The Earnix Blog: Resources for Insurance and Banking Leaders

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. 

In the quest to be agile, insurers need the ability to experiment and innovate with machine learning models in a mode that gives them maximum flexibility. Earnix Model Accelerator extends the capabilities of the Earnix platform, and is designed to streamline the process of building and incorporating advanced models,.

With seemingly optimistic conditions ahead, 2024 could be the year insurers get back on track. As they do, we predict they’ll take advantage of the following trends and technology developments.