Browsing by Author "Ekanayake, E. M. P."

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    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.