Big Data Analytics in the Banking Sector: Guidelines and Lessons Learned from the CaixaBank Case

A large number of EU organizations already leverage Big Data pools to drive value and investments. This trend also applies to the banking sector. As a specific example, CaixaBank currently manages more than 300 different data sources (more than 4 PetaBytes of data and increasing) and more than 700 internal and external active users and services are processing them every day. In order to harness value from such high-volume and high-variety of data, banks need to resolve several challenges, such as finding efficient ways to perform Big Data analytics and to provide solutions that help to increase the involvement of bank employees, the true decision makers. In this book chapter, we describe how these challenges are resolved by the self-service solution developed within the I-BiDaaS project. In more detail, we present three CaixaBank use cases, namely i) Analysis of relationships through IP addresses; ii) Advanced Analysis of bank transfer payment in financial terminal; and iii) Enhanced control of customers to online banking, and describe how the corresponding requirements are mapped to specific technical and business KPIs. For each use case, we present the architecture, data analysis and visualization provided by the I-BiDaaS solution, reporting on the achieved results, domain-specific impact, and lessons learned