Ensuring secure, continuous and accessible information flow in a data-based world
The Seenity system handles the ability to transfer data to the model. Only for the model. After its construction in a methodological way, the flow of the data to perform predication is a critical step in the quality of the results. That’s why Seenity sets itself the challenge to transport the data safely and allow […]
Beyond the Cloud: Unlocking Profitability in Insurance Companies
n recent years, a significant trend has emerged in the insurance industry — a mass migration to the cloud. However, the question remains: Why are insurance companies making this strategic move, and is it always the right choice? In this article, we delve into the motivations behind the cloud shift and explore alternative strategies for […]
Seizing Control: Empowering Risk Assessment with a New Platform
In the dynamic landscape of startup endeavors, a fundamental question echoes through boardrooms and pitch sessions alike: What sets you apart? How do you distinguish yourself from the multitude of competitors vying for attention and success? This inquiry is particularly pivotal for startups seeking to introduce novel concepts and innovations to the world. The first […]
No more “Black Box”
Critics argue that artificial intelligence models are akin to black boxes, impeding the assessment of risk without insight into decision-making processes. In the realms of insurance and loans, this critique gains validity due to the multifaceted nature of risk, comprising interconnected parameters and diverse datasets. Addressing this concern, Seenity has pioneered a distinctive risk assessment […]
Who Approves Your Model – Why Simulations are Key
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.
Bridging Predictive Modeling with Digital Transformation
What do we do with the Prediction outcome? Many ponder the relationship between legal engines and AI.
Seenity Creates A New Position In Insurance And Financial Companies: The Decision Intelligence Position Holder
Building a model is a creative process, primarily led by professionals who define the task for the model.
What is Relevant Data Enrichment
The AI revolution is here. And as a matter of fact, this transformative era is defined by two monumental milestones.
Exploratory Data Analysis (EDA)
The most important thing about risk assessment models is what feeds them: the information.
Game Changing MLOps for the Insurance Industry
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.
