Foundation Model-Based Predictive Analytics for Multi-Domain Decision Intelligence

Abstract

Predictive analytics is rapidly evolving through Artificial Intelligence (AI), particularly with the emergence of foundation models that enable scalable, transferable, and context-aware intelligence across multiple domains. Unlike traditional machine learning models, foundation models leverage large-scale multimodal pretraining and efficient task-specific adaptation, enabling superior reasoning, zero-shot learning, and cross-domain knowledge transfer. This paper proposes the Foundation Model-Based Predictive Analytics Framework for Multi-Domain Decision Intelligence (FMPA-MDI), an integrated architecture that combines heterogeneous data acquisition, multimodal preprocessing, semantic representation learning, transformer-based predictive reasoning, retrieval-augmented learning, knowledge graph integration, explainable AI (XAI), and intelligent decision optimization. The framework supports structured and unstructured data while incorporating transfer learning, attention mechanisms, semantic embeddings, and continuous feedback for adaptive decision-making. Mathematical formulations model feature representation, semantic similarity, predictive confidence, and optimization. The proposed framework enhances prediction accuracy, scalability, interpretability, and computational efficiency, providing a robust foundation for next-generation intelligent decision support across healthcare, finance, manufacturing, smart cities, and other enterprise domains.