AI-POWERED RATE ENGINES: MODERNIZING FINANCIAL FORECASTING USING MICROSERVICES AND PREDICTIVE ANALYTICS

Abstract

This paper presents a cloud-native architectural model for modernizing financial rate forecasting systems using microservices, Spring Boot, and AI-driven predictive analytics. Traditional rate engines suffer from performance bottlenecks, rigid infrastructure, and a lack of real-time decision support capabilities. By leveraging historical financial data and advanced time-series models integrated within microservices architecture, we design a modular, scalable, and intelligent solution deployed on Kubernetes-based infrastructure. The proposed system integrates Long Short-Term Memory (LSTM) networks with Transformer models to enhance forecasting accuracy across multiple financial instruments. Empirical analysis demonstrates improved forecasting accuracy (12–18%), enhanced system resilience with 99.95% uptime, and a 35% reduction in infrastructure costs compared to monolithic rate engines. The research contributes a novel hybrid AI framework combining reinforcement learning with ensemble methods for adaptive rate optimization, addressing the dynamic nature of financial markets.