The Earnix Blog: Resources for Insurance and Banking Leaders
May 6, 2025
By Andy How
Managing general agents (MGAs) are currently grappling with outdated pricing models that lead to inefficiencies, errors, and difficulties in adapting to market changes. By leveraging advanced technology platforms such as AI and real-time data analytics, MGAs can transform their pricing strategies, enhance accuracy, and strengthen their partnerships with insurers, ultimately securing a competitive advantage in an increasingly challenging marketplace.
April 22, 2025
By L’équipe d’Earnix
Artificial intelligence (AI) in insurance underwriting is revolutionizing carriers’ business models by enhancing their risk assessment capabilities, allowing them to deliver highly-personalized offers, and streamlining processes across functional boundaries.
April 16, 2025
By Jimmy White
The Earnix Filing Accelerator is our new codeless interface that simplifies the creation of filing documentation for proposed rate changes with departments of insurance in North America and can also be used to generate rate documentation needed by any insurer across the globe for internal audits, reviews, or approvals.
March 24, 2025
By Andy How
Managing General Agents (MGAs) are facing increasing capacity challenges due to rising inflation, geopolitical instability, and evolving risk landscapes in the insurance market. To thrive, MGAs must enhance profitability and operational efficiency while leveraging technology and data-driven insights to strengthen relationships with insurers.
March 20, 2025
By L’équipe d’Earnix
L’IA générative, soutenue par des outils tels que les copilotes et les grands modèles de langage, transforme les services financiers en améliorant l’efficacité et en personnalisant les interactions avec les clients. La maîtrise des prompts optimise les entrées des modèles d’IA, améliorant ainsi la prise de décision et rationalisant les processus dans des domaines tels que la tarification et la souscription.
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.