Massive usage of credit cards has caused an escalation of fraud. Usage of credit cards has resulted in the growth of online business advancement and ease of the e-payment system. The use of machine learning (methods) are adapted on a larger scale to detect and prevent fraud. ML algorithms play an essential role in analysing customer data. In this research article, we have conducted a comparative analysis of the literature review considering the ML techniques for credit card fraud detection (CCFD) and data confidentiality. In the end, we have proposed a hybrid solution, using the neural network (ANN) in a federated learning framework. It has been observed as an effective solution for achieving higher accuracy in CCFD while ensuring privacy.
Keywords: Artificial neural network (ANN), Credit card fraud, Federated learning, Random forest (RF) method, Support vector machine (SVM), Privacy-preserving, Blockchain
信用卡的大量使用导致欺诈升级。信用卡的使用促进了在线业务的发展和电子支付系统的简化。机器学习（方法）被更加广泛地使用来检测和预防欺诈。机器学习算法在分析客户数据方面发挥着重要作用。在这篇研究文章中，我们对用于信用卡欺诈检测 (CCFD) 和数据机密性的 ML 技术的文献综述进行了比较分析。最后，我们提出了一种在联合学习框架中利用神经网络 (ANN)的混合性解决方案。它被认为是一种在确保隐私的同时，对CCFD获得更高精确度的一种有效解决方案。