The Financial Management in State-Owned Enterprises: Leveraging Machine Learning for Bankruptcy Prevention in Zambia

This study examined factors contributing to bankruptcy in State-Owned Enterprises (SOEs) in Zambia and explored machine learning algorithms to predict bankruptcy risk using financial ratios. Using a mixed-method approach, the research aimed to: investigate financial management practices leading to bankruptcy, explore the government's role in bankruptcy prevention, and apply machine learning models. Key questions addressed included: What financial practices lead SOEs to bankruptcy? What is the government's role in prevention? How can machine learning predict bankruptcy risk? Through interviews with financial managers and analysis of SOE financial data, the study identified a lack of use of bankruptcy predictive tools and excessive government influence that reduces autonomy in financial decision-making. The research demonstrated the effectiveness of machine learning in detecting bankruptcy in SOEs. The findings offer practical recommendations for policymakers and stakeholders to enhance financial stability in SOEs.