As artificial intelligence becomes increasingly integrated into insurance underwriting, credit-based and behavior-driven scoring models are gaining global traction. While these AI systems are often developed in data-rich environments such as the United States or Europe, they are being deployed across a wide range of markets with vastly different financial systems, data quality, and socio-economic conditions. This raises important concerns about fairness, bias, and the suitability of these models in diverse contexts. This research paper will explore how AI-driven insurance scoring models perform across developed and emerging markets, with a focus on fairness-related outcomes. It will examine how differences in credit infrastructure, data availability, and consumer behavior influence model performance and risk classification. Using a combination of synthetic and publicly available datasets, the study will compare key fairness metrics such as demographic parity and equal opportunity across countries. The aim of this research is to identify where and how algorithmic bias emerges in cross-border model applications, and to propose early-stage strategies for building more equitable and context-aware insurance scoring systems.