Credit card fraud detection system using machine learning technique

Ayushi Maurya, Arun C. S. Kumar
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引用次数: 1

Abstract

Over the years, with the development of e-commerce, people are mostly making online transactions, and the risk of getting scammed has also increased. This in turn forces the financial institutions to improve continuously and upgrade their model. Machine Learning techniques were used to detect fraud in credit card transactions, but working with real-time data can be tough for machine learning to handle. Thus, implementation of blockchain techniques with machine learning to improve the efficiency and accuracy of the model. In the proposed model, Ethereum dataset has been used to check the fraudulent transaction and secure it with the help of machine learning algorithms. Out of all the classifiers XGBoost has attained the highest accuracy of 99.21% for the stated dataset.
信用卡诈骗检测系统采用机器学习技术
多年来,随着电子商务的发展,人们大多在网上进行交易,被骗的风险也增加了。这反过来又迫使金融机构不断改进和升级他们的模式。机器学习技术被用于检测信用卡交易中的欺诈行为,但处理实时数据对机器学习来说可能很难处理。因此,通过机器学习实现区块链技术,以提高模型的效率和准确性。在提出的模型中,以太坊数据集被用来检查欺诈性交易,并在机器学习算法的帮助下对其进行保护。在所有分类器中,对于所述数据集,XGBoost达到了99.21%的最高准确率。
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