International Conference on Actuarial Sciences [ICActS] 2025

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Author: search.filters.author.Napagoda, N. A. D. N.

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    PublicationOpen Access
    A Machine Learning Approach to Actuarial Life Table Estimation in Lung Cancer Patients
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Tharushika, D. D. H.; Napagoda, N. A. D. N.
    Cancer-related mortalities worldwide are most caused by lung cancer, and one of the major causes of passing worldwide is still cancer. A dangerous disease is lung cancer, which requires accurate survival modelling to assist in actuarial evaluations, public health planning, and clinical decisions. Life expectancy and mortality risk across age groups are calculated using essential tools such as actuarial life tables, but complex real-world data is frequently struggled with by traditional methods. Actuarial life tables for patients with lung cancer are created using a data set of more than 500,000 patient records with 15 key variables from 2014 to 2024 across European countries, employing Extreme Gradient Boost Accelerated Failure Time (XGBoost AFT) based survival analysis. The main objective is to develop agespecific mortality rates and life expectancy for patients with lung cancer. In contrast to earlier research that was reliant on traditional models, the nonlinear learning capabilities of XGBoost AFT models areutilized in this study to allow for more accurate estimation of mortality trends. A data-driven, machine learning approach to actuarial life table development is contributed by this study, with information about lung cancer survival patterns being provided. The understanding of survival trends, treatment planning, efficient use of healthcare resources, and assessment of the results of initiatives is aided by physicians, researchers, and policymakers. Public health initiatives focused on early identification and prevention are also guided, as well as future healthcare requirements being forecast.
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    PublicationOpen Access
    Latent Structures in Zero-Inflated Risk Domains: An Elastic–Tweedie Synergy for Claim Forecasting
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Kumarasinghe, P. B. W. S. R.; Napagoda, N. A. D. N.
    The frequency of insurance claims presents a unique modeling challenge due to high-dimensional inputs, strong feature correlations, and the dominance of zero-inflated outcomes. Conventional statistical models often fall short under these conditions, failing to capture the underlying structure of complex data sets. This study proposes an advanced predictive framework integrating Elastic Net regularization and a Tweedie-distribution-based XGBoost algorithm to address these issues in the context of motor insurance. Those methodologies were applied to the French Motor Claims data set,which contains over 678,000 policies, to distill influential variables while suppressing redundancy and noise. Lasso Regression, Elastic Net and the Boruta algorithm were employed to select relevant features. Elastic Net, in particular proved effective in identifying critical predictors including Exposure, Vehicle Age, Driver Age, BonusMalus, Area, and Fuel Type by balancing sparsity and multicollinearity. Thesefeatures were used to train both standard and Tweedie-distribution-based XGBoost models. Performance was evaluated using RMSE, MAE, and R², where the Tweedie XGBoost model guided by Elastic Net-selected features achieved the highest accuracy and explanatory power. The proposed architecture not only offers superior generalization and interpretability but also exhibits robustness in modeling skewed, zero-dominated distributions inherent to claim data. Beyond predictive enhancement, this framework has practical implications for actuarial science, particularly in dynamicpricing strategies, refined segmentation, and adaptive underwriting. This approach marks a shift toward more nuanced and scalable machine learning paradigms in insurance analytics by integrating statistically grounded feature selection with distribution-aware boosting.