###### Abstract  
Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers. outbound telemarketing; deep learning; machine learning; convolutional neural network; insurance policy loan; explainability +
Footnote †: journal: Applied  
As shown in Table 1, several studies have proposed various machine learning and deep learning (DL) prediction models to predict telemarketing success. However, because most of these studies analyzed the success of telemarketing methods carried out by banks, the extension of their results to various financial companies such as insurance and security companies involves significant limitations. In this study, we aim to predict the success of outbound telemarketing methods in the relatively sparsely researched field of insurance, with particular emphasis on insurance policy loan prediction. An insurance policy loan is a service allowing customers to withdraw and spend a portion of an insurance coverage amount in advance while maintaining coverage. Withdrawals and repayments are possible at any time, and loans can be made without any loan review procedures, such as credit evaluation and proof of income. Customers can apply through common channels such as personal computers (PCs), mobile phones, call centers, and automated teller machines (ATMs), without visiting a branch of the insurance company. These insurance policy loans are important for insurers because they have a positive effect of suppressing the increase in insurance debt as an advance payment. Therefore, insurance companies are working to increase the size of insurance policy loans, and for this purpose, increasing the efficiency of outbound telemarketing operations is crucial. However, insurance policy loan outbound telemarketing data are large-scale and high-dimensional. Predicting the success of telemarketing requires a variety of data related to customer attributes, insurance characteristics, insurance transactions, insurance policy loan transactions, and marketing campaigns. With such high-dimensional data, traditional