## 2 Background
### Convolutional Neural Networks  
CNN models are among the most popular deep learning methods. DNN learns global patterns in the input feature space using fully connected layers, whereas CNN learns the local patterns within a relatively small window. Fully connected networks need to learn new patterns appearing in new locations, but convolutional networks can be generalized by learning a small number of training samples. The convolutional operation is applied to a 3D tensor called a feature map, which consists of two spatial axes (height and width) and a depth axis.  
The convolution operation extracts small patches from the input feature map and applies the same transformation to all these patches to create an output feature map. In the convolution layer, a filter determining the output depth of the feature map is used, and the number of filters is a model hyperparameter. The output height and width can differ from the input's height and width, and padding can be used to obtain an output feature map with the same height and width as the input. Padding adds an appropriate number of rows and columns to the edges of the input feature map. Downsampling using pooling can be performed to prevent overfitting in convolutional networks. Pooling reduces the number of weights of feature maps. The maximum pooling method takes the maximum value for each channel of the input patch, whereas the average pooling method calculates and transforms the average value; both methods are commonly used. Figure 1 illustrates a general CNN architecture.  
Many studies related to CNN have shown excellent performance on unstructured data such as images, video, voice, and audio [16]. Moreover, in many studies using text data, models combining CNN have been developed and have demonstrated suitable performance [17; 18; 19; 20; 21]. The proposed model incorporates a CNN model with high-dimensional business tabular data; the related works are described briefly as follows. [PERSON] et al. [22] studied a DCNN model versus an MLP model for financial predictions. Their experimental results confirmed the effectiveness of the DCNN model for credit scoring using bank transaction data. The performance of the DCNN model was significantly better than that of an MLP model. [PERSON] et al. [23] proposed the application of a CNN model to traditional data. Twelve types of traditional tabular data were used, and ML models such as eXtreme gradient boosting (XGBoost) [24], SVM, RF, MLP, and \(k\)-nearest neighbor clustering were  
Figure 1: Illustration of convolutional neural network.  
compared with the CNN model. The performance of CNN was demonstrated to be equivalent to that of state-of-the-art XGBoost techniques. The experimental results demonstrated the importance of considering CNN models for classification tasks using traditional data. [PERSON] et al. (2018) proposed an application of a CNN model to consumer transaction data to predict mortgage defaults. They used time-series data of bank accounts and compared them with LR, MLP, RF, and ensemble models. An ensemble model composed of CNN and RF presented the best results, and the performance increased with the length of the time series examined. [PERSON] et al. (2018) proposed the incorporation of textual information into customer churn prediction (CCP) models based on a CNN. They used raw data from a financial service provider and confirmed that the inclusion of textual data in a CCP model improved its predictive performance. In addition, the experimental results showed that the CNN model outperformed the current best practices for text mining in CCP. The aforementioned studies achieved high performance using CNN models, following which the proposed approach also uses a CNN model as the base.