The Earnix Blog: Resources for Insurance and Banking Leaders

What I See for 2024 and Beyond

February 1, 2024

By Earnix Team

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.

The Rise of Synthetic Data

November 13, 2023

By Earnix Team

Data is an invaluable asset in any business, but particularly in businesses such as insurance and banking, where the products are intangibles, instead of physical goods. Much of these businesses’ revenue, profitability and market share are driven by using data as a competitive weapon.

Person Holding A Green Plant

October 23, 2023

By Earnix Team

In our new industry research report, “Insurance Operations in a Changing Industry,” respondents felt strongly that “changes to the macroeconomic environment” would have the most impact on their company.
Automatic Generalized Linear Models (GLM) in Insurance & Banking

March 28, 2023

By Luba Orlovsky

To make a cake, you need to have specific ingredients: eggs, milk, flour, etc. The same applies to a model, you need to have variables such as age, tenure, and price.