Credit card customers comprise a volatile subset of a banks' client base. As such, banks would like to predict in advance which of those clients are likely to attrite, so as to approach them with proactive marketing campaigns. Neuronets have found great application in many classification problems. Credit card attrition is a poorly investigated subtopic that poses many challenges, such as highly imbalanced datasets. The goal of this research is to construct a feed-forward neuronet that can overcome such obstacles and thus accurately classify credit card attrition. To this end, we employ a weights and structure determination (WASD) algorithm that facilitates the development of a competitive and all around robust classifier whilst accounting for the shortcomings of traditional back propagation neuronets. This is supported by the fact that when compared with some of the best performing classification models that MATLAB's classification learner app offers, the power softplus activated WASD neuronet demonstrated either superior or highly competitive performance across all metrics.
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27 February 2023
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Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
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Mourtas, S. D., Katsikis, V. N., & Sahas, R. (2023). Credit Card Attrition Classification Through Neuronets. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 86-93). European Publisher. https://doi.org/10.15405/epct.23021.11