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Credit Card Fraud Detection using Machine Learning and Deep Learning Techniques

机译:使用机器学习和深度学习技术的信用卡欺诈检测

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In general, fraudulent activities are always intended to cause financial detriment to the second party. With the aggrandizement of digital money in various countries, the fraudulent activities will be even more increased. Credit card companies and Banks lose billions to such fraudulent activities every year, where it accounts to a huge part of their revenue and affects the jobs of various employees. The proposed research work discusses more about the different fraudulent activities associated with credit cards. While all of them cannot be dealt simultaneously, this research work discusses how Machine Learning and Neural Networks can be used to determine the potential fraudsters by referring to their previous mistakes and details of previous fraudsters. Machine Learning algorithms such as Multinomial Naive Bayes, Random Forest Regression, Logistic Regression, Support Vector Machine and a basic Neural Network are also used.
机译:一般而言,欺诈活动始终旨在造成对第二方的财政问题。随着各国的数字货币的委屈,欺诈活动将更加增加。信用卡公司和银行每年丢失数百万的欺诈活动,在其中占他们的大部分收入,并影响了各种员工的工作。拟议的研究工作更多地讨论了与信用卡相关的不同欺诈活动的更多信息。虽然所有这些研究都不能同时处理,但该研究讨论了如何通过参考他们之前的错误和先前欺诈者的错误和细节来使用机器学习和神经网络来确定潜在的欺诈者。还使用了多项式天真贝叶斯,随机森林回归,逻辑回归,支持向量机和基本神经网络的机器学习算法。

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