基于机器学习算法的在线支付交易欺诈检测

Darshan Aladakatti, Gagana P, Ashwini Kodipalli, Shoaib Kamal
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引用次数: 0

摘要

网上支付交易是一种使用数字货币进行支付的交易。从家具到服装,从食品到药品,从小玩意到家电等等,世界各地的消费者都喜欢在线支付购买几乎所有东西。网上交易现在已经发展成许多平台。这是许多公司为其客户提供的最有效的方法之一。它不仅有助于建立公司的收入,而且影响公司的增长。让我们不要忘记,有好处也有坏处,在指尖完成繁琐的交易的最大好处是害怕欺诈活动,我们辛苦赚来的钱可能在几秒钟内被盗。这些活动可以通过机器学习算法通过提供有关这些事务的足够数据来确定。我们已经使用了机器学习算法,如SVM(支持向量机)、LR(逻辑回归)、朴素贝叶斯、决策树和随机福雷斯特。随机森林分类器的准确率达到99.94%,优于其他分类器
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fraud detection in Online Payment Transaction using Machine Learning Algorithms
Online payment transaction is a transaction in which payment is made using digitalized currency. Customers all over the world prefer online payments to purchase almost everything from furniture to clothing, from food to medicines, from gadgets to appliances, and whatnot. the online transaction has now evolved into many platforms. It is one of the most efficient methods provided by many companies to its customer. It not only helps build the company's revenue but also impacts the growth of the company. Let us not forget that with pros come the cons with the greatest advantage of having tedious transactions done at the fingertips comes the fear of fraudulent activity in which our hard-earned money can get theft within seconds. These activities can be determined by machine learning algorithms by feeding adequate data about these transactions. We have used machine learning algorithms such as SVM (Support Vector Machine), LR (Logistic regression), Naive Bayes, Decision tree, and Random Forrest for the same. It is observed that Random Forest classifier has outperformed comparing to other classifiers with the accuracy of 99.94%
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