AI-Enabled Advanced Decision Intelligence Framework for Optimizing Credit Risk Management in Saudi Enterprises

Artificial intelligence is reshaping credit risk management by enabling lenders to process high-dimensional financial, transactional and behavioral information at a speed and granularity that conventional scorecards cannot easily match. Yet stronger prediction alone does not constitute decision intelligence. Credit decisions are economically consequential, regulated, path-dependent and exposed to model risk, data drift, unfairness, cybersecurity threats and weak organizational accountability. This review develops an AI-enabled advanced decision intelligence framework for optimizing credit risk management in Saudi enterprises. Using a structured integrative review of peer-reviewed literature, the paper synthesizes evidence on machine-learning credit scoring, explainable artificial intelligence, alternative data, class imbalance, profit-sensitive evaluation, model governance and Saudi digital-finance conditions. The synthesis indicates that ensemble and nonlinear models often improve discriminatory power, but their value depends on calibrated probabilities, stable explanations, portfolio-level economics, rigorous validation and human oversight. The proposed framework therefore links enterprise data, predictive analytics, explainability, risk appetite, workflow orchestration and continuous monitoring rather than treating credit scoring as an isolated classification task. Particular attention is given to Saudi requirements for trustworthy data use, Shariah-sensitive product structures, financial-sector digitalization and Vision 2030 objectives. The review concludes that Saudi enterprises should adopt a governed decision-intelligence architecture in which AI augments rather than replaces accountable credit judgment, and where performance is evaluated through accuracy, fairness, interpretability, robustness and economic outcomes simultaneously.