Ferris Wheel at Night

April 9, 2024

By Equipo de 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 Equipo de 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 Equipo de 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 Equipo de 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.

Synthetic Data Generation and Its Key Benefits in Insurance

January 10, 2024

By Chen Ben Gal

We will focus on some of the technical aspects behind the generation of Synthetic Data, present some detailed information as to its effectiveness in mimicking original data statistical properties, and present some interesting applications which go beyond replication of the original data.