AI-Driven Accounting and Sensing Applications for Investment Management

¹ Dean of Business and Innovative Education Faculty, Tashkent International University, Tashkent, Uzbekistan
² Deputy Director for Organizational and Financial Affairs, Tashkent International University, Tashkent, Uzbekistan
³ Dean of Tourism and Economics Faculty, Kokand University, Kokand City, Uzbekistan
⁴ PhD, Economics and Management Industry of Tashkent Institute of Chemical Technology (TCTI), Tashkent, Uzbekistan
⁵ Candidate of Physico-Mathematical Sciences, Associate Professor of the Department of Applied Mechanics, Tashkent State Transport University, Tashkent, Uzbekistan

^* Corresponding author: ifoziljonov@tsue.uz

Abstract

Al-driven accounting and sensing applications have enabled the formulation of multiple investment decision-support models with considerable predictive accuracy, real-time responsiveness, and cost-efficiency benefits. Advancements in algorithmic sensing on financial datasets are challenging traditional conceptions of risk assessment and portfolio diversification, and in the process, opening up windows of opportunity for redefining the analytical frameworks associated with investment management practices. As little is known about where AI-led innovation is gaining momentum beyond institutional finance and automated trading systems, the purpose of this study is to map in what subsectors of investment management it is perceived to gain traction. Drawing on data from regression analysis and correlation matrices in emerging market contexts, we identify a long tail of niche applications and sector-specific tools in which a total of 142 unique AI-enabled platforms operate, including use cases such as fraud detection, asset rebalancing, and environmental, social, and governance (ESG) forecasting. Our findings reveal a strong, positive correlation coefficient (r = 0.78) between AI integration levels and portfolio performance outcomes. However, financial analysts do not passively comply. Rather, their professional judgment and domain expertise are integrated into the adaptive learning processes of AI systems. The article concludes by identifying critical implementation challenges, reflecting on the application of machine learning and sensor fusion in the field of investment analytics, and proposing suggestions for future interdisciplinary research. The resulting insights enrich understandings of the workings of AI-accounting convergence in experiences of decision-making optimization and risk-adjusted return enhancement.