Ming Ruixing, Abdelrahman Osama, Innab Nisreen, Ibrahim Mohamed Hanafy Kotb
School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China.
Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia.
PeerJ Comput Sci. 2024 Jun 28;10:e2088. doi: 10.7717/peerj-cs.2088. eCollection 2024.
Fraudulent activities especially in auto insurance and credit card transactions impose significant financial losses on businesses and individuals. To overcome this issue, we propose a novel approach for fraud detection, combining convolutional neural networks (CNNs) with support vector machine (SVM), k nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT) algorithms. The core of this methodology lies in utilizing the deep features extracted from the CNNs as inputs to various machine learning models, thus significantly contributing to the enhancement of fraud detection accuracy and efficiency. Our results demonstrate superior performance compared to previous studies, highlighting our model's potential for widespread adoption in combating fraudulent activities.
欺诈活动，尤其是汽车保险和信用卡交易中的欺诈行为，给企业和个人带来了巨大的经济损失。为了克服这一问题，我们提出了一种新颖的欺诈检测方法，将卷积神经网络（CNN）与支持向量机（SVM）、k近邻（KNN）、朴素贝叶斯（NB）和决策树（DT）算法相结合。这种方法的核心在于利用从CNN中提取的深度特征作为各种机器学习模型的输入，从而显著提高欺诈检测的准确性和效率。我们的结果表明，与先前的研究相比，我们的方法具有更优的性能，凸显了我们的模型在打击欺诈活动中广泛应用的潜力。
