Analytics based on big data computing can benefit today's banking and financial organizations on many aspects, and provide much valuable information for organizations to achieve more intelligent trading, which can help them to gain a great competitive advantage. However, the large scale of data and the critical latency analytics requirement in finance poses a great challenge for current system architecture. In this paper, we first analyze the challenges brought by the financial latency critical big data computing, then propose a discussion on how to handle these challenges from a perspective of multi-level system. We also talk about current researches on low latency in different system levels. The discussions and conclusions in the paper can be useful to the banking and financial organizations with the critical latency requirement of big data analytics.