How Large Language Models Are Revolutionizing Banking Regulatory Interpretation

1. New Scenarios for AI in Regulatory Interpretation

With the rapid development of China’s financial industry and the emergence of new business models, regulators have introduced various policies that banks must interpret. Traditional manual reading of extensive regulatory documents is time‑consuming and prone to errors. The rise of AI, especially large‑model technologies, offers the possibility of more precise, efficient, and intelligent regulatory interpretation for commercial banks.

2. New Practices of AI in Regulatory Interpretation

Commercial banks train large‑scale datasets to build AI models with strong generalization, massive parameters, and advanced data‑processing capabilities, enabling them to understand massive unstructured data. Using a CO‑STAR framework and delimiter‑based prompt methodology, banks self‑train and fine‑tune large models, applying Retrieval‑Augmented Generation (RAG) to improve answer accuracy.

Prompt Engineering Methodology (10 steps)

Role : Define the role the prompt should assume, equipped with necessary knowledge and skills.

Background : Provide sufficient context about the task.

Skill : List key skills required to complete the task.

Constraints : Set rules and limits such as time range or data reliability.

Output : Specify the desired format and content of the result.

Workflows : Outline step‑by‑step procedures to achieve the goal.

Initialization : Have the role introduce itself, review skills, and confirm constraints.

Interaction and Confirmation : Confirm with the user before each critical step.

Problem Solving and Completeness : Resolve issues and ensure task completeness.

Completion and Next Steps Inquiry : After finishing, ask for satisfaction and possible next actions.

Following these steps maximizes interaction efficiency with large language models, delivering precise, industry‑specific regulatory insights.

3. Capabilities Enhanced by Large Models

1) Automated Processing and Parsing : Automatically handle and analyze massive regulatory texts using NLP and machine‑learning algorithms, extracting key information and reducing manual workload.

2) Precise Matching and Recommendation : Tailor regulatory interpretations to a bank’s specific business scenarios, offering personalized advice and highlighting potential risk points based on historical data.

3) Risk Warning and Prediction : Combine historical and real‑time transaction data to predict compliance risks, providing early warnings that help banks mitigate violations and improve operational efficiency.

Conclusion

The advancement of AI technology brings significant changes to banking regulatory interpretation, enabling higher efficiency, accuracy, and risk reduction. As AI applications continue to expand, commercial banks are expected to move toward increasingly intelligent, efficient, and secure operations.