arXiv:2602.19591v3 Announce Type: replace-cross Abstract: Small and Medium Enterprises (SMEs) constitute 99.9% of U.S. businesses and generate 44% of economic activity, yet systematically identifying high-potential SMEs remains an open challenge. We introduce SME-HGT, a Heterogeneous Graph Transformer framework that predicts which SBIR Phase I awardees will advance to Phase II funding using exclusively public data. We construct a heterogeneous graph with 32,268 company nodes, 124 research topic nodes, and 13 government agency nodes connected by approximately 99,000 edges across three semantic
The proliferation of AI/ML techniques for predictive analytics, combined with the increasing availability of public business and government funding data, enables sophisticated SME evaluation.
Accurate identification of high-potential SMEs can significantly improve capital allocation by government agencies and private investors, fostering innovation and economic growth.
The ability to predict SME success with greater precision through AI changes how early-stage funding decisions are made, potentially democratizing access to capital for deserving companies.
- · High-potential SMEs
- · Government funding agencies
- · Venture capital firms
- · AI/ML companies specializing in financial prediction
- · Legacy SME evaluation methods
- · Underperforming SMEs that previously received funding
- · Human-centric due diligence processes
Government agencies become more efficient in their funding programs, directing capital to statistically more promising ventures.
Private investors leverage similar AI models, creating a more data-driven and competitive landscape for SME funding.
The overall innovation ecosystem accelerates as fewer resources are wasted on low-potential ventures, leading to faster technological progress.
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Read at arXiv cs.AI