Work directly to model claims using generalized linear methods inside Dataiku’s no-code VisualML interface.Explore !
The goal of this adapt and apply solution is to show actuaries how Dataiku can be used to create an insurance pricing model based on historical claim data, conduct extensive Exploratory Data Analysis, and push their models to production. More details on the specifics of the solution can be found on the knowledge base. This solution makes use of the Generalized Linear Models plugin to enable the training of GLMs in Dataiku’s VisualML interface.
As the insurance competitive landscape intensifies with the entry of new digital native players, the emergence of new risks, and growing consumer volatility, reinforcing efficiency in pricing strategies becomes a must tackle for insurers. Leveraging Generalized Linear Models (GLMs) for consumer claims modeling is a common market practice approach with a deep, rich, and proven track record.
However, historical set-ups for building and approving GLMs are often outdated and lack modern data science and analytic capabilities, acting as a barrier to developing dynamic pricing strategies, embracing specific risks, and opening integration of differentiating data science-supported pricing approaches to attract new ranges of customers.
Retaining the ability to deliver effective and approved GLMs within a modern data science and analytics solution ensures that the value of these techniques is retained, while dramatically improving the efficiency and effectiveness of the teams developing them. This is achieved by enhancing upstream and downstream processes, including data wrangling and API connectivity, improving existing workflows or pricing modules incrementally with machine learning, as and when desired, and establishing effective governance, analytic best-practice, and centralization of workflows, without sacrificing agility. Embarking on this journey acts as the first step to building modern insurance pricing solutions.