Fraud credit card transaction detection using hybrid multilayer perceptron-random forest method
Alexander Subagio () and
Ditdit Nugeraha Utama ()
Edelweiss Applied Science and Technology, 2025, vol. 9, issue 3, 2482-2494
Abstract:
Credit card fraud is a leading crime with rapid growth in the world. This is due to credit cards being one of the most popular payment options worldwide. To address this problem, there needs to be a robust and efficient method to accurately identify fraudulent transactions. This study aims to investigate the performance of a hybrid method that combines Multilayer Perceptron (MLP) as a feature extractor and a Random Forest (RF) classifier for detecting fraudulent credit card transactions. The MLP is used to capture complex patterns in the transaction data, while the RF classifier is used to make robust and accurate predictions. The performance of the proposed model was compared with standalone MLP and RF using several evaluation metrics. The proposed method achieved the best performance among other methods, with an accuracy of 99.949%, precision of 87.097%, recall of 82.653%, and F1-score of 84.817%. This result shows the ability of the proposed method by combining the strengths of MLP as a feature extractor and RF as a classifier, offering an effective and robust method for fraud detection. This research shows the potential of hybrid methods in addressing financial challenges and provides further advancement in fraud detection systems.
Keywords: Credit Card Fraud Detection; Machine Learning; Multilayer Perceptron; Random Forest. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/5823/2085 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:3:p:2482-2494:id:5823
Access Statistics for this article
More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().