International Conference on Actuarial Sciences [ICActS] 2025

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4496

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    PublicationOpen Access
    Climate-Based Agri-Insurance Method for Paddy Production in Sri Lanka
    (Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Vijayakumar, J
    Climate change has emerged as a major threat to agriculture globally, and Sri Lanka is no exception. The districts of Ampara, Anuradhapura, and Polonnaruwa are the main producers of paddy. In recent decades, these regions have experienced greater climate variability, leading to unstable harvests and posing financial risks for paddy farmers. This study examines the potential of Weather Index Insurance (WII) as an effective tool to mitigate income losses caused by extreme weather events. Historical data on paddy yields were combined with daily weather records. The analysis focused on relationships between paddy yields and weather variables: total rainfall, average temperature, maximum and minimum temperatures, and extreme monthly temperatures in the regions for both the Maha and Yala cultivation seasons. Regression models identified significant correlations, and insurance indices were designed for each district and season, with pure premiums calculated based on these relationships. The results indicate that total rainfall is the most significant factor influencing yield variability across all three districts in different seasons. The proposed insurance models were able to reduce income variability by 15–19%. These findings indicate that rainfall is the most reliable basis for climate-resilient paddy insurance in these regions. This data-driven framework for index-based agricultural insurance provides insights to enhance farmer resilience, reduce economic vulnerability for farmers, and support the long-term sustainability of production in those regions.
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    PublicationOpen Access
    Quantifying Future Flood Risk in Sri Lanka: A Smart Data Approach for Insurance Pricing and Strategy
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Premaratne, C
    Sri 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.