Variable Selection and Asymmetric Links to Predict Credit Card Fraud

Francesco Giordano (), Michele La Rocca (), Marcella Niglio () and Marialuisa Restaino ()
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Francesco Giordano: Università degli Studi di Salerno
Michele La Rocca: Università degli Studi di Salerno
Marcella Niglio: Università degli Studi di Salerno
Marialuisa Restaino: Università degli Studi di Salerno

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2024, pp 198-204 from Springer

Abstract: Abstract Credit card fraud identification is a challenging problem for different reasons: it needs to be suddenly detected; it is based on the use of huge data sets that have to be properly managed; the number of fraudulent transactions is definitely lower than the number of genuine transactions and then, this imbalance requires the use of proper statistical models. Here we discuss how the data reduction, performed through the variable selection, can be combined with the use of Generalized Linear Models with asymmetric link functions which are able to handle imbalanced data. We illustrate how these theoretical results can be used for credit card fraud-detection purposes.

Keywords: credit card fraud; imbalanced data; asymmetric link; variable selection (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-64273-9_33

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DOI: 10.1007/978-3-031-64273-9_33

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