Hybrid Causal-Predictive Framework for Data Asset Valuation and Regulatory-Integrated Financial Reporting in Manufacturing Enterprises

Abstract

This research propose a hybrid causal-predictive framework for data asset valuation and regulatory-integrated financial reporting in manufacturing enterprises, addressing the dual challenge of quantifying intangible data value while ensuring compliance with evolving financial standards. The system integrates partial least squares structural equation modeling (PLS-SEM) to establish causal relationships between latent data asset constructs and observed financial performance metrics, robustly capturing non-linear interactions typical in manufacturing datasets. A hierarchical transformer architecture concurrently processes regulatory texts, dynamically scoring compliance urgency through temporal and semantic attention mechanisms, which we formalize as a Regulatory Pressure Index (RPI). These components are unified in a multi-objective decision curve analysis that balances valuation insights against regulatory risks, visualized through an interactive efficient frontier dashboard. The proposed method advances conventional valuation approaches by simultaneously resolving the epistemic uncertainty of data asset valuation and the temporal volatility of reporting requirements. Experimental integration with existing ERP pipelines demonstrates practical feasibility, as the system automatically generates XBRL-tagged disclosures while maintaining interoperability with legacy financial reporting tools. Our framework contributes to both academic research and industrial practice by providing a theoretically grounded yet operationally adaptable solution for data-driven financial decision-making under regulatory uncertainty. The results suggest significant improvements in valuation accuracy and compliance responsiveness compared to static valuation models, particularly for manufacturing firms with complex data ecosystems.