TY - CHAP
T1 - Big data analytics in the banking sector
T2 - Guidelines and lessons learned from the CaixaBank case
AU - Alexopoulos, Andreas
AU - Becerra, Yolanda
AU - Boehm, Omer
AU - Bravos, George
AU - Chatzigiannakis, Vasilis
AU - Cugnasco, Cesare
AU - Demetriou, Giorgos
AU - Eleftheriou, Iliada
AU - Fodor, Lidija
AU - Fotis, Spiros
AU - Ioannidis, Sotiris
AU - Jakovetic, Dusan
AU - Kallipolitis, Leonidas
AU - Katusic, Vlatka
AU - Kavakli, Evangelia
AU - Kopanaki, Despina
AU - Leventis, Christoforos
AU - Marcos, Mario Maawad
AU - de Pozuelo, Ramon Martin
AU - Martínez, Miquel
AU - Milosevic, Nemanja
AU - Montanera, Enric Pere Pages
AU - Ristow, Gerald
AU - Ruiz-Ocampo, Hernan
AU - Sakellariou, Rizos
AU - Sirvent, Raül
AU - Skrbic, Srdjan
AU - Spais, Ilias
AU - Vasiliadis, Giorgos
AU - Vinov, Michael
N1 - Publisher Copyright:
© The Author(s) 2022. All rights reserved.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - 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.
AB - 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.
KW - Advanced analytics
KW - Banking
KW - Big data analytics
KW - Security applications
KW - Self-service solution
KW - Visualisations
UR - http://www.scopus.com/inward/record.url?scp=85161836448&partnerID=8YFLogxK
U2 - 10.1007/9783030783075_13
DO - 10.1007/9783030783075_13
M3 - Chapter
AN - SCOPUS:85161836448
SN - 9783030783068
SP - 273
EP - 297
BT - Technologies and Applications for Big Data Value
PB - Springer International Publishing
ER -