The role of diversity and ensemble learning in credit card fraud detection

Gian Marco Paldino (), Bertrand Lebichot, Yann-Aël Le Borgne, Wissam Siblini, Frédéric Oblé, Giacomo Boracchi and Gianluca Bontempi
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Gian Marco Paldino: Université Libre de Bruxelles
Bertrand Lebichot: Université Libre de Bruxelles
Yann-Aël Le Borgne: Université Libre de Bruxelles
Wissam Siblini: Research, Development and Innovation
Frédéric Oblé: Research, Development and Innovation
Giacomo Boracchi: Politecnico di Milano
Gianluca Bontempi: Université Libre de Bruxelles

Advances in Data Analysis and Classification, 2024, vol. 18, issue 1, No 9, 193-217

Abstract: Abstract The number of daily credit card transactions is inexorably growing: the e-commerce market expansion and the recent constraints for the Covid-19 pandemic have significantly increased the use of electronic payments. The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component of the detection process. Standard machine learning techniques are widely employed, but inadequate for the evolving nature of customers behavior entailing continuous changes in the underlying data distribution. his problem is often tackled by discarding past knowledge, despite its potential relevance in the case of recurrent concepts. Appropriate exploitation of historical knowledge is necessary: we propose a learning strategy that relies on diversity-based ensemble learning and allows to preserve past concepts and reuse them for a faster adaptation to changes. In our experiments, we adopt several state-of-the-art diversity measures and we perform comparisons with various other learning approaches. We assess the effectiveness of our proposed learning strategy on extracts of two real datasets from two European countries, containing more than 30 M and 50 M transactions, provided by our industrial partner, Worldline, a leading company in the field.

Keywords: Finance; Fraud detection; Concept drift; Ensemble learning; Diversity; 68T05; Learning; and; adaptive; systems; in; artificial; intelligence (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11634-022-00515-5

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