International Conference on Actuarial Sciences [ICActS] 2025

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    PublicationOpen Access
    Forecasting Monthly Electricity Consumption for Energy Planning and Policy Development in Sri Lanka
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Pathirana, D; Arachchige, C. N. P. G
    For effective energy planning, grid stability, and policy development especially in emerging nations like Sri Lanka accurate electricity consumption projections is essential. The goal of this project is to create a reliable model that can forecast the Ceylon Electricity Board's (CEB) monthly electricity consumption using a large dataset that includes macroeconomic variables, market indicators, peak demand, energy generation sources, and weather data. Autoregressive Distributed Lag (ARDL), Random Forest, and eXtreme Gradient Boosting (XGBoost) are the three models whose redicting performance is compared in this study. The most pertinent predictors were chosen using Recursive Feature Elimination with Cross-Validation (RFECV). Although XGBoost performed well throughout training, overfitting was a problem. ARDL was interpretable, however it was unable to detect longterm cointegration and could not represent non-linear connections. With the best accuracy and dependability on the test dataset without overfitting, Random Forest turned out to be the best model whereas Monthly Sales by Tariff in LKR, Fuel Cost by Power Stations in LKR, Electricity Generation from Thermal Coal in CEB (Gwh), Electricity Generation from Mannar Wind in CEB (Gwh), Day Peak Demand (MW), Night Peak Demand (MW), Average Monthly Rainfall (mm), and Gross Domestic Product (GDP) in LKR were the eight most important factors that were found to be involved in forecasting electricity consumption. On the test dataset, Random Forest, the best model chosen, had an accuracy of 77.34%, a Mean Absolute Percentage Error (MAPE) of 22.67%, a Root Mean Square Error (RMSE) of 12.62, and a Mean Absolute Error (MAE) of 11.11. However, the models might not be able to reflect long-term structural changes like the switch to electric vehicles or widespread adoption of renewable energy sources, and the study did not account for new elements like government policy reforms or energy efficiency initiatives. Nevertheless, the results show that machine learning, in particular Random Forest, can improve Sri Lankan electricity consumption predictions to aid in sustainable energy planning and policy choices.
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    PublicationOpen Access
    Classifying Credit Card Payment Risk among Senior Citizens using Machine Learning on Limited Data
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Jayathilaka, G. A. M. K.; Ekanayake, E. M. P.; Appuhami, P. A. D. A. N.
    Sri Lanka is experiencing a significant demographic transition in view of lower fertility, enhanced life expectancy, and international migration, all of which have accounted for a higher proportion of senior citizens. The credit risk associated with a rising percentage of elderly population demands an investigation into the payment habits of senior citizens using credit facilities, for their own benefits and the sustainability of financial institutions. Addressing this issue, machine learning techniques are employed in this study in order to develop a viable model for classifying the credit card payment riskposed by the senior citizens based on their demographic and financial metrics at a leading private bank in Sri Lanka. Predictive dual-category classifications comprising of on-time payers and risky payers that include both late payers and dormant users are achieved using the machine learning algorithms of Logistic Regression, Random Forest, Support Vector Machine, Naïve Bayes, Extreme Gradient Boosting, and Multi-Layer Perceptron Neural Networks. Hyperparameter tuning and model performance ptimization were accomplished using Grid Search Cross-Validation, with models beingassessed by Accuracy, Precision, Recall, F1 Score, and AUC-ROC. Of those modelling techniques, the Random Forest excelled with 85% Accuracy, 85% Precision, 85.45% Recall, 84.99% F1 Score, and a high AUC-ROC score of 88 in classifying the risk posed by senior citizens, identifying payment settlement, average monthly credit card spending, average monthly debit, and number of credit card transactions as the key contributing variables. This classification method could be recommended for financial institutions with limited databases in setting credit limits tailored to senior citizens’ payment behaviour, reducing risk and promoting sustainable financial management for seniors.