PDF(Pubmed)
Abstract：
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.
摘要：
欺诈活动，特别是在汽车保险和信用卡交易中，给企业和个人造成了巨大的经济损失。为了克服这个问题，我们提出了一种新的欺诈检测方法，将卷积神经网络(CNN)与支持向量机(SVM)相结合，k最近邻(KNN)，朴素贝叶斯(NB)，和决策树(DT)算法。这种方法的核心在于利用从CNN提取的深层特征作为各种机器学习模型的输入。从而大大有助于提高欺诈检测的准确性和效率。我们的研究结果表明，与以前的研究相比，我们的研究表现优异，突出了我们的模式在打击欺诈活动中被广泛采用的潜力。
