AI capabilities are rapidly maturing, and they represent a competitive advantage for insurers. Data science departments are tasked with developing AI-driven improvements for current solutions and processes.
But, inaccessible, sensitive, or insufficient training data hinder the innovation efforts of data science teams. Today, insurers should focus on speeding up and improving data access if they want to boost AI capabilities and projects.
Our experts present how adding synthetic data to your data science team’s toolbox could help solve data agility and access challenges. They explore questions such as:
Emna is Statice's go-to expert on deep learning matters. She is a Machine Learning Engineer with a computer science engineering degree. During her career, she worked on sequential data, like videos, unstructured and structured datasets. Emna enjoys being part of the entire solution process, supporting data architecture, and building ready-to-production pipelines using the newest technologies.
Matteo is a data privacy researcher and Statice's in-house expert in the world of privacy evaluations. He leads the research on the privacy side and ensures our synthesization technology remains ahead of the privacy research. After a Ph.D. in Astroparticle Physics, Matteo was a postdoctoral researcher at the Humboldt University in Berlin, working on cosmology.
Benjamin heads up the commercial efforts at Statice. He works with Statice's partners to help them overcome their data access and privacy challenges with privacy-preserving synthetic data. He gets excited about data-driven innovation and has spent the last eight years working in data-heavy B2B startups and scaleups.