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Flood impacts are increasing globally due to growing exposure and climate variability, placing pressure on traditional disaster risk reduction (DRR) approaches such as structural flood protection and post-event humanitarian response. In this context, disaster risk finance (DRF) instruments, including parametric insurance and catastrophe bonds, are increasingly explored as complementary tools to support rapid response and recovery.While parametric approaches have gained traction for hazards such as earthquakes and tropical cyclones, flooding poses particular challenges for DRR applications due to its spatial heterogeneity and the complex relationship between rainfall, inundation, and impacts. These challenges are often expressed through basis risk, where modelled triggers do not align with experienced losses, undermining trust and effectiveness.Drawing on more than five years of applied work supporting DRF initiatives, we will reflect on practical lessons from developing flood parametric insurance solutions under data-sparse conditions. Using a rainfall-based parametric insurance scheme for Pacific Island nations as a case study, we will examine the end-to-end workflow linking hazard data, event set generation, trigger definition, and payment certification, with particular attention paid to how uncertainty is managed and communicated.The case study illustrates how choices around input datasets, spatial scale, exposure representation, and local climate characteristics shape basis risk, and how these trade-offs can be made explicit to stakeholders. We will show that, while flood parametric insurance remains challenging, advances in hazard modelling and analytical workflows are improving its viability as a DRR instrument when designed with an explicit focus on uncertainty and user needs.We will conclude by discussing how insights from frontline implementation can inform the design of parametric instruments that support disaster preparedness, response, and climate adaptation