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.
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.
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.
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/