Mapping the InsurTech landscape: a taxonomy of successful business models

The rapid evolution of digital technologies has given rise to new business models and value creation pathways in the insurance sector, commonly grouped under the umbrella of InsurTech. While existing literature has acknowledged this trend, a comprehensive, empirically-grounded classification of these models remains underdeveloped. This study addresses this gap by developing a robust taxonomy of InsurTech business models and empirically deriving a set of business model archetypes. By synthesizing insights from academic literature, industry reports, and a hand-collected dataset of 100 leading firms from the 2023 InsurTech100 list, we identify five key dimensions of InsurTech innovation: target market, revenue model, value proposition, technology, and emerging business model innovations. Critically, this research moves beyond descriptive classification by applying a multi-method clustering framework suited for categorical data, employing K-means, K-modes, and Gower’s distance with hierarchical clustering and K-medoids, to uncover four distinct and stable business model archetypes: Technology-Focused Enablers, Customer-Centric Disruptors, Embedded Ecosystem Integrators, and Specialized Niche Providers. Cross-method validation using the Adjusted Rand Index demonstrates strong convergence across methods (ARI = 0.86–1.00), with silhouette scores ranging from 0.51 (K-means, Euclidean) to 0.68 (Gower’s distance), confirming moderately strong cluster separation. These archetypes reveal the dominant strategic configurations in the InsurTech landscape, highlighting how firms either enable incumbents with advanced data analytics, disrupt the sector with user-friendly platforms, integrate into broader digital ecosystems, or specialize in underserved risk segments. The findings provide a clear and structured understanding of the competitive landscape, offering insights for insurers, technology vendors, and regulators. The paper concludes by discussing the theoretical and practical implications of these archetypes, acknowledging the study’s limitations, and outlining specific avenues for future research.