An Empirical Study on the Application of Large Language Models in Financial Regulatory Question Answering and Risk Screening and the Enhancement of Regulatory Efficiency
TL;DR
An intelligent assistant system for financial regulation based on large language models is built, and a regulatory question-answering module and a risk-screening module are introduced to enhance the efficiency of supervision.
Abstract
The Financial Regulatory Office has added significantly to its database of text data but has not increased staff. Large language models have strong natural language understanding and generation abilities, and a new technical path for automating regulatory Q&A and intelligent risk screening has been provided. Build an intelligent assistant system for financial regulation based on large language models in this paper, and introduce a regulatory question-answering module and a risk-screening module. A controlled experiment was carried out in the actual work environment of a local financial regulatory office during the 2025 fiscal year. According to the above experimental results, LLM assistance has reduced the regulatory question response time by 82.6%, improved the accuracy of risk screening by 19.3 percentage points, increased the average daily effective workload of regulatory staff to 2.8 times, and raised the overall regulatory efficiency index by 156.7%. Provide empirical support and operational references for the financial regulatory authorities in utilising artificial intelligence technology to enhance the efficiency of supervision.