Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1943
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dc.contributor.authorBandara, H. M. M. T-
dc.contributor.authorSamarasinghe, D. P-
dc.contributor.authorManchanayake, S. M. A. M-
dc.contributor.authorPerera, L. P. J-
dc.contributor.authorKumaradasa, K. C-
dc.contributor.authorPemadasa, N-
dc.contributor.authorSamarasinghe, P-
dc.date.accessioned2022-04-07T04:48:10Z-
dc.date.available2022-04-07T04:48:10Z-
dc.date.issued2019-12-05-
dc.identifier.citationH. M. M. T. Bandara et al., "Analyzing Payment Behaviors And Introducing An Optimal Credit Limit," 2019 International Conference on Advancements in Computing (ICAC), 2019, pp. 68-72, doi: 10.1109/ICAC49085.2019.9103404.en_US
dc.identifier.isbn978-1-7281-4170-1-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1943-
dc.description.abstractIdentifying an optimal credit limit plays a vital role in telecommunication industry as the credit limit given to customers is influence on the market, revenue stabilization and customer retention. Most of the time service providers offer a fixed credit limit for customers which may cause customer dissatisfaction and loss of potential revenue. Therefore, it is essential to determine an optimal credit limit that maintains customer satisfaction while stabilizing the company revenue. Clustering algorithms were used to group customers with similar payment and usage behaviors. Then the optimal credit limit derived for each cluster is applicable to all the customers within the cluster. In order to identify the most suitable clustering algorithm, cluster validation statistics namely, Silhouette and Dunn indexes were used in this research. Based on the scores generated from these statistics KMeans algorithm was chosen. Furthermore, the quality of the KMeans clustering was evaluated using Silhouette score and the Elbow method. The optimal number of clusters are identified by those validation statistics. The significance of this approach is that the optimal credit limits generated by these clustering models suit dynamic behaviors of the customer which in turn increases customer satisfaction while contributing to reducing customer churn and potential loss of revenue.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2019 International Conference on Advancements in Computing (ICAC);Pages 68-72-
dc.subjectAnalyzing Paymenten_US
dc.subjectIntroducingen_US
dc.subjectPayment Behaviorsen_US
dc.subjectOptimal Credit Limiten_US
dc.titleAnalyzing Payment Behaviors And Introducing An Optimal Credit Limiten_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC49085.2019.9103404en_US
Appears in Collections:Research Papers - IEEE
Research Publications -Dept of Information Technology

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