Big data in the banking environment. A new motivation for the development of cuban banking (Review)
Keywords:
big data; elearning; artificial intelligenceAbstract
The financial sector has traditionally been one of the sectors most prone to investment in technology, especially related to data. It is not strange then to see how the main vendors launch Big Data solutions oriented to this market such as wealth management, risk control, commercial banking, investment banking, variable market, loans, credit analysis, or currency exchange, for put some examples. This research on the use of Big Data in the financial sector is close to the reality of Cuban Banking; yet to know this powerful computational tool, which will allow great advances in the development of the sector for better financial stability and a service provision at the level of the first world countries. In recent years some of the Big Data solutions have managed to appear as the best use cases and a barrage of specialized companies have launched themselves to offerin creasingly complete solutions. The main objective of this publication is to motivate executives and specialists in the banking sector, to find on the subject, an analysis and development tool for the implementation of Big Data in Cuban Banking; giving way to computerization and landing the reality of the country to these important resources of today.
References
BeijiZou, Q, Guanglu. S, Weipeng,J, Xiaoning,P. Zeguang, L.: (2017) Data Science – Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE, Changsha, China, September 22-24, 2017, Proceedings, Part II. Communications in Computer and InformationScience 728, Springer 2017, ISBN 978-981-10-6387-9
Costa, C., Santos, M.Y. (2017) A Conceptual Modelforthe Professional Profile of a Data Scientist. WorldCIST (2): 453-463.
Chazal, F Bertrand, M.: (2017). An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists. CoRRabs/1710.04019
Wardah Z., Ismail, N., Nurazean. M, Alias, R. A: (2017). Skills Sets Towards Becoming Effective Data Scientists. KMO: 97-106.
Isaac A. J.,, Kyoung-Yun K. : (2015)Systematic Service Product Requirement Analysis with Online Customer Review Data. J. Integrated Design & Process Science 19(2): 25-48
López. J., Álvaro. L., Jiménez. B.,y Takeda(2018) .A Improving cash logistics in bank branches by coupling machine learning and robust optimization. ExpertSyst. Appl. 92: 236-255
Olson, D., Wu. D. (2017). Predictive Data Mining Models. Computational Risk Management, Springer, ISBN 978-981-10-2542-6, pp. 1-97
Prabha. D., y Krishnamurthi:. (2014) I A Rough Set Approach for Customer Segmentation. Data ScienceJournal 13: 1-11
Roy Laurens, R.,. Zou , C.C (2016). Using Credit/Debit Card Dynamic Soft Descriptor as Fraud Prevention System for Merchant. GLOBECOM: 1-7
Yi Yin, A(2017): Scientists. Fundamental Requirements to Deal with their Research Data in the Big Data Era. ERCIM News 2017(109)
