International Conference on Actuarial Sciences [ICActS] 2025

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

Browse

Filters

202521

Advanced Search

Filter by

Settings

Type: search.filters.itemtype.Article

Search Results

Now showing 1 - 10 of 21
  • Thumbnail Image
    PublicationOpen Access
    Modelling the Indicative Rate of the USD/LKR SPOT Exchange Rate in Sri Lanka
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Rajapaksha, R. G. S. N.; Kumarasiri, P. V. A. L.; Sathsarani, T. V. I. A.; Rambukkana, P. P.; Botheju, W. S. R.; Guruge, M. L.; Peiris, T. S. G.
    This study develops and validates a time series model to forecast Sri Lanka’s daily indicative USD/LKR spot exchange rate using ARIMA and ARCH methods using data from 1st of January 2021 to 4th of June 2025, sourced from Central Bank of Sri Lanka. The original series was first differenced to achieve stationarity since it is not stationary. According to the sample ACF and PACF of stationary series, three candidate models were augmented with an ARCH(2) variance specification based on residual diagnostics. After comparing AIC, SIC, Hannan Quinn metrics and log likelihood, the ARIMA(1,1,1)+ARCH(2) was identified as the best possible model. The diagnostic tests confirmed that residuals are identically and independently distributed without remaining heteroskedasticity. Insample forecasting yielded a MAPE of 0.32% and a Theil U statistic of 0.0036, while out-of-sample validation (June 5 to July 4, 2025) produced a MAPE of 0.087% and a bias proportion near zero, highlighting the model the model’s predictive accuracy. By focusing only on the internal pattern of the exchange rate, this research creates a strong short term forecasting tool for Sri Lanka's volatile currencyenvironment laying ground work for adding outside factors in future improvements.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    PublicationOpen Access
    Event Detection and Latency Analysis in High Frequency Trading Dashboards
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) de Silva, U; Perera, S; Liyanage, U.P; Erandi, H
    High frequency trading relies on millisecond-level decisions, where profitability is strongly influenced by both market responsiveness and system latency. Traditional dashboards offer real-time visualizations but fall short in detecting abrupt regime shifts or quantifying latency. This study presents an AI-aided Market Pulse and Latency Panel that integrates candlestick pattern recognition, change point detection and latency measurement into a unified dashboard. The system detects technical patterns, identifies structural market shifts, and quantifies infrastructural bottlenecks. Experimental results demonstrate that the panel enhances situational awareness by combining event detection with latency analytics, providing traders with actionable insights for strategy adjustment and infrastructural optimization.
  • 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
    The Impact of Digital Learning Readiness on Academic Performance and Student Engagement in Sri Lanka
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Nuwanthika, W. A. N.; Thathsarani, U.S.
    The rapid shift to virtual learning settings has unveiled disparities in preparedness, involvement, and scholarly performance among Sri Lankan government university undergraduate students. This study investigates the impact of Digital Learning Readiness (DLR), Teacher Support (TS), Perceived Usefulness (PU), and Motivation (MDL) on Student Engagement (ENG) and Academic Performance (AP). The general aim is to develop and validate a structural model that explains the mechanisms by which psychological and environmental factors lead to academic performance in online learning contexts. Quantitative research design was employed. A standardized questionnaire was completed by 301 undergraduate students sampled through simple random sampling across ten government universities. Data were analyzed using Structural Equation Modeling (SEM) supplemented by Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Reliability and validity were tested using Cronbach's alpha, Average Variance Extracted (AVE), Composite Reliability (CR),and fit indices for the model through SPSS and SmartPLS. Greater digital learning readiness strongly facilitates student motivation, engagement, and academic achievement. Perceived digital tool usefulness mediates the influence of readiness on academic performance to some extent. Motivation and engagement also have central mediating roles. Support from teachers has a positive impact on motivation, which reinforces student engagement. The study confirms that digital readiness, motivational factors, perceived technology usefulness, and supportive pedagogy are integrated to influence academic performance in digital learning settings. The results have theoretical and practical suggestions for increasing the efficiency of digital learning in the higher education system of Sri Lanka.
