In this article, the integration of data science tools into innovative working capital management approachesis analyzed in the context of increasing financial stability in enterprises. The relevance of the topic is grounded in thegrowing complexity of business processes and the necessity to optimize liquidity, receivables, and inventory turnoverusing predictive analytics, machine learning, and real-time monitoring. The study emphasizes the role of data-drivendecision-making in enhancing operational efficiency and sustainability. Furthermore, the research evaluates theapplication of advanced statistical models in forecasting financial risks and improving resource allocation. Empiricalanalysis is conducted on selected enterprises in Uzbekistan, highlighting the positive correlation between data scienceimplementation and improved financial indicators such as DSCR, Z-Score, and liquidity ratios. The study concludes witha set of practical recommendations for enterprises seeking to modernize their capital management strategies throughdata science.
| Mualliflar | Doniyor Khoshimov, Ilmurod Kungratov Kuzibay ugli |
|---|---|
| Jurnal | Innovation science and technologiy |
| Nashr sanasi | 2025-06-25 |
| Jild | 1 |
| Son | 6 |
| Betlar | 68-75 |
| Til | Ingliz |
| DOI | 10.5281/zenodo.17446566 |
DOI: 10.5281/zenodo.17446566 · Maqolaning asl sahifasi
data science, working capital, financial stability, machine learning, predictive analytics, cash flow, liquidity, Z-score, DSCR, inventory, accounts receivable, decision-making, real-time data, regression analysis, optimization, risk forecasting, financial strategy, enterprise performance.
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