Cognitive Liquidity Engines: Reinventing Capital Flow Optimization through AI-Native Cloud Microservices - Rajender Chilukala (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Artificial intelligence (AI) integrated with cloud-native microservices has radically changed the operating environment of financial systems. The paper presents the idea of Cognitive Liquidity Engines (CLEs)—an AI-inclusive structure established to enhance the flow of capital via adaptive, data-informed automation. The study, applying a simulation of a dataset, explores the statistical association of AI process automation, risk-adjusted returns, operational latency, and capital efficiency. One-way ANOVA results reveal very significant differences (p < 0.001) among treatment groups, thus confirming the role of AI-native automation in liquidity enhancement. Besides, model normality and robustness are assured by residual analysis and Q-Q plots, which makes the statistical conclusions dependable. The results suggest that AI-powered microservices can reallocate liquidity resources in real-time, minimize capital circulation latency, and increase the accuracy of financial operations forecasting. The primary model for infrastructure of the future generation, which are AI-native financial, is provided by this research, thereby setting the stage for smart, self-correcting, and durable liquidity ecosystems.