Luba Orlovsky

Earnix Automatic GLM preserves the transparency and regulatory trust of traditional GLMs while automating complex feature engineering to capture non-linear effects, enabling explainable, auditable models with stronger predictive performance and faster deployment in regulated financial environments.

Multi-Rule Execution, a recent enhancement to the Earnix solution set, is poised to allow carriers to dramatically “up their games.” Insurers can now give customers the power to change their policy provisions on their own, avoiding the headache of unexpected premium adjustments. Consumers can adjust coverages online, add new items to their policies, or alter the set of covered individuals, all in a fully-governed way, thanks to this new flexible rule mapping capability.

In the rapidly evolving world of ML and AI, the power of predictive analytics is undeniable. Yet as we harness this new power, we must also navigate the complex landscape of fairness and ethics. We must also do all we can to create equal opportunities and outcomes across different groups of individuals, regardless of their race, gender, age, or other sensitive attributes.

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.

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.