Data Mining Solutions towards Bank Telemarketing Predicting
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Lending in international markets has become more restricted, and the focus of funding has shifted to domestic customers and their deposits. This desire has created a demand for knowledge about customer deposit behavior, especially customer reactions to telemarketing campaigns. Therefore, promotional transaction banking strategies shifted from traditional methods to advanced data analytics and machine learning techniques to keep pace with the growth of the banking industry. In a telemarketing business, the ability to select potential customers for purchase is important, because it reduces processing time and operational costs. This paper presents a data mining solution using machine learning techniques to predict customer response to a bank's telemarketing campaign through clustering similar groups by the data analysis. Whereas, the dataset groups are pre-processed and using three types of algorithms: k-nearest neighbor (KNN), support vector machines (SVM), and decision trees (DTs), for modeling. The tests were conducted on a real dataset that resembles the actual information associated with the bank client, telemarketing calls, and the results of the customer's bank time deposits as a result of the call. The experimental results show that DTs has superior performance in some settings with a true positive rate of over 90%, outperforming their equivalent methods.
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