Authors:
Jaya Ram Menda
Addresses:
Department of Information Technology, Cognizant Technology Solutions, Austin, Texas, United States of America.
Abstract:
This study examines the growing need for adaptive financial intelligence as institutions confront volatility, fragmented data ecosystems, and decision cycles increasingly influenced by automated analytics. The research proposes an integrated architecture for LLM-infused financial optimisation, supported by continuous machine learning feedback, that enables systems to learn from operational outcomes, refine internal models, and improve predictive accuracy over time. Using a mixed-methods approach that combines quantitative evaluation of optimisation gains with qualitative analysis of system behaviours, the study investigates how composite AI elements complement one another to create a self-directed improvement loop. The findings indicate that LLM components enhance interpretability, reasoning patterns, and alignment with contextual decisions, while machine learning feedback surfaces deviations, risks, and economic patterns that guide ongoing recalibration. Empirical patterns suggest that financial strategies supported by this architecture improve portfolio responsiveness, compliance accuracy, and anomaly detection while reducing model drift and intervention costs. The investigation advances academic understanding by illustrating how bidirectional learning cycles influence financial decision stability and create more resilient optimisation processes. In practice, the framework offers institutions a path toward financial automation that maintains transparency and governance, avoids overfitting, and adapts to evolving market conditions.
Keywords: Adaptive Financial Intelligence; Model Drift Mitigation; Predictive Financial Analytics; LLM Infused Financial Systems; Financial Optimisation Architecture; Continuous Machine Learning Feedback.
Received: 12/12/2024, Revised: 14/02/2025, Accepted: 26/03/2025, Published: 23/12/2025
DOI: 10.64091/ATITP.2025.000181
AVE Trends in Intelligent Technoprise Letters, 2025 Vol. 2 No. 4 , Pages: 180-192