International Conference on Actuarial Sciences [ICActS] 2025

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

Browse

Filters

20252

Advanced Search

Filter by

Settings

Author: search.filters.author.Premarathna, L. P. N. DHas files: Yes

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Designing an Economic Scenario Generator for Financial Risk Management of Low-Income Households in Sri Lanka: A Review
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Peiris, K. G. H. S.; Premarathna, L. P. N. D
    Low-income households in Sri Lanka face increasing financial vulnerabilities driven by unstable income, high dependence on essential goods, and exposure to inflation and external shocks. Economic Scenario Generators (ESGs), widely used in institutional risk management, offer a structured way to model uncertainty but have rarely been adapted for household-level applications. This review synthesizes literature on ESG methodologies, household financial risk in developing economies, and Sri Lanka’s socio-economic realities. It highlights the need for a household-oriented ESG framework that integrates macroeconomic shocks with micro-level financial behavior to support budgeting, debt avoidance, and policy interventions.
  • Thumbnail Image
    PublicationOpen Access
    A Poisson Mixture Model of Claim Counts to Improve Insurance Claim Predictions Using Incomplete Data/ Asymmetric Data: A Case Study with Telematics Insurance
    (2025-10-10) Peiris, K. G. H. S.; Sampath, J. K. H.; Premarathna, L. P. N. D
    In the evolving landscape of insurance analytics, integrating traditional and telematics data is pivotal for enhancing the accuracy of claim predictions. This study introduces a two-fold approach utilizing a Poisson mixture model to merge these distinct data streams effectively. Initially, we apply the Poisson mixture model to traditional insurance features common to both datasets, employing Hamiltonian Monte Carlo (HMC) and Metropolis-Hastings algorithms separately for model fitting. Subsequently,the predicted claim counts derived from the Poisson mixture model are used as an offset to fit a Poisson generalized linear model (GLM) exclusively with telematics-based features. Our focus is on assessing the suitability of HMC and Metropolis-Hastings for addressing data integration challenges within Poisson mixture frameworks. Comparative analysis reveals that while HMC demands more computational time to achieve convergence, it exhibits superior performance in parameter estimation in scenarios with increased model complexity. This study underscores the potential of advanced Monte Carlo methods in refining predictive models by leveraging the synergy between traditional and telematics data sources.