arXiv:2605.27845v1 Announce Type: cross Abstract: Financial and economic research often relies on structured supply-chain disclosures and commercial databases. In China, supplier--customer disclosure is typically limited to major partners of listed firms, leaving unlisted firms and long-tail inter-firm links poorly captured in structured data. Public web evidence can partly complement this gap through corporate, government, and trade-media disclosures; however, full-text web mining at scale is costly because pages are often inaccessible or expensive to process with large language models (LLMs)
The proliferation of LLMs and the increasing demand for granular supply chain visibility, especially in complex markets like China, are converging to enable new discovery methods.
This development offers a scalable solution to a long-standing data gap in emerging markets, improving financial analysis, risk management, and strategic planning for entities operating globally.
Traditional reliance on structured financial disclosures for supply chain mapping is supplemented by advanced AI-driven web mining, revealing previously hidden inter-firm relationships and improving data accessibility.
- · Financial analysts
- · Supply chain risk managers
- · LLM developers
- · Global manufacturers
- · Commercial data providers reliant solely on structured disclosures
- · Firms with opaque supply chain practices
Improved visibility into Chinese supply chains for unlisted firms and long-tail links becomes possible.
Enhanced data availability leads to more accurate economic models and better-informed investment decisions regarding Chinese firms.
Increased transparency could foster greater competition or expose vulnerabilities, leading to shifts in global manufacturing strategies and partner selection.
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Read at arXiv cs.AI