Advertisement

Risk-Driven Analytics for Banking IoT Strategy

  • F. KhanboubiEmail author
  • A. Boulmakoul
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 266)

Abstract

New communication technologies have a significant impact on the management of banking activities and strongly influence their ecosystem. Key innovations include big data analytics, artificial intelligence, data science, digital currency, social media, blockchain and the Internet of Things (IoT). Many of these technologies are interdependent. IoT is the interconnection of uniquely identifiable integrated computing devices within the infrastructure of the surrounding computer network. In the field of banking services, the interconnection of those integrated equipment should allow the automation of several legacy processes. As the digital transformation driven by IoT begins to take root, new business models and products emerge. This opens new frontiers for innovation that can change customer behavior in the banking industry. The objective of this chapter is to highlight and illustrate the different uses of IoT in banking. We also analyze the impact of digital risks based on the Internet of Things on the traditional banking processes. It would be of interest to bring together the different types of digital risks that have a similar impact on bank’s processes. We analyze those technologies to implement the digital transformation using new practices. We develop an approach based on a bipartite graph associating the processes and risks of the banking sector. From this, we deduce a strategy to safely lead the integration of connected objects into the banking industry.

Keywords

Internet of Things Bank Digital risks Digital strategy Galois lattice Fuzzy concept Process modeling Holistic analytics 

References

  1. 1.
    Dey, N., Wagh, S., Mahalle, P., Pathan, M. (eds.): Applied Machine Learning for Smart Data Analysis. CRC Press, Boca Raton (2019).  https://doi.org/10.1201/9780429440953
  2. 2.
  3. 3.
    Alan Goode: Goode intelligence: biometrics for banking: best practices and barriers to adoption (2018)Google Scholar
  4. 4.
    Dey, N., Hassanien, A.E., Bhatt, C., Ashour, A.S., Satapathy, S.C. (eds.): Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Springer, Berlin (2018)Google Scholar
  5. 5.
  6. 6.
    Juniper Research: Contactless to account for more than 1 in 2 POS transactions globally by 2022. https://www.juniperresearch.com/press/press-releases/contactless-account-more-than-1-in-2-pos-trans (2017)
  7. 7.
    GSMA Intelligence: The mobile economy sub-saharan Africa. https://www.gsmaintelligence.com/research/?file=809c442550e5487f3b1d025fdc70e23b&download (2018)
  8. 8.
    Mezghani, K., Aloulou, W.: Business Transformations in the Era of Digitalization. Advances in E-Business Research (1935–2700). IGI Global (2019). ISBN 9781522572633Google Scholar
  9. 9.
    Takalkar, V., Mahalle, P.N.: Trust-based access control in multi-role environment of online networks. Springer J. Wirel. Pers. Commun. (2018). 11277-017-5078-2Google Scholar
  10. 10.
    International Organization for Standardization (ISO): ISO 9000:2015: Quality Management Systems—Fundamentals and Vocabulary, International Organization for Standardization. https://www.iso.org/standard/45481.html (2015)
  11. 11.
    Debauche, B., Mégard, P.: BPM Business Process Management, Pilotage Métier de l’entreprise, Lavoisier (2004)Google Scholar
  12. 12.
    Ganter, B., Wille, R.: Conceptual scaling. In: Roberts, F. (ed.) Applications of Combinatorics and Graph Theory to the Biological and Social Sciences, pp. 139–167. Springer (1989)Google Scholar
  13. 13.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Inc., New York, Secaucus, NJ, USA (1997) ISBN: 3540627715Google Scholar
  14. 14.
    Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel (1982)Google Scholar
  15. 15.
    Wille, R.: Concept lattices and conceptual knowledge systems. Comput. Math. Appl. 23(6–9), 493–515 (1992)CrossRefGoogle Scholar
  16. 16.
    Wille, R.: Line diagrams of hierarchical concept systems. Int. Classif. 11(2), 77–86 (1984)Google Scholar
  17. 17.
    Barbut, M., Monjardet, B.: Ordre et Classification: Algèbre et Combinatoire, vol. 2. Hachette Université, Paris (1970)zbMATHGoogle Scholar
  18. 18.
    Atkin, R.: Mathematical Structure in Human Affairs. Heinemann, London (1974)Google Scholar
  19. 19.
    Yevtushenko, S.A.: System of data analysis “Concept Explorer”. In: Proceedings of the 7th national conference on Artificial Intelligence KII-2000, pp. 127–134, Russia (2000)Google Scholar
  20. 20.
    Burusco, A., Fuentes-Gonzales, R.: Concept lattice defined from implication operators. Fuzzy Sets Syst. (2000)Google Scholar
  21. 21.
    Georgescu, G., Popescu, A.: Concept lattices and similarity in non-commutative fuzzy logic. Fundam. Informaticae 53(1), 23–54 (2002)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Yahia S.B., Jaoua, A.: Discovering knowledge from fuzzy concept lattice. In: Kandel, A., Last, M., Bunke, H. (eds.) Data Mining and Computational Intelligence, pp. 167–190. Physica-Verlag (2001)Google Scholar
  23. 23.
    Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Theory and Applications. Prentice Hall, Upper Saddle River, NJ (1995)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LIM Laboratory, IOS, Department of Computer Science, FSTMHassan II University of CasablancaMohammediaMorocco

Personalised recommendations