## 1 Introduction  
Data-driven decision-making process [1; 2; 3; 4; 5; 6; 7] has been playing an essential part of the critical responses to the stringent business environment [4; 8; 9; 10; 11; 12; 13]. Identifying profitable or costly customer is crucial for businesses to maximize the returns, preserve a long-term relationship with the customers, and sustain a competitive advantage [14]. In the banking industry, there are several opportunities yet to be considered in sustaining a competitive advantage. Improving database marketing efficiency is still one of the main issues that requires intensive investigation. The nature of bank marketing data presents a challenge that is facing the researchers in business analytics [3; 7; 10]. The low volume of the potential target/important customer data (i.e., imbalanced data distribution) is a major challenge in extracting the latent knowledge in banks marketing data [1; 3; 10]. There is still an insisting need for handling the imbalanced dataset distribution reliably [15; 16; 17]; commonly used approaches [1; 18; 19; 20; 21; 15] impose processing overhead or lead to loss of information. Artificial Neural Network (ANN) models have been used broadly in marketing to predict the behavior of customers [22; 23]. ANNs are supposed to discover non-linear relationships from complex domain data, e.g., in bank telemarketing data [14; 24]. Interestingly, ANNs are able to generalize the \"trained\" model to predict the relationships of unseen inputs. In some business cases ANNs are highly competitive as they can handle any type of input data distribution [25; 26; 27; 28]. Modifying an ANN to make it cost-sensitive is a promising approach to enhance its performance and mitigate the effects of imbalanced data distribution. Cost-sensitive methods preserve the quality of original datasets, in contrast to other methods (e.g., pre-processing the original dataset by re-sampling techniques to adjust the skewed distribution of classes) which may degrade the quality of the data [29]. In practice, bank telemarketing data are highly imbalanced (e.g., 11% of the contacted clients in a marketing campaign may be interested to accept an offer). Therefore, a cost-sensitive ANN model is a strong candidate [1; 30] to predict the willingness of bank clients to take a term deposit in a telemarketing database. Researchers and practitioners in the field of electronic commerce have been striving to streamline and promote various business processes. For a few decades, different interesting research efforts have attempted to improve understanding of the behavior of customers using ANNs [1; 14; 22; 23; 24; 26; 27; 31]. However, cost-sensitive algorithms have been with marginal interest to the researchers in bank marketing, while pre-processing the input dataset by re-sampling techniques to solve imbalanced class distribution have gained significant interest [1; 15]. This research seeks unleashing the potentials of cost-sensitive analysis in providing enhanced bank marketing models, and reliable handling of imbalanced data distributions. Predicting the potential bank clients who are willing to apply for a term deposit would reduce marketing costs by saving campaigns wasted efforts and resources. On the other hand, contacting uninterested clients is an incurred cost with marginal returns. It is a matter of fact that wrong prejudgments on client intentions (e.g., willing or not willing to accept an offer) have unequal consequent costs [29; 30]. A marketing manager would expect a relatively higher cost of not contacting a potential client who is willing to invest than contacting an uninterested client. Therefore, it is with high value to find a reliable prediction model that takes misclassification costs into account. Cost-sensitive approach has been proposed before to overcome the imbalanced nature of a bank telemarketing data. Cost-sensitive learning, by re-weighting instances [32], leveraging only nine features of the whole feature set was proposed in [30].