Banks have always been quite wary of incorporating new ideals and approaches in technology evolution. Often they wait years before a new breakthrough idea has had a chance to sink in after which they seem to go through an assessment phase before they sow the oats into their own systems. Banks have a huge amount of data to work with and there are an immense amount of risk management requirements that need to be captured at the same time. The almost immediate benefits from incorporating semantic web can be transformed by the use of an internal corporate wide linked data solution allowing the security cautious banker into the realm of data connectivity that not only provides further insights but also more contextual meaning. Once the REST approach is accepted, utilizing semantic web is only a stones throw away as resource on HTTP further starts to take shape with useful metadata. Semantic Web can be used to build profiles of individuals and companies for credit risk assessment to conduct more data sensitive analysis for compliance. It can even be used to manage securities information as well as to semantically build enrichment of market insights. A market data itself holds an immense amount of information beyond just a dashboard of historical data graphs. Furthermore, aggregated news sources can even project a global view of what investment strategies can be made and even text forecasting becomes a more semantically agreeable option via rich natural language processing with sentiment analysis. There is a lot of untapped potential in the overlapping associations and applications of natural language processing, semantic web, and machine learning to the real-world of finance all driven through fundamentally on uncertainty of events and human behavior. Such banks as JPMorgan and Goldman Sachs have already started to embark on their semantic expeditions to control the entanglements of data complexity and exchange as well as the inexplicable frontiers of risk management.
