INTEGRATING DATA SCIENCE INTO INNOVATIVE APPROACHES TO WORKING CAPITAL MANAGEMENT FOR ENHANCING FINANCIAL STABILITY IN ENTERPRISES
DOI:
https://doi.org/10.5281/zenodo.17446566Keywords:
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.Abstract
In this article, the integration of data science tools into innovative working capital management approaches
is analyzed in the context of increasing financial stability in enterprises. The relevance of the topic is grounded in the
growing complexity of business processes and the necessity to optimize liquidity, receivables, and inventory turnover
using predictive analytics, machine learning, and real-time monitoring. The study emphasizes the role of data-driven
decision-making in enhancing operational efficiency and sustainability. Furthermore, the research evaluates the
application of advanced statistical models in forecasting financial risks and improving resource allocation. Empirical
analysis is conducted on selected enterprises in Uzbekistan, highlighting the positive correlation between data science
implementation and improved financial indicators such as DSCR, Z-Score, and liquidity ratios. The study concludes with
a set of practical recommendations for enterprises seeking to modernize their capital management strategies through
data science.
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