Ahmad Hadeel, Kasasbeh Bassam, Aldabaybah Balqees, Rawashdeh Enas
Department of Computer Science, Applied Science Private University, Amman, 11931 Jordan.
Department of Management Information Systems, Albalqa' Applied University, Amman, 11931 Jordan.
Int J Inf Technol. 2023;15(1):325-333. doi: 10.1007/s41870-022-00987-w. Epub 2022 Jun 21.
Credit card fraud is a growing problem nowadays and it has escalated during COVID-19 due to the authorities in many countries requiring people to use cashless transactions. Every year, billions of Euros are lost due to credit card fraud transactions, therefore, fraud detection systems are essential for financial institutions. As the classes' distribution is not equally represented in the credit card dataset, the machine learning trains the model according to the majority class which leads to inaccurate fraud predictions. For that, in this research, we mainly focus on processing unbalanced data by using an under-sampling technique to get more accurate and better results with different machine learning algorithms. We propose a framework that is based on clustering the dataset using fuzzy C-means and selecting similar fraud and normal instances that have the same features, which guarantees the integrity between the data features.
信用卡欺诈如今是一个日益严重的问题，并且在新冠疫情期间由于许多国家的当局要求人们进行无现金交易而有所升级。每年，由于信用卡欺诈交易损失数十亿欧元，因此，欺诈检测系统对金融机构至关重要。由于信用卡数据集中各类别的分布不均衡，机器学习根据多数类别训练模型，这导致欺诈预测不准确。为此，在本研究中，我们主要专注于通过使用欠采样技术处理不平衡数据，以便在不同的机器学习算法下获得更准确和更好的结果。我们提出了一个框架，该框架基于使用模糊C均值对数据集进行聚类，并选择具有相同特征的相似欺诈和正常实例，这保证了数据特征之间的完整性。
