ABSTRACT
Big data technologies have a strong impact on different industries, which began in the last decade and continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated its operating activities mostly on the analysis of structured data. However, with the support of big data technologies, hidden information from semi-structured and unstructured data from various sources could be harvested. Recent research and practice indicates that such information can be interesting for the decision-making process. In this chapter, we discuss the impact of big data analytics to the financial sector with emphasis on analysis of textual data. We present several commonly used data-driven case studies adopted by different institutions from the financial sector that can be replicated in any institution, using textual data for gaining new valuable insights: keyword detection, name entity recognition, gender prediction, sentiment analysis, topic extraction, and social network analysis. Although we use original data from real financial case studies, the identity of financial institutions providing sources is not revealed.
