The use of Big Data in financial services, its current applications and risks

Big data is a very important issue today and is talked about a lot for many different reasons. It is both appreciated and desired but also feared. Almost everything today creates data, whether it is people activities on social media, weather sensors, financial transactions, commercial logs or plane activities in an airport. All of this data has been recognised to be a potential amazing opportunity for companies and organisations. With the use of big data algorithms, it is easier to understand people and systems better and therefore better understand and predict behaviours. In this dissertation, I tried to find out how big data is actually used in financial services, and if it actually brings competitive advantages. In order to do so, I conducted a series of five in depth interviews with participants from different organisations, with all different points of view on the subject. My research revealed that big data analysis can be and is indeed used in financial services companies, especially banks and insurances. My research revealed two main uses. First of all, big data analysis gives a better picture of customers and therefore allows for the possibility of better segmentation of clients which leads to a more efficient way of targeting the customers with relevant products and services. This is particularly useful for marketing strategies. Second of all, and this goes especially for banks and insurances, the possibility to limit and assess some risks; in particular fraud risks and the credit risk of potential clients. This allows a more efficient loan pricing for companies. But big data is not without issues, and there are some challenges and risks that come with the development of big data strategies