Named entity recognition (NER) is a fundamental task in natural language processing, concerned with identifying spans of texts belonging to a set of pre-defined categories (e.g. person, organisation and location names). In the financial and insurance domain, apart from using the generic NER system to extract person, organisation and location names, more often, the task is altered to predict domain-specific categories, such as fiscal year, currency and expense. Those systems are tailored to extract useful information from financial documents like annual reports and insurance claims. Moreover, NER also plays an important role in other crucial financial pipelines, such as search engines, knowledge graph extraction and aspects-based sentiment analysis. The talk will first give a brief introduction to different NER applications in the financial and insurance domains, including real-world examples to show the impact of NER in those areas. Next, an industry system is used as an illustration to show the differences between the industry and academia NER systems. Finally, the last part of the talk will focus on the challenges and opportunities of the NER task, including topics.