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Statistical Data-Generative Machine Learning-Based Credit Card Fraud Detection Systems

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Publicado en:Mathematics vol. 13, no. 15 (2025), p. 2446-2461
Autor principal: Feng Xiaomei
Otros Autores: Song-Kyoo, Kim
Publicado:
MDPI AG
Materias:
American Express Co
Machine learning
Datasets
Hypothesis testing
Sampling techniques
Hypotheses
Artificial neural networks
Credit card fraud
Fraud prevention
Decision making
Missing data
Algorithms
Credit card processing
Decision trees
Financial institutions
Efficiency
Acceso en línea:Citation/Abstract
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Resumen:This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. We analyzed two distinct datasets: one consisting of European cardholders and the other from American Express, applying multiple machine learning algorithms, including Artificial Neural Networks, Convolutional Neural Networks, and Gradient Boosted Decision Trees, as well as others. Notably, the Gradient Boosted Decision Tree demonstrated superior predictive performance, with accuracy increasing by 4.53%, reaching 96.92% on the European cardholders dataset. Mode imputation significantly improved data quality, enabling stable and reliable analysis of merged datasets with up to 50% missing values. Hypothesis testing confirmed that the performance of the merged dataset was statistically significant compared to the original datasets. This study highlights the importance of robust data handling techniques in developing effective fraud detection systems, setting the stage for future research on combining different datasets and improving predictive accuracy in the financial sector.
ISSN:2227-7390
DOI:10.3390/math13152446
Fuente:Engineering Database