Mapfre to Showcase AI and Embedded Insurance Innovation at ITC Vegas 2026

Mapfre will participate for the third consecutive year in ITC Vegas, the largest global innovation event in the insurance sector, which will be held from 29 September to 1 October in Las Vegas (United States).

The insurer thus reaffirms its commitment to innovation and the development of new formulas, products, and services that will allow it to respond to the challenges of transformation in the industry and within its own operations.

Mapfre will continue to be the only Spanish insurer with a presence and will participate in some of the most relevant conversations of the programme with Leire Jiménez, Chief Innovation Officer of Mapfre and CEO of MAWDY, and Carlos Cendra, Scouting & Investment Lead of corporate innovation at Mapfre.

The multinational's presence in ITC Vegas is part of a strategy that prioritises innovation as a core and cross-cutting driver of the company's growth. This strategy, embodied in the innovation model launched in 2025, also focuses on efficiency and the development of new capabilities for the Group.

The first presentation will take place on 29 September, when Carlos Cendra will present the talk “The Next Insurance Channel Isn't a Channel”, where he will analyse how the points of contact between customers and insurers are evolving in a context marked by the rise of embedded insurance, AI-based assistants, and new digital experiences.

On September 30, Leire Jiménez will lead the session “Embedded Insurance, Reimagined: From Distribution to Orchestration to Agentic Growth.” The talk will explore how the B2B2C model is evolving from traditional distribution schemes to orchestrated ecosystems and experiences powered by AI agents.

With thousands of attendees from around the world and a broad representation of insurers, insurtech startups, technology companies, and investors, ITC Vegas has established itself as one of the leading international forums for discussing the future of insurance, new technologies, and emerging distribution models.