Abstract
The unprecedented growth of issues generated in banking sector is extremely huge. It is important to prevent customer churn by retaining existing customers and acquiring new customers so that is important for analyzing. Since data stored in the databases of banks are generally complex and are of varying dimensions such as consumer loan, debt collection, credit reporting and mortgage, the procedure for data analysis becomes very difficult. This paper presents a simplified framework for clustering the various issues by using a combination of data mining techniques. Hence in huge datasets issues from recorded by the customers are clustered using an efficient clustering algorithm. The parameters such as execution time and prediction accuracy are used to compare the results of the algorithms.
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Naveen Sundar, G., Narmadha, D., Jebapriya, S., Malathy, M. (2019). Optimized Methodology for Hassle-Free Clustering of Customer Issues in Banking. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_42
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DOI: https://doi.org/10.1007/978-981-13-0617-4_42
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