Premaratne, C2026-01-112025-10-10978-624-6010-14-02783 – 8862https://rda.sliit.lk/handle/123456789/4516Sri Lanka is increasingly vulnerable to flooding due to climate change, unplanned urban expansion, and insufficient infrastructure resilience. Despite this, the current insurance regulatory framework under the Risk-Based Capital (RBC) regime does not explicitly incorporate a catastrophic (CAT) risk charge for natural disasters such as floods. This paper proposes a novel framework for quantifying future flood risk in Sri Lanka using a smart data approach that integrates hydraulic modeling (HEC-RAS), geographic information systems (GIS), and machine learning (ML). The framework enables the generation of flood hazard maps, estimation of event probabilities, and calculation of expected losses at property level. A simulation-based approach is then used to determine the capital required to cover extreme loss events, which can serve as the basis for a CAT risk charge. Although full implementation is pending, this paperpresents an illustrative model using synthetic data to demonstrate the methodology and its potential implications. By embedding flood risk into pricing and strategic decisions, this approach aims to improve insurance sector resilience and inform regulatory advancement. The results highlight the feasibility and urgency of adopting data-driven tools to better manage climate-induced risks in Sri Lanka.enClimateFlood riskHazard modelingSmart dataArticlehttps://doi.org/10.54389/CLYO8878