Virtual Summit 2020 - Model Explainability in Banking and Insurance
Machine learning models can significantly enhance prediction accuracy, but their complexity makes them hard to understand and monitor. In this session we will explore new tools (such as Ubi-insurance) that can break the trade-off between accuracy and explainability – a critical step for the future evolution of machine learning in the finance and insurance industries.
Ori Katz, Research Scientist at Earnix, brings you everything you need to know about Model Explanability.
In this recording, Ori discusses:
- Explainabiltiy vs accuracy
- Uses of exaplainability
- Explainability research
- And more