Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.

中文翻译：

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使用机器学习和深度学习技术在医疗保健领域进行金融欺诈检测

医疗保健行业是重要的行业之一，不仅可以在健康方面收集大量数据，还可以在财务方面收集大量数据。由于电子支付的不断增强，信用卡的使用在医疗保健领域发生了重大欺诈，而信用卡欺诈监控一直是不同服务提供商财务状况方面的挑战。因此，有必要对欺诈检测系统进行持续改进。各种欺诈场景不断发生，对财务损失造成巨大影响。许多技术（如网络钓鱼或类病毒木马）主要用于收集有关信用卡及其所有者详细信息的敏感信息。因此，应该有有效的技术来识别信用卡中不同类型的欺诈行为。在本文中，各种机器学习和深度学习方法用于检测信用卡欺诈，不同的算法，如朴素贝叶斯、逻辑回归、K-最近邻 (KNN)、随机森林和序列卷积神经网络训练交易的其他标准和异常特征，以检测信用卡欺诈。为了评估模型的准确性，使用了公开可用的数据。不同的算法结果将准确率可视化为 96.1%、94.8%、95.89%、97.58% 和 92.3%，对应于各种方法，如朴素贝叶斯、逻辑回归、K-最近邻 (KNN)、随机森林和序列分别是卷积神经网络。