Deep Learning Modeling of Consumer Payment Behavior and Security Perception

With the rapid development of mobile payments, understanding the dynamic relationship between consumer payment behavior and security perception is crucial for enhancing payment security and optimizing user experience. This paper proposes the PaySenseNet model, which integrates Long Short-Term Memory (LSTM) networks, Deep Neural Networks (DNN), and a multi-task learning module, aiming to uncover the bidirectional interactive mechanism between payment behavior and security perception. The experimental results show that PaySenseNet outperforms traditional models across all datasets. On the PaySim dataset, the model achieved an accuracy of 88.6%, precision of 87.9%, MSE of 0.105, and a coefficient of determination R2 of 0.89; on the Credit Card Fraud Detection dataset, the accuracy was 87.3%, F1 score of 86.8%, MSE of 0.112, and R2 of 0.87; on the Mobile Payment User Behavior Data dataset, the accuracy was 88.1%, precision was 87.4%, MSE was 0.109, and R2 was 0.88.