Cognitive Liquidity Engines: Reinventing Capital Flow Optimization through AI-Native Cloud Microservices

Authors

  • Rajender Chilukala

Keywords:

AI-Native Cloud Microservices; Cognitive Liquidity Engine; Capital Flow Optimization; Risk-Adjusted Return; Operational Latency; Financial Automation; Statistical Modeling; ANOVA; Predictive Analytics; FinTech.

Abstract

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.

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Published

27.04.2024

How to Cite

Rajender Chilukala. (2024). Cognitive Liquidity Engines: Reinventing Capital Flow Optimization through AI-Native Cloud Microservices. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 5188 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7899

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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