses more data and sophisticated math to make better predictions and decisions. But adoption of ML has been held back by the technology’s “black-box” nature—you can’t run a credit model safely or accurately if you can’t explain its decisions.

The existing explainability techniques have serious flaws: they're inaccurate, inconsistent, and slow. We are working to change that.

In this white paper, we cover:
  • Four kinds of explainability techniques
  • Putting explainability techniques to the test
  • Gradient boosted trees
  • Neural networks

Get your copy of the white paper to see how we are changing the status quo with Zest Automated Machine Learning (ZAML™).

Download your copy of "Most AI Explainability is Snake Oil. Ours Isn't."