The Earnix Blog: Resources for Insurance and Banking Leaders
April 28, 2026
By Yuval Ben Dror
Découvrez comment la surveillance des données axée sur les KPI permet aux assureurs et aux banques à distinguer le bruit statistique des risques métier réels, en reliant directement la dérive des données à des indicateurs tels que le ratio de sinistralité, le taux de conversion et la rentabilité.
October 27, 2025
By L’équipe d’Earnix
Face à la hausse des coûts et à une concurrence féroce, les assureurs doivent exploiter l’analytique avancée et la tarification dynamique afin d’atteindre une rentabilité durable en 2025 et dans les années à venir.
October 19, 2025
By Yuval Ben Dror & Eyal Bar Natan
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.
September 18, 2025
By Noga Aloni
Earnix is introducing Elevate Data, a governed and scalable foundation built to handle the rising volume, variety, and velocity of insurance data. It streamlines access and governance so insurers can reduce risk, keep models accurate, and align business with analytics at speed.
July 24, 2025
By Yuval Ben Dror
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.
March 3, 2025
By Yuval Ben Dror & Yitzhak Yahalom
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.