Yuval Ben Dror

Discover how Model to Rating Structure Distillation helps insurers transform complex machine learning models into transparent, production-ready rating structures—preserving predictive performance while meeting governance, operational, and regulatory requirements.

Discover how KPI-focused data monitoring helps insurers and banks distinguish statistical noise from real business risk by connecting data drift directly to outcomes like loss ratio, conversion, and profitability.

Discover how Earnix’s Hierarchical Level Selector intelligently merges categorical hierarchies to create cleaner, faster, and more accurate predictive models. This innovative feature helps insurers and banks simplify complex data structures, improving both model performance and explainability.

Discover how Smart Grouping revolutionizes the way insurers and banks tackle categorical features, improving model clarity and efficiency through the innovative Smart Grouping technique in Auto-GLM, leading to more interpretable and credible analytical outcomes.

XGBoost (Extreme Gradient Boosting) builds on the idea of boosting with several optimizations, such as regularization techniques to control overfitting, parallelization to improve computation speed, and handling sparse data more efficiently. These improvements allow XGBoost to achieve high performance, making it one of the most popular machine learning algorithms in practice today. 

What’s the best way to handle the analytical challenges facing insurers and banks today? We at Earnix believe it starts with asking the right questions, challenging assumptions, and finding innovative ways to move forward.