DECISION INTELLIGENCE FRAMEWORK INTEGRATING BIG DATA ANALYTICS FOR ENTERPRISE FINANCIAL RISK MANAGEMENT UNDER SAUDI VISION 2030

Abstract

Enterprise financial risk management is being reshaped by the convergence of high-volume data, machine learning, explainable analytics, and digital decision support. Yet many organizations still separate data engineering, predictive modelling, risk governance, and managerial judgment, creating a gap between analytical insight and accountable action. This review develops a Decision Intelligence Framework integrating Big Data Analytics for enterprise financial risk management in the context of Saudi Vision 2030. Using a structured integrative review, the study synthesizes peer-reviewed evidence on big data analytics capabilities, enterprise risk management, credit and fraud analytics, explainable artificial intelligence, banking digitalization, and Saudi financial-sector transformation. The synthesis identifies five recurring requirements: trusted multi - source data, risk-oriented analytical capability, explainability and model governance, decision orchestration with human accountability, and continuous learning from outcomes. The proposed framework links these requirements through a closed-loop architecture that converts internal and external data into risk signals, scenarios, recommended actions, approvals, controls, and feedback.