Construction Logic and Algorithmic Evolution Pathways of Large-Scale Growth Models for SMEs

Abstract

This paper develops a conceptual foundation-model framework for predicting SME growth in the digital economy. It argues that traditional, linear and data-scarce approaches cannot capture the non-linear, networked and multimodal nature of contemporary SME trajectories. Building on organisational life-cycle theory and the resource-based view, the study proposes an architecture that fuses structured financial statements, unstructured textual disclosures and graph-based relational data into shared latent representations. Transformer-based models, graph neural networks and contrastive multimodal learning are combined to generate time-varying, firm-level assessments of growth and default risk, accompanied by natural-language diagnostic reports. The paper further discusses how transfer learning, synthetic data and retrieval-augmented prediction can mitigate cold-start problems, enhance causal interpretability and improve responsiveness to macro shocks. At the macro level, the framework has implications for easing bank–firm information asymmetries, expanding financial inclusion and rethinking the role of financial intermediaries in an AI-driven credit ecosystem.