Fraud Detection on E-Commerce Transactions Using Machine Learning Techniques

Abstract

Fraud detection is an important aspect of e-commerce transactions as it helps to prevent fraudulent activities such as unauthorized transactions, identity theft, and account takeovers. Recently, machine learning algorithms have been widely used in the literature to detect fraud in e-commerce transactions. These algorithms work by learning patterns in the data that indicate fraudulent activity. Pattern detection involves discovering the discriminative features in the data, such as unusual transaction amounts, locations, or behaviors that are out of the normal range for a particular user, to feed the machine learning method. In this study, four basic machine learning algorithms (decision tree, logistic regression, random forest, and extreme gradient boosting) are used to detect fraud in e-commerce transactions using a newly created dataset including various features about online shopping activities on Boyner Group's e-commerce website and mobile application. The study contributes to the literature by trying different machine learning classifiers and utilizing different features that differ from current approaches in the literature.

Keywords

• fraud detection
• e-commerce
• machine learning
• feature engineering

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