The trading volume of China's secondary bond market reached CNY 2,735.44 trillion in 2024. The majority of transactions were conducted through the over-the-counter (OTC) market. In this market, brokers, buyers, and sellers send request-for-quote (RFQ) messages via group or private chats, generating an RFQ pool containing tens of thousands of requests. Brokers search through this pool to match suitable counterparties and facilitate transactions between them. The faster they match, the more trading opportunities and profits they can obtain. However, as RFQs are unstructured text messages, manual matching is time-consuming. Therefore, we built a system to accelerate this process. The core task of this system is to automatically parse RFQs into structured data, which faces two key challenges: highly domain-specific jargon in RFQs and the rapid evolution of jargon patterns. To address these challenges, we propose an RFQ parsing method based on fine-tuned large language models (LLMs). It consists of three sequential components: (1) Message Chunking, which decomposes an RFQ message into shorter items; (2) Few-shot Retrieval, which retrieves similar historical RFQs; and (3) Record Extraction, which generates structured RFQ records leveraging retrieved demonstrations. Compared with baseline methods, this approach delivers significant performance gains and exhibits superior adaptability to novel expressions. Building on this methodology, we developed Chat2Trade, a trading assistance system that parses RFQs and supports rapid filtering and querying. Chat2Trade transforms the brokers' traditional workflow from actively searching for relevant RFQs into a passive, real-time, push-based paradigm. The system has been deployed at China Securities, one of the largest securities firms in China, for nearly one year. It improves brokers' average trading volume by 3.67 times compared to traditional manual methods.

Recommendation systems are effective tools for information filtering and discovery. These systems are widely applied across various consumer sectors and hold significant potential for applications in professional domains to enhance work efficiency. However, supporting decision-making in professional contexts requires not only providing recommendation results but also offering explanations to persuade users to adopt the suggestions. Taking the task in the primary bond market as an example, where sales staff seek potential investors for bonds, this paper presents the development and deployment of a recommendation system designed for a professional setting. The system provides a set of key features as explanations for its recommendations. In this process, we observe that current explanation methods may select redundant and spurious features, which can undermine the persuasive impact of the explanations. To address this issue, we propose a method named ConciseExplain, which leverages a mask training strategy and gradient descent to directly identify a concise set of features. We conduct experiments on real-world and synthetic datasets. Our method achieves relative improvements of 6.1% and 12.4% over the best-performing baseline on redundant and spurious metrics, respectively. Our method also outperforms the baseline method in online manual evaluations. Moreover, during the one-year official deployment of our system at China Securities Co., Ltd. (a leading brokerage firm in China), we observed a continuous improvement in the accuracy of the recommendation system. This suggests that, with concise explanations, a positive feedback loop might be established between recommendation outcomes and investment decisions.