Large Model Applications in the Financial Sector: Practices, Knowledge Graphs, Ethics, and Ecosystem

Introduction – The article outlines the rapid advancement of large AI models and their impact on the third wave of information technology, emphasizing the pivotal role of data as a production factor.

1. Development Trends of Large Models – Discusses the shift from fragmented small‑model systems to unified large‑model platforms like ChatGPT, highlighting improvements in language ability, intent recognition, contextual writing, logical reasoning, code generation, and domain‑specific expertise.

2. Financial Scenario Applications – Describes how large models are integrated into various financial business lines such as customer service, investment advisory, risk control, research, investment banking, and quantitative trading, both for external client‑facing services and internal workflow automation.

3. Knowledge Graphs in the Large‑Model Era – Explains the continued relevance of knowledge graphs for long‑range context linking and hidden semantic resources, and categorises their application patterns based on data source (internal vs. external) and interaction target (human vs. system).

4. LightGPT – A Financial Large Model – Introduces LightGPT, its safety mechanisms (corpus security, model security, evaluation), compliance with over 5,000 financial regulations, and performance advantages in legality, factuality, and value alignment.

5. Ethical and Governance Issues – Highlights professional self‑restraint, content‑based permission control, generation error mitigation, and attribution tagging to trace responsibility for model outputs.

6. Application Scenarios – Classifies model usage into reconstruction (replacing manual tasks), embedding (Copilot‑style assistance), and native (autonomous agents) across writing, compliance, advisory, operations, and research domains.

7. Future Development Path – Summarises the evolution from text‑only to multimodal understanding, from Copilot to autonomous agents, and the strategic choice between general‑purpose versus vertical models and between model‑centric and control‑center architectures.

Conclusion – The article concludes with a Q&A session reinforcing that the four primary financial use cases are investment advisory, research, operations, and compliance, and emphasizes the importance of building robust middle‑layer control systems to fully realise large‑model potential in finance.