Systematic Review of Big Data in the Financial Sector: Satisfaction and Loyalty

Huamán K.N.A.; Munayco R.E.F.; Castro L.J.T.; Rodriguez O.F.A. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/leird2024.1.1.468

Resumen

This systematic review, carried out under the PRISMA methodology, aims to investigate the implementation of Big Data Analytics in the customer management system that proposes customer loyalty and satisfaction, recognizing the challenges they try to overcome, the updated methods and identifying if it is beneficial for the financial sector. Studies in the financial, technological and telecommunications sectors were considered to facilitate the transfer of information from different sectors. 9 articles were analyzed, with the main results being that the Big Data system stores and organizes large volumes of data with AI and algorithms, that the complexity of the data, the broad classification of characteristics and confidentiality are the problems addressed; that combinations of machine learning, “k-means”, “clustering” and Naive Bayes are the most used methods; that is, the performance of the proposed models exceeds classical statistical methods. In general, the articles cover aspects such as data type, extraction techniques, and the use of algorithms to automate data fitting. Finally, a comparison of the client's needs with the innovative methods offered by the BDA is developed.

Instituciones: Universidad Tecnológica del Perú