AI-Driven Predictive Analytics for Supply Chain Resilience, Financial Risk Management, and Digital Marketing Strategy: A Unified Business Intelligence Framework

Abstract

The arrival of artificial intelligence and big-data analytics at scale has begun to redraw the strategic map of the modern enterprise. Three areas sit close to the center of that change: how firms manage their supply chains, how they assess financial risk, and how they think about digital marketing. Each has its own substantial literature, yet the three are seldom examined together inside a single, properly governed analytical architecture. This paper sets out to close that gap. We develop and empirically validate a Unified Business Intelligence (UBI) framework that brings machine-learning engines, predictive analytics pipelines, and explainable AI modules into one coherent three-layer design that spans all three domains. The framework is grounded in a structured synthesis of forty-three peer-reviewed studies published between 2023 and 2026 and is supplemented by multi-domain benchmark experiments. The results are consistent and, in places, striking. In supply chain management, the framework reaches 94.1% disruption-prediction accuracy, a 22.9 percentage-point lift over domain-specific baselines and pulls demand-forecast MAPE down from 12.4% to 7.8%. In financial risk, ensemble-transformer hybrids deliver an AUC-ROC of 0.93 on portfolio stress testing and an F1 of 91.4% on credit-risk classification. In marketing, AI-orchestrated cross-domain targeting raises campaign ROI from 14.2% to 45.3%, and churn-prediction recall climbs from 68.0% to 84.7%. Across the seven capability dimensions assessed among them cross-domain integration, real-time processing, and embedded explainability the UBI framework outperforms traditional BI, siloed AI, and integrated MIS benchmarks; no existing paradigm achieves all of these at once. Twelve concrete research gaps are mapped, spanning federated learning, regulatory-grade explainability, privacy-preserving marketing analytics, and questions of geographic generalizability, with a structured agenda proposed for each. The findings are directly relevant to practitioners moving enterprise AI into production, and to policymakers crafting governance for cross-domain algorithmic decision-making.