<p>This study aimed to identify and characterize behavioral subtypes of potentially problematic gambling patterns using real-world data from a major Danish gambling provider. A two-stage clustering approach was applied to behavioral data from over 650,000 users. In the first stage, k-means clustering segmented the population based on standardized behavioral indicators. In the second stage, subtypes were identified within selected clusters using k-medoids clustering. The procedure was replicated in an independent dataset (<i>N</i>= 619,441) to assess structural stability. The analysis revealed clear behavioral heterogeneity across gambling profiles. Subtypes differed systematically in intensity, reloads, product focus, and risk-related patterns such as chasing and nighttime gambling. The overall cluster structure was consistent across datasets, supporting the robustness and generalizability of the identified subtypes. Gambling-related risk patterns does not follow a single behavioral pattern. The data-driven subtypes of potentially problematic gambling identified in this study highlight the heterogeneity of gambling-related risk and offer a foundation for more nuanced approaches to risk detection based on gambling patterns.</p>