Developing a framework based on machine learning algorithms to optimize organizational cost forecasting
Subject Areas : Information Science
1 -
Keywords: Enterprise cost forecasting, machine learning, data-driven model, resource optimization, data analysis, linear regression, financial management,
Abstract :
Abstract:
In today's world, accurately predicting organizational costs to optimize resource allocation and enhance productivity is one of the key challenges faced by managers. Rapid economic changes and the increasing complexity of financial processes have further highlighted the necessity of data-driven forecasting methods. This study presents a machine learning-based framework aimed at improving the accuracy of organizational cost prediction and facilitating more precise financial planning in organizations.
In this research, real financial and operational data from the Health Insurance Organization over a five-year period were collected and, after preprocessing, used to train predictive models. The primary model employed in this study is linear regression, and its performance was evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE).
The results demonstrate that the linear regression model can accurately predict variations in organizational costs and identify key factors influencing expenses. The proposed framework can assist organizations in improving budget planning, financial resource management, and reducing prediction errors. Furthermore, the findings of this research provide a scientific foundation for developing predictive models in other organizational domains, facilitating more informed strategic decision-making for managers
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