Large Language Models in Financial Risk Management: Applications, Challenges, and Future Outlook

Introduction As large model technology matures since 2022, it has moved from research to industrialization and become a prominent trend, especially for future AGI development. While offering broad application potential, large models also introduce risks that must be managed safely, particularly in finance where they can support risk management and prevention.

1. Risk Classification and Large Model Mapping Financial risk management covers market, credit, reputation, operational, technical, anti‑fraud, and geopolitical risks. Each risk type aligns with specific large model use cases, such as market monitoring reports, credit approval assistance, sentiment analysis for reputation, multimodal anomaly detection for operations, and graph‑based techniques for anti‑fraud.

2. Large Model Applications in Finance The evolution from traditional scorecards to large language models (LLMs) has enabled handling of unstructured data, improving credit reporting, sentiment analysis, and fraud detection. LLMs reduce costs compared to manual processes and accelerate the digital transformation of financial services.

3. Business Flow and Information Flow Examples LLMs are applied across customer service, marketing, query answering, approval document generation, risk factor selection, office automation, and IT operations. They enhance efficiency in call handling, content creation, knowledge retrieval, code generation, and log analysis.

4. Challenges and Data Space Concept Challenges include model hallucinations, limited performance on sparse data, and high computational costs. The emerging data‑space paradigm, originating from the EU, provides decentralized, trustworthy data exchange mechanisms using blockchain and homomorphic encryption, supporting secure multi‑party data sharing for AI models.

5. Future Outlook Anticipated advances include lower model costs, improved anti‑fraud capabilities, expanded data‑space integration, multimodal fusion, and enhanced risk decision support. Large models are expected to play a pivotal role in next‑generation risk management, enabling real‑time, accurate, and secure financial operations.