---
title: Improving GLM models in insurance and banking
description: "\"Discover how Earnix is revolutionizing GLMs in insurance and banking with advanced techniques for handling hierarchical categorical data, leading to more accurate and reliable models. Benefit from improved risk assessment, pricing strategies, and decision-making. Read more at the original source.\""
image: https://insnerds.com/hubfs/News%20Feed%20Featured%20Image.jpg
---

 1 min read

# Improving GLM models in insurance and banking

[![Picture of Insurance Nerds Editorial Team](https://insnerds.com/hubfs/insurance_nerds_logo.png) Insurance Nerds Editorial Team ](https://insnerds.com/news/author/insurance-nerds-editorial-team) :  Oct 20, 2025 2:56:31 PM

![Improving GLM models in insurance and banking](https://insnerds.com/hubfs/News%20Feed%20Featured%20Image.jpg)

### Improving Generalised Linear Models in Insurance and Banking

Generalised linear models (GLMs) are key players in the insurance and banking industries, widely used for analytics. However, creating accurate and interpretable models can be a challenge, particularly when it comes to dealing with hierarchical categorical data. This type of data is quite common in insurance datasets, and improper handling can lead to several issues.

### The Challenges of Modeling

One significant problem with GLMs is that using the incorrect level of detail can induce overfitting. Overfitting occurs when a model is too tailored to the specific dataset, leading to poor performance on new data. Other challenges include multicollinearity, where predictors are highly correlated, making it difficult to assess their individual effects. Additionally, slow training times and unstable coefficients can negatively impact model reliability, affecting decision-making processes.

### Earnix's Approach

Fortunately, Earnix is actively working to improve these modeling techniques to create more efficient and reliable GLMs. Their focus is on refining the handling of hierarchical categorical data, which aims to address the common pitfalls associated with GLMs.

### Who Benefits?

This advancement could significantly benefit actuaries, underwriters, and data analysts in insurance and banking sectors. By implementing more robust GLMs, these professionals can derive better insights, leading to improved risk assessment, pricing strategies, and overall performance in their respective fields.

In summary, addressing the complexities of GLMs in the insurance and banking industries is critical for enhancing analytical accuracy. With Earnix’s focus on improving these models, the potential for more reliable data-driven decision-making is promising.

Original Source: [https://fintech.global/2025/10/20/improving-glm-models-in-insurance-and-banking/](https://fintech.global/2025/10/20/improving-glm-models-in-insurance-and-banking/)

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