Consumer complaints of consumer financial protection bureau via two-stage residual one-dimensional convolutional neural network (TSR1DCNN)

Abstract

The Consumer Financial Protection Bureau (CFPB) is a government body responsible for safeguarding consumers from financial fraud and abuse. Managing customer complaints is one of the key tasks undertaken by the CFPB. However, the sheer volume of complaints received can overwhelm the bureau's resources, hindering prompt and efficient resolution. To address this challenge, we propose a novel approach called the Two-Stage Residual One-Dimensional Convolutional Neural Network (TSR1DCNN) to optimize the processing of consumer complaints at the CFPB. In this study, we conducted comprehensive experiments, including Ablation Experiment 1 (AE1) and Ablation Experiment 2 (AE2), to evaluate the effectiveness of our proposed TSR1DCNN model. AE1 involved removing the first Conv1D layer, while AE2 removed the Batch Normalization layer. These experiments allowed us to assess the impact of removing specific components on the overall performance of the model. Furthermore, we compared our TSR1DCNN model with other popular deep learning architectures, including 1DCNN, LSTM, and BLSTM, to provide a comprehensive analysis of our proposed approach. Using a dataset of 555,957 consumer complaints received by the CFPB, we trained and tested the TSR1DCNN model, as well as the ablated versions in AE1 and AE2, alongside the 1DCNN, LSTM, and BLSTM models. The results showed that the TSR1DCNN model achieved an impressive accuracy of 78.07% on the training set and 76.53% on the test set. In comparison, AE1 achieved an accuracy of 69.63% with a loss of 1.1207, while AE2 achieved an accuracy of 71.00% with a loss of 1.0583. The performance of the TSR1DCNN model outperformed the other deep learning architectures, including 1DCNN, LSTM, and BLSTM, indicating its superiority in handling consumer complaints effectively. These results demonstrate the superiority of the TSR1DCNN model over the ablated versions in AE1 and AE2, as well as its superiority over other commonly used deep learning architectures. By incorporating advanced neural network architectures such as 1DCNN, LSTM, and BLSTM, and considering the specific modules where our proposed method operates, we provide a promising solution for enhancing the efficiency and effectiveness of complaint-handling processes in organizations facing a large volume of complaints, such as the CFPB.