Wooden Blocks Men Stack on Top of Each Other Making a Pyramid

October 19, 2025

By Yuval Ben Dror & Eyal Bar Natan

Discover how Earnix’s Hierarchical Level Selector intelligently merges categorical hierarchies to create cleaner, faster, and more accurate predictive models. This innovative feature helps insurers and banks simplify complex data structures, improving both model performance and explainability.

Elevate Data: Turning Today’s Data Complexity into Tomorrow’s Competitive Edge

September 18, 2025

By Noga Aloni

Earnix is introducing Elevate Data, a governed and scalable foundation built to handle the rising volume, variety, and velocity of insurance data. It streamlines access and governance so insurers can reduce risk, keep models accurate, and align business with analytics at speed.

Colorful Cubes And Puzzle Piece

September 17, 2025

By Barak Rozenwax

Earnix AI Studio brings insurance-grade AI into production - fast, accountable, and built for pricing, underwriting, and distribution. With governance and testing built in, it turns scattered experiments into repeatable business outcomes.

colleagues joining hands

September 15, 2025

By Earnix Team

With SOC 2 Type II compliance, Earnix gives customers greater confidence in data security and operational reliability, streamlining vendor reviews and supporting regulatory requirements.

Colorful Cubes And Puzzle Piece

August 24, 2025

By Earnix Team

Adrian Coupland, Head of EMEA Insurance at Earnix, emphasizes that AI is transforming the insurance industry by enhancing product design, pricing, and customer engagement through the integration of predictive and generative technologies, enabling better decision-making, operational efficiency, compliance, and tailored coverage.

One Black Chess Piece Separated From Red Pawn Chess Pieces

July 24, 2025

By Yuval Ben Dror

Discover how Smart Grouping revolutionizes the way insurers and banks tackle categorical features, improving model clarity and efficiency through the innovative Smart Grouping technique in Auto-GLM, leading to more interpretable and credible analytical outcomes.