The Seenity Blog

Updating a risk-assessment model to match shifts in reality is fundamentally essential. If the model fails to adapt, or in other words, doesn’t ‘breathe’ with current circumstances, the outcomes can be detrimental.

What do we do with the Prediction outcome? Many ponder the relationship between legal engines and AI.

Building a model is a creative process, primarily led by professionals who define the task for the model.

The AI revolution is here. And as a matter of fact, this transformative era is defined by two monumental milestones.

The most important thing about risk assessment models is what feeds them: the information.

DevOps strives to streamline software development by bridging the gap between development and operations teams, delivering high-quality, reliable software faster and with less risk. Similarly, MLOps aims to manage the entire life cycle of machine learning projects, including data management, model creation, deployment and monitoring. By automating these processes, MLOps can help ensure that models are accurate, reliable and scalable.

Insurance companies have relied heavily upon the Generalized Linear Model (GLM) for many years, and some still utilize it today. Before AI revolutionized our lives, implementing this model was achieved by categorizing users into groups based on factors such as age and cost of asset; these parameters were then applied to a formula that used limited computing power in order to draw conclusions.

Insurance companies face numerous challenges that can be addressed with the help of advanced AI. However, in the process of generating AI-powered risk models, it’s often hard to create the accurate correlation between the task on-hand and the AI environment.