  • Thumbnail Image
    PublicationOpen Access
    On the Fundamentals of Angle Trisectors of a Triangle
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Amarasinghe, I
    Over the years of the history of elementary and advanced geometry, trisecting a given angle into three equal parts, was prominent and given more attention. Nevertheless, it is evident that there is a significant research gap of the standard angle trisectors, the lengths of the angle trisectors and the relationships amongst other standard line segments in a triangle. In this paper, we address this gap by developing a purely geometric framework, supplemented with advanced algebraic methods, to obtainclosed-form expressions for internal angle trisectors in a Euclidean triangle. Using the circumcircle, similarity arguments, and Ptolemy’s Theorem, we derive polynomial relations and solve the associated cubic equations explicitly through Cardano’s method. The explicit determination of angle trisector lengths has not been previously available in closed form. Most approaches are trigonometric, but the trisector and related lengths were implicit or incomplete. Moreover, we present few very useful, novel, interesting lemmas, fundamental theorems, and corollaries related to two-dimensional angle trisectors in Euclidean triangles without using any trigonometric, vector algebra or complex number methods.
  • 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.
  • Thumbnail Image
    PublicationOpen Access
    Exploring the Determinants of Medical Insurance Expenses: A Quantile Regression Approach
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Rathnayake, K; Somasiri, D; Abeygunawardana, T; Nugegoda, K; Fernando, N; Guruge, M. L.; Peiris, T. S. G.
    Healthcare insurance costs are influenced by a combination of biological and socioeconomic factors. This study investigates how age, body mass index (BMI), gender, and discount eligibility affect medical insurance expenses in the United States, using data from 1,338 individuals. Due to the right-skewed distribution of expenses, quantile regression was applied at the 25th, 50th, and 75th percentiles, providing insights across low-, medium-, and high-cost groups. Results show that age and BMI consistently increase insurance expenses, with stronger effects among high-cost patients. Genderdifferences also emerged, with females incurring higher costs than males at certain expenditure levels. Discount eligibility significantly reduced expenses across all quantiles. In contrast, the number of children was not a significant predictor and was excluded from the final model. Compared to ordinary least squares regression, quantile regression provided a more accurate assessment of cost determinants in skewed data. These findings highlight the importance of adopting advanced modeling approachesin insurance pricing and suggest that targeted policies addressing individuals having high BMI and equitable discount programs could improve healthcare affordability and risk management.
  • Thumbnail Image
    PublicationOpen Access
    Development of Time Series Model to Predict the Weekly Percentage of Python Programming Language usage
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Gunawardane, D. M. N. M.; Herath, H. M. P. T.; Pitiyekumbura, W. S.; Samodhika, P. L. D.; Athauda, A. M. B. T.; Amarasinghe,E. J. C. U.; Peiris, T. S. G.
    Python's super popular and getting bigger fast. Figuring out how it will be used is super important for planning what to teach, training tech workers, and making good rules, especially in places like Sri Lanka that are just now getting into digital stuff. Therefore, this study aims to predict the weekly global usage of Python. We looked at data from April 21, 2019, to April 21, 2024. We got 262 weeks. This data is entered into Kaggle from Google search interest scores (Nextmillionaire, 2023). This dataset shows the highest interest score for Python in the general world. After trying out a bunch of models, theARIMA (1,1,1) model with seasonal stuff seemed like the best fit. We taught the model with data from April 21, 2019, to January 28, 2024 (250 weeks) and checked it with data from February 4, 2024, to April 21, 2024 (12 weeks). We tested the model to make sure it was doing things right, and the leftovers looked random, which is a good thing. The MAPE (Mean Absolute Percentage Error) for the validation data is 6.04%. This shows the ARIMA model is pretty good at guessing Python usage over time. Because theguesses are pretty accurate and consistent, it looks like Python usage of global is going up steadily. This means Python is a big deal with both Data Science & Analytics, Machine Learning & AI, Cloud Computing & DevOps, Automation & Scripting. This info should help schools, training places, and the government make smart choices about teaching digital skills.