Advanced Decision-Making Models for Digital Transformation of Brokerage Operations in Saudi Arabia

Digital transformation within securities brokerage increasingly presents a decision-making challenge rather than a straightforward technology-acquisition issue. Brokerage firms must evaluate options such as artificial intelligence, robo-advisory, application programming interfaces, cloud infrastructure, process automation, advanced analytics, and hybrid advisory models, while concurrently safeguarding investors, managing model risk, ensuring regulatory compliance, and preserving trust. This review develops an integrative framework for advanced decision-making models customized to brokerage operations in Saudi Arabia. It integrates recent peer-reviewed evidence on financial artificial intelligence, robo-advisory, digital banking, explainability, algorithm aversion, customer adoption, and financial-service governance. The analysis contends that no single optimization model can drive Saudi brokerage transformation. Instead, it supports the combination of multi-criteria decision analysis, predictive analytics, scenario analysis, explainable artificial intelligence, and staged human governance. The proposed architecture connects strategic value, operational workability, customer outcomes, Shariah-sensitive design, data governance, cybersecurity, and regulatory accountability. The synthesis points to a continuing tension: although automation boosts scalability and consistency, insufficiently governed or opaque automation can diminish adoption and heighten operational, conduct, or reputational risks. Accordingly, the paper proposes an adaptive decision cycle in which digital initiatives are systematically diagnosed, prioritized, piloted, governed, and either scaled or discontinued based on measurable evidence. The review also identifies research gaps related to Saudi brokerage-specific datasets, comparative decision-model performance, Arabic explainability, investor heterogeneity, hybrid human–algorithm designs, and longitudinal regulatory outcomes.