UTILIZING DEEP LEARNING AND PERSUASIVE TECHNOLOGY TO INCREASE USER TRUST IN DIGITAL PAYMENTS

The paper examines the foundations of user trust in digital payments, using quantitative analysis of data from 277 participants. The study focuses on the associations between perceived security, convenience, privacy protection, and user trust in digital payments. Our findings suggest that all three attributes, safety, security, and privacy, are powerful positive correlates of trust. Furthermore, past experience with digital payments (PEDP) modifies these relationships, suggesting that the impact of these attributes on trust can have significant implications for engaged user digital payments are greatly enhanced We can use persuasive design principles and advanced deep learning to enhance user trust. The study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for enhanced feature extraction and temporal sequence analysis of user interactions and preferences within digital payment environments. This model is complemented by a hybrid optimization algorithm merging the Zebra Optimization Algorithm (ZOA) and Seagull Optimization Algorithm (SOA), aimed at refining system performance and user engagement through iterative enhancement of persuasive elements. This paper lays the groundwork for further research on other factors affecting the use of trust and cultural diversity in digital payment channels. Combining mind-blowing technologies with sophisticated deep learning techniques, it seeks to develop digital payment systems that not only secure transactions but also provide lasting user trust, satisfaction planning and implementation improvements.