This study illustrates a data-driven recommendation system aiming to optimize payment gateway selection utilizing an innovative API architecture built with Java Spring Boot. This project uses hybrid machine learning (ML) models that combine collaborative filtering and deep neural networks to create a scalable RESTful API that provides tailored payment gateway suggestions based on user behavior, transaction context, and gateway performance indicators. The methodology entails gathering and preparing transactional data from many sources, organizing it for ML model training, and employing stringent validation techniques to assure forecast accuracy. The resulting API, which is implemented using Spring Boot, gives stakeholders-merchants, developers, and end users-actionable insights into gateway suitability, transaction success rates, and processing patterns. Key findings show that states or regions with high gateway availability may not always result in optimal transaction outcomes, indicating inefficiencies that can be addressed through intelligent recommendations. The technology improves recommendation accuracy by 25% and reduces transaction abandonment by 15%, proving its ability to simplify complicated payment ecosystems and improve decision-making for smooth financial operations.
Java Spring Boot, Machine Learning, Hybrid Models, Payment Gateway Recommendations, Transaction Data, User Behavior Analysis, Fintech Dashboard, API Integration
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