Big Data in the Finance and Insurance Sectors

The finance and insurance sector by nature has been an intensively data-driven industry, managing large quantities of customer data and with areas such as capital market trading having used data analytics for some time. The advent of big data in financial services can bring numerous advantages to financial institutions: enhanced levels of customer insight, engagement, and experience through the digitization of financial products and services and with the increasing trend of customers interacting with brands or organizations in the digital space; enhanced fraud detection and prevention capabilities through the use of big data it is now possible to use larger datasets to identify trends that indicate fraud; and enhanced market trading analysis, where trading strategies which make the use of sophisticated computer algorithms to rapidly trade the financial markets. This chapter identifies the drivers related with the evolution of the sector, like the impact of regulations, and changing business models, together with the associated constraints related with legacy culture and infrastructures, and data privacy and security issues. The findings, after analysing the requirements and the technologies currently available, show that there are still research challenges to develop the technologies to their full potential in order to provide competitive and effective solutions. These challenges appear at all levels of the big data chain and involve a wide set of different technologies, which would make necessary a prioritization of the investments in R&D, for example, real-time aspects, better data quality techniques, scalability of data management and processing, and better sentiment classification methods.