Typically, women are scored with a lower financial risk than men. However, the understanding of variables and indicators that lead to such results, are not fully understood. Furthermore, the stochastic nature of the data makes it difficult to generate a suitable profile to offer an adequate financial portfolio to the women segment. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the results. In this research, machine learning techniques are used for data analysis. In this way, faster, more accurate results are obtained than in traditional models (such as statistical models or linear programming) in addition to their scalability.
CITATION STYLE
Lozano-Medina, J. I., Hervert-Escobar, L., & Hernandez-Gress, N. (2020). Risk profiles of financial service portfolio for women segment using machine learning algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12143 LNCS, pp. 561–574). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-50436-6_42
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