CORPORATE DEBT OPTIMIZATION THROUGH PREDICTIVE FINANCIAL ANALYTICS: EVIDENCE AND STRATEGIC IMPLICATIONS FOR SAUDI ARABIA

Saudi Arabias Vision 2030 is deepening the role of private-sector credit and debt capital markets in financing economic diversification, infrastructure, real estate, industrial expansion and technology-led growth. As financing channels widen, Saudi enterprises face a more complex question than simply obtaining debt: they must determine an economically efficient, resilient and governable debt structure under changing cash flows, interest rates, project risks and market conditions. This paper develops a predictive financial analytics framework for corporate debt optimization in Saudi Arabia. It adopts an integrative review and evidence-synthesis approach combining recent peer-reviewed research on capital-structure prediction, corporate default, machine learning, explainable artificial intelligence and debt-structure dynamics with official Saudi financial-sector evidence. The review shows that machine-learning methods can improve prediction of target leverage, financing actions, default probabilities and loss severity when data quality and out-of-sample validation are adequate. Saudi Central Bank evidence also indicates strong corporate credit expansion in 2024 while listed non-financial companies experienced lower median leverage, illustrating the simultaneous availability of financing and the importance of disciplined balance-sheet management. The proposed framework connects enterprise data, cash-flow forecasting, target-leverage estimation, debt -structure simulation, stress testing, explainab ility and governance.