AI-Driven Accounting and Sensing Applications for Investment Management
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 tradition...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01020.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849431398798065664 |
|---|---|
| author | Foziljonov Ibrohimjon Yuldashev Jahongir Turgunov Jasurbek Khujamurodov Askarjon Rozhkova Elena |
| author_facet | Foziljonov Ibrohimjon Yuldashev Jahongir Turgunov Jasurbek Khujamurodov Askarjon Rozhkova Elena |
| author_sort | Foziljonov Ibrohimjon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-acefdc278cb247e19f3bf69ccbad5f60 |
| institution | Kabale University |
| issn | 2261-2424 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj-art-acefdc278cb247e19f3bf69ccbad5f602025-08-20T03:27:40ZengEDP SciencesSHS Web of Conferences2261-24242025-01-012160102010.1051/shsconf/202521601020shsconf_iciaites2025_01020AI-Driven Accounting and Sensing Applications for Investment ManagementFoziljonov Ibrohimjon0Yuldashev Jahongir1Turgunov Jasurbek2Khujamurodov Askarjon3Rozhkova Elena4Dean of Business and Innovative Education Faculty, Tashkent International UniversityDeputy Director for Organizational and Financial Affairs, Tashkent International UniversityDean of Tourism and Economics Faculty, Kokand UniversityPhD, Economics and Management Industry of Tashkent Institute of Chemical Technology (TCTI)Candidate of Physico-Mathematical Sciences, Associate Professor of the Department of Applied Mechanics, Tashkent State Transport UniversityAl-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.https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01020.pdf |
| spellingShingle | Foziljonov Ibrohimjon Yuldashev Jahongir Turgunov Jasurbek Khujamurodov Askarjon Rozhkova Elena AI-Driven Accounting and Sensing Applications for Investment Management SHS Web of Conferences |
| title | AI-Driven Accounting and Sensing Applications for Investment Management |
| title_full | AI-Driven Accounting and Sensing Applications for Investment Management |
| title_fullStr | AI-Driven Accounting and Sensing Applications for Investment Management |
| title_full_unstemmed | AI-Driven Accounting and Sensing Applications for Investment Management |
| title_short | AI-Driven Accounting and Sensing Applications for Investment Management |
| title_sort | ai driven accounting and sensing applications for investment management |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2025/07/shsconf_iciaites2025_01020.pdf |
| work_keys_str_mv | AT foziljonovibrohimjon aidrivenaccountingandsensingapplicationsforinvestmentmanagement AT yuldashevjahongir aidrivenaccountingandsensingapplicationsforinvestmentmanagement AT turgunovjasurbek aidrivenaccountingandsensingapplicationsforinvestmentmanagement AT khujamurodovaskarjon aidrivenaccountingandsensingapplicationsforinvestmentmanagement AT rozhkovaelena aidrivenaccountingandsensingapplicationsforinvestmentmanagement |