The Earnix Blog: Resources for Insurance and Banking Leaders

Person Reaching Out to a Robot

January 5, 2025

By Yuval Ben Dror & Eyal Bar Natan

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. 

Ferris Wheel at Night

April 9, 2024

By L’équipe d’Earnix

Today there’s a new opportunity to take advantage of new innovations and capabilities in the insurance industry. AI now plays a crucial role in insurance analytics due to its ability to process vast amounts of data, identify patterns, make predictions, and automate tasks.

Time Lapse Photography of Cars on Road during Night Time

March 28, 2024

By L’équipe d’Earnix

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,.

Four Key Predictions for the Insurance Industry in 2024

February 7, 2024

By L’équipe d’Earnix

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.

What I See for 2024 and Beyond

February 1, 2024

By L’équipe d’Earnix

As 2023 fades from view and 2024 comes into focus, Reuven Shnaps, Earnix Chief Analytics Officer, takes a step back to assess where pricing, rating, and underwriting stand today, and where they may be headed.

AI & Machine Learning Explainability - Unlocking the Black Box

January 15, 2024

By Luba Orlovsky

In the bustling world of insurance, a quiet revolution is unfolding. It's not led by charismatic CEOs or flamboyant industry disruptors. Instead, it's powered by something far more enigmatic: machine learning (ML). But there's a catch – these oracles are often inscrutable, their predictions shrouded in mystery. This is where the concept of ML explainability enters the discussion, turning the spotlight on the inner workings of these digital soothsayers.