Temporal–Cross-Modal Expert Ensemble for Trustworthy Financial Decisions

In corporate finance, deploying AI agents for financial modeling requires integrating diverse data streams into a reliable and auditable decision-making framework. Existing multimodal and large language models provide strong representations but often miss clear temporal ordering, cross-modal coordination, and adaptive rule enforcement in real-world financial practice. To address these gaps, this paper introduces HAFIN (Hierarchical Adaptive Financial Intelligence Network), a rule-aware framework built upon the Typhoon2-70B foundation. HAFIN combines multi-scale temporal modeling, cross-modal fusion, sparse expert routing, soft rule propagation, Bayesian uncertainty modeling, and selective attention in a single architecture. The model captures fine and coarse financial patterns through time-aware embedding and aligns data types through simple gated fusion. Sparse expert routing improves focus and efficiency. Soft rule propagation incorporates regulatory limits into learning. Bayesian inference refines uncertainty handling, and selective attention highlights salient signals. The result is a robust, interpretable, and compliance-driven system designed for high-stakes multimodal financial analysis, directly supporting the needs of financial modeling and finance transformation leaders.