AI Neural Network Architectures For Personalized Payment Systems: Exploring Machine Learning's Role In Real-Time Consumer Insights

Abstract

The study considers the convergence of advanced and scalable AI technologies with financial services to understand the advancements achieved in user experience through the next stage of a personalized augmented financial system. Machine learning resembles advanced data analytics that is predominantly used for predicting the characteristics of individuals using real-time data and learning as the inputs change. The role of advanced data analytics or machine learning is explored to predict the preferences and characteristics of individuals in real time and to extend the application to personalized payments where digital money or assets are transferred between individuals. The existing literature mainly focuses on the development of predictive models that are unable to learn and adapt to individual expenditure patterns, subsequently suggesting the need for research to design AI neural network architectures that navigate the trade-offs in the inference of consumer preferences in real-time expenses with a primary focus on consumer characteristics.
The study presents multiple novel contributions in aiding the development of automated neural networks for personalized payments from the very successful practitioner-driven features of immensely important applications. One aspect of building an automated neural network for personalized payments is to discuss the exchange of digital assets between retailers and customers using consumer characteristic inference in real-time, as characteristics of goods received are continuously altered, leading to their adaptation over time to reflect consumer preferences at any given moment. The practice of this leads to new nested expert levels to be defined to systematically explore a consumer’s expenses through consumer characteristics in real-time. The contribution here lies in the a priori definition of automated layers that make up the localized snapshot of a consumer. This can, and has been, extended to transaction privacy. The second major contribution is in the adaptation of federated learning to evolutionary algorithms, thus improving transaction efficiency and, most importantly, increasing customer satisfaction as a result. Thirdly, the overall architecture depicts the final evolutionary adapted ANN architecture that is efficient and situated to automate the real-time relevant prediction in group dynamics. The findings are threefold. First, experts in the contractual agreement chains that receive data from their superiors must make inferences about their superiors when their expert level of inference becomes relevant to marketing. Second, neural networks are not always designed to be the most efficient for inferring the characteristics of a database, but recently they have been improved upon so that they are efficient in optimizing performance characteristics.