Bloomberg's AI Group has developed a range of natural language processing (NLP) applications that transform how our clients interact with financial data and news. From solutions such as IB (Instant Bloomberg) NLP, which enables the extraction of key information from trader dialogue, to News Summarization, which provides concise and accurate summaries of market-moving news, our NLP applications are providing business insights and enabling financial professionals to make more informed business and investment decisions.

However, building industry-grade NLP applications for the financial domain is a complex task. In this talk, we will highlight some of the challenging technical requirements we have encountered while developing these applications, including: efficiently deploying high-precision models while meeting our clients' stringent latency requirements; ensuring the factuality and accuracy of LLM outputs, something that is particularly important in the high-stakes financial domain; and evaluating and maintaining the accuracy of our NLP models over time, as market conditions and financial data evolve.