Big data analytics in the banking sector: Guidelines and lessons learned from the CaixaBank case

Andreas Alexopoulos, Yolanda Becerra, Omer Boehm, George Bravos, Vasilis Chatzigiannakis, Cesare Cugnasco, Giorgos Demetriou, Iliada Eleftheriou, Lidija Fodor, Spiros Fotis, Sotiris Ioannidis, Dusan Jakovetic, Leonidas Kallipolitis, Vlatka Katusic, Evangelia Kavakli, Despina Kopanaki, Christoforos Leventis, Mario Maawad Marcos, Ramon Martin de Pozuelo, Miquel MartínezNemanja Milosevic, Enric Pere Pages Montanera, Gerald Ristow, Hernan Ruiz-Ocampo, Rizos Sakellariou, Raül Sirvent, Srdjan Skrbic, Ilias Spais, Giorgos Vasiliadis, Michael Vinov

Producción científica: Capítulo del libroCapítulorevisión exhaustiva

2 Citas (Scopus)

Resumen

A large number of EU organisations 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 chapter, we describe how these challenges are resolved by the self-service solution developed within the I-BiDaaS project. We present three CaixaBank use cases in more detail, namely, (1) analysis of relationships through IP addresses, (2) advanced analysis of bank transfer payment in financial terminals and (3) Enhanced control of customers in 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 visualisation provided by the I-BiDaaS solution, reporting on the achieved results, domain-specific impact and lessons learned.

Idioma originalInglés
Título de la publicación alojadaTechnologies and Applications for Big Data Value
EditorialSpringer International Publishing
Páginas273-297
Número de páginas25
ISBN (versión digital)9783030783075
ISBN (versión impresa)9783030783068
DOI
EstadoPublicada - 28 abr 2022
Publicado de forma externa

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