Computational Linguistics Applications in AI-Based Investment and Cost Structuring Models

¹ Associate Professor, Head of the Faculty of Pedagogy, the Nordic International University, Tashkent, Uzbekistan
² Dsc, professor, Head of the Department “Social Sciences”, Tashkent State Transport University, Tashkent, Uzbekistan
³ PhD, Senior Teacher of "Uzbek and foreign languages" department of the International Islamic Academy of Uzbekistan, Tashkent, Uzbekistan
⁴ Senior Teacher of "Foreign Languages" department of the Tashkent State Technical University, Tashkent, Uzbekistan
⁵ Teacher, English Language department, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University ("TIIAME" NRU), Tashkent, Uzbekistan

^* Corresponding author: Cool.miralieva@mail.ru

Abstract

Artificial intelligence is an influential technological paradigm that is quickly and comprehensively transforming financial decision-making systems worldwide. This study aims to focus on computational and economic implications of computational linguistics techniques and to enrich the existing literature in AI-driven investment modeling. Empirically, we draw on a cross-sectional research project, assessing the predictive power of linguistic data processing algorithms and the experiences of financial analysts in cost structuring contexts. A regression-based model consisting of semantic, syntactic, and pragmatic dimensions was created and estimated through correlation and multivariate regression analysis. The findings indicate that natural language processing efficiency and machine learning advancements have significant impacts on investment planning accuracy. Computational linguistics is deeply embedded in algorithmic financial models and reshapes cost forecasting frameworks through data-driven semantic interpretation. Closing the research gaps would contribute to the development of much-needed intelligent financial infrastructures. AI-enabled linguistics modeling promotes scalable optimization and context-aware applications of financial analytics, and realizes cost transparency improvements in automated investment systems. This paper offers computational insights and reflections to provide financial analysts with key information to best apply linguistically driven AI tools while being aware of contextual modeling constraints. This future research agenda provides ample scope for future empirical investigation and interdisciplinary science on semantic modeling, cost structuring, investment forecasting, and automated financial systems.