@inbook {Katsikis2024,
	title = {A Weights Direct Determination Neural Network for~Credit Card Attrition Analysis},
	booktitle = {Machine Learning Approaches in Financial Analytics},
	year = {2024},
	pages = {325{\textendash}346},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Cham},
	abstract = {Cost reduction is a component that contributes to both the profitability and longevity of a corporation, especially in the case of a financial institution, and can be accomplished through greater client retention. Particularly, credit card customers comprise a volatile subset of a bank{\textquoteright}s 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. Credit card attrition is generally a poorly investigated subtopic with a variety of challenges, like highly imbalanced datasets. This article utilizes neural networks to address the challenges of credit card attrition since they have found great application in many classification problems. More particularly, to overcome the shortcomings of traditional back propagation neural networks, we construct a multi-input trigonometrically activated weights and structure determination (MTA-WASD) neural network which incorporates structure trimming as well as other techniques that boost its training speed as well as diminish the danger and the subsequent detrimental effects of overfitting. When applied to three publicly available datasets, the MTA-WASD neural network demonstrated either superior or highly competitive performance across all metrics, compared to some of the best-performing classification models that MATLAB{\textquoteright}s classification learner app offers.},
	isbn = {978-3-031-61037-0},
	doi = {10.1007/978-3-031-61037-0_16},
	url = {https://doi.org/10.1007/978-3-031-61037-0_16},
	author = {Vasilios N. Katsikis and Spyridon D. Mourtas and Romanos Sahas and Balios, Dimitris},
	editor = {Maglaras, Leandros A. and Das, Sonali and Tripathy, Naliniprava and Patnaik, Srikanta}
}
